Generative Adversarial Networks (GAN)

L'objectif de ce TP est de créer et manipuler un Generative Adversarial Networks (GAN) afin de générer des images de chiffres manuscrits à partir d'une base de données d'exemples.

Vous aurez besoin des outils présents dans le fichier gan_tools_cuda.py que vous devrez copier dans votre répertoire Jupyter afin de pouvoir l'importer. Regardez bien les fonctions et classes définies dans ce fichier : il y a les codes permettant de télécharger la base de donnée d'images, de faire les conversions entre images et vecteurs, de générer les vecteurs de bruit à donner en entrée du générateur et de gérer l’affichage des résultats sous forme d'exemples d’images générées par le générateur et des indices de qualité du générateur et du discriminateur.

1. Importation des librairies et de gan_tools_cuda.py

import torch
from torch import nn, optim
from torch.autograd.variable import Variable

import gan_tools_cuda as gt

2. Chargement des données d'apprentissage et création du "loader"

Le loader présentera au réseau les données par paquet de 100, dans un ordre aléatoire.

# Load data
data = gt.mnist_data()
# Create loader with data, so that we can iterate over it
data_loader = torch.utils.data.DataLoader(data, batch_size=100, shuffle=True)
# Num batches
num_batches = len(data_loader)

Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-images-idx3-ubyte.gz

Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-labels-idx1-ubyte.gz

Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-images-idx3-ubyte.gz

Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz

Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw
Processing...
Done!

/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:479: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)

3. Création du discriminateur

Créez une classe Python DiscriminatorNet décrivant la structure du réseau discriminateur.

Le réseau est composé de trois couches cachées et d'une couche de sortie. Nous utiliserons le module "séquentiel" de Pytorch qui décrit l’ordre des différentes transformations à appliquer aux données dans chaque couche.

L'entrée du réseau est un vecteur avec 784 valeurs (une image 28x28), la sortie est une valeur unique, allant de 0 (fausse image) à 1 (vraie image) grâce à l'utilisation d'une fonction sigmoïde.

Les trois couches cachées utilisent une fonction LeakyReLU pour transformer leur sortie en valeurs quasi positives, avec alpha = 0,2. Elles utilisent également une fonction Dropout pour définir aléatoirement à zéro 30% des valeurs de sortie des neurones des couches cachées (il a été prouvé que cela augmentait les performances du réseau en empêchant le sur-apprentissage). Le nombre de neurones dans chaque couche cachée est respectivement de 1024, 512 et 256.

La classe possède une méthode "forward" permettant de calculer la sortie du réseau à partir d'un vecteur d'entrée (représenaant une image).

class DiscriminatorNet(torch.nn.Module):

    def __init__(self):
        super(DiscriminatorNet, self).__init__()
        n_features = 784
        n_out = 1
        
        self.hidden0 = nn.Sequential( 
            nn.Linear(n_features, 1024),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.3)
        )
      

        self.hidden1 = nn.Sequential(
            nn.Linear(1024, 512),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.3)
        )

        self.hidden2 = nn.Sequential(
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.3)
        )
            
        self.out = nn.Sequential(
            torch.nn.Linear(256, n_out),
            torch.nn.Sigmoid()
        )

    def forward(self, x):
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = self.hidden2(x)
        x = self.out(x)
        return x
    
  

4. Création du générateur

Créez une classe Python GeneratorNet décrivant la structure du réseau générateurs. Le réseau est composé de trois couches cachées et d'une couche de sortie. L'entrée du réseau est un vecteur avec 100 valeurs, la sortie est une image avec 784 valeurs (utilisez nn.Tanh() au lieu de nn.Sigmoid() pour convertir les valeurs de sortie). Les trois couches cachées utilisent une fonction LeakyReLU pour transformer leur sortie en valeurs quasi positives, avec alpha = 0,2, mais pas de Dropout ici. Le nombre de neurones dans chaque couche cachée est respectivement de 256, 512 et 1024

• Le générateur suréchantillonne l'entrée en utilisant plusieurs modules nn.ConvTranspose2d séparés par nn.BatchNorm2d et nn.ReLU.
• À la fin de la séquence, le réseau utilise nn.Tanh() pour écraser les sorties à (-1,1).
• Le vecteur aléatoire d'entrée a une taille nznz. La sortie a une taille nc \times 64 \times 64nc×64×64, où ncnc est le nombre de canaux.

class GeneratorNet(torch.nn.Module):
    """
    3 couches cachées generative neural network
    """

    def __init__(self):
        super(GeneratorNet, self).__init__()
        n_features = 100
        n_out = 784

        self.hidden0 = nn.Sequential(
            nn.Linear(n_features, 256),
            nn.LeakyReLU(0.2)
        )

        self.hidden1 = nn.Sequential(
            nn.Linear(256, 512),
            nn.LeakyReLU(0.2)
        )

        self.hidden2 = nn.Sequential(
            nn.Linear(512, 1024),
            nn.LeakyReLU(0.2)
        )

        self.out = nn.Sequential(
            nn.Linear(1024, n_out),
            nn.Tanh()
        )

    def forward(self, x):
       
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = self.hidden2(x)
        x = self.out(x)
        return x

5. Creation du processus d'apprentissage

Initialisez les instances d'un discriminateur et d'un générateurs. Créez deux optimiseurs de type Adam pour les deux réseaux. Définissez la fonction de perte: ici, nous choisissons une fonction d'entropie croisée binaire pour vérifier si le discriminateur trouve la bonne réponse.

discriminator = DiscriminatorNet()
generator = GeneratorNet()
if torch.cuda.is_available():
    discriminator.cuda()
    generator.cuda()
    
# Optimizers
d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), lr=0.0002)

# Loss function 
loss = nn.BCELoss()

Créez une fonction train_generator et une fonction train_discriminator. Expliquez les différentes étapes du processus d'apprentissage.

D'après le cours, si nous remplaçons vᵢ = D(xᵢ) et yᵢ=1 (pour tout i) dans la définition de la perte BCE, nous obtenons la perte liée aux images réelles. A l'inverse, si on fixe vᵢ = D(G(zᵢ)) et yᵢ=0 pour tout i, on obtient la perte liée aux images fake.

Dans le modèle mathématique d'un GAN , le gradient de celle-ci devait être ascendant, mais PyTorch et la plupart des autres frameworks de Machine Learning minimisent généralement les fonctions à la place. Puisque la maximisation d'une fonction est équivalente à la minimisation de son négatif, et que le terme BCE-Loss a un signe moins, nous n'avons pas besoin de nous soucier du signe.

De plus, nous pouvons observer que les cibles des images réelles sont toujours des 1, tandis que les cibles des images fausses sont des 0. On utilise les fonctions reel_data_target et fake_data_target définies dans le fichier gan_tools.

En additionnant ces deux pertes du discriminateur, nous obtenons la perte totale du mini-batch pour le discriminateur.

def train_discriminator(optimizer, real_data, fake_data):
    # Reset gradients
    optimizer.zero_grad()
    
    # 1.1 Train on Real Data
    prediction_real = discriminator(real_data)
    # Calculate error and backpropagate
    error_real = loss(prediction_real, gt.real_data_target(real_data.size(0)))
    error_real.backward()

    # 1.2 Train on Fake Data
    prediction_fake = discriminator(fake_data)
    # Calculate error and backpropagate
    error_fake = loss(prediction_fake, gt.fake_data_target(real_data.size(0)))
    error_fake.backward()
    
    # 1.3 Update weights with gradients
    optimizer.step()
    
    # Return error
    return error_real + error_fake, prediction_real, prediction_fake

Nous calculerons les gradients séparément, puis nous les mettrons à jour ensemble.

Plutôt que de minimiser log(1- D(G(z)), l'entraînement du générateur pour maximiser log D(G(z)) fournira des gradients beaucoup plus forts au début de l'entraînement. Les deux pertes peuvent être échangées de manière interchangeable puisqu'elles entraînent la même dynamique pour le Générateur et le Discriminateur. La maximisation de log D(G(z)) est équivalente à la minimisation de sa valeur négative et, comme la définition de la perte BCE-Loss comporte un signe moins, il n'est pas nécessaire de tenir compte du signe. De même que pour le Discriminateur, si nous fixons vᵢ = D(G(zᵢ)) et yᵢ=1 ∀ i, nous obtenons la perte souhaitée à minimiser.

def train_generator(optimizer, fake_data):
    # 2. Train Generator
    # Reset gradients
    optimizer.zero_grad()
    # Sample noise and generate fake data
    prediction = discriminator(fake_data)
    # Calculate error and backpropagate
    error = loss(prediction, gt.real_data_target(prediction.size(0)))
    error.backward()
    # Update weights with gradients
    optimizer.step()
    # Return error
    return error

6. Apprentissage et visualisation:

Pour chaque itération et pour chaque lot ("batch") de données, les étapes d'apprentissage sont les suivantes:

• Transformez le lot de données en variables Torch à l'aide des fonctions Variable et gt.images_to_vectors. Ce sont les vraies données pour l'étape suivante.

• Créez des vecteurs de bruit de taille 100 avec gt.noise (autant que de vraies images dans le lot), puis générez de fausses données avec le générateur.

• Former le discriminateur sur les fausses données et les données réelles, en utilisant l'optimiseur défini ci-dessus (d_optimizer).

• Créez d'autres vecteurs de bruit, taille 100, avec gt.noise.

• Entrainez le générateur sur les vecteurs de bruit.

• Visualisez un échantillon d'images générées avec gt.plot_gan. Cette fonction nécessite le numéro d'itération et de lot, ainsi que le nombre total de lots et le nom du générateur.

Attention :

Il se peut que le système se "bloque" dans une configuration où la sortie du générateur est la même quelle que soit l'entrée. Si toutes les images des exemples de sortie sont identiques après une dizaine d'itérations et que D(x) = 1 et D(G(z)) = 0, il faut relancer l'apprentissage (redémarrer le noyau).

num_epochs=20
for epoch in range(num_epochs):
  
    for n_batch, (real_batch,_) in enumerate(data_loader):
        n = real_batch.size(0)
        #Transformez le lot de données en variables Torch à l'aide des fonctions Variable et gt.images_to_vectors. 
        #Ce sont les vraies données pour l'étape suivante.
        # 1 Train Discriminator
        # 1.1 Prepare real data
        real_data = Variable(gt.images_to_vectors(real_batch))
       
        
            
        # 1.2 Generate fake data with the Generator
        #""" A COMPLETER : Créer des vecteurs bruit """
        #(les gradients ne sont donc pas calculés pour le générateur)
        fake_data = generator(gt.noise(n)).detach()
        #""" A COMPLETER : Générer des images avec le générateur """
        
        # 1.3 Train Discriminator 
       # """ A COMPLETER : Entrainer le discriminateur """
        d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, real_data, fake_data)

 
        # 2 Train Generator
        # 2.1 Generate noise
        fake_data = generator(gt.noise(n))
                      
        # 2.2 Train Generator
        #"""  A COMPLETER : Entrainer le generateur """
        g_error = train_generator(g_optimizer, fake_data)
                              
        # Generate images from a fixed noise input and visualize the output
        gt.plot_gan(epoch, n_batch, num_batches, generator)

        # Display status Logs
        gt.logger.display_status(
                epoch, num_epochs, n_batch, num_batches,
                d_error, g_error, d_pred_real, d_pred_fake
            )

  

Epoch: 0, Batch Num: [0/600]

Epoch: [0/20], Batch Num: [0/600]
Discriminator Loss: 1.3757, Generator Loss: 0.6997
D(x): 0.5020, D(G(z)): 0.4965
Epoch: [0/20], Batch Num: [1/600]
Discriminator Loss: 1.2533, Generator Loss: 0.6947
D(x): 0.5697, D(G(z)): 0.4985
Epoch: [0/20], Batch Num: [2/600]
Discriminator Loss: 1.1399, Generator Loss: 0.6885
D(x): 0.6421, D(G(z)): 0.5015
Epoch: [0/20], Batch Num: [3/600]
Discriminator Loss: 1.0170, Generator Loss: 0.6786
D(x): 0.7323, D(G(z)): 0.5057
Epoch: [0/20], Batch Num: [4/600]
Discriminator Loss: 0.9390, Generator Loss: 0.6612
D(x): 0.8037, D(G(z)): 0.5131
Epoch: [0/20], Batch Num: [5/600]
Discriminator Loss: 0.8756, Generator Loss: 0.6338
D(x): 0.8770, D(G(z)): 0.5247
Epoch: [0/20], Batch Num: [6/600]
Discriminator Loss: 0.8576, Generator Loss: 0.5975
D(x): 0.9311, D(G(z)): 0.5442
Epoch: [0/20], Batch Num: [7/600]
Discriminator Loss: 0.8783, Generator Loss: 0.5560
D(x): 0.9677, D(G(z)): 0.5704
Epoch: [0/20], Batch Num: [8/600]
Discriminator Loss: 0.9397, Generator Loss: 0.5054
D(x): 0.9818, D(G(z)): 0.6015
Epoch: [0/20], Batch Num: [9/600]
Discriminator Loss: 1.0182, Generator Loss: 0.4571
D(x): 0.9894, D(G(z)): 0.6342
Epoch: [0/20], Batch Num: [10/600]
Discriminator Loss: 1.1329, Generator Loss: 0.4214
D(x): 0.9917, D(G(z)): 0.6743
Epoch: [0/20], Batch Num: [11/600]
Discriminator Loss: 1.2116, Generator Loss: 0.4033
D(x): 0.9912, D(G(z)): 0.6983
Epoch: [0/20], Batch Num: [12/600]
Discriminator Loss: 1.2775, Generator Loss: 0.3852
D(x): 0.9894, D(G(z)): 0.7168
Epoch: [0/20], Batch Num: [13/600]
Discriminator Loss: 1.3234, Generator Loss: 0.3940
D(x): 0.9852, D(G(z)): 0.7279
Epoch: [0/20], Batch Num: [14/600]
Discriminator Loss: 1.2922, Generator Loss: 0.4148
D(x): 0.9753, D(G(z)): 0.7168
Epoch: [0/20], Batch Num: [15/600]
Discriminator Loss: 1.2624, Generator Loss: 0.4539
D(x): 0.9585, D(G(z)): 0.7034
Epoch: [0/20], Batch Num: [16/600]
Discriminator Loss: 1.2129, Generator Loss: 0.5100
D(x): 0.9253, D(G(z)): 0.6771
Epoch: [0/20], Batch Num: [17/600]
Discriminator Loss: 1.1521, Generator Loss: 0.5946
D(x): 0.8661, D(G(z)): 0.6338
Epoch: [0/20], Batch Num: [18/600]
Discriminator Loss: 1.0823, Generator Loss: 0.7206
D(x): 0.7975, D(G(z)): 0.5742
Epoch: [0/20], Batch Num: [19/600]
Discriminator Loss: 1.0828, Generator Loss: 0.8469
D(x): 0.7008, D(G(z)): 0.5150
Epoch: [0/20], Batch Num: [20/600]
Discriminator Loss: 1.0993, Generator Loss: 0.9372
D(x): 0.6058, D(G(z)): 0.4478
Epoch: [0/20], Batch Num: [21/600]
Discriminator Loss: 1.1323, Generator Loss: 1.0164
D(x): 0.5413, D(G(z)): 0.4012
Epoch: [0/20], Batch Num: [22/600]
Discriminator Loss: 1.1501, Generator Loss: 1.0378
D(x): 0.5153, D(G(z)): 0.3812
Epoch: [0/20], Batch Num: [23/600]
Discriminator Loss: 1.1575, Generator Loss: 0.9534
D(x): 0.5174, D(G(z)): 0.3881
Epoch: [0/20], Batch Num: [24/600]
Discriminator Loss: 1.1552, Generator Loss: 0.8324
D(x): 0.5576, D(G(z)): 0.4304
Epoch: [0/20], Batch Num: [25/600]
Discriminator Loss: 1.1874, Generator Loss: 0.6943
D(x): 0.5998, D(G(z)): 0.4872
Epoch: [0/20], Batch Num: [26/600]
Discriminator Loss: 1.2665, Generator Loss: 0.6062
D(x): 0.6455, D(G(z)): 0.5598
Epoch: [0/20], Batch Num: [27/600]
Discriminator Loss: 1.4218, Generator Loss: 0.5423
D(x): 0.6558, D(G(z)): 0.6277
Epoch: [0/20], Batch Num: [28/600]
Discriminator Loss: 1.5001, Generator Loss: 0.5569
D(x): 0.6419, D(G(z)): 0.6477
Epoch: [0/20], Batch Num: [29/600]
Discriminator Loss: 1.5554, Generator Loss: 0.6346
D(x): 0.6102, D(G(z)): 0.6479
Epoch: [0/20], Batch Num: [30/600]
Discriminator Loss: 1.6531, Generator Loss: 0.7014
D(x): 0.5315, D(G(z)): 0.6269
Epoch: [0/20], Batch Num: [31/600]
Discriminator Loss: 1.6781, Generator Loss: 0.7924
D(x): 0.4631, D(G(z)): 0.5802
Epoch: [0/20], Batch Num: [32/600]
Discriminator Loss: 1.7457, Generator Loss: 0.8311
D(x): 0.3935, D(G(z)): 0.5352
Epoch: [0/20], Batch Num: [33/600]
Discriminator Loss: 1.7417, Generator Loss: 0.8616
D(x): 0.3742, D(G(z)): 0.5153
Epoch: [0/20], Batch Num: [34/600]
Discriminator Loss: 1.6515, Generator Loss: 0.8278
D(x): 0.4019, D(G(z)): 0.5099
Epoch: [0/20], Batch Num: [35/600]
Discriminator Loss: 1.5516, Generator Loss: 0.8314
D(x): 0.4403, D(G(z)): 0.5055
Epoch: [0/20], Batch Num: [36/600]
Discriminator Loss: 1.4324, Generator Loss: 0.8331
D(x): 0.4787, D(G(z)): 0.4918
Epoch: [0/20], Batch Num: [37/600]
Discriminator Loss: 1.3024, Generator Loss: 0.8581
D(x): 0.5380, D(G(z)): 0.4891
Epoch: [0/20], Batch Num: [38/600]
Discriminator Loss: 1.1468, Generator Loss: 0.9232
D(x): 0.6018, D(G(z)): 0.4669
Epoch: [0/20], Batch Num: [39/600]
Discriminator Loss: 1.0202, Generator Loss: 1.0655
D(x): 0.6513, D(G(z)): 0.4417
Epoch: [0/20], Batch Num: [40/600]
Discriminator Loss: 0.8959, Generator Loss: 1.1934
D(x): 0.6860, D(G(z)): 0.4015
Epoch: [0/20], Batch Num: [41/600]
Discriminator Loss: 0.7477, Generator Loss: 1.3638
D(x): 0.7228, D(G(z)): 0.3417
Epoch: [0/20], Batch Num: [42/600]
Discriminator Loss: 0.6334, Generator Loss: 1.5860
D(x): 0.7651, D(G(z)): 0.3026
Epoch: [0/20], Batch Num: [43/600]
Discriminator Loss: 0.5010, Generator Loss: 1.8039
D(x): 0.8094, D(G(z)): 0.2490
Epoch: [0/20], Batch Num: [44/600]
Discriminator Loss: 0.4071, Generator Loss: 2.0720
D(x): 0.8376, D(G(z)): 0.2032
Epoch: [0/20], Batch Num: [45/600]
Discriminator Loss: 0.3182, Generator Loss: 2.2691
D(x): 0.8673, D(G(z)): 0.1589
Epoch: [0/20], Batch Num: [46/600]
Discriminator Loss: 0.2585, Generator Loss: 2.5395
D(x): 0.9028, D(G(z)): 0.1429
Epoch: [0/20], Batch Num: [47/600]
Discriminator Loss: 0.2147, Generator Loss: 2.5651
D(x): 0.9261, D(G(z)): 0.1271
Epoch: [0/20], Batch Num: [48/600]
Discriminator Loss: 0.1938, Generator Loss: 2.7570
D(x): 0.9452, D(G(z)): 0.1269
Epoch: [0/20], Batch Num: [49/600]
Discriminator Loss: 0.1694, Generator Loss: 2.7636
D(x): 0.9674, D(G(z)): 0.1262
Epoch: [0/20], Batch Num: [50/600]
Discriminator Loss: 0.1716, Generator Loss: 2.7915
D(x): 0.9700, D(G(z)): 0.1299
Epoch: [0/20], Batch Num: [51/600]
Discriminator Loss: 0.1924, Generator Loss: 2.6971
D(x): 0.9700, D(G(z)): 0.1481
Epoch: [0/20], Batch Num: [52/600]
Discriminator Loss: 0.2190, Generator Loss: 2.5869
D(x): 0.9731, D(G(z)): 0.1723
Epoch: [0/20], Batch Num: [53/600]
Discriminator Loss: 0.2729, Generator Loss: 2.3712
D(x): 0.9607, D(G(z)): 0.2033
Epoch: [0/20], Batch Num: [54/600]
Discriminator Loss: 0.3433, Generator Loss: 2.0457
D(x): 0.9607, D(G(z)): 0.2577
Epoch: [0/20], Batch Num: [55/600]
Discriminator Loss: 0.5324, Generator Loss: 1.6961
D(x): 0.9153, D(G(z)): 0.3379
Epoch: [0/20], Batch Num: [56/600]
Discriminator Loss: 0.8184, Generator Loss: 1.2114
D(x): 0.8659, D(G(z)): 0.4533
Epoch: [0/20], Batch Num: [57/600]
Discriminator Loss: 1.0401, Generator Loss: 0.8257
D(x): 0.8643, D(G(z)): 0.5675
Epoch: [0/20], Batch Num: [58/600]
Discriminator Loss: 1.2592, Generator Loss: 0.6095
D(x): 0.8683, D(G(z)): 0.6518
Epoch: [0/20], Batch Num: [59/600]
Discriminator Loss: 1.3357, Generator Loss: 0.4950
D(x): 0.8982, D(G(z)): 0.6840
Epoch: [0/20], Batch Num: [60/600]
Discriminator Loss: 1.3016, Generator Loss: 0.4461
D(x): 0.9417, D(G(z)): 0.7032
Epoch: [0/20], Batch Num: [61/600]
Discriminator Loss: 1.2379, Generator Loss: 0.4373
D(x): 0.9880, D(G(z)): 0.7041
Epoch: [0/20], Batch Num: [62/600]
Discriminator Loss: 1.2578, Generator Loss: 0.4644
D(x): 0.9912, D(G(z)): 0.7103
Epoch: [0/20], Batch Num: [63/600]
Discriminator Loss: 1.1733, Generator Loss: 0.5047
D(x): 0.9958, D(G(z)): 0.6868
Epoch: [0/20], Batch Num: [64/600]
Discriminator Loss: 1.0763, Generator Loss: 0.5611
D(x): 0.9980, D(G(z)): 0.6567
Epoch: [0/20], Batch Num: [65/600]
Discriminator Loss: 0.9887, Generator Loss: 0.6345
D(x): 0.9973, D(G(z)): 0.6256
Epoch: [0/20], Batch Num: [66/600]
Discriminator Loss: 0.9078, Generator Loss: 0.6993
D(x): 0.9985, D(G(z)): 0.5945
Epoch: [0/20], Batch Num: [67/600]
Discriminator Loss: 0.8236, Generator Loss: 0.7486
D(x): 0.9978, D(G(z)): 0.5588
Epoch: [0/20], Batch Num: [68/600]
Discriminator Loss: 0.7643, Generator Loss: 0.8112
D(x): 0.9986, D(G(z)): 0.5324
Epoch: [0/20], Batch Num: [69/600]
Discriminator Loss: 0.7060, Generator Loss: 0.8751
D(x): 0.9973, D(G(z)): 0.5040
Epoch: [0/20], Batch Num: [70/600]
Discriminator Loss: 0.6686, Generator Loss: 0.9255
D(x): 0.9986, D(G(z)): 0.4858
Epoch: [0/20], Batch Num: [71/600]
Discriminator Loss: 0.6529, Generator Loss: 0.9738
D(x): 0.9986, D(G(z)): 0.4777
Epoch: [0/20], Batch Num: [72/600]
Discriminator Loss: 0.6296, Generator Loss: 0.9920
D(x): 0.9984, D(G(z)): 0.4653
Epoch: [0/20], Batch Num: [73/600]
Discriminator Loss: 0.6636, Generator Loss: 0.9755
D(x): 0.9985, D(G(z)): 0.4823
Epoch: [0/20], Batch Num: [74/600]
Discriminator Loss: 0.6659, Generator Loss: 0.9740
D(x): 0.9965, D(G(z)): 0.4819
Epoch: [0/20], Batch Num: [75/600]
Discriminator Loss: 0.6878, Generator Loss: 0.9486
D(x): 0.9988, D(G(z)): 0.4945
Epoch: [0/20], Batch Num: [76/600]
Discriminator Loss: 0.7214, Generator Loss: 0.9181
D(x): 0.9974, D(G(z)): 0.5094
Epoch: [0/20], Batch Num: [77/600]
Discriminator Loss: 0.7737, Generator Loss: 0.9087
D(x): 0.9985, D(G(z)): 0.5334
Epoch: [0/20], Batch Num: [78/600]
Discriminator Loss: 0.8364, Generator Loss: 0.8875
D(x): 0.9985, D(G(z)): 0.5606
Epoch: [0/20], Batch Num: [79/600]
Discriminator Loss: 0.9387, Generator Loss: 0.9201
D(x): 0.9981, D(G(z)): 0.5983
Epoch: [0/20], Batch Num: [80/600]
Discriminator Loss: 0.9371, Generator Loss: 0.9294
D(x): 0.9961, D(G(z)): 0.5995
Epoch: [0/20], Batch Num: [81/600]
Discriminator Loss: 0.8749, Generator Loss: 1.0195
D(x): 0.9957, D(G(z)): 0.5743
Epoch: [0/20], Batch Num: [82/600]
Discriminator Loss: 0.8026, Generator Loss: 1.2085
D(x): 0.9891, D(G(z)): 0.5405
Epoch: [0/20], Batch Num: [83/600]
Discriminator Loss: 0.6476, Generator Loss: 1.4802
D(x): 0.9806, D(G(z)): 0.4604
Epoch: [0/20], Batch Num: [84/600]
Discriminator Loss: 0.4984, Generator Loss: 1.8845
D(x): 0.9684, D(G(z)): 0.3670
Epoch: [0/20], Batch Num: [85/600]
Discriminator Loss: 0.3566, Generator Loss: 2.4574
D(x): 0.9203, D(G(z)): 0.2314
Epoch: [0/20], Batch Num: [86/600]
Discriminator Loss: 0.2886, Generator Loss: 2.9607
D(x): 0.9030, D(G(z)): 0.1646
Epoch: [0/20], Batch Num: [87/600]
Discriminator Loss: 0.2324, Generator Loss: 3.3977
D(x): 0.9119, D(G(z)): 0.1261
Epoch: [0/20], Batch Num: [88/600]
Discriminator Loss: 0.1666, Generator Loss: 3.4194
D(x): 0.9203, D(G(z)): 0.0760
Epoch: [0/20], Batch Num: [89/600]
Discriminator Loss: 0.1434, Generator Loss: 3.5815
D(x): 0.9496, D(G(z)): 0.0845
Epoch: [0/20], Batch Num: [90/600]
Discriminator Loss: 0.1398, Generator Loss: 3.3752
D(x): 0.9745, D(G(z)): 0.1055
Epoch: [0/20], Batch Num: [91/600]
Discriminator Loss: 0.1742, Generator Loss: 3.0946
D(x): 0.9831, D(G(z)): 0.1417
Epoch: [0/20], Batch Num: [92/600]
Discriminator Loss: 0.2990, Generator Loss: 2.7808
D(x): 0.9862, D(G(z)): 0.2341
Epoch: [0/20], Batch Num: [93/600]
Discriminator Loss: 0.4710, Generator Loss: 2.6480
D(x): 0.9834, D(G(z)): 0.3487
Epoch: [0/20], Batch Num: [94/600]
Discriminator Loss: 0.4440, Generator Loss: 3.0964
D(x): 0.9635, D(G(z)): 0.3193
Epoch: [0/20], Batch Num: [95/600]
Discriminator Loss: 0.5219, Generator Loss: 3.3658
D(x): 0.8752, D(G(z)): 0.3004
Epoch: [0/20], Batch Num: [96/600]
Discriminator Loss: 1.2233, Generator Loss: 2.5355
D(x): 0.5219, D(G(z)): 0.2663
Epoch: [0/20], Batch Num: [97/600]
Discriminator Loss: 1.5884, Generator Loss: 1.2441
D(x): 0.4920, D(G(z)): 0.4067
Epoch: [0/20], Batch Num: [98/600]
Discriminator Loss: 1.8034, Generator Loss: 0.6282
D(x): 0.6402, D(G(z)): 0.6709
Epoch: [0/20], Batch Num: [99/600]
Discriminator Loss: 2.0546, Generator Loss: 0.6380
D(x): 0.6808, D(G(z)): 0.7555
Epoch: 0, Batch Num: [100/600]

Epoch: [0/20], Batch Num: [100/600]
Discriminator Loss: 2.0057, Generator Loss: 0.7656
D(x): 0.7039, D(G(z)): 0.7589
Epoch: [0/20], Batch Num: [101/600]
Discriminator Loss: 2.0250, Generator Loss: 1.1344
D(x): 0.6194, D(G(z)): 0.6761
Epoch: [0/20], Batch Num: [102/600]
Discriminator Loss: 1.6289, Generator Loss: 1.3661
D(x): 0.5502, D(G(z)): 0.4884
Epoch: [0/20], Batch Num: [103/600]
Discriminator Loss: 1.2518, Generator Loss: 1.5980
D(x): 0.6560, D(G(z)): 0.4149
Epoch: [0/20], Batch Num: [104/600]
Discriminator Loss: 0.7981, Generator Loss: 1.8476
D(x): 0.7662, D(G(z)): 0.3217
Epoch: [0/20], Batch Num: [105/600]
Discriminator Loss: 0.5618, Generator Loss: 2.3718
D(x): 0.8224, D(G(z)): 0.2550
Epoch: [0/20], Batch Num: [106/600]
Discriminator Loss: 0.4530, Generator Loss: 2.7787
D(x): 0.8677, D(G(z)): 0.2268
Epoch: [0/20], Batch Num: [107/600]
Discriminator Loss: 0.3115, Generator Loss: 2.9076
D(x): 0.9033, D(G(z)): 0.1719
Epoch: [0/20], Batch Num: [108/600]
Discriminator Loss: 0.3001, Generator Loss: 3.2308
D(x): 0.8968, D(G(z)): 0.1429
Epoch: [0/20], Batch Num: [109/600]
Discriminator Loss: 0.2864, Generator Loss: 3.1464
D(x): 0.9199, D(G(z)): 0.1650
Epoch: [0/20], Batch Num: [110/600]
Discriminator Loss: 0.2895, Generator Loss: 3.3187
D(x): 0.9270, D(G(z)): 0.1796
Epoch: [0/20], Batch Num: [111/600]
Discriminator Loss: 0.3755, Generator Loss: 2.8984
D(x): 0.9124, D(G(z)): 0.2203
Epoch: [0/20], Batch Num: [112/600]
Discriminator Loss: 0.4398, Generator Loss: 2.6751
D(x): 0.8507, D(G(z)): 0.1858
Epoch: [0/20], Batch Num: [113/600]
Discriminator Loss: 0.3714, Generator Loss: 2.6324
D(x): 0.8860, D(G(z)): 0.1808
Epoch: [0/20], Batch Num: [114/600]
Discriminator Loss: 0.3039, Generator Loss: 2.7980
D(x): 0.9056, D(G(z)): 0.1571
Epoch: [0/20], Batch Num: [115/600]
Discriminator Loss: 0.2057, Generator Loss: 2.9515
D(x): 0.9415, D(G(z)): 0.1239
Epoch: [0/20], Batch Num: [116/600]
Discriminator Loss: 0.1903, Generator Loss: 3.0568
D(x): 0.9648, D(G(z)): 0.1353
Epoch: [0/20], Batch Num: [117/600]
Discriminator Loss: 0.1890, Generator Loss: 3.0937
D(x): 0.9754, D(G(z)): 0.1457
Epoch: [0/20], Batch Num: [118/600]
Discriminator Loss: 0.2634, Generator Loss: 3.2799
D(x): 0.9653, D(G(z)): 0.1898
Epoch: [0/20], Batch Num: [119/600]
Discriminator Loss: 0.2386, Generator Loss: 3.2300
D(x): 0.9631, D(G(z)): 0.1619
Epoch: [0/20], Batch Num: [120/600]
Discriminator Loss: 0.2684, Generator Loss: 3.4721
D(x): 0.9554, D(G(z)): 0.1813
Epoch: [0/20], Batch Num: [121/600]
Discriminator Loss: 0.3528, Generator Loss: 3.5226
D(x): 0.8916, D(G(z)): 0.1728
Epoch: [0/20], Batch Num: [122/600]
Discriminator Loss: 0.3932, Generator Loss: 3.4124
D(x): 0.8850, D(G(z)): 0.1959
Epoch: [0/20], Batch Num: [123/600]
Discriminator Loss: 0.4349, Generator Loss: 3.3915
D(x): 0.8791, D(G(z)): 0.2198
Epoch: [0/20], Batch Num: [124/600]
Discriminator Loss: 0.5224, Generator Loss: 3.2788
D(x): 0.8699, D(G(z)): 0.2663
Epoch: [0/20], Batch Num: [125/600]
Discriminator Loss: 0.7063, Generator Loss: 3.0236
D(x): 0.8080, D(G(z)): 0.3151
Epoch: [0/20], Batch Num: [126/600]
Discriminator Loss: 0.9791, Generator Loss: 2.3889
D(x): 0.7672, D(G(z)): 0.4154
Epoch: [0/20], Batch Num: [127/600]
Discriminator Loss: 1.6270, Generator Loss: 1.6560
D(x): 0.6527, D(G(z)): 0.5714
Epoch: [0/20], Batch Num: [128/600]
Discriminator Loss: 2.3135, Generator Loss: 1.0631
D(x): 0.6043, D(G(z)): 0.7322
Epoch: [0/20], Batch Num: [129/600]
Discriminator Loss: 3.3252, Generator Loss: 0.5853
D(x): 0.5236, D(G(z)): 0.8656
Epoch: [0/20], Batch Num: [130/600]
Discriminator Loss: 4.0215, Generator Loss: 0.2905
D(x): 0.5304, D(G(z)): 0.9252
Epoch: [0/20], Batch Num: [131/600]
Discriminator Loss: 4.5425, Generator Loss: 0.1464
D(x): 0.5152, D(G(z)): 0.9545
Epoch: [0/20], Batch Num: [132/600]
Discriminator Loss: 4.6483, Generator Loss: 0.1203
D(x): 0.5145, D(G(z)): 0.9604
Epoch: [0/20], Batch Num: [133/600]
Discriminator Loss: 4.5506, Generator Loss: 0.1208
D(x): 0.4705, D(G(z)): 0.9590
Epoch: [0/20], Batch Num: [134/600]
Discriminator Loss: 4.1835, Generator Loss: 0.1369
D(x): 0.4786, D(G(z)): 0.9429
Epoch: [0/20], Batch Num: [135/600]
Discriminator Loss: 3.4126, Generator Loss: 0.2342
D(x): 0.5837, D(G(z)): 0.9210
Epoch: [0/20], Batch Num: [136/600]
Discriminator Loss: 2.6324, Generator Loss: 0.4036
D(x): 0.6446, D(G(z)): 0.8661
Epoch: [0/20], Batch Num: [137/600]
Discriminator Loss: 1.9153, Generator Loss: 0.7278
D(x): 0.6619, D(G(z)): 0.7449
Epoch: [0/20], Batch Num: [138/600]
Discriminator Loss: 1.4548, Generator Loss: 1.1038
D(x): 0.5977, D(G(z)): 0.5576
Epoch: [0/20], Batch Num: [139/600]
Discriminator Loss: 1.0761, Generator Loss: 1.4134
D(x): 0.6160, D(G(z)): 0.4031
Epoch: [0/20], Batch Num: [140/600]
Discriminator Loss: 0.8119, Generator Loss: 1.7640
D(x): 0.6759, D(G(z)): 0.3083
Epoch: [0/20], Batch Num: [141/600]
Discriminator Loss: 0.7429, Generator Loss: 1.9919
D(x): 0.6613, D(G(z)): 0.2273
Epoch: [0/20], Batch Num: [142/600]
Discriminator Loss: 0.5646, Generator Loss: 2.1301
D(x): 0.7626, D(G(z)): 0.2289
Epoch: [0/20], Batch Num: [143/600]
Discriminator Loss: 0.5102, Generator Loss: 2.1464
D(x): 0.7785, D(G(z)): 0.2090
Epoch: [0/20], Batch Num: [144/600]
Discriminator Loss: 0.4331, Generator Loss: 2.0524
D(x): 0.8613, D(G(z)): 0.2373
Epoch: [0/20], Batch Num: [145/600]
Discriminator Loss: 0.4878, Generator Loss: 1.8296
D(x): 0.8755, D(G(z)): 0.2875
Epoch: [0/20], Batch Num: [146/600]
Discriminator Loss: 0.5691, Generator Loss: 1.6582
D(x): 0.9043, D(G(z)): 0.3652
Epoch: [0/20], Batch Num: [147/600]
Discriminator Loss: 0.6970, Generator Loss: 1.6181
D(x): 0.9036, D(G(z)): 0.4314
Epoch: [0/20], Batch Num: [148/600]
Discriminator Loss: 0.8336, Generator Loss: 1.4877
D(x): 0.8885, D(G(z)): 0.4894
Epoch: [0/20], Batch Num: [149/600]
Discriminator Loss: 0.9185, Generator Loss: 1.6651
D(x): 0.8533, D(G(z)): 0.5129
Epoch: [0/20], Batch Num: [150/600]
Discriminator Loss: 0.9745, Generator Loss: 1.7946
D(x): 0.7520, D(G(z)): 0.4655
Epoch: [0/20], Batch Num: [151/600]
Discriminator Loss: 1.3522, Generator Loss: 1.8568
D(x): 0.5674, D(G(z)): 0.4696
Epoch: [0/20], Batch Num: [152/600]
Discriminator Loss: 1.4831, Generator Loss: 1.6831
D(x): 0.4961, D(G(z)): 0.4592
Epoch: [0/20], Batch Num: [153/600]
Discriminator Loss: 1.8555, Generator Loss: 1.4483
D(x): 0.3750, D(G(z)): 0.4657
Epoch: [0/20], Batch Num: [154/600]
Discriminator Loss: 2.3840, Generator Loss: 1.0873
D(x): 0.2793, D(G(z)): 0.5554
Epoch: [0/20], Batch Num: [155/600]
Discriminator Loss: 2.5592, Generator Loss: 0.8425
D(x): 0.3098, D(G(z)): 0.6657
Epoch: [0/20], Batch Num: [156/600]
Discriminator Loss: 2.7537, Generator Loss: 0.7005
D(x): 0.3332, D(G(z)): 0.7338
Epoch: [0/20], Batch Num: [157/600]
Discriminator Loss: 2.8382, Generator Loss: 0.7531
D(x): 0.3216, D(G(z)): 0.7407
Epoch: [0/20], Batch Num: [158/600]
Discriminator Loss: 2.7322, Generator Loss: 0.8191
D(x): 0.3435, D(G(z)): 0.7207
Epoch: [0/20], Batch Num: [159/600]
Discriminator Loss: 2.1532, Generator Loss: 1.1463
D(x): 0.4042, D(G(z)): 0.6271
Epoch: [0/20], Batch Num: [160/600]
Discriminator Loss: 1.7042, Generator Loss: 1.6353
D(x): 0.4595, D(G(z)): 0.5319
Epoch: [0/20], Batch Num: [161/600]
Discriminator Loss: 1.4963, Generator Loss: 1.9447
D(x): 0.4473, D(G(z)): 0.4194
Epoch: [0/20], Batch Num: [162/600]
Discriminator Loss: 1.2780, Generator Loss: 2.3246
D(x): 0.4593, D(G(z)): 0.2986
Epoch: [0/20], Batch Num: [163/600]
Discriminator Loss: 1.1015, Generator Loss: 2.1820
D(x): 0.5048, D(G(z)): 0.2592
Epoch: [0/20], Batch Num: [164/600]
Discriminator Loss: 0.9572, Generator Loss: 2.2909
D(x): 0.5741, D(G(z)): 0.2818
Epoch: [0/20], Batch Num: [165/600]
Discriminator Loss: 0.8590, Generator Loss: 2.0758
D(x): 0.6387, D(G(z)): 0.2985
Epoch: [0/20], Batch Num: [166/600]
Discriminator Loss: 0.8500, Generator Loss: 2.0418
D(x): 0.7050, D(G(z)): 0.3491
Epoch: [0/20], Batch Num: [167/600]
Discriminator Loss: 0.8134, Generator Loss: 2.2371
D(x): 0.7248, D(G(z)): 0.3416
Epoch: [0/20], Batch Num: [168/600]
Discriminator Loss: 0.7569, Generator Loss: 2.5149
D(x): 0.7520, D(G(z)): 0.3397
Epoch: [0/20], Batch Num: [169/600]
Discriminator Loss: 0.7376, Generator Loss: 2.8681
D(x): 0.7346, D(G(z)): 0.3097
Epoch: [0/20], Batch Num: [170/600]
Discriminator Loss: 0.8856, Generator Loss: 3.0549
D(x): 0.6582, D(G(z)): 0.3129
Epoch: [0/20], Batch Num: [171/600]
Discriminator Loss: 0.9488, Generator Loss: 2.9372
D(x): 0.6192, D(G(z)): 0.2714
Epoch: [0/20], Batch Num: [172/600]
Discriminator Loss: 1.1630, Generator Loss: 2.4872
D(x): 0.5530, D(G(z)): 0.2882
Epoch: [0/20], Batch Num: [173/600]
Discriminator Loss: 1.2253, Generator Loss: 2.0293
D(x): 0.5919, D(G(z)): 0.3943
Epoch: [0/20], Batch Num: [174/600]
Discriminator Loss: 1.1550, Generator Loss: 1.9717
D(x): 0.6662, D(G(z)): 0.4420
Epoch: [0/20], Batch Num: [175/600]
Discriminator Loss: 1.4188, Generator Loss: 1.8792
D(x): 0.6209, D(G(z)): 0.5219
Epoch: [0/20], Batch Num: [176/600]
Discriminator Loss: 1.5129, Generator Loss: 2.0554
D(x): 0.5929, D(G(z)): 0.5384
Epoch: [0/20], Batch Num: [177/600]
Discriminator Loss: 1.6055, Generator Loss: 1.9217
D(x): 0.4794, D(G(z)): 0.4466
Epoch: [0/20], Batch Num: [178/600]
Discriminator Loss: 1.5354, Generator Loss: 1.9267
D(x): 0.5363, D(G(z)): 0.4640
Epoch: [0/20], Batch Num: [179/600]
Discriminator Loss: 1.5365, Generator Loss: 1.8693
D(x): 0.5542, D(G(z)): 0.4971
Epoch: [0/20], Batch Num: [180/600]
Discriminator Loss: 1.5685, Generator Loss: 1.6900
D(x): 0.5531, D(G(z)): 0.5089
Epoch: [0/20], Batch Num: [181/600]
Discriminator Loss: 1.3998, Generator Loss: 1.8437
D(x): 0.6168, D(G(z)): 0.5190
Epoch: [0/20], Batch Num: [182/600]
Discriminator Loss: 1.4275, Generator Loss: 2.1761
D(x): 0.5807, D(G(z)): 0.4679
Epoch: [0/20], Batch Num: [183/600]
Discriminator Loss: 1.2889, Generator Loss: 2.0070
D(x): 0.5728, D(G(z)): 0.4082
Epoch: [0/20], Batch Num: [184/600]
Discriminator Loss: 1.2336, Generator Loss: 1.8823
D(x): 0.5808, D(G(z)): 0.3866
Epoch: [0/20], Batch Num: [185/600]
Discriminator Loss: 1.0873, Generator Loss: 1.9294
D(x): 0.6637, D(G(z)): 0.4191
Epoch: [0/20], Batch Num: [186/600]
Discriminator Loss: 1.0522, Generator Loss: 1.9696
D(x): 0.6867, D(G(z)): 0.4189
Epoch: [0/20], Batch Num: [187/600]
Discriminator Loss: 0.8979, Generator Loss: 2.1970
D(x): 0.7452, D(G(z)): 0.3877
Epoch: [0/20], Batch Num: [188/600]
Discriminator Loss: 0.6575, Generator Loss: 2.4053
D(x): 0.7830, D(G(z)): 0.3052
Epoch: [0/20], Batch Num: [189/600]
Discriminator Loss: 0.5843, Generator Loss: 2.5890
D(x): 0.8193, D(G(z)): 0.2889
Epoch: [0/20], Batch Num: [190/600]
Discriminator Loss: 0.5317, Generator Loss: 2.7182
D(x): 0.8279, D(G(z)): 0.2608
Epoch: [0/20], Batch Num: [191/600]
Discriminator Loss: 0.5641, Generator Loss: 2.9022
D(x): 0.7709, D(G(z)): 0.2174
Epoch: [0/20], Batch Num: [192/600]
Discriminator Loss: 0.4754, Generator Loss: 2.7208
D(x): 0.8240, D(G(z)): 0.2120
Epoch: [0/20], Batch Num: [193/600]
Discriminator Loss: 0.5251, Generator Loss: 2.7104
D(x): 0.8163, D(G(z)): 0.2372
Epoch: [0/20], Batch Num: [194/600]
Discriminator Loss: 0.5117, Generator Loss: 2.5804
D(x): 0.8407, D(G(z)): 0.2527
Epoch: [0/20], Batch Num: [195/600]
Discriminator Loss: 0.5327, Generator Loss: 2.4419
D(x): 0.8800, D(G(z)): 0.2912
Epoch: [0/20], Batch Num: [196/600]
Discriminator Loss: 0.6154, Generator Loss: 2.3948
D(x): 0.8686, D(G(z)): 0.3437
Epoch: [0/20], Batch Num: [197/600]
Discriminator Loss: 0.6782, Generator Loss: 2.4333
D(x): 0.8502, D(G(z)): 0.3625
Epoch: [0/20], Batch Num: [198/600]
Discriminator Loss: 0.7137, Generator Loss: 2.1507
D(x): 0.8151, D(G(z)): 0.3544
Epoch: [0/20], Batch Num: [199/600]
Discriminator Loss: 0.9986, Generator Loss: 2.1189
D(x): 0.7806, D(G(z)): 0.4560
Epoch: 0, Batch Num: [200/600]

Epoch: [0/20], Batch Num: [200/600]
Discriminator Loss: 0.9086, Generator Loss: 2.0211
D(x): 0.8197, D(G(z)): 0.4333
Epoch: [0/20], Batch Num: [201/600]
Discriminator Loss: 0.9055, Generator Loss: 2.0067
D(x): 0.7908, D(G(z)): 0.4377
Epoch: [0/20], Batch Num: [202/600]
Discriminator Loss: 0.8385, Generator Loss: 2.0879
D(x): 0.8175, D(G(z)): 0.4100
Epoch: [0/20], Batch Num: [203/600]
Discriminator Loss: 0.6694, Generator Loss: 2.6079
D(x): 0.8426, D(G(z)): 0.3517
Epoch: [0/20], Batch Num: [204/600]
Discriminator Loss: 0.5598, Generator Loss: 2.8838
D(x): 0.8498, D(G(z)): 0.2806
Epoch: [0/20], Batch Num: [205/600]
Discriminator Loss: 0.4382, Generator Loss: 3.1786
D(x): 0.8889, D(G(z)): 0.2179
Epoch: [0/20], Batch Num: [206/600]
Discriminator Loss: 0.3378, Generator Loss: 3.6241
D(x): 0.9109, D(G(z)): 0.1870
Epoch: [0/20], Batch Num: [207/600]
Discriminator Loss: 0.2100, Generator Loss: 3.8903
D(x): 0.9392, D(G(z)): 0.1267
Epoch: [0/20], Batch Num: [208/600]
Discriminator Loss: 0.1872, Generator Loss: 3.8112
D(x): 0.9524, D(G(z)): 0.1212
Epoch: [0/20], Batch Num: [209/600]
Discriminator Loss: 0.2041, Generator Loss: 3.7566
D(x): 0.9628, D(G(z)): 0.1400
Epoch: [0/20], Batch Num: [210/600]
Discriminator Loss: 0.2619, Generator Loss: 3.6942
D(x): 0.9677, D(G(z)): 0.1890
Epoch: [0/20], Batch Num: [211/600]
Discriminator Loss: 0.2675, Generator Loss: 3.3797
D(x): 0.9692, D(G(z)): 0.1978
Epoch: [0/20], Batch Num: [212/600]
Discriminator Loss: 0.3590, Generator Loss: 2.9914
D(x): 0.9764, D(G(z)): 0.2688
Epoch: [0/20], Batch Num: [213/600]
Discriminator Loss: 0.4237, Generator Loss: 2.9625
D(x): 0.9682, D(G(z)): 0.2909
Epoch: [0/20], Batch Num: [214/600]
Discriminator Loss: 0.4924, Generator Loss: 2.9242
D(x): 0.9705, D(G(z)): 0.3416
Epoch: [0/20], Batch Num: [215/600]
Discriminator Loss: 0.6234, Generator Loss: 2.8422
D(x): 0.9479, D(G(z)): 0.3907
Epoch: [0/20], Batch Num: [216/600]
Discriminator Loss: 0.6726, Generator Loss: 2.9595
D(x): 0.9298, D(G(z)): 0.3948
Epoch: [0/20], Batch Num: [217/600]
Discriminator Loss: 0.8335, Generator Loss: 2.9506
D(x): 0.8965, D(G(z)): 0.4508
Epoch: [0/20], Batch Num: [218/600]
Discriminator Loss: 1.0198, Generator Loss: 2.7615
D(x): 0.7836, D(G(z)): 0.4316
Epoch: [0/20], Batch Num: [219/600]
Discriminator Loss: 1.3710, Generator Loss: 2.3265
D(x): 0.7505, D(G(z)): 0.5249
Epoch: [0/20], Batch Num: [220/600]
Discriminator Loss: 1.6645, Generator Loss: 1.8983
D(x): 0.7498, D(G(z)): 0.6652
Epoch: [0/20], Batch Num: [221/600]
Discriminator Loss: 2.1903, Generator Loss: 1.3833
D(x): 0.7528, D(G(z)): 0.7828
Epoch: [0/20], Batch Num: [222/600]
Discriminator Loss: 2.7846, Generator Loss: 1.1118
D(x): 0.7619, D(G(z)): 0.8626
Epoch: [0/20], Batch Num: [223/600]
Discriminator Loss: 3.1516, Generator Loss: 0.7451
D(x): 0.7429, D(G(z)): 0.9099
Epoch: [0/20], Batch Num: [224/600]
Discriminator Loss: 3.4502, Generator Loss: 0.4626
D(x): 0.6580, D(G(z)): 0.9191
Epoch: [0/20], Batch Num: [225/600]
Discriminator Loss: 3.8689, Generator Loss: 0.4014
D(x): 0.5788, D(G(z)): 0.9288
Epoch: [0/20], Batch Num: [226/600]
Discriminator Loss: 4.1244, Generator Loss: 0.4059
D(x): 0.4931, D(G(z)): 0.9321
Epoch: [0/20], Batch Num: [227/600]
Discriminator Loss: 3.6857, Generator Loss: 0.4365
D(x): 0.5729, D(G(z)): 0.9180
Epoch: [0/20], Batch Num: [228/600]
Discriminator Loss: 3.1983, Generator Loss: 0.7137
D(x): 0.5785, D(G(z)): 0.8782
Epoch: [0/20], Batch Num: [229/600]
Discriminator Loss: 2.5742, Generator Loss: 1.2122
D(x): 0.6557, D(G(z)): 0.8231
Epoch: [0/20], Batch Num: [230/600]
Discriminator Loss: 2.0029, Generator Loss: 2.0332
D(x): 0.6684, D(G(z)): 0.6982
Epoch: [0/20], Batch Num: [231/600]
Discriminator Loss: 1.3821, Generator Loss: 3.6414
D(x): 0.6537, D(G(z)): 0.4803
Epoch: [0/20], Batch Num: [232/600]
Discriminator Loss: 1.1986, Generator Loss: 4.8706
D(x): 0.6045, D(G(z)): 0.3288
Epoch: [0/20], Batch Num: [233/600]
Discriminator Loss: 1.2239, Generator Loss: 5.2146
D(x): 0.4872, D(G(z)): 0.1799
Epoch: [0/20], Batch Num: [234/600]
Discriminator Loss: 1.2970, Generator Loss: 4.8424
D(x): 0.4805, D(G(z)): 0.1455
Epoch: [0/20], Batch Num: [235/600]
Discriminator Loss: 0.8832, Generator Loss: 4.1107
D(x): 0.6294, D(G(z)): 0.1865
Epoch: [0/20], Batch Num: [236/600]
Discriminator Loss: 0.9230, Generator Loss: 3.7021
D(x): 0.6983, D(G(z)): 0.2893
Epoch: [0/20], Batch Num: [237/600]
Discriminator Loss: 1.1147, Generator Loss: 3.6811
D(x): 0.7233, D(G(z)): 0.4258
Epoch: [0/20], Batch Num: [238/600]
Discriminator Loss: 0.9427, Generator Loss: 3.8535
D(x): 0.7152, D(G(z)): 0.3127
Epoch: [0/20], Batch Num: [239/600]
Discriminator Loss: 1.3040, Generator Loss: 4.1377
D(x): 0.5689, D(G(z)): 0.2845
Epoch: [0/20], Batch Num: [240/600]
Discriminator Loss: 1.2124, Generator Loss: 3.8424
D(x): 0.5810, D(G(z)): 0.2978
Epoch: [0/20], Batch Num: [241/600]
Discriminator Loss: 1.4023, Generator Loss: 3.1724
D(x): 0.5452, D(G(z)): 0.3327
Epoch: [0/20], Batch Num: [242/600]
Discriminator Loss: 1.4698, Generator Loss: 2.9145
D(x): 0.5864, D(G(z)): 0.3599
Epoch: [0/20], Batch Num: [243/600]
Discriminator Loss: 1.4432, Generator Loss: 2.5519
D(x): 0.6519, D(G(z)): 0.4249
Epoch: [0/20], Batch Num: [244/600]
Discriminator Loss: 1.6462, Generator Loss: 2.6780
D(x): 0.6372, D(G(z)): 0.4593
Epoch: [0/20], Batch Num: [245/600]
Discriminator Loss: 1.4753, Generator Loss: 3.1936
D(x): 0.5806, D(G(z)): 0.4117
Epoch: [0/20], Batch Num: [246/600]
Discriminator Loss: 1.6904, Generator Loss: 2.7215
D(x): 0.5042, D(G(z)): 0.3362
Epoch: [0/20], Batch Num: [247/600]
Discriminator Loss: 1.4428, Generator Loss: 2.4317
D(x): 0.6080, D(G(z)): 0.4065
Epoch: [0/20], Batch Num: [248/600]
Discriminator Loss: 1.7317, Generator Loss: 2.5980
D(x): 0.5633, D(G(z)): 0.4570
Epoch: [0/20], Batch Num: [249/600]
Discriminator Loss: 2.0449, Generator Loss: 2.1086
D(x): 0.4631, D(G(z)): 0.4101
Epoch: [0/20], Batch Num: [250/600]
Discriminator Loss: 2.0100, Generator Loss: 1.7823
D(x): 0.5111, D(G(z)): 0.5429
Epoch: [0/20], Batch Num: [251/600]
Discriminator Loss: 2.4089, Generator Loss: 1.6016
D(x): 0.4946, D(G(z)): 0.5957
Epoch: [0/20], Batch Num: [252/600]
Discriminator Loss: 2.2947, Generator Loss: 1.6971
D(x): 0.4943, D(G(z)): 0.5506
Epoch: [0/20], Batch Num: [253/600]
Discriminator Loss: 2.5003, Generator Loss: 1.5763
D(x): 0.3989, D(G(z)): 0.5616
Epoch: [0/20], Batch Num: [254/600]
Discriminator Loss: 2.6157, Generator Loss: 1.6664
D(x): 0.4193, D(G(z)): 0.5546
Epoch: [0/20], Batch Num: [255/600]
Discriminator Loss: 2.1071, Generator Loss: 1.5496
D(x): 0.4873, D(G(z)): 0.5196
Epoch: [0/20], Batch Num: [256/600]
Discriminator Loss: 1.8149, Generator Loss: 1.7503
D(x): 0.4820, D(G(z)): 0.4274
Epoch: [0/20], Batch Num: [257/600]
Discriminator Loss: 1.6699, Generator Loss: 1.8347
D(x): 0.5744, D(G(z)): 0.5022
Epoch: [0/20], Batch Num: [258/600]
Discriminator Loss: 1.0655, Generator Loss: 2.3592
D(x): 0.7107, D(G(z)): 0.3988
Epoch: [0/20], Batch Num: [259/600]
Discriminator Loss: 1.0344, Generator Loss: 2.4587
D(x): 0.6506, D(G(z)): 0.3040
Epoch: [0/20], Batch Num: [260/600]
Discriminator Loss: 0.8716, Generator Loss: 2.7074
D(x): 0.6824, D(G(z)): 0.2422
Epoch: [0/20], Batch Num: [261/600]
Discriminator Loss: 0.4163, Generator Loss: 3.2893
D(x): 0.8099, D(G(z)): 0.1551
Epoch: [0/20], Batch Num: [262/600]
Discriminator Loss: 0.4096, Generator Loss: 3.4558
D(x): 0.8224, D(G(z)): 0.1444
Epoch: [0/20], Batch Num: [263/600]
Discriminator Loss: 0.3184, Generator Loss: 3.5489
D(x): 0.8336, D(G(z)): 0.0902
Epoch: [0/20], Batch Num: [264/600]
Discriminator Loss: 0.2801, Generator Loss: 3.7337
D(x): 0.8491, D(G(z)): 0.0827
Epoch: [0/20], Batch Num: [265/600]
Discriminator Loss: 0.2361, Generator Loss: 4.1468
D(x): 0.8858, D(G(z)): 0.0662
Epoch: [0/20], Batch Num: [266/600]
Discriminator Loss: 0.1748, Generator Loss: 4.0025
D(x): 0.9143, D(G(z)): 0.0665
Epoch: [0/20], Batch Num: [267/600]
Discriminator Loss: 0.1312, Generator Loss: 4.1672
D(x): 0.9359, D(G(z)): 0.0553
Epoch: [0/20], Batch Num: [268/600]
Discriminator Loss: 0.1615, Generator Loss: 4.4024
D(x): 0.9334, D(G(z)): 0.0651
Epoch: [0/20], Batch Num: [269/600]
Discriminator Loss: 0.1176, Generator Loss: 4.5093
D(x): 0.9410, D(G(z)): 0.0482
Epoch: [0/20], Batch Num: [270/600]
Discriminator Loss: 0.0938, Generator Loss: 4.7856
D(x): 0.9635, D(G(z)): 0.0517
Epoch: [0/20], Batch Num: [271/600]
Discriminator Loss: 0.0810, Generator Loss: 4.4808
D(x): 0.9593, D(G(z)): 0.0357
Epoch: [0/20], Batch Num: [272/600]
Discriminator Loss: 0.0900, Generator Loss: 4.3926
D(x): 0.9639, D(G(z)): 0.0464
Epoch: [0/20], Batch Num: [273/600]
Discriminator Loss: 0.0808, Generator Loss: 4.5429
D(x): 0.9717, D(G(z)): 0.0487
Epoch: [0/20], Batch Num: [274/600]
Discriminator Loss: 0.0916, Generator Loss: 4.3356
D(x): 0.9674, D(G(z)): 0.0539
Epoch: [0/20], Batch Num: [275/600]
Discriminator Loss: 0.1133, Generator Loss: 3.9325
D(x): 0.9564, D(G(z)): 0.0611
Epoch: [0/20], Batch Num: [276/600]
Discriminator Loss: 0.1539, Generator Loss: 4.3792
D(x): 0.9538, D(G(z)): 0.0891
Epoch: [0/20], Batch Num: [277/600]
Discriminator Loss: 0.1395, Generator Loss: 4.6449
D(x): 0.9459, D(G(z)): 0.0685
Epoch: [0/20], Batch Num: [278/600]
Discriminator Loss: 0.1330, Generator Loss: 4.8289
D(x): 0.9521, D(G(z)): 0.0723
Epoch: [0/20], Batch Num: [279/600]
Discriminator Loss: 0.1440, Generator Loss: 4.7882
D(x): 0.9436, D(G(z)): 0.0757
Epoch: [0/20], Batch Num: [280/600]
Discriminator Loss: 0.1555, Generator Loss: 5.1488
D(x): 0.9328, D(G(z)): 0.0721
Epoch: [0/20], Batch Num: [281/600]
Discriminator Loss: 0.2216, Generator Loss: 5.2763
D(x): 0.8892, D(G(z)): 0.0673
Epoch: [0/20], Batch Num: [282/600]
Discriminator Loss: 0.2164, Generator Loss: 4.9849
D(x): 0.8759, D(G(z)): 0.0573
Epoch: [0/20], Batch Num: [283/600]
Discriminator Loss: 0.2838, Generator Loss: 5.0490
D(x): 0.8629, D(G(z)): 0.0686
Epoch: [0/20], Batch Num: [284/600]
Discriminator Loss: 0.1968, Generator Loss: 4.7905
D(x): 0.9079, D(G(z)): 0.0836
Epoch: [0/20], Batch Num: [285/600]
Discriminator Loss: 0.2356, Generator Loss: 4.3804
D(x): 0.8919, D(G(z)): 0.0842
Epoch: [0/20], Batch Num: [286/600]
Discriminator Loss: 0.2215, Generator Loss: 4.1736
D(x): 0.9076, D(G(z)): 0.0947
Epoch: [0/20], Batch Num: [287/600]
Discriminator Loss: 0.2095, Generator Loss: 4.0381
D(x): 0.9183, D(G(z)): 0.1050
Epoch: [0/20], Batch Num: [288/600]
Discriminator Loss: 0.2304, Generator Loss: 4.4215
D(x): 0.9248, D(G(z)): 0.1172
Epoch: [0/20], Batch Num: [289/600]
Discriminator Loss: 0.2084, Generator Loss: 4.7047
D(x): 0.9254, D(G(z)): 0.1064
Epoch: [0/20], Batch Num: [290/600]
Discriminator Loss: 0.2328, Generator Loss: 4.6516
D(x): 0.8906, D(G(z)): 0.0903
Epoch: [0/20], Batch Num: [291/600]
Discriminator Loss: 0.1920, Generator Loss: 4.8816
D(x): 0.9265, D(G(z)): 0.0914
Epoch: [0/20], Batch Num: [292/600]
Discriminator Loss: 0.2264, Generator Loss: 5.2001
D(x): 0.8966, D(G(z)): 0.0811
Epoch: [0/20], Batch Num: [293/600]
Discriminator Loss: 0.2442, Generator Loss: 4.7204
D(x): 0.8922, D(G(z)): 0.0918
Epoch: [0/20], Batch Num: [294/600]
Discriminator Loss: 0.2652, Generator Loss: 4.4690
D(x): 0.9024, D(G(z)): 0.1201
Epoch: [0/20], Batch Num: [295/600]
Discriminator Loss: 0.2115, Generator Loss: 4.1499
D(x): 0.9308, D(G(z)): 0.1137
Epoch: [0/20], Batch Num: [296/600]
Discriminator Loss: 0.2321, Generator Loss: 4.6099
D(x): 0.9269, D(G(z)): 0.1277
Epoch: [0/20], Batch Num: [297/600]
Discriminator Loss: 0.2710, Generator Loss: 4.4073
D(x): 0.9163, D(G(z)): 0.1284
Epoch: [0/20], Batch Num: [298/600]
Discriminator Loss: 0.3584, Generator Loss: 4.5914
D(x): 0.8850, D(G(z)): 0.1474
Epoch: [0/20], Batch Num: [299/600]
Discriminator Loss: 0.3294, Generator Loss: 4.4059
D(x): 0.8963, D(G(z)): 0.1613
Epoch: 0, Batch Num: [300/600]

Epoch: [0/20], Batch Num: [300/600]
Discriminator Loss: 0.3378, Generator Loss: 4.4186
D(x): 0.8675, D(G(z)): 0.1273
Epoch: [0/20], Batch Num: [301/600]
Discriminator Loss: 0.4077, Generator Loss: 4.3169
D(x): 0.8571, D(G(z)): 0.1558
Epoch: [0/20], Batch Num: [302/600]
Discriminator Loss: 0.4715, Generator Loss: 4.0077
D(x): 0.8285, D(G(z)): 0.1581
Epoch: [0/20], Batch Num: [303/600]
Discriminator Loss: 0.5292, Generator Loss: 3.6661
D(x): 0.8507, D(G(z)): 0.2064
Epoch: [0/20], Batch Num: [304/600]
Discriminator Loss: 0.5801, Generator Loss: 3.5109
D(x): 0.8343, D(G(z)): 0.2295
Epoch: [0/20], Batch Num: [305/600]
Discriminator Loss: 0.5487, Generator Loss: 4.1193
D(x): 0.8902, D(G(z)): 0.3074
Epoch: [0/20], Batch Num: [306/600]
Discriminator Loss: 0.4946, Generator Loss: 4.4040
D(x): 0.8420, D(G(z)): 0.2181
Epoch: [0/20], Batch Num: [307/600]
Discriminator Loss: 0.5920, Generator Loss: 4.4235
D(x): 0.8268, D(G(z)): 0.2416
Epoch: [0/20], Batch Num: [308/600]
Discriminator Loss: 0.6398, Generator Loss: 4.2284
D(x): 0.8224, D(G(z)): 0.2246
Epoch: [0/20], Batch Num: [309/600]
Discriminator Loss: 0.7078, Generator Loss: 3.9516
D(x): 0.8109, D(G(z)): 0.2872
Epoch: [0/20], Batch Num: [310/600]
Discriminator Loss: 0.5823, Generator Loss: 4.0908
D(x): 0.8709, D(G(z)): 0.2844
Epoch: [0/20], Batch Num: [311/600]
Discriminator Loss: 0.6484, Generator Loss: 4.1293
D(x): 0.8631, D(G(z)): 0.3056
Epoch: [0/20], Batch Num: [312/600]
Discriminator Loss: 0.6123, Generator Loss: 3.7420
D(x): 0.8523, D(G(z)): 0.2774
Epoch: [0/20], Batch Num: [313/600]
Discriminator Loss: 0.6142, Generator Loss: 3.7879
D(x): 0.8713, D(G(z)): 0.3241
Epoch: [0/20], Batch Num: [314/600]
Discriminator Loss: 0.6176, Generator Loss: 3.4247
D(x): 0.8799, D(G(z)): 0.3153
Epoch: [0/20], Batch Num: [315/600]
Discriminator Loss: 0.5878, Generator Loss: 3.7192
D(x): 0.8503, D(G(z)): 0.2774
Epoch: [0/20], Batch Num: [316/600]
Discriminator Loss: 0.5088, Generator Loss: 3.8903
D(x): 0.8703, D(G(z)): 0.2698
Epoch: [0/20], Batch Num: [317/600]
Discriminator Loss: 0.5097, Generator Loss: 4.1630
D(x): 0.8598, D(G(z)): 0.2175
Epoch: [0/20], Batch Num: [318/600]
Discriminator Loss: 0.5365, Generator Loss: 4.0946
D(x): 0.8161, D(G(z)): 0.1743
Epoch: [0/20], Batch Num: [319/600]
Discriminator Loss: 0.2974, Generator Loss: 4.8686
D(x): 0.9298, D(G(z)): 0.1600
Epoch: [0/20], Batch Num: [320/600]
Discriminator Loss: 0.2551, Generator Loss: 4.8189
D(x): 0.9063, D(G(z)): 0.0935
Epoch: [0/20], Batch Num: [321/600]
Discriminator Loss: 0.2400, Generator Loss: 4.7758
D(x): 0.9305, D(G(z)): 0.1047
Epoch: [0/20], Batch Num: [322/600]
Discriminator Loss: 0.1495, Generator Loss: 5.1347
D(x): 0.9612, D(G(z)): 0.0884
Epoch: [0/20], Batch Num: [323/600]
Discriminator Loss: 0.1547, Generator Loss: 6.0189
D(x): 0.9762, D(G(z)): 0.1095
Epoch: [0/20], Batch Num: [324/600]
Discriminator Loss: 0.1670, Generator Loss: 5.4164
D(x): 0.9753, D(G(z)): 0.1122
Epoch: [0/20], Batch Num: [325/600]
Discriminator Loss: 0.1739, Generator Loss: 5.2561
D(x): 0.9803, D(G(z)): 0.1218
Epoch: [0/20], Batch Num: [326/600]
Discriminator Loss: 0.1957, Generator Loss: 4.1296
D(x): 0.9809, D(G(z)): 0.1426
Epoch: [0/20], Batch Num: [327/600]
Discriminator Loss: 0.1950, Generator Loss: 4.4456
D(x): 0.9791, D(G(z)): 0.1417
Epoch: [0/20], Batch Num: [328/600]
Discriminator Loss: 0.2748, Generator Loss: 4.5575
D(x): 0.9744, D(G(z)): 0.1940
Epoch: [0/20], Batch Num: [329/600]
Discriminator Loss: 0.3059, Generator Loss: 4.4916
D(x): 0.9854, D(G(z)): 0.2148
Epoch: [0/20], Batch Num: [330/600]
Discriminator Loss: 0.2334, Generator Loss: 4.8871
D(x): 0.9771, D(G(z)): 0.1700
Epoch: [0/20], Batch Num: [331/600]
Discriminator Loss: 0.2066, Generator Loss: 5.4307
D(x): 0.9760, D(G(z)): 0.1512
Epoch: [0/20], Batch Num: [332/600]
Discriminator Loss: 0.1633, Generator Loss: 5.2715
D(x): 0.9731, D(G(z)): 0.1149
Epoch: [0/20], Batch Num: [333/600]
Discriminator Loss: 0.2096, Generator Loss: 5.9224
D(x): 0.9366, D(G(z)): 0.1162
Epoch: [0/20], Batch Num: [334/600]
Discriminator Loss: 0.2443, Generator Loss: 5.2586
D(x): 0.9232, D(G(z)): 0.1258
Epoch: [0/20], Batch Num: [335/600]
Discriminator Loss: 0.2647, Generator Loss: 4.8717
D(x): 0.8936, D(G(z)): 0.1077
Epoch: [0/20], Batch Num: [336/600]
Discriminator Loss: 0.3286, Generator Loss: 4.4946
D(x): 0.8676, D(G(z)): 0.1311
Epoch: [0/20], Batch Num: [337/600]
Discriminator Loss: 0.3014, Generator Loss: 3.8580
D(x): 0.8753, D(G(z)): 0.1359
Epoch: [0/20], Batch Num: [338/600]
Discriminator Loss: 0.4657, Generator Loss: 3.4570
D(x): 0.8498, D(G(z)): 0.2205
Epoch: [0/20], Batch Num: [339/600]
Discriminator Loss: 0.5175, Generator Loss: 2.9128
D(x): 0.8587, D(G(z)): 0.2714
Epoch: [0/20], Batch Num: [340/600]
Discriminator Loss: 0.7476, Generator Loss: 2.9062
D(x): 0.7871, D(G(z)): 0.2940
Epoch: [0/20], Batch Num: [341/600]
Discriminator Loss: 0.9251, Generator Loss: 2.3504
D(x): 0.7261, D(G(z)): 0.3415
Epoch: [0/20], Batch Num: [342/600]
Discriminator Loss: 1.1914, Generator Loss: 2.0162
D(x): 0.6800, D(G(z)): 0.4262
Epoch: [0/20], Batch Num: [343/600]
Discriminator Loss: 1.5330, Generator Loss: 1.8931
D(x): 0.6103, D(G(z)): 0.4815
Epoch: [0/20], Batch Num: [344/600]
Discriminator Loss: 1.5510, Generator Loss: 1.7129
D(x): 0.6655, D(G(z)): 0.5245
Epoch: [0/20], Batch Num: [345/600]
Discriminator Loss: 1.5849, Generator Loss: 1.5490
D(x): 0.6838, D(G(z)): 0.5592
Epoch: [0/20], Batch Num: [346/600]
Discriminator Loss: 1.5829, Generator Loss: 1.1877
D(x): 0.6436, D(G(z)): 0.5055
Epoch: [0/20], Batch Num: [347/600]
Discriminator Loss: 1.4055, Generator Loss: 1.5322
D(x): 0.7102, D(G(z)): 0.5312
Epoch: [0/20], Batch Num: [348/600]
Discriminator Loss: 1.2521, Generator Loss: 1.6479
D(x): 0.7364, D(G(z)): 0.5101
Epoch: [0/20], Batch Num: [349/600]
Discriminator Loss: 1.1557, Generator Loss: 1.7793
D(x): 0.7388, D(G(z)): 0.4847
Epoch: [0/20], Batch Num: [350/600]
Discriminator Loss: 1.3250, Generator Loss: 1.8958
D(x): 0.7003, D(G(z)): 0.4750
Epoch: [0/20], Batch Num: [351/600]
Discriminator Loss: 1.2288, Generator Loss: 1.9919
D(x): 0.7205, D(G(z)): 0.4936
Epoch: [0/20], Batch Num: [352/600]
Discriminator Loss: 0.9772, Generator Loss: 2.1081
D(x): 0.7385, D(G(z)): 0.3933
Epoch: [0/20], Batch Num: [353/600]
Discriminator Loss: 0.9898, Generator Loss: 2.2842
D(x): 0.7301, D(G(z)): 0.4145
Epoch: [0/20], Batch Num: [354/600]
Discriminator Loss: 0.9732, Generator Loss: 2.3749
D(x): 0.6853, D(G(z)): 0.3409
Epoch: [0/20], Batch Num: [355/600]
Discriminator Loss: 0.8764, Generator Loss: 2.3410
D(x): 0.7081, D(G(z)): 0.3178
Epoch: [0/20], Batch Num: [356/600]
Discriminator Loss: 0.9863, Generator Loss: 2.3610
D(x): 0.6530, D(G(z)): 0.3327
Epoch: [0/20], Batch Num: [357/600]
Discriminator Loss: 1.0412, Generator Loss: 2.1764
D(x): 0.6662, D(G(z)): 0.3406
Epoch: [0/20], Batch Num: [358/600]
Discriminator Loss: 0.8415, Generator Loss: 2.2480
D(x): 0.7666, D(G(z)): 0.3726
Epoch: [0/20], Batch Num: [359/600]
Discriminator Loss: 0.8847, Generator Loss: 2.5500
D(x): 0.7512, D(G(z)): 0.3469
Epoch: [0/20], Batch Num: [360/600]
Discriminator Loss: 0.8525, Generator Loss: 2.7454
D(x): 0.7385, D(G(z)): 0.3187
Epoch: [0/20], Batch Num: [361/600]
Discriminator Loss: 0.8883, Generator Loss: 3.0345
D(x): 0.6984, D(G(z)): 0.2491
Epoch: [0/20], Batch Num: [362/600]
Discriminator Loss: 0.6770, Generator Loss: 3.2126
D(x): 0.7642, D(G(z)): 0.2394
Epoch: [0/20], Batch Num: [363/600]
Discriminator Loss: 0.6818, Generator Loss: 3.6033
D(x): 0.7424, D(G(z)): 0.2228
Epoch: [0/20], Batch Num: [364/600]
Discriminator Loss: 0.8657, Generator Loss: 3.5201
D(x): 0.6610, D(G(z)): 0.1718
Epoch: [0/20], Batch Num: [365/600]
Discriminator Loss: 0.7704, Generator Loss: 3.1906
D(x): 0.6986, D(G(z)): 0.2068
Epoch: [0/20], Batch Num: [366/600]
Discriminator Loss: 0.6863, Generator Loss: 2.9705
D(x): 0.7384, D(G(z)): 0.2110
Epoch: [0/20], Batch Num: [367/600]
Discriminator Loss: 0.7620, Generator Loss: 3.0217
D(x): 0.7373, D(G(z)): 0.2138
Epoch: [0/20], Batch Num: [368/600]
Discriminator Loss: 0.7306, Generator Loss: 3.0261
D(x): 0.7514, D(G(z)): 0.2508
Epoch: [0/20], Batch Num: [369/600]
Discriminator Loss: 0.5604, Generator Loss: 3.0055
D(x): 0.7998, D(G(z)): 0.2193
Epoch: [0/20], Batch Num: [370/600]
Discriminator Loss: 0.8189, Generator Loss: 3.2061
D(x): 0.7349, D(G(z)): 0.2355
Epoch: [0/20], Batch Num: [371/600]
Discriminator Loss: 0.8259, Generator Loss: 2.8850
D(x): 0.7013, D(G(z)): 0.2174
Epoch: [0/20], Batch Num: [372/600]
Discriminator Loss: 1.0870, Generator Loss: 2.5400
D(x): 0.6417, D(G(z)): 0.2385
Epoch: [0/20], Batch Num: [373/600]
Discriminator Loss: 0.9984, Generator Loss: 2.0578
D(x): 0.6919, D(G(z)): 0.3125
Epoch: [0/20], Batch Num: [374/600]
Discriminator Loss: 1.0087, Generator Loss: 2.1666
D(x): 0.7480, D(G(z)): 0.3971
Epoch: [0/20], Batch Num: [375/600]
Discriminator Loss: 1.1479, Generator Loss: 2.3249
D(x): 0.6966, D(G(z)): 0.3655
Epoch: [0/20], Batch Num: [376/600]
Discriminator Loss: 0.9389, Generator Loss: 2.7871
D(x): 0.7302, D(G(z)): 0.3498
Epoch: [0/20], Batch Num: [377/600]
Discriminator Loss: 0.7718, Generator Loss: 3.2262
D(x): 0.7398, D(G(z)): 0.2620
Epoch: [0/20], Batch Num: [378/600]
Discriminator Loss: 0.6473, Generator Loss: 3.3029
D(x): 0.7499, D(G(z)): 0.2045
Epoch: [0/20], Batch Num: [379/600]
Discriminator Loss: 0.6817, Generator Loss: 3.1151
D(x): 0.7233, D(G(z)): 0.1618
Epoch: [0/20], Batch Num: [380/600]
Discriminator Loss: 0.5807, Generator Loss: 2.9312
D(x): 0.7709, D(G(z)): 0.1775
Epoch: [0/20], Batch Num: [381/600]
Discriminator Loss: 0.4236, Generator Loss: 2.9158
D(x): 0.8563, D(G(z)): 0.1749
Epoch: [0/20], Batch Num: [382/600]
Discriminator Loss: 0.3306, Generator Loss: 3.2439
D(x): 0.9140, D(G(z)): 0.1737
Epoch: [0/20], Batch Num: [383/600]
Discriminator Loss: 0.2632, Generator Loss: 3.6253
D(x): 0.9311, D(G(z)): 0.1474
Epoch: [0/20], Batch Num: [384/600]
Discriminator Loss: 0.1779, Generator Loss: 4.6465
D(x): 0.9368, D(G(z)): 0.0905
Epoch: [0/20], Batch Num: [385/600]
Discriminator Loss: 0.2007, Generator Loss: 5.2087
D(x): 0.9201, D(G(z)): 0.0925
Epoch: [0/20], Batch Num: [386/600]
Discriminator Loss: 0.1361, Generator Loss: 5.6504
D(x): 0.9253, D(G(z)): 0.0448
Epoch: [0/20], Batch Num: [387/600]
Discriminator Loss: 0.1723, Generator Loss: 5.4397
D(x): 0.9152, D(G(z)): 0.0332
Epoch: [0/20], Batch Num: [388/600]
Discriminator Loss: 0.1206, Generator Loss: 6.1569
D(x): 0.9430, D(G(z)): 0.0390
Epoch: [0/20], Batch Num: [389/600]
Discriminator Loss: 0.1155, Generator Loss: 5.5913
D(x): 0.9386, D(G(z)): 0.0378
Epoch: [0/20], Batch Num: [390/600]
Discriminator Loss: 0.1269, Generator Loss: 4.9678
D(x): 0.9491, D(G(z)): 0.0597
Epoch: [0/20], Batch Num: [391/600]
Discriminator Loss: 0.1634, Generator Loss: 4.7428
D(x): 0.9493, D(G(z)): 0.0751
Epoch: [0/20], Batch Num: [392/600]
Discriminator Loss: 0.1940, Generator Loss: 4.8659
D(x): 0.9641, D(G(z)): 0.1206
Epoch: [0/20], Batch Num: [393/600]
Discriminator Loss: 0.2070, Generator Loss: 4.7886
D(x): 0.9711, D(G(z)): 0.1414
Epoch: [0/20], Batch Num: [394/600]
Discriminator Loss: 0.2567, Generator Loss: 5.8451
D(x): 0.9452, D(G(z)): 0.1476
Epoch: [0/20], Batch Num: [395/600]
Discriminator Loss: 0.2658, Generator Loss: 5.6535
D(x): 0.9275, D(G(z)): 0.1347
Epoch: [0/20], Batch Num: [396/600]
Discriminator Loss: 0.3585, Generator Loss: 5.3229
D(x): 0.8706, D(G(z)): 0.1294
Epoch: [0/20], Batch Num: [397/600]
Discriminator Loss: 0.4910, Generator Loss: 5.3154
D(x): 0.8239, D(G(z)): 0.1482
Epoch: [0/20], Batch Num: [398/600]
Discriminator Loss: 0.6025, Generator Loss: 4.1603
D(x): 0.8322, D(G(z)): 0.2335
Epoch: [0/20], Batch Num: [399/600]
Discriminator Loss: 0.6588, Generator Loss: 4.4807
D(x): 0.8618, D(G(z)): 0.3018
Epoch: 0, Batch Num: [400/600]

Epoch: [0/20], Batch Num: [400/600]
Discriminator Loss: 0.8637, Generator Loss: 3.8195
D(x): 0.7915, D(G(z)): 0.3371
Epoch: [0/20], Batch Num: [401/600]
Discriminator Loss: 1.1138, Generator Loss: 3.1757
D(x): 0.7231, D(G(z)): 0.3864
Epoch: [0/20], Batch Num: [402/600]
Discriminator Loss: 1.5666, Generator Loss: 2.7434
D(x): 0.7470, D(G(z)): 0.5415
Epoch: [0/20], Batch Num: [403/600]
Discriminator Loss: 1.8187, Generator Loss: 2.6013
D(x): 0.6786, D(G(z)): 0.6137
Epoch: [0/20], Batch Num: [404/600]
Discriminator Loss: 2.5149, Generator Loss: 1.5563
D(x): 0.5964, D(G(z)): 0.7367
Epoch: [0/20], Batch Num: [405/600]
Discriminator Loss: 2.8111, Generator Loss: 0.7896
D(x): 0.5935, D(G(z)): 0.8126
Epoch: [0/20], Batch Num: [406/600]
Discriminator Loss: 2.9330, Generator Loss: 0.5005
D(x): 0.6860, D(G(z)): 0.8784
Epoch: [0/20], Batch Num: [407/600]
Discriminator Loss: 3.0145, Generator Loss: 0.3111
D(x): 0.7703, D(G(z)): 0.9052
Epoch: [0/20], Batch Num: [408/600]
Discriminator Loss: 2.9508, Generator Loss: 0.2850
D(x): 0.7619, D(G(z)): 0.9126
Epoch: [0/20], Batch Num: [409/600]
Discriminator Loss: 2.9008, Generator Loss: 0.2856
D(x): 0.7282, D(G(z)): 0.8778
Epoch: [0/20], Batch Num: [410/600]
Discriminator Loss: 2.2671, Generator Loss: 0.6555
D(x): 0.8052, D(G(z)): 0.8255
Epoch: [0/20], Batch Num: [411/600]
Discriminator Loss: 1.6237, Generator Loss: 1.1743
D(x): 0.8351, D(G(z)): 0.7028
Epoch: [0/20], Batch Num: [412/600]
Discriminator Loss: 1.3443, Generator Loss: 1.8150
D(x): 0.7760, D(G(z)): 0.5617
Epoch: [0/20], Batch Num: [413/600]
Discriminator Loss: 1.1458, Generator Loss: 1.9022
D(x): 0.8387, D(G(z)): 0.5271
Epoch: [0/20], Batch Num: [414/600]
Discriminator Loss: 1.3398, Generator Loss: 1.7302
D(x): 0.8493, D(G(z)): 0.5665
Epoch: [0/20], Batch Num: [415/600]
Discriminator Loss: 1.2380, Generator Loss: 1.1740
D(x): 0.8716, D(G(z)): 0.6396
Epoch: [0/20], Batch Num: [416/600]
Discriminator Loss: 1.5691, Generator Loss: 0.8763
D(x): 0.8362, D(G(z)): 0.7196
Epoch: [0/20], Batch Num: [417/600]
Discriminator Loss: 1.6766, Generator Loss: 0.4624
D(x): 0.8669, D(G(z)): 0.7723
Epoch: [0/20], Batch Num: [418/600]
Discriminator Loss: 1.8481, Generator Loss: 0.3247
D(x): 0.8534, D(G(z)): 0.8032
Epoch: [0/20], Batch Num: [419/600]
Discriminator Loss: 1.8624, Generator Loss: 0.3008
D(x): 0.8527, D(G(z)): 0.8074
Epoch: [0/20], Batch Num: [420/600]
Discriminator Loss: 1.8794, Generator Loss: 0.2948
D(x): 0.8618, D(G(z)): 0.8091
Epoch: [0/20], Batch Num: [421/600]
Discriminator Loss: 1.7642, Generator Loss: 0.3647
D(x): 0.8253, D(G(z)): 0.7593
Epoch: [0/20], Batch Num: [422/600]
Discriminator Loss: 1.4364, Generator Loss: 0.4694
D(x): 0.8551, D(G(z)): 0.7003
Epoch: [0/20], Batch Num: [423/600]
Discriminator Loss: 1.2661, Generator Loss: 0.6384
D(x): 0.8717, D(G(z)): 0.6370
Epoch: [0/20], Batch Num: [424/600]
Discriminator Loss: 1.1418, Generator Loss: 0.9491
D(x): 0.8711, D(G(z)): 0.5926
Epoch: [0/20], Batch Num: [425/600]
Discriminator Loss: 0.9564, Generator Loss: 1.1156
D(x): 0.9174, D(G(z)): 0.5572
Epoch: [0/20], Batch Num: [426/600]
Discriminator Loss: 0.9834, Generator Loss: 1.2297
D(x): 0.8968, D(G(z)): 0.5508
Epoch: [0/20], Batch Num: [427/600]
Discriminator Loss: 0.9950, Generator Loss: 1.0573
D(x): 0.8894, D(G(z)): 0.5552
Epoch: [0/20], Batch Num: [428/600]
Discriminator Loss: 1.0495, Generator Loss: 1.0541
D(x): 0.9108, D(G(z)): 0.5976
Epoch: [0/20], Batch Num: [429/600]
Discriminator Loss: 1.0904, Generator Loss: 0.9624
D(x): 0.8999, D(G(z)): 0.6134
Epoch: [0/20], Batch Num: [430/600]
Discriminator Loss: 1.1362, Generator Loss: 0.9815
D(x): 0.8894, D(G(z)): 0.6262
Epoch: [0/20], Batch Num: [431/600]
Discriminator Loss: 1.0766, Generator Loss: 1.1177
D(x): 0.8730, D(G(z)): 0.5925
Epoch: [0/20], Batch Num: [432/600]
Discriminator Loss: 0.9921, Generator Loss: 1.3311
D(x): 0.8675, D(G(z)): 0.5596
Epoch: [0/20], Batch Num: [433/600]
Discriminator Loss: 0.8906, Generator Loss: 1.5313
D(x): 0.8492, D(G(z)): 0.5012
Epoch: [0/20], Batch Num: [434/600]
Discriminator Loss: 0.7964, Generator Loss: 1.9437
D(x): 0.8010, D(G(z)): 0.4149
Epoch: [0/20], Batch Num: [435/600]
Discriminator Loss: 0.7392, Generator Loss: 2.3470
D(x): 0.7797, D(G(z)): 0.3585
Epoch: [0/20], Batch Num: [436/600]
Discriminator Loss: 0.7456, Generator Loss: 2.7541
D(x): 0.6985, D(G(z)): 0.2838
Epoch: [0/20], Batch Num: [437/600]
Discriminator Loss: 0.7623, Generator Loss: 3.2118
D(x): 0.6397, D(G(z)): 0.2150
Epoch: [0/20], Batch Num: [438/600]
Discriminator Loss: 0.8586, Generator Loss: 2.9694
D(x): 0.5496, D(G(z)): 0.1778
Epoch: [0/20], Batch Num: [439/600]
Discriminator Loss: 1.0126, Generator Loss: 2.6910
D(x): 0.4825, D(G(z)): 0.1834
Epoch: [0/20], Batch Num: [440/600]
Discriminator Loss: 1.0929, Generator Loss: 2.0530
D(x): 0.4684, D(G(z)): 0.2210
Epoch: [0/20], Batch Num: [441/600]
Discriminator Loss: 1.1882, Generator Loss: 1.4595
D(x): 0.5184, D(G(z)): 0.3663
Epoch: [0/20], Batch Num: [442/600]
Discriminator Loss: 1.3562, Generator Loss: 1.0952
D(x): 0.5165, D(G(z)): 0.4566
Epoch: [0/20], Batch Num: [443/600]
Discriminator Loss: 1.3760, Generator Loss: 0.8087
D(x): 0.6128, D(G(z)): 0.5585
Epoch: [0/20], Batch Num: [444/600]
Discriminator Loss: 1.5048, Generator Loss: 0.8477
D(x): 0.6391, D(G(z)): 0.6219
Epoch: [0/20], Batch Num: [445/600]
Discriminator Loss: 1.4412, Generator Loss: 0.9277
D(x): 0.6475, D(G(z)): 0.6138
Epoch: [0/20], Batch Num: [446/600]
Discriminator Loss: 1.4301, Generator Loss: 1.0640
D(x): 0.6293, D(G(z)): 0.5865
Epoch: [0/20], Batch Num: [447/600]
Discriminator Loss: 1.2869, Generator Loss: 1.3038
D(x): 0.6142, D(G(z)): 0.5161
Epoch: [0/20], Batch Num: [448/600]
Discriminator Loss: 1.2356, Generator Loss: 1.6444
D(x): 0.5797, D(G(z)): 0.4517
Epoch: [0/20], Batch Num: [449/600]
Discriminator Loss: 1.1447, Generator Loss: 1.9549
D(x): 0.5765, D(G(z)): 0.4004
Epoch: [0/20], Batch Num: [450/600]
Discriminator Loss: 0.9784, Generator Loss: 2.0486
D(x): 0.5874, D(G(z)): 0.3154
Epoch: [0/20], Batch Num: [451/600]
Discriminator Loss: 1.0952, Generator Loss: 1.8060
D(x): 0.5500, D(G(z)): 0.3265
Epoch: [0/20], Batch Num: [452/600]
Discriminator Loss: 0.9854, Generator Loss: 1.6963
D(x): 0.6403, D(G(z)): 0.3897
Epoch: [0/20], Batch Num: [453/600]
Discriminator Loss: 0.9131, Generator Loss: 1.6264
D(x): 0.6889, D(G(z)): 0.3915
Epoch: [0/20], Batch Num: [454/600]
Discriminator Loss: 0.9527, Generator Loss: 1.5740
D(x): 0.7157, D(G(z)): 0.4345
Epoch: [0/20], Batch Num: [455/600]
Discriminator Loss: 0.9495, Generator Loss: 1.6693
D(x): 0.7098, D(G(z)): 0.4339
Epoch: [0/20], Batch Num: [456/600]
Discriminator Loss: 0.9575, Generator Loss: 1.6531
D(x): 0.7057, D(G(z)): 0.4247
Epoch: [0/20], Batch Num: [457/600]
Discriminator Loss: 0.8948, Generator Loss: 2.0974
D(x): 0.7011, D(G(z)): 0.3927
Epoch: [0/20], Batch Num: [458/600]
Discriminator Loss: 0.9058, Generator Loss: 2.0725
D(x): 0.6624, D(G(z)): 0.3580
Epoch: [0/20], Batch Num: [459/600]
Discriminator Loss: 0.9951, Generator Loss: 2.2703
D(x): 0.6189, D(G(z)): 0.3489
Epoch: [0/20], Batch Num: [460/600]
Discriminator Loss: 1.0670, Generator Loss: 2.2192
D(x): 0.5913, D(G(z)): 0.3660
Epoch: [0/20], Batch Num: [461/600]
Discriminator Loss: 1.0103, Generator Loss: 2.0042
D(x): 0.6365, D(G(z)): 0.3811
Epoch: [0/20], Batch Num: [462/600]
Discriminator Loss: 1.0494, Generator Loss: 2.1390
D(x): 0.6728, D(G(z)): 0.4380
Epoch: [0/20], Batch Num: [463/600]
Discriminator Loss: 1.0843, Generator Loss: 1.7961
D(x): 0.6679, D(G(z)): 0.4423
Epoch: [0/20], Batch Num: [464/600]
Discriminator Loss: 1.0367, Generator Loss: 1.9582
D(x): 0.7137, D(G(z)): 0.4652
Epoch: [0/20], Batch Num: [465/600]
Discriminator Loss: 1.0717, Generator Loss: 1.8306
D(x): 0.6884, D(G(z)): 0.4710
Epoch: [0/20], Batch Num: [466/600]
Discriminator Loss: 1.1154, Generator Loss: 1.9660
D(x): 0.6624, D(G(z)): 0.4589
Epoch: [0/20], Batch Num: [467/600]
Discriminator Loss: 1.2075, Generator Loss: 1.9492
D(x): 0.6153, D(G(z)): 0.4649
Epoch: [0/20], Batch Num: [468/600]
Discriminator Loss: 1.3347, Generator Loss: 1.7411
D(x): 0.5606, D(G(z)): 0.4733
Epoch: [0/20], Batch Num: [469/600]
Discriminator Loss: 1.3871, Generator Loss: 1.6085
D(x): 0.5650, D(G(z)): 0.4809
Epoch: [0/20], Batch Num: [470/600]
Discriminator Loss: 1.5842, Generator Loss: 1.3166
D(x): 0.5260, D(G(z)): 0.5508
Epoch: [0/20], Batch Num: [471/600]
Discriminator Loss: 1.7264, Generator Loss: 0.9696
D(x): 0.5363, D(G(z)): 0.5869
Epoch: [0/20], Batch Num: [472/600]
Discriminator Loss: 1.9889, Generator Loss: 0.6204
D(x): 0.5201, D(G(z)): 0.6905
Epoch: [0/20], Batch Num: [473/600]
Discriminator Loss: 2.1805, Generator Loss: 0.4402
D(x): 0.5215, D(G(z)): 0.7436
Epoch: [0/20], Batch Num: [474/600]
Discriminator Loss: 2.0492, Generator Loss: 0.4010
D(x): 0.5884, D(G(z)): 0.7544
Epoch: [0/20], Batch Num: [475/600]
Discriminator Loss: 2.2361, Generator Loss: 0.3614
D(x): 0.5732, D(G(z)): 0.7880
Epoch: [0/20], Batch Num: [476/600]
Discriminator Loss: 2.1687, Generator Loss: 0.3923
D(x): 0.6164, D(G(z)): 0.7891
Epoch: [0/20], Batch Num: [477/600]
Discriminator Loss: 2.1202, Generator Loss: 0.4029
D(x): 0.5900, D(G(z)): 0.7740
Epoch: [0/20], Batch Num: [478/600]
Discriminator Loss: 2.0298, Generator Loss: 0.4168
D(x): 0.5784, D(G(z)): 0.7473
Epoch: [0/20], Batch Num: [479/600]
Discriminator Loss: 1.9404, Generator Loss: 0.4578
D(x): 0.5974, D(G(z)): 0.7362
Epoch: [0/20], Batch Num: [480/600]
Discriminator Loss: 1.7845, Generator Loss: 0.5338
D(x): 0.5892, D(G(z)): 0.6810
Epoch: [0/20], Batch Num: [481/600]
Discriminator Loss: 1.7554, Generator Loss: 0.6433
D(x): 0.5511, D(G(z)): 0.6490
Epoch: [0/20], Batch Num: [482/600]
Discriminator Loss: 1.6627, Generator Loss: 0.7037
D(x): 0.5317, D(G(z)): 0.5978
Epoch: [0/20], Batch Num: [483/600]
Discriminator Loss: 1.5099, Generator Loss: 0.8377
D(x): 0.5440, D(G(z)): 0.5526
Epoch: [0/20], Batch Num: [484/600]
Discriminator Loss: 1.5169, Generator Loss: 0.9287
D(x): 0.5028, D(G(z)): 0.5075
Epoch: [0/20], Batch Num: [485/600]
Discriminator Loss: 1.5134, Generator Loss: 0.9106
D(x): 0.5016, D(G(z)): 0.5007
Epoch: [0/20], Batch Num: [486/600]
Discriminator Loss: 1.4228, Generator Loss: 0.9163
D(x): 0.5131, D(G(z)): 0.4865
Epoch: [0/20], Batch Num: [487/600]
Discriminator Loss: 1.4457, Generator Loss: 0.8474
D(x): 0.5150, D(G(z)): 0.4914
Epoch: [0/20], Batch Num: [488/600]
Discriminator Loss: 1.3729, Generator Loss: 0.8347
D(x): 0.5501, D(G(z)): 0.4900
Epoch: [0/20], Batch Num: [489/600]
Discriminator Loss: 1.3466, Generator Loss: 0.8285
D(x): 0.5548, D(G(z)): 0.5007
Epoch: [0/20], Batch Num: [490/600]
Discriminator Loss: 1.3227, Generator Loss: 0.7707
D(x): 0.5751, D(G(z)): 0.5108
Epoch: [0/20], Batch Num: [491/600]
Discriminator Loss: 1.3361, Generator Loss: 0.9200
D(x): 0.5637, D(G(z)): 0.4975
Epoch: [0/20], Batch Num: [492/600]
Discriminator Loss: 1.3108, Generator Loss: 0.9030
D(x): 0.5753, D(G(z)): 0.5008
Epoch: [0/20], Batch Num: [493/600]
Discriminator Loss: 1.2987, Generator Loss: 1.0030
D(x): 0.5352, D(G(z)): 0.4585
Epoch: [0/20], Batch Num: [494/600]
Discriminator Loss: 1.3362, Generator Loss: 1.1080
D(x): 0.5130, D(G(z)): 0.4473
Epoch: [0/20], Batch Num: [495/600]
Discriminator Loss: 1.3319, Generator Loss: 1.1165
D(x): 0.4955, D(G(z)): 0.4183
Epoch: [0/20], Batch Num: [496/600]
Discriminator Loss: 1.2648, Generator Loss: 1.0867
D(x): 0.5191, D(G(z)): 0.4232
Epoch: [0/20], Batch Num: [497/600]
Discriminator Loss: 1.1916, Generator Loss: 1.0685
D(x): 0.5296, D(G(z)): 0.3943
Epoch: [0/20], Batch Num: [498/600]
Discriminator Loss: 1.2433, Generator Loss: 1.1376
D(x): 0.5181, D(G(z)): 0.4052
Epoch: [0/20], Batch Num: [499/600]
Discriminator Loss: 1.2194, Generator Loss: 1.1542
D(x): 0.5253, D(G(z)): 0.4003
Epoch: 0, Batch Num: [500/600]

Epoch: [0/20], Batch Num: [500/600]
Discriminator Loss: 1.1486, Generator Loss: 1.1845
D(x): 0.5528, D(G(z)): 0.3996
Epoch: [0/20], Batch Num: [501/600]
Discriminator Loss: 1.0560, Generator Loss: 1.2711
D(x): 0.5840, D(G(z)): 0.3790
Epoch: [0/20], Batch Num: [502/600]
Discriminator Loss: 1.0526, Generator Loss: 1.3692
D(x): 0.5795, D(G(z)): 0.3676
Epoch: [0/20], Batch Num: [503/600]
Discriminator Loss: 1.0229, Generator Loss: 1.4933
D(x): 0.5674, D(G(z)): 0.3344
Epoch: [0/20], Batch Num: [504/600]
Discriminator Loss: 0.9839, Generator Loss: 1.5374
D(x): 0.5705, D(G(z)): 0.3162
Epoch: [0/20], Batch Num: [505/600]
Discriminator Loss: 1.0121, Generator Loss: 1.6814
D(x): 0.5647, D(G(z)): 0.3146
Epoch: [0/20], Batch Num: [506/600]
Discriminator Loss: 0.9718, Generator Loss: 1.6185
D(x): 0.5732, D(G(z)): 0.2973
Epoch: [0/20], Batch Num: [507/600]
Discriminator Loss: 0.8523, Generator Loss: 1.7571
D(x): 0.6166, D(G(z)): 0.2725
Epoch: [0/20], Batch Num: [508/600]
Discriminator Loss: 0.8095, Generator Loss: 1.7674
D(x): 0.6354, D(G(z)): 0.2754
Epoch: [0/20], Batch Num: [509/600]
Discriminator Loss: 0.8257, Generator Loss: 1.8444
D(x): 0.6200, D(G(z)): 0.2657
Epoch: [0/20], Batch Num: [510/600]
Discriminator Loss: 0.8307, Generator Loss: 1.8691
D(x): 0.6408, D(G(z)): 0.2766
Epoch: [0/20], Batch Num: [511/600]
Discriminator Loss: 0.7211, Generator Loss: 1.9878
D(x): 0.6750, D(G(z)): 0.2495
Epoch: [0/20], Batch Num: [512/600]
Discriminator Loss: 0.6933, Generator Loss: 2.0553
D(x): 0.6877, D(G(z)): 0.2499
Epoch: [0/20], Batch Num: [513/600]
Discriminator Loss: 0.6338, Generator Loss: 2.1294
D(x): 0.7101, D(G(z)): 0.2281
Epoch: [0/20], Batch Num: [514/600]
Discriminator Loss: 0.6418, Generator Loss: 2.3913
D(x): 0.6942, D(G(z)): 0.2151
Epoch: [0/20], Batch Num: [515/600]
Discriminator Loss: 0.5960, Generator Loss: 2.4338
D(x): 0.7075, D(G(z)): 0.1997
Epoch: [0/20], Batch Num: [516/600]
Discriminator Loss: 0.6030, Generator Loss: 2.2881
D(x): 0.7241, D(G(z)): 0.2146
Epoch: [0/20], Batch Num: [517/600]
Discriminator Loss: 0.5676, Generator Loss: 2.5491
D(x): 0.7283, D(G(z)): 0.1988
Epoch: [0/20], Batch Num: [518/600]
Discriminator Loss: 0.6106, Generator Loss: 2.2876
D(x): 0.7029, D(G(z)): 0.1967
Epoch: [0/20], Batch Num: [519/600]
Discriminator Loss: 0.6054, Generator Loss: 2.4001
D(x): 0.7319, D(G(z)): 0.2265
Epoch: [0/20], Batch Num: [520/600]
Discriminator Loss: 0.6297, Generator Loss: 2.2620
D(x): 0.7320, D(G(z)): 0.2324
Epoch: [0/20], Batch Num: [521/600]
Discriminator Loss: 0.7399, Generator Loss: 2.2095
D(x): 0.7024, D(G(z)): 0.2765
Epoch: [0/20], Batch Num: [522/600]
Discriminator Loss: 0.7701, Generator Loss: 2.2951
D(x): 0.7139, D(G(z)): 0.3085
Epoch: [0/20], Batch Num: [523/600]
Discriminator Loss: 0.8351, Generator Loss: 2.2933
D(x): 0.6676, D(G(z)): 0.2877
Epoch: [0/20], Batch Num: [524/600]
Discriminator Loss: 0.8405, Generator Loss: 2.0897
D(x): 0.6711, D(G(z)): 0.3035
Epoch: [0/20], Batch Num: [525/600]
Discriminator Loss: 0.9873, Generator Loss: 1.8748
D(x): 0.6187, D(G(z)): 0.3139
Epoch: [0/20], Batch Num: [526/600]
Discriminator Loss: 0.8235, Generator Loss: 2.0938
D(x): 0.6953, D(G(z)): 0.3059
Epoch: [0/20], Batch Num: [527/600]
Discriminator Loss: 0.9736, Generator Loss: 1.8095
D(x): 0.6204, D(G(z)): 0.2931
Epoch: [0/20], Batch Num: [528/600]
Discriminator Loss: 0.9134, Generator Loss: 1.8510
D(x): 0.6943, D(G(z)): 0.3521
Epoch: [0/20], Batch Num: [529/600]
Discriminator Loss: 0.8840, Generator Loss: 2.0397
D(x): 0.7014, D(G(z)): 0.3389
Epoch: [0/20], Batch Num: [530/600]
Discriminator Loss: 0.8072, Generator Loss: 2.3508
D(x): 0.7012, D(G(z)): 0.3063
Epoch: [0/20], Batch Num: [531/600]
Discriminator Loss: 0.7169, Generator Loss: 2.4544
D(x): 0.7072, D(G(z)): 0.2520
Epoch: [0/20], Batch Num: [532/600]
Discriminator Loss: 0.7669, Generator Loss: 2.5117
D(x): 0.6643, D(G(z)): 0.2106
Epoch: [0/20], Batch Num: [533/600]
Discriminator Loss: 0.6541, Generator Loss: 2.5520
D(x): 0.7486, D(G(z)): 0.2371
Epoch: [0/20], Batch Num: [534/600]
Discriminator Loss: 0.5366, Generator Loss: 2.5924
D(x): 0.7786, D(G(z)): 0.1968
Epoch: [0/20], Batch Num: [535/600]
Discriminator Loss: 0.4829, Generator Loss: 2.7945
D(x): 0.8093, D(G(z)): 0.1958
Epoch: [0/20], Batch Num: [536/600]
Discriminator Loss: 0.4426, Generator Loss: 3.0354
D(x): 0.8076, D(G(z)): 0.1659
Epoch: [0/20], Batch Num: [537/600]
Discriminator Loss: 0.5152, Generator Loss: 2.9206
D(x): 0.7823, D(G(z)): 0.1602
Epoch: [0/20], Batch Num: [538/600]
Discriminator Loss: 0.4681, Generator Loss: 3.4649
D(x): 0.8471, D(G(z)): 0.2271
Epoch: [0/20], Batch Num: [539/600]
Discriminator Loss: 0.4049, Generator Loss: 3.4827
D(x): 0.8454, D(G(z)): 0.1802
Epoch: [0/20], Batch Num: [540/600]
Discriminator Loss: 0.5233, Generator Loss: 3.3731
D(x): 0.8105, D(G(z)): 0.1842
Epoch: [0/20], Batch Num: [541/600]
Discriminator Loss: 0.4343, Generator Loss: 3.6656
D(x): 0.8215, D(G(z)): 0.1549
Epoch: [0/20], Batch Num: [542/600]
Discriminator Loss: 0.5151, Generator Loss: 3.0899
D(x): 0.7780, D(G(z)): 0.1752
Epoch: [0/20], Batch Num: [543/600]
Discriminator Loss: 0.6280, Generator Loss: 2.8664
D(x): 0.7558, D(G(z)): 0.2150
Epoch: [0/20], Batch Num: [544/600]
Discriminator Loss: 0.8383, Generator Loss: 2.4980
D(x): 0.7286, D(G(z)): 0.3034
Epoch: [0/20], Batch Num: [545/600]
Discriminator Loss: 1.1020, Generator Loss: 1.8477
D(x): 0.6766, D(G(z)): 0.4093
Epoch: [0/20], Batch Num: [546/600]
Discriminator Loss: 1.6987, Generator Loss: 1.6460
D(x): 0.5912, D(G(z)): 0.5453
Epoch: [0/20], Batch Num: [547/600]
Discriminator Loss: 1.8480, Generator Loss: 1.5389
D(x): 0.5727, D(G(z)): 0.6116
Epoch: [0/20], Batch Num: [548/600]
Discriminator Loss: 2.3487, Generator Loss: 1.0578
D(x): 0.4355, D(G(z)): 0.6251
Epoch: [0/20], Batch Num: [549/600]
Discriminator Loss: 2.2174, Generator Loss: 1.0377
D(x): 0.5092, D(G(z)): 0.6789
Epoch: [0/20], Batch Num: [550/600]
Discriminator Loss: 2.7294, Generator Loss: 1.0003
D(x): 0.4230, D(G(z)): 0.7273
Epoch: [0/20], Batch Num: [551/600]
Discriminator Loss: 2.5798, Generator Loss: 0.9633
D(x): 0.4298, D(G(z)): 0.6772
Epoch: [0/20], Batch Num: [552/600]
Discriminator Loss: 2.3156, Generator Loss: 1.0999
D(x): 0.4805, D(G(z)): 0.6486
Epoch: [0/20], Batch Num: [553/600]
Discriminator Loss: 1.8557, Generator Loss: 1.4084
D(x): 0.4941, D(G(z)): 0.5454
Epoch: [0/20], Batch Num: [554/600]
Discriminator Loss: 1.2408, Generator Loss: 2.5082
D(x): 0.5922, D(G(z)): 0.4050
Epoch: [0/20], Batch Num: [555/600]
Discriminator Loss: 0.9540, Generator Loss: 3.1253
D(x): 0.6246, D(G(z)): 0.2896
Epoch: [0/20], Batch Num: [556/600]
Discriminator Loss: 0.7917, Generator Loss: 3.7396
D(x): 0.6234, D(G(z)): 0.1698
Epoch: [0/20], Batch Num: [557/600]
Discriminator Loss: 0.5222, Generator Loss: 4.2356
D(x): 0.7388, D(G(z)): 0.1366
Epoch: [0/20], Batch Num: [558/600]
Discriminator Loss: 0.5701, Generator Loss: 3.9677
D(x): 0.6888, D(G(z)): 0.1081
Epoch: [0/20], Batch Num: [559/600]
Discriminator Loss: 0.5119, Generator Loss: 4.1337
D(x): 0.7525, D(G(z)): 0.1348
Epoch: [0/20], Batch Num: [560/600]
Discriminator Loss: 0.4672, Generator Loss: 3.6722
D(x): 0.7574, D(G(z)): 0.0931
Epoch: [0/20], Batch Num: [561/600]
Discriminator Loss: 0.3737, Generator Loss: 3.4717
D(x): 0.8335, D(G(z)): 0.1392
Epoch: [0/20], Batch Num: [562/600]
Discriminator Loss: 0.4166, Generator Loss: 3.8651
D(x): 0.8125, D(G(z)): 0.1482
Epoch: [0/20], Batch Num: [563/600]
Discriminator Loss: 0.3404, Generator Loss: 3.7055
D(x): 0.8484, D(G(z)): 0.1289
Epoch: [0/20], Batch Num: [564/600]
Discriminator Loss: 0.4481, Generator Loss: 3.7489
D(x): 0.7913, D(G(z)): 0.1376
Epoch: [0/20], Batch Num: [565/600]
Discriminator Loss: 0.4358, Generator Loss: 3.6423
D(x): 0.8149, D(G(z)): 0.1517
Epoch: [0/20], Batch Num: [566/600]
Discriminator Loss: 0.5364, Generator Loss: 3.6154
D(x): 0.7789, D(G(z)): 0.1786
Epoch: [0/20], Batch Num: [567/600]
Discriminator Loss: 0.4864, Generator Loss: 3.7216
D(x): 0.7874, D(G(z)): 0.1529
Epoch: [0/20], Batch Num: [568/600]
Discriminator Loss: 0.6548, Generator Loss: 3.5130
D(x): 0.7395, D(G(z)): 0.1588
Epoch: [0/20], Batch Num: [569/600]
Discriminator Loss: 0.5863, Generator Loss: 3.2726
D(x): 0.7865, D(G(z)): 0.2041
Epoch: [0/20], Batch Num: [570/600]
Discriminator Loss: 0.6460, Generator Loss: 3.0779
D(x): 0.7444, D(G(z)): 0.1894
Epoch: [0/20], Batch Num: [571/600]
Discriminator Loss: 0.9295, Generator Loss: 2.9488
D(x): 0.7108, D(G(z)): 0.2886
Epoch: [0/20], Batch Num: [572/600]
Discriminator Loss: 0.8421, Generator Loss: 2.5525
D(x): 0.7113, D(G(z)): 0.2444
Epoch: [0/20], Batch Num: [573/600]
Discriminator Loss: 0.9099, Generator Loss: 3.0412
D(x): 0.7398, D(G(z)): 0.3109
Epoch: [0/20], Batch Num: [574/600]
Discriminator Loss: 0.7702, Generator Loss: 3.0890
D(x): 0.8201, D(G(z)): 0.3372
Epoch: [0/20], Batch Num: [575/600]
Discriminator Loss: 0.7253, Generator Loss: 3.5575
D(x): 0.7548, D(G(z)): 0.2338
Epoch: [0/20], Batch Num: [576/600]
Discriminator Loss: 0.5552, Generator Loss: 4.1565
D(x): 0.7841, D(G(z)): 0.1815
Epoch: [0/20], Batch Num: [577/600]
Discriminator Loss: 0.6895, Generator Loss: 3.9858
D(x): 0.7307, D(G(z)): 0.1820
Epoch: [0/20], Batch Num: [578/600]
Discriminator Loss: 0.7562, Generator Loss: 3.8925
D(x): 0.6887, D(G(z)): 0.1423
Epoch: [0/20], Batch Num: [579/600]
Discriminator Loss: 0.6630, Generator Loss: 2.8363
D(x): 0.7587, D(G(z)): 0.1939
Epoch: [0/20], Batch Num: [580/600]
Discriminator Loss: 0.6217, Generator Loss: 2.7417
D(x): 0.8328, D(G(z)): 0.2677
Epoch: [0/20], Batch Num: [581/600]
Discriminator Loss: 0.6751, Generator Loss: 3.1393
D(x): 0.8489, D(G(z)): 0.2945
Epoch: [0/20], Batch Num: [582/600]
Discriminator Loss: 0.5685, Generator Loss: 4.0275
D(x): 0.8782, D(G(z)): 0.2839
Epoch: [0/20], Batch Num: [583/600]
Discriminator Loss: 0.4684, Generator Loss: 4.6400
D(x): 0.8307, D(G(z)): 0.1593
Epoch: [0/20], Batch Num: [584/600]
Discriminator Loss: 0.3629, Generator Loss: 4.9793
D(x): 0.8156, D(G(z)): 0.0756
Epoch: [0/20], Batch Num: [585/600]
Discriminator Loss: 0.3336, Generator Loss: 5.4266
D(x): 0.8382, D(G(z)): 0.0872
Epoch: [0/20], Batch Num: [586/600]
Discriminator Loss: 0.3088, Generator Loss: 5.3266
D(x): 0.8428, D(G(z)): 0.0745
Epoch: [0/20], Batch Num: [587/600]
Discriminator Loss: 0.1994, Generator Loss: 4.6596
D(x): 0.8995, D(G(z)): 0.0676
Epoch: [0/20], Batch Num: [588/600]
Discriminator Loss: 0.1892, Generator Loss: 5.3610
D(x): 0.9064, D(G(z)): 0.0691
Epoch: [0/20], Batch Num: [589/600]
Discriminator Loss: 0.1751, Generator Loss: 4.9585
D(x): 0.9365, D(G(z)): 0.0849
Epoch: [0/20], Batch Num: [590/600]
Discriminator Loss: 0.1222, Generator Loss: 5.9010
D(x): 0.9523, D(G(z)): 0.0614
Epoch: [0/20], Batch Num: [591/600]
Discriminator Loss: 0.0827, Generator Loss: 6.5904
D(x): 0.9674, D(G(z)): 0.0413
Epoch: [0/20], Batch Num: [592/600]
Discriminator Loss: 0.0821, Generator Loss: 7.0481
D(x): 0.9559, D(G(z)): 0.0286
Epoch: [0/20], Batch Num: [593/600]
Discriminator Loss: 0.0637, Generator Loss: 6.4821
D(x): 0.9630, D(G(z)): 0.0217
Epoch: [0/20], Batch Num: [594/600]
Discriminator Loss: 0.0811, Generator Loss: 7.0804
D(x): 0.9604, D(G(z)): 0.0175
Epoch: [0/20], Batch Num: [595/600]
Discriminator Loss: 0.0481, Generator Loss: 7.1159
D(x): 0.9759, D(G(z)): 0.0220
Epoch: [0/20], Batch Num: [596/600]
Discriminator Loss: 0.0535, Generator Loss: 6.1330
D(x): 0.9749, D(G(z)): 0.0216
Epoch: [0/20], Batch Num: [597/600]
Discriminator Loss: 0.1048, Generator Loss: 5.7750
D(x): 0.9836, D(G(z)): 0.0729
Epoch: [0/20], Batch Num: [598/600]
Discriminator Loss: 0.1471, Generator Loss: 4.8065
D(x): 0.9812, D(G(z)): 0.1006
Epoch: [0/20], Batch Num: [599/600]
Discriminator Loss: 0.2751, Generator Loss: 5.0674
D(x): 0.9801, D(G(z)): 0.1850
Epoch: 1, Batch Num: [0/600]

Epoch: [1/20], Batch Num: [0/600]
Discriminator Loss: 0.2440, Generator Loss: 5.3547
D(x): 0.9780, D(G(z)): 0.1700
Epoch: [1/20], Batch Num: [1/600]
Discriminator Loss: 0.2805, Generator Loss: 6.0067
D(x): 0.9577, D(G(z)): 0.1718
Epoch: [1/20], Batch Num: [2/600]
Discriminator Loss: 0.2998, Generator Loss: 5.9869
D(x): 0.9411, D(G(z)): 0.1764
Epoch: [1/20], Batch Num: [3/600]
Discriminator Loss: 0.2472, Generator Loss: 6.2311
D(x): 0.9187, D(G(z)): 0.1165
Epoch: [1/20], Batch Num: [4/600]
Discriminator Loss: 0.3954, Generator Loss: 5.6887
D(x): 0.8293, D(G(z)): 0.0892
Epoch: [1/20], Batch Num: [5/600]
Discriminator Loss: 0.4845, Generator Loss: 5.6694
D(x): 0.8141, D(G(z)): 0.1390
Epoch: [1/20], Batch Num: [6/600]
Discriminator Loss: 0.4590, Generator Loss: 4.6255
D(x): 0.8266, D(G(z)): 0.1601
Epoch: [1/20], Batch Num: [7/600]
Discriminator Loss: 0.6753, Generator Loss: 3.8724
D(x): 0.7977, D(G(z)): 0.2463
Epoch: [1/20], Batch Num: [8/600]
Discriminator Loss: 0.7337, Generator Loss: 3.9309
D(x): 0.8271, D(G(z)): 0.3128
Epoch: [1/20], Batch Num: [9/600]
Discriminator Loss: 0.6241, Generator Loss: 4.2000
D(x): 0.8396, D(G(z)): 0.2828
Epoch: [1/20], Batch Num: [10/600]
Discriminator Loss: 0.6892, Generator Loss: 5.1277
D(x): 0.7994, D(G(z)): 0.2845
Epoch: [1/20], Batch Num: [11/600]
Discriminator Loss: 0.4678, Generator Loss: 5.6946
D(x): 0.8005, D(G(z)): 0.1439
Epoch: [1/20], Batch Num: [12/600]
Discriminator Loss: 0.6080, Generator Loss: 5.1809
D(x): 0.7313, D(G(z)): 0.1222
Epoch: [1/20], Batch Num: [13/600]
Discriminator Loss: 0.5089, Generator Loss: 4.9032
D(x): 0.7553, D(G(z)): 0.1090
Epoch: [1/20], Batch Num: [14/600]
Discriminator Loss: 0.4940, Generator Loss: 4.5344
D(x): 0.7464, D(G(z)): 0.1111
Epoch: [1/20], Batch Num: [15/600]
Discriminator Loss: 0.5630, Generator Loss: 3.2114
D(x): 0.7555, D(G(z)): 0.1323
Epoch: [1/20], Batch Num: [16/600]
Discriminator Loss: 0.6074, Generator Loss: 3.3786
D(x): 0.8324, D(G(z)): 0.2530
Epoch: [1/20], Batch Num: [17/600]
Discriminator Loss: 0.5794, Generator Loss: 3.4535
D(x): 0.8210, D(G(z)): 0.2353
Epoch: [1/20], Batch Num: [18/600]
Discriminator Loss: 0.6746, Generator Loss: 3.0433
D(x): 0.7590, D(G(z)): 0.2492
Epoch: [1/20], Batch Num: [19/600]
Discriminator Loss: 0.8054, Generator Loss: 2.8170
D(x): 0.6959, D(G(z)): 0.2038
Epoch: [1/20], Batch Num: [20/600]
Discriminator Loss: 0.8450, Generator Loss: 2.4325
D(x): 0.7118, D(G(z)): 0.2633
Epoch: [1/20], Batch Num: [21/600]
Discriminator Loss: 0.8644, Generator Loss: 2.0715
D(x): 0.7380, D(G(z)): 0.2931
Epoch: [1/20], Batch Num: [22/600]
Discriminator Loss: 1.0922, Generator Loss: 1.9181
D(x): 0.7041, D(G(z)): 0.3460
Epoch: [1/20], Batch Num: [23/600]
Discriminator Loss: 1.0371, Generator Loss: 1.8601
D(x): 0.7735, D(G(z)): 0.4590
Epoch: [1/20], Batch Num: [24/600]
Discriminator Loss: 1.0787, Generator Loss: 2.2901
D(x): 0.7382, D(G(z)): 0.3990
Epoch: [1/20], Batch Num: [25/600]
Discriminator Loss: 1.3555, Generator Loss: 2.2120
D(x): 0.6415, D(G(z)): 0.3759
Epoch: [1/20], Batch Num: [26/600]
Discriminator Loss: 1.4471, Generator Loss: 1.8218
D(x): 0.5923, D(G(z)): 0.3740
Epoch: [1/20], Batch Num: [27/600]
Discriminator Loss: 1.2946, Generator Loss: 1.5933
D(x): 0.6525, D(G(z)): 0.4348
Epoch: [1/20], Batch Num: [28/600]
Discriminator Loss: 1.3042, Generator Loss: 1.6352
D(x): 0.6925, D(G(z)): 0.4556
Epoch: [1/20], Batch Num: [29/600]
Discriminator Loss: 1.2611, Generator Loss: 1.5015
D(x): 0.7245, D(G(z)): 0.4842
Epoch: [1/20], Batch Num: [30/600]
Discriminator Loss: 1.4540, Generator Loss: 1.9068
D(x): 0.6609, D(G(z)): 0.4666
Epoch: [1/20], Batch Num: [31/600]
Discriminator Loss: 1.3638, Generator Loss: 1.7375
D(x): 0.6636, D(G(z)): 0.4577
Epoch: [1/20], Batch Num: [32/600]
Discriminator Loss: 1.2123, Generator Loss: 2.1615
D(x): 0.6786, D(G(z)): 0.3877
Epoch: [1/20], Batch Num: [33/600]
Discriminator Loss: 1.1330, Generator Loss: 1.9546
D(x): 0.6648, D(G(z)): 0.3730
Epoch: [1/20], Batch Num: [34/600]
Discriminator Loss: 1.3297, Generator Loss: 1.7346
D(x): 0.6422, D(G(z)): 0.3961
Epoch: [1/20], Batch Num: [35/600]
Discriminator Loss: 1.2936, Generator Loss: 1.6407
D(x): 0.6969, D(G(z)): 0.4683
Epoch: [1/20], Batch Num: [36/600]
Discriminator Loss: 0.9108, Generator Loss: 2.0477
D(x): 0.8168, D(G(z)): 0.4307
Epoch: [1/20], Batch Num: [37/600]
Discriminator Loss: 0.9377, Generator Loss: 2.5519
D(x): 0.8323, D(G(z)): 0.4484
Epoch: [1/20], Batch Num: [38/600]
Discriminator Loss: 0.7558, Generator Loss: 2.8456
D(x): 0.7475, D(G(z)): 0.2712
Epoch: [1/20], Batch Num: [39/600]
Discriminator Loss: 0.8263, Generator Loss: 3.1043
D(x): 0.7358, D(G(z)): 0.2338
Epoch: [1/20], Batch Num: [40/600]
Discriminator Loss: 0.7602, Generator Loss: 3.0713
D(x): 0.7115, D(G(z)): 0.1801
Epoch: [1/20], Batch Num: [41/600]
Discriminator Loss: 0.6777, Generator Loss: 2.8073
D(x): 0.7912, D(G(z)): 0.2480
Epoch: [1/20], Batch Num: [42/600]
Discriminator Loss: 0.7111, Generator Loss: 2.9474
D(x): 0.8124, D(G(z)): 0.2701
Epoch: [1/20], Batch Num: [43/600]
Discriminator Loss: 0.6299, Generator Loss: 2.6301
D(x): 0.8529, D(G(z)): 0.3022
Epoch: [1/20], Batch Num: [44/600]
Discriminator Loss: 0.6219, Generator Loss: 3.2359
D(x): 0.8883, D(G(z)): 0.3250
Epoch: [1/20], Batch Num: [45/600]
Discriminator Loss: 0.5019, Generator Loss: 3.7616
D(x): 0.8927, D(G(z)): 0.2659
Epoch: [1/20], Batch Num: [46/600]
Discriminator Loss: 0.4230, Generator Loss: 4.7722
D(x): 0.8774, D(G(z)): 0.1960
Epoch: [1/20], Batch Num: [47/600]
Discriminator Loss: 0.4787, Generator Loss: 5.1251
D(x): 0.8385, D(G(z)): 0.1595
Epoch: [1/20], Batch Num: [48/600]
Discriminator Loss: 0.5350, Generator Loss: 4.9347
D(x): 0.8184, D(G(z)): 0.1478
Epoch: [1/20], Batch Num: [49/600]
Discriminator Loss: 0.6426, Generator Loss: 4.0593
D(x): 0.7461, D(G(z)): 0.1746
Epoch: [1/20], Batch Num: [50/600]
Discriminator Loss: 0.5462, Generator Loss: 3.3777
D(x): 0.8233, D(G(z)): 0.1969
Epoch: [1/20], Batch Num: [51/600]
Discriminator Loss: 0.6741, Generator Loss: 2.8187
D(x): 0.8489, D(G(z)): 0.3075
Epoch: [1/20], Batch Num: [52/600]
Discriminator Loss: 0.7461, Generator Loss: 3.0868
D(x): 0.8596, D(G(z)): 0.3614
Epoch: [1/20], Batch Num: [53/600]
Discriminator Loss: 0.8438, Generator Loss: 3.0248
D(x): 0.8354, D(G(z)): 0.3710
Epoch: [1/20], Batch Num: [54/600]
Discriminator Loss: 0.9015, Generator Loss: 3.1065
D(x): 0.8014, D(G(z)): 0.3786
Epoch: [1/20], Batch Num: [55/600]
Discriminator Loss: 0.8907, Generator Loss: 2.9403
D(x): 0.7306, D(G(z)): 0.3085
Epoch: [1/20], Batch Num: [56/600]
Discriminator Loss: 1.1243, Generator Loss: 2.4102
D(x): 0.7037, D(G(z)): 0.3500
Epoch: [1/20], Batch Num: [57/600]
Discriminator Loss: 1.2254, Generator Loss: 2.2462
D(x): 0.7453, D(G(z)): 0.4394
Epoch: [1/20], Batch Num: [58/600]
Discriminator Loss: 1.2264, Generator Loss: 2.2007
D(x): 0.7579, D(G(z)): 0.4954
Epoch: [1/20], Batch Num: [59/600]
Discriminator Loss: 1.2572, Generator Loss: 1.9949
D(x): 0.7845, D(G(z)): 0.5043
Epoch: [1/20], Batch Num: [60/600]
Discriminator Loss: 1.1215, Generator Loss: 2.2597
D(x): 0.7573, D(G(z)): 0.4676
Epoch: [1/20], Batch Num: [61/600]
Discriminator Loss: 1.3260, Generator Loss: 2.3041
D(x): 0.7149, D(G(z)): 0.4900
Epoch: [1/20], Batch Num: [62/600]
Discriminator Loss: 1.5219, Generator Loss: 1.7998
D(x): 0.6726, D(G(z)): 0.4926
Epoch: [1/20], Batch Num: [63/600]
Discriminator Loss: 1.5194, Generator Loss: 1.6543
D(x): 0.6506, D(G(z)): 0.4793
Epoch: [1/20], Batch Num: [64/600]
Discriminator Loss: 1.5112, Generator Loss: 1.5239
D(x): 0.7388, D(G(z)): 0.5631
Epoch: [1/20], Batch Num: [65/600]
Discriminator Loss: 1.4737, Generator Loss: 1.3360
D(x): 0.7910, D(G(z)): 0.6103
Epoch: [1/20], Batch Num: [66/600]
Discriminator Loss: 1.4563, Generator Loss: 1.3232
D(x): 0.8095, D(G(z)): 0.6203
Epoch: [1/20], Batch Num: [67/600]
Discriminator Loss: 1.4418, Generator Loss: 1.7050
D(x): 0.7244, D(G(z)): 0.5739
Epoch: [1/20], Batch Num: [68/600]
Discriminator Loss: 1.5152, Generator Loss: 1.5211
D(x): 0.6710, D(G(z)): 0.5345
Epoch: [1/20], Batch Num: [69/600]
Discriminator Loss: 1.6335, Generator Loss: 1.4354
D(x): 0.6705, D(G(z)): 0.5534
Epoch: [1/20], Batch Num: [70/600]
Discriminator Loss: 1.8528, Generator Loss: 1.1562
D(x): 0.6130, D(G(z)): 0.5988
Epoch: [1/20], Batch Num: [71/600]
Discriminator Loss: 2.0192, Generator Loss: 0.8365
D(x): 0.6236, D(G(z)): 0.6652
Epoch: [1/20], Batch Num: [72/600]
Discriminator Loss: 1.9184, Generator Loss: 0.5612
D(x): 0.6997, D(G(z)): 0.7078
Epoch: [1/20], Batch Num: [73/600]
Discriminator Loss: 2.0063, Generator Loss: 0.5304
D(x): 0.7473, D(G(z)): 0.7634
Epoch: [1/20], Batch Num: [74/600]
Discriminator Loss: 1.8117, Generator Loss: 0.7827
D(x): 0.8139, D(G(z)): 0.7566
Epoch: [1/20], Batch Num: [75/600]
Discriminator Loss: 1.6704, Generator Loss: 0.8657
D(x): 0.7813, D(G(z)): 0.6804
Epoch: [1/20], Batch Num: [76/600]
Discriminator Loss: 1.5192, Generator Loss: 1.2827
D(x): 0.7695, D(G(z)): 0.6212
Epoch: [1/20], Batch Num: [77/600]
Discriminator Loss: 1.1717, Generator Loss: 1.6369
D(x): 0.7841, D(G(z)): 0.5297
Epoch: [1/20], Batch Num: [78/600]
Discriminator Loss: 1.0687, Generator Loss: 1.9764
D(x): 0.7721, D(G(z)): 0.4372
Epoch: [1/20], Batch Num: [79/600]
Discriminator Loss: 0.8959, Generator Loss: 2.3163
D(x): 0.7687, D(G(z)): 0.3632
Epoch: [1/20], Batch Num: [80/600]
Discriminator Loss: 0.9095, Generator Loss: 2.3784
D(x): 0.7218, D(G(z)): 0.3512
Epoch: [1/20], Batch Num: [81/600]
Discriminator Loss: 0.8895, Generator Loss: 2.2051
D(x): 0.7042, D(G(z)): 0.3030
Epoch: [1/20], Batch Num: [82/600]
Discriminator Loss: 0.9384, Generator Loss: 1.8834
D(x): 0.7540, D(G(z)): 0.3551
Epoch: [1/20], Batch Num: [83/600]
Discriminator Loss: 0.9284, Generator Loss: 1.7420
D(x): 0.7568, D(G(z)): 0.3986
Epoch: [1/20], Batch Num: [84/600]
Discriminator Loss: 0.9498, Generator Loss: 1.6822
D(x): 0.7764, D(G(z)): 0.4310
Epoch: [1/20], Batch Num: [85/600]
Discriminator Loss: 0.9544, Generator Loss: 1.5711
D(x): 0.8209, D(G(z)): 0.4738
Epoch: [1/20], Batch Num: [86/600]
Discriminator Loss: 0.9747, Generator Loss: 1.6747
D(x): 0.7834, D(G(z)): 0.4535
Epoch: [1/20], Batch Num: [87/600]
Discriminator Loss: 1.0437, Generator Loss: 1.8452
D(x): 0.7460, D(G(z)): 0.4594
Epoch: [1/20], Batch Num: [88/600]
Discriminator Loss: 0.9637, Generator Loss: 1.8636
D(x): 0.7780, D(G(z)): 0.4324
Epoch: [1/20], Batch Num: [89/600]
Discriminator Loss: 1.3828, Generator Loss: 1.7207
D(x): 0.6216, D(G(z)): 0.4380
Epoch: [1/20], Batch Num: [90/600]
Discriminator Loss: 1.2900, Generator Loss: 1.5660
D(x): 0.6462, D(G(z)): 0.4407
Epoch: [1/20], Batch Num: [91/600]
Discriminator Loss: 1.2834, Generator Loss: 1.2699
D(x): 0.6949, D(G(z)): 0.5023
Epoch: [1/20], Batch Num: [92/600]
Discriminator Loss: 1.3948, Generator Loss: 1.1245
D(x): 0.6785, D(G(z)): 0.5468
Epoch: [1/20], Batch Num: [93/600]
Discriminator Loss: 1.4562, Generator Loss: 1.1115
D(x): 0.7148, D(G(z)): 0.5887
Epoch: [1/20], Batch Num: [94/600]
Discriminator Loss: 1.4675, Generator Loss: 1.1008
D(x): 0.7458, D(G(z)): 0.6283
Epoch: [1/20], Batch Num: [95/600]
Discriminator Loss: 1.4814, Generator Loss: 0.9876
D(x): 0.7381, D(G(z)): 0.6322
Epoch: [1/20], Batch Num: [96/600]
Discriminator Loss: 1.6665, Generator Loss: 1.0028
D(x): 0.6871, D(G(z)): 0.6475
Epoch: [1/20], Batch Num: [97/600]
Discriminator Loss: 1.7653, Generator Loss: 0.9827
D(x): 0.6587, D(G(z)): 0.6919
Epoch: [1/20], Batch Num: [98/600]
Discriminator Loss: 1.8093, Generator Loss: 0.9017
D(x): 0.6584, D(G(z)): 0.6848
Epoch: [1/20], Batch Num: [99/600]
Discriminator Loss: 1.8452, Generator Loss: 0.7056
D(x): 0.6255, D(G(z)): 0.6911
Epoch: 1, Batch Num: [100/600]

Epoch: [1/20], Batch Num: [100/600]
Discriminator Loss: 2.0245, Generator Loss: 0.5976
D(x): 0.6104, D(G(z)): 0.7205
Epoch: [1/20], Batch Num: [101/600]
Discriminator Loss: 2.0362, Generator Loss: 0.5873
D(x): 0.6118, D(G(z)): 0.7375
Epoch: [1/20], Batch Num: [102/600]
Discriminator Loss: 2.0667, Generator Loss: 0.5805
D(x): 0.6083, D(G(z)): 0.7209
Epoch: [1/20], Batch Num: [103/600]
Discriminator Loss: 1.7915, Generator Loss: 0.5704
D(x): 0.7168, D(G(z)): 0.7262
Epoch: [1/20], Batch Num: [104/600]
Discriminator Loss: 1.8837, Generator Loss: 0.6831
D(x): 0.6597, D(G(z)): 0.7104
Epoch: [1/20], Batch Num: [105/600]
Discriminator Loss: 1.7493, Generator Loss: 0.6629
D(x): 0.6986, D(G(z)): 0.6991
Epoch: [1/20], Batch Num: [106/600]
Discriminator Loss: 1.7695, Generator Loss: 0.7572
D(x): 0.6626, D(G(z)): 0.6658
Epoch: [1/20], Batch Num: [107/600]
Discriminator Loss: 1.6814, Generator Loss: 0.9166
D(x): 0.6398, D(G(z)): 0.6263
Epoch: [1/20], Batch Num: [108/600]
Discriminator Loss: 1.4949, Generator Loss: 1.0250
D(x): 0.6613, D(G(z)): 0.5931
Epoch: [1/20], Batch Num: [109/600]
Discriminator Loss: 1.6324, Generator Loss: 1.1906
D(x): 0.6101, D(G(z)): 0.5872
Epoch: [1/20], Batch Num: [110/600]
Discriminator Loss: 1.5546, Generator Loss: 0.9736
D(x): 0.6026, D(G(z)): 0.5669
Epoch: [1/20], Batch Num: [111/600]
Discriminator Loss: 1.6785, Generator Loss: 0.9903
D(x): 0.5754, D(G(z)): 0.5960
Epoch: [1/20], Batch Num: [112/600]
Discriminator Loss: 1.6222, Generator Loss: 0.8207
D(x): 0.6009, D(G(z)): 0.6025
Epoch: [1/20], Batch Num: [113/600]
Discriminator Loss: 1.7293, Generator Loss: 0.7476
D(x): 0.5792, D(G(z)): 0.6160
Epoch: [1/20], Batch Num: [114/600]
Discriminator Loss: 1.9161, Generator Loss: 0.6711
D(x): 0.5708, D(G(z)): 0.6288
Epoch: [1/20], Batch Num: [115/600]
Discriminator Loss: 1.6583, Generator Loss: 0.6529
D(x): 0.6201, D(G(z)): 0.6299
Epoch: [1/20], Batch Num: [116/600]
Discriminator Loss: 1.6623, Generator Loss: 0.6249
D(x): 0.6222, D(G(z)): 0.6458
Epoch: [1/20], Batch Num: [117/600]
Discriminator Loss: 1.7189, Generator Loss: 0.7067
D(x): 0.6307, D(G(z)): 0.6483
Epoch: [1/20], Batch Num: [118/600]
Discriminator Loss: 1.6539, Generator Loss: 0.7962
D(x): 0.6068, D(G(z)): 0.6202
Epoch: [1/20], Batch Num: [119/600]
Discriminator Loss: 1.8402, Generator Loss: 0.8498
D(x): 0.5259, D(G(z)): 0.6016
Epoch: [1/20], Batch Num: [120/600]
Discriminator Loss: 1.7243, Generator Loss: 0.9497
D(x): 0.5261, D(G(z)): 0.5507
Epoch: [1/20], Batch Num: [121/600]
Discriminator Loss: 1.6600, Generator Loss: 1.0020
D(x): 0.5063, D(G(z)): 0.5287
Epoch: [1/20], Batch Num: [122/600]
Discriminator Loss: 1.5038, Generator Loss: 0.9344
D(x): 0.5527, D(G(z)): 0.5311
Epoch: [1/20], Batch Num: [123/600]
Discriminator Loss: 1.5437, Generator Loss: 0.9785
D(x): 0.5601, D(G(z)): 0.5452
Epoch: [1/20], Batch Num: [124/600]
Discriminator Loss: 1.4784, Generator Loss: 1.0525
D(x): 0.5631, D(G(z)): 0.5317
Epoch: [1/20], Batch Num: [125/600]
Discriminator Loss: 1.4288, Generator Loss: 1.1007
D(x): 0.5631, D(G(z)): 0.4860
Epoch: [1/20], Batch Num: [126/600]
Discriminator Loss: 1.4199, Generator Loss: 1.1417
D(x): 0.5216, D(G(z)): 0.4670
Epoch: [1/20], Batch Num: [127/600]
Discriminator Loss: 1.4773, Generator Loss: 1.1955
D(x): 0.4978, D(G(z)): 0.4397
Epoch: [1/20], Batch Num: [128/600]
Discriminator Loss: 1.2760, Generator Loss: 1.2308
D(x): 0.5472, D(G(z)): 0.4390
Epoch: [1/20], Batch Num: [129/600]
Discriminator Loss: 1.2079, Generator Loss: 1.4230
D(x): 0.5567, D(G(z)): 0.4002
Epoch: [1/20], Batch Num: [130/600]
Discriminator Loss: 1.2110, Generator Loss: 1.3123
D(x): 0.5557, D(G(z)): 0.3948
Epoch: [1/20], Batch Num: [131/600]
Discriminator Loss: 1.1346, Generator Loss: 1.4915
D(x): 0.5916, D(G(z)): 0.4045
Epoch: [1/20], Batch Num: [132/600]
Discriminator Loss: 1.0618, Generator Loss: 1.3682
D(x): 0.6005, D(G(z)): 0.3782
Epoch: [1/20], Batch Num: [133/600]
Discriminator Loss: 1.0348, Generator Loss: 1.6657
D(x): 0.5988, D(G(z)): 0.3507
Epoch: [1/20], Batch Num: [134/600]
Discriminator Loss: 0.9344, Generator Loss: 1.7401
D(x): 0.6270, D(G(z)): 0.3203
Epoch: [1/20], Batch Num: [135/600]
Discriminator Loss: 0.9539, Generator Loss: 1.8596
D(x): 0.6028, D(G(z)): 0.2703
Epoch: [1/20], Batch Num: [136/600]
Discriminator Loss: 0.8831, Generator Loss: 1.6557
D(x): 0.6083, D(G(z)): 0.2689
Epoch: [1/20], Batch Num: [137/600]
Discriminator Loss: 0.8215, Generator Loss: 1.9150
D(x): 0.6713, D(G(z)): 0.2893
Epoch: [1/20], Batch Num: [138/600]
Discriminator Loss: 0.7657, Generator Loss: 1.9076
D(x): 0.6916, D(G(z)): 0.2770
Epoch: [1/20], Batch Num: [139/600]
Discriminator Loss: 0.6801, Generator Loss: 1.9343
D(x): 0.6999, D(G(z)): 0.2412
Epoch: [1/20], Batch Num: [140/600]
Discriminator Loss: 0.7680, Generator Loss: 2.0374
D(x): 0.6882, D(G(z)): 0.2767
Epoch: [1/20], Batch Num: [141/600]
Discriminator Loss: 0.7040, Generator Loss: 1.9666
D(x): 0.6985, D(G(z)): 0.2472
Epoch: [1/20], Batch Num: [142/600]
Discriminator Loss: 0.6880, Generator Loss: 2.1051
D(x): 0.7251, D(G(z)): 0.2580
Epoch: [1/20], Batch Num: [143/600]
Discriminator Loss: 0.6634, Generator Loss: 1.9951
D(x): 0.7422, D(G(z)): 0.2535
Epoch: [1/20], Batch Num: [144/600]
Discriminator Loss: 0.7027, Generator Loss: 1.9948
D(x): 0.7365, D(G(z)): 0.2727
Epoch: [1/20], Batch Num: [145/600]
Discriminator Loss: 0.6725, Generator Loss: 2.1110
D(x): 0.7287, D(G(z)): 0.2421
Epoch: [1/20], Batch Num: [146/600]
Discriminator Loss: 0.7168, Generator Loss: 2.2380
D(x): 0.7262, D(G(z)): 0.2682
Epoch: [1/20], Batch Num: [147/600]
Discriminator Loss: 0.7324, Generator Loss: 2.1367
D(x): 0.7024, D(G(z)): 0.2488
Epoch: [1/20], Batch Num: [148/600]
Discriminator Loss: 0.7200, Generator Loss: 1.8305
D(x): 0.7369, D(G(z)): 0.2953
Epoch: [1/20], Batch Num: [149/600]
Discriminator Loss: 0.7264, Generator Loss: 1.7049
D(x): 0.7232, D(G(z)): 0.2713
Epoch: [1/20], Batch Num: [150/600]
Discriminator Loss: 0.7690, Generator Loss: 1.9191
D(x): 0.7431, D(G(z)): 0.3104
Epoch: [1/20], Batch Num: [151/600]
Discriminator Loss: 0.8586, Generator Loss: 1.8314
D(x): 0.7000, D(G(z)): 0.3245
Epoch: [1/20], Batch Num: [152/600]
Discriminator Loss: 0.8807, Generator Loss: 1.7711
D(x): 0.6793, D(G(z)): 0.2906
Epoch: [1/20], Batch Num: [153/600]
Discriminator Loss: 0.9751, Generator Loss: 1.9320
D(x): 0.6900, D(G(z)): 0.3596
Epoch: [1/20], Batch Num: [154/600]
Discriminator Loss: 0.8514, Generator Loss: 1.7860
D(x): 0.7010, D(G(z)): 0.3147
Epoch: [1/20], Batch Num: [155/600]
Discriminator Loss: 1.0897, Generator Loss: 1.7451
D(x): 0.6160, D(G(z)): 0.3455
Epoch: [1/20], Batch Num: [156/600]
Discriminator Loss: 1.0089, Generator Loss: 1.5329
D(x): 0.6804, D(G(z)): 0.3642
Epoch: [1/20], Batch Num: [157/600]
Discriminator Loss: 1.0857, Generator Loss: 1.4200
D(x): 0.6796, D(G(z)): 0.4080
Epoch: [1/20], Batch Num: [158/600]
Discriminator Loss: 1.0444, Generator Loss: 1.6959
D(x): 0.6561, D(G(z)): 0.3886
Epoch: [1/20], Batch Num: [159/600]
Discriminator Loss: 1.0547, Generator Loss: 1.5199
D(x): 0.6492, D(G(z)): 0.3657
Epoch: [1/20], Batch Num: [160/600]
Discriminator Loss: 1.3742, Generator Loss: 1.2914
D(x): 0.5769, D(G(z)): 0.4061
Epoch: [1/20], Batch Num: [161/600]
Discriminator Loss: 1.1642, Generator Loss: 1.2459
D(x): 0.6962, D(G(z)): 0.4742
Epoch: [1/20], Batch Num: [162/600]
Discriminator Loss: 1.1186, Generator Loss: 1.5679
D(x): 0.6639, D(G(z)): 0.4078
Epoch: [1/20], Batch Num: [163/600]
Discriminator Loss: 1.1589, Generator Loss: 1.5593
D(x): 0.6808, D(G(z)): 0.4466
Epoch: [1/20], Batch Num: [164/600]
Discriminator Loss: 1.1507, Generator Loss: 1.5149
D(x): 0.6176, D(G(z)): 0.3613
Epoch: [1/20], Batch Num: [165/600]
Discriminator Loss: 1.1540, Generator Loss: 1.6016
D(x): 0.6058, D(G(z)): 0.3518
Epoch: [1/20], Batch Num: [166/600]
Discriminator Loss: 1.4157, Generator Loss: 1.3640
D(x): 0.5556, D(G(z)): 0.4232
Epoch: [1/20], Batch Num: [167/600]
Discriminator Loss: 1.0116, Generator Loss: 1.4225
D(x): 0.7635, D(G(z)): 0.4545
Epoch: [1/20], Batch Num: [168/600]
Discriminator Loss: 1.0827, Generator Loss: 1.3821
D(x): 0.6924, D(G(z)): 0.4321
Epoch: [1/20], Batch Num: [169/600]
Discriminator Loss: 1.1501, Generator Loss: 1.6141
D(x): 0.6942, D(G(z)): 0.4300
Epoch: [1/20], Batch Num: [170/600]
Discriminator Loss: 0.9788, Generator Loss: 1.8569
D(x): 0.7053, D(G(z)): 0.3812
Epoch: [1/20], Batch Num: [171/600]
Discriminator Loss: 1.0219, Generator Loss: 2.0618
D(x): 0.6398, D(G(z)): 0.2848
Epoch: [1/20], Batch Num: [172/600]
Discriminator Loss: 0.9441, Generator Loss: 1.8397
D(x): 0.6377, D(G(z)): 0.2808
Epoch: [1/20], Batch Num: [173/600]
Discriminator Loss: 0.8263, Generator Loss: 1.6090
D(x): 0.6849, D(G(z)): 0.2674
Epoch: [1/20], Batch Num: [174/600]
Discriminator Loss: 0.8413, Generator Loss: 1.6117
D(x): 0.7685, D(G(z)): 0.3617
Epoch: [1/20], Batch Num: [175/600]
Discriminator Loss: 0.8024, Generator Loss: 1.5888
D(x): 0.7688, D(G(z)): 0.3567
Epoch: [1/20], Batch Num: [176/600]
Discriminator Loss: 0.8616, Generator Loss: 1.8042
D(x): 0.7786, D(G(z)): 0.3884
Epoch: [1/20], Batch Num: [177/600]
Discriminator Loss: 0.8142, Generator Loss: 2.1080
D(x): 0.7641, D(G(z)): 0.3364
Epoch: [1/20], Batch Num: [178/600]
Discriminator Loss: 0.7450, Generator Loss: 2.1220
D(x): 0.7143, D(G(z)): 0.2521
Epoch: [1/20], Batch Num: [179/600]
Discriminator Loss: 0.8113, Generator Loss: 2.1478
D(x): 0.6814, D(G(z)): 0.2563
Epoch: [1/20], Batch Num: [180/600]
Discriminator Loss: 0.7620, Generator Loss: 1.8858
D(x): 0.7023, D(G(z)): 0.2446
Epoch: [1/20], Batch Num: [181/600]
Discriminator Loss: 0.6939, Generator Loss: 1.8813
D(x): 0.7985, D(G(z)): 0.3154
Epoch: [1/20], Batch Num: [182/600]
Discriminator Loss: 0.7821, Generator Loss: 1.9502
D(x): 0.7893, D(G(z)): 0.3487
Epoch: [1/20], Batch Num: [183/600]
Discriminator Loss: 0.7485, Generator Loss: 2.2225
D(x): 0.7701, D(G(z)): 0.3107
Epoch: [1/20], Batch Num: [184/600]
Discriminator Loss: 0.6232, Generator Loss: 2.2277
D(x): 0.7677, D(G(z)): 0.2464
Epoch: [1/20], Batch Num: [185/600]
Discriminator Loss: 0.9001, Generator Loss: 2.1033
D(x): 0.6742, D(G(z)): 0.2722
Epoch: [1/20], Batch Num: [186/600]
Discriminator Loss: 0.7880, Generator Loss: 1.7829
D(x): 0.7262, D(G(z)): 0.2938
Epoch: [1/20], Batch Num: [187/600]
Discriminator Loss: 0.7748, Generator Loss: 1.7835
D(x): 0.7747, D(G(z)): 0.3090
Epoch: [1/20], Batch Num: [188/600]
Discriminator Loss: 0.7217, Generator Loss: 2.1105
D(x): 0.7745, D(G(z)): 0.3110
Epoch: [1/20], Batch Num: [189/600]
Discriminator Loss: 0.7717, Generator Loss: 2.1561
D(x): 0.7526, D(G(z)): 0.3127
Epoch: [1/20], Batch Num: [190/600]
Discriminator Loss: 0.8601, Generator Loss: 2.2600
D(x): 0.7288, D(G(z)): 0.3268
Epoch: [1/20], Batch Num: [191/600]
Discriminator Loss: 0.8588, Generator Loss: 2.0742
D(x): 0.6990, D(G(z)): 0.2829
Epoch: [1/20], Batch Num: [192/600]
Discriminator Loss: 0.8707, Generator Loss: 2.1126
D(x): 0.7270, D(G(z)): 0.3159
Epoch: [1/20], Batch Num: [193/600]
Discriminator Loss: 1.0008, Generator Loss: 1.8880
D(x): 0.7106, D(G(z)): 0.3424
Epoch: [1/20], Batch Num: [194/600]
Discriminator Loss: 0.7387, Generator Loss: 1.9103
D(x): 0.7827, D(G(z)): 0.3140
Epoch: [1/20], Batch Num: [195/600]
Discriminator Loss: 0.8711, Generator Loss: 2.0345
D(x): 0.7347, D(G(z)): 0.3465
Epoch: [1/20], Batch Num: [196/600]
Discriminator Loss: 0.8018, Generator Loss: 2.0687
D(x): 0.7401, D(G(z)): 0.2866
Epoch: [1/20], Batch Num: [197/600]
Discriminator Loss: 0.8803, Generator Loss: 2.0682
D(x): 0.6924, D(G(z)): 0.2781
Epoch: [1/20], Batch Num: [198/600]
Discriminator Loss: 0.8696, Generator Loss: 2.0922
D(x): 0.7228, D(G(z)): 0.3133
Epoch: [1/20], Batch Num: [199/600]
Discriminator Loss: 0.7694, Generator Loss: 2.1709
D(x): 0.7486, D(G(z)): 0.2786
Epoch: 1, Batch Num: [200/600]

Epoch: [1/20], Batch Num: [200/600]
Discriminator Loss: 0.9038, Generator Loss: 2.1094
D(x): 0.7586, D(G(z)): 0.3472
Epoch: [1/20], Batch Num: [201/600]
Discriminator Loss: 0.7788, Generator Loss: 2.1873
D(x): 0.7591, D(G(z)): 0.3081
Epoch: [1/20], Batch Num: [202/600]
Discriminator Loss: 0.7284, Generator Loss: 2.3894
D(x): 0.7721, D(G(z)): 0.2817
Epoch: [1/20], Batch Num: [203/600]
Discriminator Loss: 0.7022, Generator Loss: 2.7067
D(x): 0.7747, D(G(z)): 0.2526
Epoch: [1/20], Batch Num: [204/600]
Discriminator Loss: 0.6716, Generator Loss: 2.5858
D(x): 0.7594, D(G(z)): 0.2501
Epoch: [1/20], Batch Num: [205/600]
Discriminator Loss: 0.6784, Generator Loss: 2.5104
D(x): 0.7460, D(G(z)): 0.2193
Epoch: [1/20], Batch Num: [206/600]
Discriminator Loss: 0.6629, Generator Loss: 2.4898
D(x): 0.7864, D(G(z)): 0.2551
Epoch: [1/20], Batch Num: [207/600]
Discriminator Loss: 0.4907, Generator Loss: 2.5240
D(x): 0.8522, D(G(z)): 0.2358
Epoch: [1/20], Batch Num: [208/600]
Discriminator Loss: 0.5672, Generator Loss: 2.6999
D(x): 0.7914, D(G(z)): 0.1994
Epoch: [1/20], Batch Num: [209/600]
Discriminator Loss: 0.5156, Generator Loss: 2.6685
D(x): 0.8313, D(G(z)): 0.2184
Epoch: [1/20], Batch Num: [210/600]
Discriminator Loss: 0.5367, Generator Loss: 3.2010
D(x): 0.8435, D(G(z)): 0.2415
Epoch: [1/20], Batch Num: [211/600]
Discriminator Loss: 0.5352, Generator Loss: 2.9677
D(x): 0.7979, D(G(z)): 0.1732
Epoch: [1/20], Batch Num: [212/600]
Discriminator Loss: 0.3726, Generator Loss: 2.7089
D(x): 0.8435, D(G(z)): 0.1386
Epoch: [1/20], Batch Num: [213/600]
Discriminator Loss: 0.4727, Generator Loss: 3.1640
D(x): 0.8326, D(G(z)): 0.1825
Epoch: [1/20], Batch Num: [214/600]
Discriminator Loss: 0.5267, Generator Loss: 2.7425
D(x): 0.8193, D(G(z)): 0.1752
Epoch: [1/20], Batch Num: [215/600]
Discriminator Loss: 0.3355, Generator Loss: 2.9898
D(x): 0.8915, D(G(z)): 0.1714
Epoch: [1/20], Batch Num: [216/600]
Discriminator Loss: 0.5171, Generator Loss: 3.0616
D(x): 0.8362, D(G(z)): 0.1919
Epoch: [1/20], Batch Num: [217/600]
Discriminator Loss: 0.5493, Generator Loss: 3.0614
D(x): 0.8293, D(G(z)): 0.2140
Epoch: [1/20], Batch Num: [218/600]
Discriminator Loss: 0.5376, Generator Loss: 3.0975
D(x): 0.8389, D(G(z)): 0.2184
Epoch: [1/20], Batch Num: [219/600]
Discriminator Loss: 0.6040, Generator Loss: 2.8980
D(x): 0.7839, D(G(z)): 0.1856
Epoch: [1/20], Batch Num: [220/600]
Discriminator Loss: 0.5571, Generator Loss: 2.8367
D(x): 0.8203, D(G(z)): 0.2147
Epoch: [1/20], Batch Num: [221/600]
Discriminator Loss: 0.5779, Generator Loss: 2.8794
D(x): 0.8079, D(G(z)): 0.2163
Epoch: [1/20], Batch Num: [222/600]
Discriminator Loss: 0.5458, Generator Loss: 2.7755
D(x): 0.8687, D(G(z)): 0.2487
Epoch: [1/20], Batch Num: [223/600]
Discriminator Loss: 0.6102, Generator Loss: 2.9927
D(x): 0.7825, D(G(z)): 0.2060
Epoch: [1/20], Batch Num: [224/600]
Discriminator Loss: 0.5444, Generator Loss: 2.9940
D(x): 0.8166, D(G(z)): 0.2046
Epoch: [1/20], Batch Num: [225/600]
Discriminator Loss: 0.6883, Generator Loss: 2.9943
D(x): 0.7816, D(G(z)): 0.2386
Epoch: [1/20], Batch Num: [226/600]
Discriminator Loss: 0.7443, Generator Loss: 2.8804
D(x): 0.7661, D(G(z)): 0.2561
Epoch: [1/20], Batch Num: [227/600]
Discriminator Loss: 0.7861, Generator Loss: 2.6626
D(x): 0.7720, D(G(z)): 0.2880
Epoch: [1/20], Batch Num: [228/600]
Discriminator Loss: 0.9284, Generator Loss: 2.3715
D(x): 0.7365, D(G(z)): 0.3031
Epoch: [1/20], Batch Num: [229/600]
Discriminator Loss: 1.0718, Generator Loss: 2.2454
D(x): 0.6974, D(G(z)): 0.3448
Epoch: [1/20], Batch Num: [230/600]
Discriminator Loss: 1.2177, Generator Loss: 1.8470
D(x): 0.6515, D(G(z)): 0.3621
Epoch: [1/20], Batch Num: [231/600]
Discriminator Loss: 1.3543, Generator Loss: 1.8831
D(x): 0.7035, D(G(z)): 0.4731
Epoch: [1/20], Batch Num: [232/600]
Discriminator Loss: 1.4018, Generator Loss: 2.0650
D(x): 0.6872, D(G(z)): 0.4703
Epoch: [1/20], Batch Num: [233/600]
Discriminator Loss: 1.3646, Generator Loss: 1.9105
D(x): 0.6455, D(G(z)): 0.4041
Epoch: [1/20], Batch Num: [234/600]
Discriminator Loss: 1.6171, Generator Loss: 1.7461
D(x): 0.5819, D(G(z)): 0.4155
Epoch: [1/20], Batch Num: [235/600]
Discriminator Loss: 1.4976, Generator Loss: 1.6019
D(x): 0.6528, D(G(z)): 0.4279
Epoch: [1/20], Batch Num: [236/600]
Discriminator Loss: 1.2554, Generator Loss: 1.6993
D(x): 0.6994, D(G(z)): 0.4518
Epoch: [1/20], Batch Num: [237/600]
Discriminator Loss: 1.3390, Generator Loss: 1.7638
D(x): 0.6832, D(G(z)): 0.4494
Epoch: [1/20], Batch Num: [238/600]
Discriminator Loss: 1.0554, Generator Loss: 1.9368
D(x): 0.7104, D(G(z)): 0.3895
Epoch: [1/20], Batch Num: [239/600]
Discriminator Loss: 1.2125, Generator Loss: 1.8775
D(x): 0.6102, D(G(z)): 0.3417
Epoch: [1/20], Batch Num: [240/600]
Discriminator Loss: 1.1382, Generator Loss: 2.1234
D(x): 0.7038, D(G(z)): 0.3551
Epoch: [1/20], Batch Num: [241/600]
Discriminator Loss: 1.0686, Generator Loss: 1.9584
D(x): 0.6880, D(G(z)): 0.3406
Epoch: [1/20], Batch Num: [242/600]
Discriminator Loss: 1.0293, Generator Loss: 1.7722
D(x): 0.6723, D(G(z)): 0.3311
Epoch: [1/20], Batch Num: [243/600]
Discriminator Loss: 0.8833, Generator Loss: 1.6316
D(x): 0.7646, D(G(z)): 0.3673
Epoch: [1/20], Batch Num: [244/600]
Discriminator Loss: 1.0127, Generator Loss: 1.8280
D(x): 0.7450, D(G(z)): 0.4003
Epoch: [1/20], Batch Num: [245/600]
Discriminator Loss: 0.9296, Generator Loss: 2.2694
D(x): 0.7829, D(G(z)): 0.4018
Epoch: [1/20], Batch Num: [246/600]
Discriminator Loss: 0.9952, Generator Loss: 2.3325
D(x): 0.6769, D(G(z)): 0.3080
Epoch: [1/20], Batch Num: [247/600]
Discriminator Loss: 0.9803, Generator Loss: 2.2664
D(x): 0.6827, D(G(z)): 0.2922
Epoch: [1/20], Batch Num: [248/600]
Discriminator Loss: 1.2185, Generator Loss: 1.9713
D(x): 0.5702, D(G(z)): 0.3045
Epoch: [1/20], Batch Num: [249/600]
Discriminator Loss: 0.8702, Generator Loss: 1.7014
D(x): 0.7260, D(G(z)): 0.3253
Epoch: [1/20], Batch Num: [250/600]
Discriminator Loss: 1.0325, Generator Loss: 1.6680
D(x): 0.7377, D(G(z)): 0.4231
Epoch: [1/20], Batch Num: [251/600]
Discriminator Loss: 0.9821, Generator Loss: 1.6656
D(x): 0.7452, D(G(z)): 0.4037
Epoch: [1/20], Batch Num: [252/600]
Discriminator Loss: 0.9210, Generator Loss: 1.8789
D(x): 0.7503, D(G(z)): 0.3860
Epoch: [1/20], Batch Num: [253/600]
Discriminator Loss: 0.8481, Generator Loss: 2.4204
D(x): 0.7672, D(G(z)): 0.3519
Epoch: [1/20], Batch Num: [254/600]
Discriminator Loss: 1.0265, Generator Loss: 2.2742
D(x): 0.6149, D(G(z)): 0.2464
Epoch: [1/20], Batch Num: [255/600]
Discriminator Loss: 0.9905, Generator Loss: 2.0476
D(x): 0.6369, D(G(z)): 0.2775
Epoch: [1/20], Batch Num: [256/600]
Discriminator Loss: 1.0234, Generator Loss: 1.7832
D(x): 0.6754, D(G(z)): 0.3284
Epoch: [1/20], Batch Num: [257/600]
Discriminator Loss: 0.9815, Generator Loss: 1.5655
D(x): 0.7049, D(G(z)): 0.3654
Epoch: [1/20], Batch Num: [258/600]
Discriminator Loss: 0.8935, Generator Loss: 1.7980
D(x): 0.7352, D(G(z)): 0.3475
Epoch: [1/20], Batch Num: [259/600]
Discriminator Loss: 0.9874, Generator Loss: 1.9922
D(x): 0.7684, D(G(z)): 0.4327
Epoch: [1/20], Batch Num: [260/600]
Discriminator Loss: 0.9892, Generator Loss: 2.1566
D(x): 0.6785, D(G(z)): 0.3388
Epoch: [1/20], Batch Num: [261/600]
Discriminator Loss: 0.9689, Generator Loss: 2.1780
D(x): 0.6983, D(G(z)): 0.3415
Epoch: [1/20], Batch Num: [262/600]
Discriminator Loss: 0.9561, Generator Loss: 2.3758
D(x): 0.6801, D(G(z)): 0.2881
Epoch: [1/20], Batch Num: [263/600]
Discriminator Loss: 0.9540, Generator Loss: 2.0937
D(x): 0.6988, D(G(z)): 0.3221
Epoch: [1/20], Batch Num: [264/600]
Discriminator Loss: 0.9259, Generator Loss: 2.1812
D(x): 0.7147, D(G(z)): 0.3229
Epoch: [1/20], Batch Num: [265/600]
Discriminator Loss: 0.9273, Generator Loss: 2.2571
D(x): 0.7132, D(G(z)): 0.2921
Epoch: [1/20], Batch Num: [266/600]
Discriminator Loss: 0.7961, Generator Loss: 2.2735
D(x): 0.7639, D(G(z)): 0.3201
Epoch: [1/20], Batch Num: [267/600]
Discriminator Loss: 0.6315, Generator Loss: 2.2869
D(x): 0.8211, D(G(z)): 0.2953
Epoch: [1/20], Batch Num: [268/600]
Discriminator Loss: 0.6316, Generator Loss: 2.9530
D(x): 0.8386, D(G(z)): 0.2745
Epoch: [1/20], Batch Num: [269/600]
Discriminator Loss: 0.6677, Generator Loss: 2.5744
D(x): 0.7803, D(G(z)): 0.2625
Epoch: [1/20], Batch Num: [270/600]
Discriminator Loss: 0.6093, Generator Loss: 3.4905
D(x): 0.7777, D(G(z)): 0.2129
Epoch: [1/20], Batch Num: [271/600]
Discriminator Loss: 0.5362, Generator Loss: 3.8831
D(x): 0.8249, D(G(z)): 0.2327
Epoch: [1/20], Batch Num: [272/600]
Discriminator Loss: 0.6385, Generator Loss: 4.1762
D(x): 0.7727, D(G(z)): 0.2269
Epoch: [1/20], Batch Num: [273/600]
Discriminator Loss: 0.6147, Generator Loss: 3.8705
D(x): 0.7954, D(G(z)): 0.2237
Epoch: [1/20], Batch Num: [274/600]
Discriminator Loss: 0.6158, Generator Loss: 3.4176
D(x): 0.8040, D(G(z)): 0.2562
Epoch: [1/20], Batch Num: [275/600]
Discriminator Loss: 0.6720, Generator Loss: 3.8937
D(x): 0.8134, D(G(z)): 0.2838
Epoch: [1/20], Batch Num: [276/600]
Discriminator Loss: 0.7709, Generator Loss: 3.8525
D(x): 0.8074, D(G(z)): 0.3177
Epoch: [1/20], Batch Num: [277/600]
Discriminator Loss: 0.8458, Generator Loss: 3.7535
D(x): 0.7728, D(G(z)): 0.3288
Epoch: [1/20], Batch Num: [278/600]
Discriminator Loss: 1.0031, Generator Loss: 4.1187
D(x): 0.7317, D(G(z)): 0.3408
Epoch: [1/20], Batch Num: [279/600]
Discriminator Loss: 0.9799, Generator Loss: 4.1339
D(x): 0.7026, D(G(z)): 0.2910
Epoch: [1/20], Batch Num: [280/600]
Discriminator Loss: 0.8693, Generator Loss: 3.8413
D(x): 0.7969, D(G(z)): 0.3719
Epoch: [1/20], Batch Num: [281/600]
Discriminator Loss: 0.9295, Generator Loss: 4.9272
D(x): 0.7789, D(G(z)): 0.3696
Epoch: [1/20], Batch Num: [282/600]
Discriminator Loss: 0.8473, Generator Loss: 5.2160
D(x): 0.7373, D(G(z)): 0.2486
Epoch: [1/20], Batch Num: [283/600]
Discriminator Loss: 0.7372, Generator Loss: 5.7717
D(x): 0.7798, D(G(z)): 0.2448
Epoch: [1/20], Batch Num: [284/600]
Discriminator Loss: 0.6286, Generator Loss: 6.0482
D(x): 0.8528, D(G(z)): 0.2610
Epoch: [1/20], Batch Num: [285/600]
Discriminator Loss: 0.6191, Generator Loss: 6.8693
D(x): 0.9004, D(G(z)): 0.2849
Epoch: [1/20], Batch Num: [286/600]
Discriminator Loss: 0.7551, Generator Loss: 6.7564
D(x): 0.8971, D(G(z)): 0.3230
Epoch: [1/20], Batch Num: [287/600]
Discriminator Loss: 1.1677, Generator Loss: 5.8796
D(x): 0.8249, D(G(z)): 0.3978
Epoch: [1/20], Batch Num: [288/600]
Discriminator Loss: 1.5860, Generator Loss: 6.7582
D(x): 0.6888, D(G(z)): 0.4279
Epoch: [1/20], Batch Num: [289/600]
Discriminator Loss: 1.9986, Generator Loss: 5.1930
D(x): 0.6559, D(G(z)): 0.5134
Epoch: [1/20], Batch Num: [290/600]
Discriminator Loss: 2.2458, Generator Loss: 4.8630
D(x): 0.6467, D(G(z)): 0.6318
Epoch: [1/20], Batch Num: [291/600]
Discriminator Loss: 1.8884, Generator Loss: 4.7339
D(x): 0.6653, D(G(z)): 0.5514
Epoch: [1/20], Batch Num: [292/600]
Discriminator Loss: 2.4206, Generator Loss: 5.5290
D(x): 0.5706, D(G(z)): 0.5823
Epoch: [1/20], Batch Num: [293/600]
Discriminator Loss: 1.7899, Generator Loss: 5.2521
D(x): 0.5962, D(G(z)): 0.4101
Epoch: [1/20], Batch Num: [294/600]
Discriminator Loss: 1.8470, Generator Loss: 5.0267
D(x): 0.5980, D(G(z)): 0.4294
Epoch: [1/20], Batch Num: [295/600]
Discriminator Loss: 1.6758, Generator Loss: 4.8769
D(x): 0.6051, D(G(z)): 0.3984
Epoch: [1/20], Batch Num: [296/600]
Discriminator Loss: 1.7690, Generator Loss: 4.0716
D(x): 0.6759, D(G(z)): 0.4945
Epoch: [1/20], Batch Num: [297/600]
Discriminator Loss: 1.4975, Generator Loss: 4.8097
D(x): 0.7332, D(G(z)): 0.4864
Epoch: [1/20], Batch Num: [298/600]
Discriminator Loss: 1.7299, Generator Loss: 4.5633
D(x): 0.6356, D(G(z)): 0.4269
Epoch: [1/20], Batch Num: [299/600]
Discriminator Loss: 1.3674, Generator Loss: 5.7512
D(x): 0.7053, D(G(z)): 0.4017
Epoch: 1, Batch Num: [300/600]

Epoch: [1/20], Batch Num: [300/600]
Discriminator Loss: 1.6676, Generator Loss: 4.9633
D(x): 0.5705, D(G(z)): 0.3676
Epoch: [1/20], Batch Num: [301/600]
Discriminator Loss: 1.7584, Generator Loss: 3.4557
D(x): 0.5805, D(G(z)): 0.4028
Epoch: [1/20], Batch Num: [302/600]
Discriminator Loss: 1.4705, Generator Loss: 3.8597
D(x): 0.7296, D(G(z)): 0.4577
Epoch: [1/20], Batch Num: [303/600]
Discriminator Loss: 1.8006, Generator Loss: 4.2917
D(x): 0.7088, D(G(z)): 0.5720
Epoch: [1/20], Batch Num: [304/600]
Discriminator Loss: 1.3667, Generator Loss: 4.6967
D(x): 0.7488, D(G(z)): 0.4546
Epoch: [1/20], Batch Num: [305/600]
Discriminator Loss: 1.5296, Generator Loss: 5.2579
D(x): 0.6347, D(G(z)): 0.3259
Epoch: [1/20], Batch Num: [306/600]
Discriminator Loss: 1.7292, Generator Loss: 3.8928
D(x): 0.5008, D(G(z)): 0.2267
Epoch: [1/20], Batch Num: [307/600]
Discriminator Loss: 1.3779, Generator Loss: 3.2693
D(x): 0.6928, D(G(z)): 0.4184
Epoch: [1/20], Batch Num: [308/600]
Discriminator Loss: 1.8731, Generator Loss: 2.7374
D(x): 0.6520, D(G(z)): 0.5584
Epoch: [1/20], Batch Num: [309/600]
Discriminator Loss: 2.1731, Generator Loss: 2.4443
D(x): 0.6630, D(G(z)): 0.6015
Epoch: [1/20], Batch Num: [310/600]
Discriminator Loss: 2.1248, Generator Loss: 2.3872
D(x): 0.5738, D(G(z)): 0.4986
Epoch: [1/20], Batch Num: [311/600]
Discriminator Loss: 2.1092, Generator Loss: 2.4286
D(x): 0.5986, D(G(z)): 0.5638
Epoch: [1/20], Batch Num: [312/600]
Discriminator Loss: 2.3625, Generator Loss: 1.6674
D(x): 0.4802, D(G(z)): 0.4363
Epoch: [1/20], Batch Num: [313/600]
Discriminator Loss: 2.0480, Generator Loss: 1.4653
D(x): 0.6395, D(G(z)): 0.5887
Epoch: [1/20], Batch Num: [314/600]
Discriminator Loss: 2.0334, Generator Loss: 1.3353
D(x): 0.6175, D(G(z)): 0.5549
Epoch: [1/20], Batch Num: [315/600]
Discriminator Loss: 1.6639, Generator Loss: 1.7351
D(x): 0.7292, D(G(z)): 0.6011
Epoch: [1/20], Batch Num: [316/600]
Discriminator Loss: 1.5892, Generator Loss: 2.1493
D(x): 0.6433, D(G(z)): 0.4619
Epoch: [1/20], Batch Num: [317/600]
Discriminator Loss: 1.4092, Generator Loss: 2.1781
D(x): 0.6070, D(G(z)): 0.3772
Epoch: [1/20], Batch Num: [318/600]
Discriminator Loss: 1.3430, Generator Loss: 2.0116
D(x): 0.6421, D(G(z)): 0.3579
Epoch: [1/20], Batch Num: [319/600]
Discriminator Loss: 1.7594, Generator Loss: 1.3643
D(x): 0.5790, D(G(z)): 0.3994
Epoch: [1/20], Batch Num: [320/600]
Discriminator Loss: 1.3329, Generator Loss: 1.3386
D(x): 0.7014, D(G(z)): 0.4809
Epoch: [1/20], Batch Num: [321/600]
Discriminator Loss: 1.1442, Generator Loss: 1.6497
D(x): 0.7565, D(G(z)): 0.4634
Epoch: [1/20], Batch Num: [322/600]
Discriminator Loss: 1.1851, Generator Loss: 1.9470
D(x): 0.7652, D(G(z)): 0.4783
Epoch: [1/20], Batch Num: [323/600]
Discriminator Loss: 0.9987, Generator Loss: 2.0318
D(x): 0.7053, D(G(z)): 0.3354
Epoch: [1/20], Batch Num: [324/600]
Discriminator Loss: 1.1575, Generator Loss: 2.1115
D(x): 0.6018, D(G(z)): 0.2463
Epoch: [1/20], Batch Num: [325/600]
Discriminator Loss: 0.9048, Generator Loss: 1.9772
D(x): 0.6723, D(G(z)): 0.2470
Epoch: [1/20], Batch Num: [326/600]
Discriminator Loss: 0.8574, Generator Loss: 2.0428
D(x): 0.7064, D(G(z)): 0.2523
Epoch: [1/20], Batch Num: [327/600]
Discriminator Loss: 0.8630, Generator Loss: 1.5463
D(x): 0.7392, D(G(z)): 0.2985
Epoch: [1/20], Batch Num: [328/600]
Discriminator Loss: 0.9120, Generator Loss: 1.5308
D(x): 0.7844, D(G(z)): 0.3872
Epoch: [1/20], Batch Num: [329/600]
Discriminator Loss: 0.6842, Generator Loss: 1.5571
D(x): 0.8533, D(G(z)): 0.3367
Epoch: [1/20], Batch Num: [330/600]
Discriminator Loss: 0.7099, Generator Loss: 2.1164
D(x): 0.8256, D(G(z)): 0.3216
Epoch: [1/20], Batch Num: [331/600]
Discriminator Loss: 0.7287, Generator Loss: 2.0506
D(x): 0.7933, D(G(z)): 0.2865
Epoch: [1/20], Batch Num: [332/600]
Discriminator Loss: 0.8352, Generator Loss: 2.3132
D(x): 0.7188, D(G(z)): 0.2573
Epoch: [1/20], Batch Num: [333/600]
Discriminator Loss: 0.6900, Generator Loss: 2.1394
D(x): 0.7437, D(G(z)): 0.2139
Epoch: [1/20], Batch Num: [334/600]
Discriminator Loss: 0.7320, Generator Loss: 1.9427
D(x): 0.7516, D(G(z)): 0.2235
Epoch: [1/20], Batch Num: [335/600]
Discriminator Loss: 0.6657, Generator Loss: 1.9720
D(x): 0.7744, D(G(z)): 0.2226
Epoch: [1/20], Batch Num: [336/600]
Discriminator Loss: 0.7277, Generator Loss: 1.6719
D(x): 0.8092, D(G(z)): 0.3002
Epoch: [1/20], Batch Num: [337/600]
Discriminator Loss: 0.7720, Generator Loss: 1.5913
D(x): 0.7925, D(G(z)): 0.3212
Epoch: [1/20], Batch Num: [338/600]
Discriminator Loss: 0.7159, Generator Loss: 1.7539
D(x): 0.8433, D(G(z)): 0.3344
Epoch: [1/20], Batch Num: [339/600]
Discriminator Loss: 0.6708, Generator Loss: 2.1097
D(x): 0.8306, D(G(z)): 0.3050
Epoch: [1/20], Batch Num: [340/600]
Discriminator Loss: 0.7043, Generator Loss: 2.0116
D(x): 0.7783, D(G(z)): 0.2789
Epoch: [1/20], Batch Num: [341/600]
Discriminator Loss: 0.7334, Generator Loss: 2.1344
D(x): 0.7616, D(G(z)): 0.2550
Epoch: [1/20], Batch Num: [342/600]
Discriminator Loss: 0.7177, Generator Loss: 1.9192
D(x): 0.7858, D(G(z)): 0.3018
Epoch: [1/20], Batch Num: [343/600]
Discriminator Loss: 0.8283, Generator Loss: 2.0467
D(x): 0.7665, D(G(z)): 0.2874
Epoch: [1/20], Batch Num: [344/600]
Discriminator Loss: 0.7063, Generator Loss: 1.6935
D(x): 0.7801, D(G(z)): 0.2732
Epoch: [1/20], Batch Num: [345/600]
Discriminator Loss: 0.7409, Generator Loss: 1.7666
D(x): 0.7799, D(G(z)): 0.3004
Epoch: [1/20], Batch Num: [346/600]
Discriminator Loss: 0.6485, Generator Loss: 1.6801
D(x): 0.8198, D(G(z)): 0.2889
Epoch: [1/20], Batch Num: [347/600]
Discriminator Loss: 0.7787, Generator Loss: 1.7609
D(x): 0.8066, D(G(z)): 0.3586
Epoch: [1/20], Batch Num: [348/600]
Discriminator Loss: 0.7479, Generator Loss: 1.7680
D(x): 0.7946, D(G(z)): 0.3234
Epoch: [1/20], Batch Num: [349/600]
Discriminator Loss: 0.7787, Generator Loss: 1.8165
D(x): 0.8222, D(G(z)): 0.3687
Epoch: [1/20], Batch Num: [350/600]
Discriminator Loss: 0.9153, Generator Loss: 2.0216
D(x): 0.7368, D(G(z)): 0.3185
Epoch: [1/20], Batch Num: [351/600]
Discriminator Loss: 0.7612, Generator Loss: 1.8232
D(x): 0.7616, D(G(z)): 0.2618
Epoch: [1/20], Batch Num: [352/600]
Discriminator Loss: 0.7644, Generator Loss: 1.8080
D(x): 0.7498, D(G(z)): 0.2621
Epoch: [1/20], Batch Num: [353/600]
Discriminator Loss: 0.7319, Generator Loss: 1.6882
D(x): 0.7918, D(G(z)): 0.2811
Epoch: [1/20], Batch Num: [354/600]
Discriminator Loss: 0.8495, Generator Loss: 1.5749
D(x): 0.7882, D(G(z)): 0.3636
Epoch: [1/20], Batch Num: [355/600]
Discriminator Loss: 0.6547, Generator Loss: 1.4382
D(x): 0.8471, D(G(z)): 0.3260
Epoch: [1/20], Batch Num: [356/600]
Discriminator Loss: 0.7399, Generator Loss: 1.6330
D(x): 0.7982, D(G(z)): 0.3236
Epoch: [1/20], Batch Num: [357/600]
Discriminator Loss: 0.7427, Generator Loss: 1.7017
D(x): 0.8022, D(G(z)): 0.3269
Epoch: [1/20], Batch Num: [358/600]
Discriminator Loss: 0.8140, Generator Loss: 1.6682
D(x): 0.7861, D(G(z)): 0.3418
Epoch: [1/20], Batch Num: [359/600]
Discriminator Loss: 0.8737, Generator Loss: 1.7116
D(x): 0.7530, D(G(z)): 0.3314
Epoch: [1/20], Batch Num: [360/600]
Discriminator Loss: 0.7284, Generator Loss: 1.6502
D(x): 0.7662, D(G(z)): 0.2786
Epoch: [1/20], Batch Num: [361/600]
Discriminator Loss: 0.7305, Generator Loss: 1.6169
D(x): 0.7959, D(G(z)): 0.2957
Epoch: [1/20], Batch Num: [362/600]
Discriminator Loss: 0.7316, Generator Loss: 1.8570
D(x): 0.8159, D(G(z)): 0.3228
Epoch: [1/20], Batch Num: [363/600]
Discriminator Loss: 0.6715, Generator Loss: 1.6803
D(x): 0.8290, D(G(z)): 0.2883
Epoch: [1/20], Batch Num: [364/600]
Discriminator Loss: 0.7009, Generator Loss: 1.7591
D(x): 0.8359, D(G(z)): 0.3417
Epoch: [1/20], Batch Num: [365/600]
Discriminator Loss: 0.7863, Generator Loss: 1.8742
D(x): 0.7739, D(G(z)): 0.3022
Epoch: [1/20], Batch Num: [366/600]
Discriminator Loss: 0.7232, Generator Loss: 1.5337
D(x): 0.7823, D(G(z)): 0.2936
Epoch: [1/20], Batch Num: [367/600]
Discriminator Loss: 0.6419, Generator Loss: 1.7182
D(x): 0.8411, D(G(z)): 0.3215
Epoch: [1/20], Batch Num: [368/600]
Discriminator Loss: 0.7108, Generator Loss: 1.6879
D(x): 0.8303, D(G(z)): 0.3443
Epoch: [1/20], Batch Num: [369/600]
Discriminator Loss: 0.7670, Generator Loss: 1.6667
D(x): 0.8214, D(G(z)): 0.3564
Epoch: [1/20], Batch Num: [370/600]
Discriminator Loss: 0.8319, Generator Loss: 1.8269
D(x): 0.7803, D(G(z)): 0.3390
Epoch: [1/20], Batch Num: [371/600]
Discriminator Loss: 0.6741, Generator Loss: 1.8886
D(x): 0.8213, D(G(z)): 0.3245
Epoch: [1/20], Batch Num: [372/600]
Discriminator Loss: 0.7910, Generator Loss: 1.7078
D(x): 0.8058, D(G(z)): 0.3501
Epoch: [1/20], Batch Num: [373/600]
Discriminator Loss: 0.7221, Generator Loss: 1.7613
D(x): 0.8130, D(G(z)): 0.3400
Epoch: [1/20], Batch Num: [374/600]
Discriminator Loss: 0.8187, Generator Loss: 1.7656
D(x): 0.7937, D(G(z)): 0.3561
Epoch: [1/20], Batch Num: [375/600]
Discriminator Loss: 0.7272, Generator Loss: 1.5846
D(x): 0.8305, D(G(z)): 0.3484
Epoch: [1/20], Batch Num: [376/600]
Discriminator Loss: 0.7954, Generator Loss: 1.6335
D(x): 0.7989, D(G(z)): 0.3516
Epoch: [1/20], Batch Num: [377/600]
Discriminator Loss: 0.7863, Generator Loss: 1.7241
D(x): 0.8313, D(G(z)): 0.3757
Epoch: [1/20], Batch Num: [378/600]
Discriminator Loss: 0.7073, Generator Loss: 1.5703
D(x): 0.8460, D(G(z)): 0.3413
Epoch: [1/20], Batch Num: [379/600]
Discriminator Loss: 0.7470, Generator Loss: 1.5937
D(x): 0.8641, D(G(z)): 0.3898
Epoch: [1/20], Batch Num: [380/600]
Discriminator Loss: 0.8856, Generator Loss: 1.5415
D(x): 0.7920, D(G(z)): 0.3951
Epoch: [1/20], Batch Num: [381/600]
Discriminator Loss: 1.0919, Generator Loss: 1.4449
D(x): 0.7425, D(G(z)): 0.4285
Epoch: [1/20], Batch Num: [382/600]
Discriminator Loss: 0.8830, Generator Loss: 1.4380
D(x): 0.8109, D(G(z)): 0.4215
Epoch: [1/20], Batch Num: [383/600]
Discriminator Loss: 0.8926, Generator Loss: 1.4877
D(x): 0.8375, D(G(z)): 0.4539
Epoch: [1/20], Batch Num: [384/600]
Discriminator Loss: 0.9613, Generator Loss: 1.4732
D(x): 0.8247, D(G(z)): 0.4779
Epoch: [1/20], Batch Num: [385/600]
Discriminator Loss: 0.9555, Generator Loss: 1.3703
D(x): 0.8196, D(G(z)): 0.4569
Epoch: [1/20], Batch Num: [386/600]
Discriminator Loss: 0.9755, Generator Loss: 1.5196
D(x): 0.8139, D(G(z)): 0.4790
Epoch: [1/20], Batch Num: [387/600]
Discriminator Loss: 0.8442, Generator Loss: 1.5870
D(x): 0.8395, D(G(z)): 0.4272
Epoch: [1/20], Batch Num: [388/600]
Discriminator Loss: 0.9484, Generator Loss: 1.5640
D(x): 0.8101, D(G(z)): 0.4560
Epoch: [1/20], Batch Num: [389/600]
Discriminator Loss: 0.7833, Generator Loss: 1.4547
D(x): 0.8114, D(G(z)): 0.3842
Epoch: [1/20], Batch Num: [390/600]
Discriminator Loss: 0.8768, Generator Loss: 1.8017
D(x): 0.8423, D(G(z)): 0.4572
Epoch: [1/20], Batch Num: [391/600]
Discriminator Loss: 0.9379, Generator Loss: 1.3495
D(x): 0.8130, D(G(z)): 0.4525
Epoch: [1/20], Batch Num: [392/600]
Discriminator Loss: 0.9442, Generator Loss: 1.4669
D(x): 0.8733, D(G(z)): 0.5091
Epoch: [1/20], Batch Num: [393/600]
Discriminator Loss: 1.0477, Generator Loss: 1.3215
D(x): 0.8609, D(G(z)): 0.5401
Epoch: [1/20], Batch Num: [394/600]
Discriminator Loss: 1.0716, Generator Loss: 1.3067
D(x): 0.8688, D(G(z)): 0.5666
Epoch: [1/20], Batch Num: [395/600]
Discriminator Loss: 1.0941, Generator Loss: 1.4600
D(x): 0.8066, D(G(z)): 0.5148
Epoch: [1/20], Batch Num: [396/600]
Discriminator Loss: 1.0950, Generator Loss: 1.3903
D(x): 0.8063, D(G(z)): 0.5124
Epoch: [1/20], Batch Num: [397/600]
Discriminator Loss: 1.1339, Generator Loss: 1.5538
D(x): 0.7855, D(G(z)): 0.5059
Epoch: [1/20], Batch Num: [398/600]
Discriminator Loss: 1.1776, Generator Loss: 1.7259
D(x): 0.7085, D(G(z)): 0.4785
Epoch: [1/20], Batch Num: [399/600]
Discriminator Loss: 1.2767, Generator Loss: 1.6338
D(x): 0.7131, D(G(z)): 0.4931
Epoch: 1, Batch Num: [400/600]

Epoch: [1/20], Batch Num: [400/600]
Discriminator Loss: 1.0677, Generator Loss: 1.7115
D(x): 0.7602, D(G(z)): 0.4906
Epoch: [1/20], Batch Num: [401/600]
Discriminator Loss: 1.2620, Generator Loss: 1.7033
D(x): 0.7224, D(G(z)): 0.4991
Epoch: [1/20], Batch Num: [402/600]
Discriminator Loss: 1.0641, Generator Loss: 1.6412
D(x): 0.7874, D(G(z)): 0.5078
Epoch: [1/20], Batch Num: [403/600]
Discriminator Loss: 1.0644, Generator Loss: 1.8727
D(x): 0.7624, D(G(z)): 0.4878
Epoch: [1/20], Batch Num: [404/600]
Discriminator Loss: 1.0221, Generator Loss: 1.9554
D(x): 0.7732, D(G(z)): 0.4763
Epoch: [1/20], Batch Num: [405/600]
Discriminator Loss: 0.9621, Generator Loss: 2.0803
D(x): 0.7564, D(G(z)): 0.4309
Epoch: [1/20], Batch Num: [406/600]
Discriminator Loss: 1.0160, Generator Loss: 2.4440
D(x): 0.7701, D(G(z)): 0.4705
Epoch: [1/20], Batch Num: [407/600]
Discriminator Loss: 0.9189, Generator Loss: 2.6161
D(x): 0.7080, D(G(z)): 0.3241
Epoch: [1/20], Batch Num: [408/600]
Discriminator Loss: 0.8820, Generator Loss: 2.7662
D(x): 0.6791, D(G(z)): 0.2953
Epoch: [1/20], Batch Num: [409/600]
Discriminator Loss: 0.9088, Generator Loss: 2.6145
D(x): 0.6504, D(G(z)): 0.2966
Epoch: [1/20], Batch Num: [410/600]
Discriminator Loss: 0.9956, Generator Loss: 2.6331
D(x): 0.6222, D(G(z)): 0.3036
Epoch: [1/20], Batch Num: [411/600]
Discriminator Loss: 1.1916, Generator Loss: 2.2518
D(x): 0.5916, D(G(z)): 0.3751
Epoch: [1/20], Batch Num: [412/600]
Discriminator Loss: 1.3114, Generator Loss: 1.8948
D(x): 0.5932, D(G(z)): 0.4524
Epoch: [1/20], Batch Num: [413/600]
Discriminator Loss: 1.6807, Generator Loss: 1.7759
D(x): 0.5512, D(G(z)): 0.5556
Epoch: [1/20], Batch Num: [414/600]
Discriminator Loss: 2.0762, Generator Loss: 1.6067
D(x): 0.4726, D(G(z)): 0.5826
Epoch: [1/20], Batch Num: [415/600]
Discriminator Loss: 2.2468, Generator Loss: 1.1165
D(x): 0.4192, D(G(z)): 0.5728
Epoch: [1/20], Batch Num: [416/600]
Discriminator Loss: 2.8064, Generator Loss: 0.7112
D(x): 0.3859, D(G(z)): 0.7154
Epoch: [1/20], Batch Num: [417/600]
Discriminator Loss: 3.0877, Generator Loss: 0.5900
D(x): 0.4078, D(G(z)): 0.7900
Epoch: [1/20], Batch Num: [418/600]
Discriminator Loss: 3.6201, Generator Loss: 0.3080
D(x): 0.4102, D(G(z)): 0.8296
Epoch: [1/20], Batch Num: [419/600]
Discriminator Loss: 3.5458, Generator Loss: 0.1845
D(x): 0.4509, D(G(z)): 0.8820
Epoch: [1/20], Batch Num: [420/600]
Discriminator Loss: 3.5324, Generator Loss: 0.2086
D(x): 0.5607, D(G(z)): 0.9031
Epoch: [1/20], Batch Num: [421/600]
Discriminator Loss: 3.0920, Generator Loss: 0.2339
D(x): 0.5934, D(G(z)): 0.8806
Epoch: [1/20], Batch Num: [422/600]
Discriminator Loss: 2.8135, Generator Loss: 0.3981
D(x): 0.6240, D(G(z)): 0.8495
Epoch: [1/20], Batch Num: [423/600]
Discriminator Loss: 2.8667, Generator Loss: 0.5785
D(x): 0.4964, D(G(z)): 0.7881
Epoch: [1/20], Batch Num: [424/600]
Discriminator Loss: 2.5456, Generator Loss: 0.6973
D(x): 0.4235, D(G(z)): 0.6804
Epoch: [1/20], Batch Num: [425/600]
Discriminator Loss: 2.1277, Generator Loss: 0.7587
D(x): 0.4375, D(G(z)): 0.5662
Epoch: [1/20], Batch Num: [426/600]
Discriminator Loss: 2.2133, Generator Loss: 0.8710
D(x): 0.3919, D(G(z)): 0.5667
Epoch: [1/20], Batch Num: [427/600]
Discriminator Loss: 1.8264, Generator Loss: 0.7942
D(x): 0.4600, D(G(z)): 0.5426
Epoch: [1/20], Batch Num: [428/600]
Discriminator Loss: 1.8705, Generator Loss: 0.7732
D(x): 0.4911, D(G(z)): 0.5820
Epoch: [1/20], Batch Num: [429/600]
Discriminator Loss: 1.8852, Generator Loss: 0.7020
D(x): 0.4833, D(G(z)): 0.5957
Epoch: [1/20], Batch Num: [430/600]
Discriminator Loss: 1.7135, Generator Loss: 0.7387
D(x): 0.5482, D(G(z)): 0.5960
Epoch: [1/20], Batch Num: [431/600]
Discriminator Loss: 1.6524, Generator Loss: 0.8429
D(x): 0.5421, D(G(z)): 0.5684
Epoch: [1/20], Batch Num: [432/600]
Discriminator Loss: 1.3772, Generator Loss: 0.7545
D(x): 0.5901, D(G(z)): 0.5135
Epoch: [1/20], Batch Num: [433/600]
Discriminator Loss: 1.5242, Generator Loss: 0.8580
D(x): 0.5500, D(G(z)): 0.5421
Epoch: [1/20], Batch Num: [434/600]
Discriminator Loss: 1.3809, Generator Loss: 0.9228
D(x): 0.5336, D(G(z)): 0.4607
Epoch: [1/20], Batch Num: [435/600]
Discriminator Loss: 1.2801, Generator Loss: 0.9593
D(x): 0.5463, D(G(z)): 0.4326
Epoch: [1/20], Batch Num: [436/600]
Discriminator Loss: 1.4434, Generator Loss: 1.0106
D(x): 0.5100, D(G(z)): 0.4718
Epoch: [1/20], Batch Num: [437/600]
Discriminator Loss: 1.3434, Generator Loss: 1.0656
D(x): 0.5062, D(G(z)): 0.4061
Epoch: [1/20], Batch Num: [438/600]
Discriminator Loss: 1.3510, Generator Loss: 0.8886
D(x): 0.5407, D(G(z)): 0.4456
Epoch: [1/20], Batch Num: [439/600]
Discriminator Loss: 1.3030, Generator Loss: 0.8766
D(x): 0.5539, D(G(z)): 0.4466
Epoch: [1/20], Batch Num: [440/600]
Discriminator Loss: 1.3479, Generator Loss: 0.9100
D(x): 0.5946, D(G(z)): 0.5116
Epoch: [1/20], Batch Num: [441/600]
Discriminator Loss: 1.2781, Generator Loss: 0.8971
D(x): 0.6328, D(G(z)): 0.5190
Epoch: [1/20], Batch Num: [442/600]
Discriminator Loss: 1.1070, Generator Loss: 0.8653
D(x): 0.6562, D(G(z)): 0.4525
Epoch: [1/20], Batch Num: [443/600]
Discriminator Loss: 1.0673, Generator Loss: 0.9760
D(x): 0.6724, D(G(z)): 0.4510
Epoch: [1/20], Batch Num: [444/600]
Discriminator Loss: 1.1253, Generator Loss: 1.0408
D(x): 0.6414, D(G(z)): 0.4527
Epoch: [1/20], Batch Num: [445/600]
Discriminator Loss: 1.0519, Generator Loss: 1.1280
D(x): 0.6134, D(G(z)): 0.3874
Epoch: [1/20], Batch Num: [446/600]
Discriminator Loss: 1.0746, Generator Loss: 1.1480
D(x): 0.6018, D(G(z)): 0.3881
Epoch: [1/20], Batch Num: [447/600]
Discriminator Loss: 1.0307, Generator Loss: 1.1871
D(x): 0.6457, D(G(z)): 0.3867
Epoch: [1/20], Batch Num: [448/600]
Discriminator Loss: 1.0469, Generator Loss: 1.2516
D(x): 0.6121, D(G(z)): 0.3789
Epoch: [1/20], Batch Num: [449/600]
Discriminator Loss: 0.9501, Generator Loss: 1.1879
D(x): 0.6559, D(G(z)): 0.3692
Epoch: [1/20], Batch Num: [450/600]
Discriminator Loss: 0.9211, Generator Loss: 1.1518
D(x): 0.6560, D(G(z)): 0.3592
Epoch: [1/20], Batch Num: [451/600]
Discriminator Loss: 0.9050, Generator Loss: 1.2440
D(x): 0.6731, D(G(z)): 0.3552
Epoch: [1/20], Batch Num: [452/600]
Discriminator Loss: 0.9871, Generator Loss: 1.2694
D(x): 0.6574, D(G(z)): 0.3947
Epoch: [1/20], Batch Num: [453/600]
Discriminator Loss: 0.8810, Generator Loss: 1.2922
D(x): 0.6850, D(G(z)): 0.3570
Epoch: [1/20], Batch Num: [454/600]
Discriminator Loss: 0.9033, Generator Loss: 1.3042
D(x): 0.6808, D(G(z)): 0.3609
Epoch: [1/20], Batch Num: [455/600]
Discriminator Loss: 0.8915, Generator Loss: 1.3437
D(x): 0.6863, D(G(z)): 0.3602
Epoch: [1/20], Batch Num: [456/600]
Discriminator Loss: 0.7407, Generator Loss: 1.3230
D(x): 0.7628, D(G(z)): 0.3452
Epoch: [1/20], Batch Num: [457/600]
Discriminator Loss: 0.7700, Generator Loss: 1.4263
D(x): 0.7432, D(G(z)): 0.3436
Epoch: [1/20], Batch Num: [458/600]
Discriminator Loss: 0.7512, Generator Loss: 1.4986
D(x): 0.7113, D(G(z)): 0.2996
Epoch: [1/20], Batch Num: [459/600]
Discriminator Loss: 0.7414, Generator Loss: 1.5566
D(x): 0.7182, D(G(z)): 0.2963
Epoch: [1/20], Batch Num: [460/600]
Discriminator Loss: 0.7880, Generator Loss: 1.6073
D(x): 0.6770, D(G(z)): 0.2738
Epoch: [1/20], Batch Num: [461/600]
Discriminator Loss: 0.9225, Generator Loss: 1.5149
D(x): 0.6434, D(G(z)): 0.3086
Epoch: [1/20], Batch Num: [462/600]
Discriminator Loss: 0.6934, Generator Loss: 1.3673
D(x): 0.7580, D(G(z)): 0.3096
Epoch: [1/20], Batch Num: [463/600]
Discriminator Loss: 0.7885, Generator Loss: 1.3722
D(x): 0.7234, D(G(z)): 0.3223
Epoch: [1/20], Batch Num: [464/600]
Discriminator Loss: 0.8407, Generator Loss: 1.2616
D(x): 0.7507, D(G(z)): 0.3777
Epoch: [1/20], Batch Num: [465/600]
Discriminator Loss: 0.7799, Generator Loss: 1.3686
D(x): 0.7724, D(G(z)): 0.3693
Epoch: [1/20], Batch Num: [466/600]
Discriminator Loss: 0.7838, Generator Loss: 1.4073
D(x): 0.7414, D(G(z)): 0.3401
Epoch: [1/20], Batch Num: [467/600]
Discriminator Loss: 0.7458, Generator Loss: 1.5388
D(x): 0.7178, D(G(z)): 0.2786
Epoch: [1/20], Batch Num: [468/600]
Discriminator Loss: 0.8377, Generator Loss: 1.6441
D(x): 0.6843, D(G(z)): 0.3068
Epoch: [1/20], Batch Num: [469/600]
Discriminator Loss: 0.8736, Generator Loss: 1.5156
D(x): 0.7010, D(G(z)): 0.3269
Epoch: [1/20], Batch Num: [470/600]
Discriminator Loss: 0.7891, Generator Loss: 1.3745
D(x): 0.7306, D(G(z)): 0.3361
Epoch: [1/20], Batch Num: [471/600]
Discriminator Loss: 0.8633, Generator Loss: 1.2802
D(x): 0.6918, D(G(z)): 0.3189
Epoch: [1/20], Batch Num: [472/600]
Discriminator Loss: 0.9118, Generator Loss: 1.2901
D(x): 0.7424, D(G(z)): 0.3931
Epoch: [1/20], Batch Num: [473/600]
Discriminator Loss: 0.9210, Generator Loss: 1.2470
D(x): 0.7267, D(G(z)): 0.3773
Epoch: [1/20], Batch Num: [474/600]
Discriminator Loss: 0.9114, Generator Loss: 1.3767
D(x): 0.7619, D(G(z)): 0.4099
Epoch: [1/20], Batch Num: [475/600]
Discriminator Loss: 0.9425, Generator Loss: 1.5005
D(x): 0.7116, D(G(z)): 0.3808
Epoch: [1/20], Batch Num: [476/600]
Discriminator Loss: 0.9796, Generator Loss: 1.4130
D(x): 0.6721, D(G(z)): 0.3297
Epoch: [1/20], Batch Num: [477/600]
Discriminator Loss: 1.0085, Generator Loss: 1.3435
D(x): 0.6984, D(G(z)): 0.3915
Epoch: [1/20], Batch Num: [478/600]
Discriminator Loss: 1.1210, Generator Loss: 1.3059
D(x): 0.6146, D(G(z)): 0.3621
Epoch: [1/20], Batch Num: [479/600]
Discriminator Loss: 0.9934, Generator Loss: 1.0244
D(x): 0.7346, D(G(z)): 0.4175
Epoch: [1/20], Batch Num: [480/600]
Discriminator Loss: 1.0309, Generator Loss: 1.1787
D(x): 0.7381, D(G(z)): 0.4450
Epoch: [1/20], Batch Num: [481/600]
Discriminator Loss: 1.0793, Generator Loss: 1.1685
D(x): 0.7253, D(G(z)): 0.4684
Epoch: [1/20], Batch Num: [482/600]
Discriminator Loss: 1.0152, Generator Loss: 1.3707
D(x): 0.7021, D(G(z)): 0.4220
Epoch: [1/20], Batch Num: [483/600]
Discriminator Loss: 1.1721, Generator Loss: 1.3686
D(x): 0.6115, D(G(z)): 0.3864
Epoch: [1/20], Batch Num: [484/600]
Discriminator Loss: 1.1648, Generator Loss: 1.2398
D(x): 0.6262, D(G(z)): 0.3686
Epoch: [1/20], Batch Num: [485/600]
Discriminator Loss: 0.9916, Generator Loss: 1.1631
D(x): 0.7213, D(G(z)): 0.4315
Epoch: [1/20], Batch Num: [486/600]
Discriminator Loss: 1.1852, Generator Loss: 1.2646
D(x): 0.6792, D(G(z)): 0.4659
Epoch: [1/20], Batch Num: [487/600]
Discriminator Loss: 1.2465, Generator Loss: 1.1352
D(x): 0.6527, D(G(z)): 0.4566
Epoch: [1/20], Batch Num: [488/600]
Discriminator Loss: 1.2930, Generator Loss: 1.0781
D(x): 0.6253, D(G(z)): 0.4771
Epoch: [1/20], Batch Num: [489/600]
Discriminator Loss: 1.0561, Generator Loss: 1.1686
D(x): 0.7075, D(G(z)): 0.4353
Epoch: [1/20], Batch Num: [490/600]
Discriminator Loss: 1.1480, Generator Loss: 1.1969
D(x): 0.6784, D(G(z)): 0.4453
Epoch: [1/20], Batch Num: [491/600]
Discriminator Loss: 1.0439, Generator Loss: 1.1579
D(x): 0.6794, D(G(z)): 0.4087
Epoch: [1/20], Batch Num: [492/600]
Discriminator Loss: 1.0690, Generator Loss: 1.1378
D(x): 0.6948, D(G(z)): 0.4191
Epoch: [1/20], Batch Num: [493/600]
Discriminator Loss: 1.1876, Generator Loss: 1.1837
D(x): 0.6480, D(G(z)): 0.4444
Epoch: [1/20], Batch Num: [494/600]
Discriminator Loss: 1.1236, Generator Loss: 1.2547
D(x): 0.6665, D(G(z)): 0.4103
Epoch: [1/20], Batch Num: [495/600]
Discriminator Loss: 1.1418, Generator Loss: 1.1029
D(x): 0.6766, D(G(z)): 0.4485
Epoch: [1/20], Batch Num: [496/600]
Discriminator Loss: 1.0475, Generator Loss: 1.1080
D(x): 0.7298, D(G(z)): 0.4623
Epoch: [1/20], Batch Num: [497/600]
Discriminator Loss: 0.9767, Generator Loss: 1.2860
D(x): 0.7704, D(G(z)): 0.4591
Epoch: [1/20], Batch Num: [498/600]
Discriminator Loss: 0.8418, Generator Loss: 1.5934
D(x): 0.7513, D(G(z)): 0.3745
Epoch: [1/20], Batch Num: [499/600]
Discriminator Loss: 0.9080, Generator Loss: 1.6105
D(x): 0.6714, D(G(z)): 0.3214
Epoch: 1, Batch Num: [500/600]

Epoch: [1/20], Batch Num: [500/600]
Discriminator Loss: 1.0737, Generator Loss: 1.3525
D(x): 0.5968, D(G(z)): 0.3256
Epoch: [1/20], Batch Num: [501/600]
Discriminator Loss: 0.8757, Generator Loss: 1.2705
D(x): 0.7538, D(G(z)): 0.3917
Epoch: [1/20], Batch Num: [502/600]
Discriminator Loss: 0.9055, Generator Loss: 1.2476
D(x): 0.7407, D(G(z)): 0.3872
Epoch: [1/20], Batch Num: [503/600]
Discriminator Loss: 0.8020, Generator Loss: 1.2401
D(x): 0.7764, D(G(z)): 0.3817
Epoch: [1/20], Batch Num: [504/600]
Discriminator Loss: 0.8513, Generator Loss: 1.4685
D(x): 0.7427, D(G(z)): 0.3576
Epoch: [1/20], Batch Num: [505/600]
Discriminator Loss: 0.6886, Generator Loss: 1.6290
D(x): 0.7850, D(G(z)): 0.3169
Epoch: [1/20], Batch Num: [506/600]
Discriminator Loss: 0.7186, Generator Loss: 1.6723
D(x): 0.7327, D(G(z)): 0.2724
Epoch: [1/20], Batch Num: [507/600]
Discriminator Loss: 0.7083, Generator Loss: 1.9497
D(x): 0.7429, D(G(z)): 0.2983
Epoch: [1/20], Batch Num: [508/600]
Discriminator Loss: 0.7000, Generator Loss: 1.9070
D(x): 0.7368, D(G(z)): 0.2706
Epoch: [1/20], Batch Num: [509/600]
Discriminator Loss: 0.6702, Generator Loss: 1.8727
D(x): 0.7468, D(G(z)): 0.2655
Epoch: [1/20], Batch Num: [510/600]
Discriminator Loss: 0.7218, Generator Loss: 1.8643
D(x): 0.7400, D(G(z)): 0.2640
Epoch: [1/20], Batch Num: [511/600]
Discriminator Loss: 0.6078, Generator Loss: 1.9370
D(x): 0.7969, D(G(z)): 0.2722
Epoch: [1/20], Batch Num: [512/600]
Discriminator Loss: 0.5172, Generator Loss: 2.1070
D(x): 0.8231, D(G(z)): 0.2466
Epoch: [1/20], Batch Num: [513/600]
Discriminator Loss: 0.5320, Generator Loss: 2.1740
D(x): 0.8250, D(G(z)): 0.2582
Epoch: [1/20], Batch Num: [514/600]
Discriminator Loss: 0.5297, Generator Loss: 2.4829
D(x): 0.7897, D(G(z)): 0.2081
Epoch: [1/20], Batch Num: [515/600]
Discriminator Loss: 0.5134, Generator Loss: 2.6342
D(x): 0.7597, D(G(z)): 0.1712
Epoch: [1/20], Batch Num: [516/600]
Discriminator Loss: 0.4386, Generator Loss: 2.7179
D(x): 0.7918, D(G(z)): 0.1401
Epoch: [1/20], Batch Num: [517/600]
Discriminator Loss: 0.4224, Generator Loss: 2.8451
D(x): 0.7858, D(G(z)): 0.1275
Epoch: [1/20], Batch Num: [518/600]
Discriminator Loss: 0.3907, Generator Loss: 2.6147
D(x): 0.8225, D(G(z)): 0.1446
Epoch: [1/20], Batch Num: [519/600]
Discriminator Loss: 0.3565, Generator Loss: 2.7024
D(x): 0.8377, D(G(z)): 0.1344
Epoch: [1/20], Batch Num: [520/600]
Discriminator Loss: 0.4102, Generator Loss: 2.7513
D(x): 0.8315, D(G(z)): 0.1675
Epoch: [1/20], Batch Num: [521/600]
Discriminator Loss: 0.3779, Generator Loss: 2.7842
D(x): 0.8284, D(G(z)): 0.1442
Epoch: [1/20], Batch Num: [522/600]
Discriminator Loss: 0.4593, Generator Loss: 2.7615
D(x): 0.8013, D(G(z)): 0.1539
Epoch: [1/20], Batch Num: [523/600]
Discriminator Loss: 0.4475, Generator Loss: 2.6470
D(x): 0.8230, D(G(z)): 0.1813
Epoch: [1/20], Batch Num: [524/600]
Discriminator Loss: 0.5360, Generator Loss: 2.6050
D(x): 0.7847, D(G(z)): 0.1869
Epoch: [1/20], Batch Num: [525/600]
Discriminator Loss: 0.4264, Generator Loss: 2.2686
D(x): 0.8246, D(G(z)): 0.1659
Epoch: [1/20], Batch Num: [526/600]
Discriminator Loss: 0.5024, Generator Loss: 2.5455
D(x): 0.8188, D(G(z)): 0.2059
Epoch: [1/20], Batch Num: [527/600]
Discriminator Loss: 0.5227, Generator Loss: 2.2170
D(x): 0.8107, D(G(z)): 0.2055
Epoch: [1/20], Batch Num: [528/600]
Discriminator Loss: 0.5158, Generator Loss: 2.2783
D(x): 0.7956, D(G(z)): 0.1957
Epoch: [1/20], Batch Num: [529/600]
Discriminator Loss: 0.5161, Generator Loss: 2.3100
D(x): 0.8166, D(G(z)): 0.2196
Epoch: [1/20], Batch Num: [530/600]
Discriminator Loss: 0.6167, Generator Loss: 2.2440
D(x): 0.7520, D(G(z)): 0.2189
Epoch: [1/20], Batch Num: [531/600]
Discriminator Loss: 0.6712, Generator Loss: 2.1805
D(x): 0.7643, D(G(z)): 0.2527
Epoch: [1/20], Batch Num: [532/600]
Discriminator Loss: 0.6747, Generator Loss: 2.0073
D(x): 0.7782, D(G(z)): 0.2676
Epoch: [1/20], Batch Num: [533/600]
Discriminator Loss: 0.7513, Generator Loss: 1.9177
D(x): 0.7230, D(G(z)): 0.2608
Epoch: [1/20], Batch Num: [534/600]
Discriminator Loss: 0.9219, Generator Loss: 1.8068
D(x): 0.7203, D(G(z)): 0.3419
Epoch: [1/20], Batch Num: [535/600]
Discriminator Loss: 0.8409, Generator Loss: 1.6754
D(x): 0.7511, D(G(z)): 0.3621
Epoch: [1/20], Batch Num: [536/600]
Discriminator Loss: 0.8903, Generator Loss: 1.5978
D(x): 0.7130, D(G(z)): 0.3394
Epoch: [1/20], Batch Num: [537/600]
Discriminator Loss: 0.9795, Generator Loss: 1.4651
D(x): 0.6509, D(G(z)): 0.3211
Epoch: [1/20], Batch Num: [538/600]
Discriminator Loss: 1.0538, Generator Loss: 1.2776
D(x): 0.6777, D(G(z)): 0.3494
Epoch: [1/20], Batch Num: [539/600]
Discriminator Loss: 1.1658, Generator Loss: 1.1776
D(x): 0.7151, D(G(z)): 0.4344
Epoch: [1/20], Batch Num: [540/600]
Discriminator Loss: 1.1121, Generator Loss: 1.2942
D(x): 0.7130, D(G(z)): 0.4498
Epoch: [1/20], Batch Num: [541/600]
Discriminator Loss: 1.3860, Generator Loss: 1.4335
D(x): 0.6152, D(G(z)): 0.4207
Epoch: [1/20], Batch Num: [542/600]
Discriminator Loss: 1.3675, Generator Loss: 1.4084
D(x): 0.5788, D(G(z)): 0.3994
Epoch: [1/20], Batch Num: [543/600]
Discriminator Loss: 1.3180, Generator Loss: 1.2365
D(x): 0.5883, D(G(z)): 0.3917
Epoch: [1/20], Batch Num: [544/600]
Discriminator Loss: 1.5301, Generator Loss: 0.8833
D(x): 0.5619, D(G(z)): 0.4604
Epoch: [1/20], Batch Num: [545/600]
Discriminator Loss: 1.3548, Generator Loss: 0.9594
D(x): 0.6803, D(G(z)): 0.5202
Epoch: [1/20], Batch Num: [546/600]
Discriminator Loss: 1.4020, Generator Loss: 0.9957
D(x): 0.6402, D(G(z)): 0.5177
Epoch: [1/20], Batch Num: [547/600]
Discriminator Loss: 1.4908, Generator Loss: 1.0804
D(x): 0.5911, D(G(z)): 0.4879
Epoch: [1/20], Batch Num: [548/600]
Discriminator Loss: 1.3159, Generator Loss: 1.2304
D(x): 0.6662, D(G(z)): 0.4960
Epoch: [1/20], Batch Num: [549/600]
Discriminator Loss: 1.4955, Generator Loss: 1.2929
D(x): 0.5621, D(G(z)): 0.4360
Epoch: [1/20], Batch Num: [550/600]
Discriminator Loss: 1.3638, Generator Loss: 1.3118
D(x): 0.5539, D(G(z)): 0.4142
Epoch: [1/20], Batch Num: [551/600]
Discriminator Loss: 1.1749, Generator Loss: 1.2527
D(x): 0.6167, D(G(z)): 0.4144
Epoch: [1/20], Batch Num: [552/600]
Discriminator Loss: 1.3710, Generator Loss: 1.1680
D(x): 0.6175, D(G(z)): 0.4658
Epoch: [1/20], Batch Num: [553/600]
Discriminator Loss: 1.3682, Generator Loss: 0.9760
D(x): 0.6132, D(G(z)): 0.4508
Epoch: [1/20], Batch Num: [554/600]
Discriminator Loss: 1.2265, Generator Loss: 1.0784
D(x): 0.6723, D(G(z)): 0.4723
Epoch: [1/20], Batch Num: [555/600]
Discriminator Loss: 1.2222, Generator Loss: 1.2286
D(x): 0.6575, D(G(z)): 0.4435
Epoch: [1/20], Batch Num: [556/600]
Discriminator Loss: 1.2124, Generator Loss: 1.2709
D(x): 0.6402, D(G(z)): 0.4418
Epoch: [1/20], Batch Num: [557/600]
Discriminator Loss: 1.1752, Generator Loss: 1.1902
D(x): 0.6344, D(G(z)): 0.4090
Epoch: [1/20], Batch Num: [558/600]
Discriminator Loss: 0.9899, Generator Loss: 1.3260
D(x): 0.7135, D(G(z)): 0.4113
Epoch: [1/20], Batch Num: [559/600]
Discriminator Loss: 1.0536, Generator Loss: 1.3137
D(x): 0.6674, D(G(z)): 0.4045
Epoch: [1/20], Batch Num: [560/600]
Discriminator Loss: 1.0599, Generator Loss: 1.3554
D(x): 0.6804, D(G(z)): 0.3995
Epoch: [1/20], Batch Num: [561/600]
Discriminator Loss: 1.0776, Generator Loss: 1.2139
D(x): 0.6265, D(G(z)): 0.3635
Epoch: [1/20], Batch Num: [562/600]
Discriminator Loss: 1.0481, Generator Loss: 1.3781
D(x): 0.6983, D(G(z)): 0.4032
Epoch: [1/20], Batch Num: [563/600]
Discriminator Loss: 1.0151, Generator Loss: 1.4098
D(x): 0.7127, D(G(z)): 0.3923
Epoch: [1/20], Batch Num: [564/600]
Discriminator Loss: 0.9804, Generator Loss: 1.3665
D(x): 0.6926, D(G(z)): 0.3860
Epoch: [1/20], Batch Num: [565/600]
Discriminator Loss: 0.9033, Generator Loss: 1.4033
D(x): 0.7308, D(G(z)): 0.3732
Epoch: [1/20], Batch Num: [566/600]
Discriminator Loss: 0.9362, Generator Loss: 1.4841
D(x): 0.6866, D(G(z)): 0.3292
Epoch: [1/20], Batch Num: [567/600]
Discriminator Loss: 0.7724, Generator Loss: 1.4940
D(x): 0.7621, D(G(z)): 0.3275
Epoch: [1/20], Batch Num: [568/600]
Discriminator Loss: 0.8053, Generator Loss: 1.4049
D(x): 0.7341, D(G(z)): 0.3260
Epoch: [1/20], Batch Num: [569/600]
Discriminator Loss: 0.8792, Generator Loss: 1.5280
D(x): 0.7573, D(G(z)): 0.3736
Epoch: [1/20], Batch Num: [570/600]
Discriminator Loss: 0.9386, Generator Loss: 1.4607
D(x): 0.6990, D(G(z)): 0.3409
Epoch: [1/20], Batch Num: [571/600]
Discriminator Loss: 0.9476, Generator Loss: 1.6390
D(x): 0.6989, D(G(z)): 0.3504
Epoch: [1/20], Batch Num: [572/600]
Discriminator Loss: 0.8255, Generator Loss: 1.4826
D(x): 0.7311, D(G(z)): 0.3076
Epoch: [1/20], Batch Num: [573/600]
Discriminator Loss: 0.8984, Generator Loss: 1.5296
D(x): 0.7514, D(G(z)): 0.3807
Epoch: [1/20], Batch Num: [574/600]
Discriminator Loss: 0.8031, Generator Loss: 1.6083
D(x): 0.7473, D(G(z)): 0.3273
Epoch: [1/20], Batch Num: [575/600]
Discriminator Loss: 0.7777, Generator Loss: 1.5768
D(x): 0.7479, D(G(z)): 0.3183
Epoch: [1/20], Batch Num: [576/600]
Discriminator Loss: 0.8455, Generator Loss: 1.5442
D(x): 0.7304, D(G(z)): 0.3344
Epoch: [1/20], Batch Num: [577/600]
Discriminator Loss: 0.6554, Generator Loss: 1.6081
D(x): 0.7965, D(G(z)): 0.3043
Epoch: [1/20], Batch Num: [578/600]
Discriminator Loss: 0.8109, Generator Loss: 1.6248
D(x): 0.7763, D(G(z)): 0.3289
Epoch: [1/20], Batch Num: [579/600]
Discriminator Loss: 0.8257, Generator Loss: 1.7049
D(x): 0.7541, D(G(z)): 0.3226
Epoch: [1/20], Batch Num: [580/600]
Discriminator Loss: 0.9365, Generator Loss: 1.6645
D(x): 0.6960, D(G(z)): 0.3339
Epoch: [1/20], Batch Num: [581/600]
Discriminator Loss: 0.8432, Generator Loss: 1.4379
D(x): 0.7343, D(G(z)): 0.3263
Epoch: [1/20], Batch Num: [582/600]
Discriminator Loss: 0.7845, Generator Loss: 1.5320
D(x): 0.7717, D(G(z)): 0.3314
Epoch: [1/20], Batch Num: [583/600]
Discriminator Loss: 0.7766, Generator Loss: 1.4887
D(x): 0.7904, D(G(z)): 0.3470
Epoch: [1/20], Batch Num: [584/600]
Discriminator Loss: 0.7985, Generator Loss: 1.5807
D(x): 0.7736, D(G(z)): 0.3474
Epoch: [1/20], Batch Num: [585/600]
Discriminator Loss: 0.8849, Generator Loss: 1.6394
D(x): 0.7368, D(G(z)): 0.3513
Epoch: [1/20], Batch Num: [586/600]
Discriminator Loss: 0.8510, Generator Loss: 1.7921
D(x): 0.7825, D(G(z)): 0.3707
Epoch: [1/20], Batch Num: [587/600]
Discriminator Loss: 0.8445, Generator Loss: 1.5548
D(x): 0.7781, D(G(z)): 0.3690
Epoch: [1/20], Batch Num: [588/600]
Discriminator Loss: 0.9062, Generator Loss: 1.7754
D(x): 0.7119, D(G(z)): 0.3306
Epoch: [1/20], Batch Num: [589/600]
Discriminator Loss: 0.9493, Generator Loss: 1.8484
D(x): 0.6950, D(G(z)): 0.2975
Epoch: [1/20], Batch Num: [590/600]
Discriminator Loss: 0.6729, Generator Loss: 1.7359
D(x): 0.7926, D(G(z)): 0.2902
Epoch: [1/20], Batch Num: [591/600]
Discriminator Loss: 0.7571, Generator Loss: 1.7232
D(x): 0.7791, D(G(z)): 0.3163
Epoch: [1/20], Batch Num: [592/600]
Discriminator Loss: 0.8374, Generator Loss: 1.6968
D(x): 0.7520, D(G(z)): 0.3307
Epoch: [1/20], Batch Num: [593/600]
Discriminator Loss: 0.8650, Generator Loss: 1.7531
D(x): 0.7847, D(G(z)): 0.3708
Epoch: [1/20], Batch Num: [594/600]
Discriminator Loss: 0.6942, Generator Loss: 1.9544
D(x): 0.8452, D(G(z)): 0.3374
Epoch: [1/20], Batch Num: [595/600]
Discriminator Loss: 0.8431, Generator Loss: 2.2027
D(x): 0.7368, D(G(z)): 0.2758
Epoch: [1/20], Batch Num: [596/600]
Discriminator Loss: 0.6473, Generator Loss: 2.3307
D(x): 0.7960, D(G(z)): 0.2490
Epoch: [1/20], Batch Num: [597/600]
Discriminator Loss: 0.6803, Generator Loss: 2.6721
D(x): 0.7581, D(G(z)): 0.2242
Epoch: [1/20], Batch Num: [598/600]
Discriminator Loss: 0.6201, Generator Loss: 2.7715
D(x): 0.7854, D(G(z)): 0.2059
Epoch: [1/20], Batch Num: [599/600]
Discriminator Loss: 0.4793, Generator Loss: 2.9589
D(x): 0.8845, D(G(z)): 0.2398
Epoch: 2, Batch Num: [0/600]

Epoch: [2/20], Batch Num: [0/600]
Discriminator Loss: 0.5326, Generator Loss: 3.5064
D(x): 0.8527, D(G(z)): 0.2352
Epoch: [2/20], Batch Num: [1/600]
Discriminator Loss: 0.4195, Generator Loss: 4.2679
D(x): 0.8619, D(G(z)): 0.1631
Epoch: [2/20], Batch Num: [2/600]
Discriminator Loss: 0.3451, Generator Loss: 4.3464
D(x): 0.8623, D(G(z)): 0.1251
Epoch: [2/20], Batch Num: [3/600]
Discriminator Loss: 0.3478, Generator Loss: 4.1882
D(x): 0.8470, D(G(z)): 0.1093
Epoch: [2/20], Batch Num: [4/600]
Discriminator Loss: 0.4371, Generator Loss: 4.3283
D(x): 0.8091, D(G(z)): 0.1093
Epoch: [2/20], Batch Num: [5/600]
Discriminator Loss: 0.4540, Generator Loss: 3.7972
D(x): 0.8421, D(G(z)): 0.1433
Epoch: [2/20], Batch Num: [6/600]
Discriminator Loss: 0.5518, Generator Loss: 3.3194
D(x): 0.8627, D(G(z)): 0.2256
Epoch: [2/20], Batch Num: [7/600]
Discriminator Loss: 0.4841, Generator Loss: 3.9664
D(x): 0.8695, D(G(z)): 0.1868
Epoch: [2/20], Batch Num: [8/600]
Discriminator Loss: 0.4873, Generator Loss: 3.9310
D(x): 0.8426, D(G(z)): 0.1678
Epoch: [2/20], Batch Num: [9/600]
Discriminator Loss: 0.6264, Generator Loss: 3.4663
D(x): 0.7332, D(G(z)): 0.1243
Epoch: [2/20], Batch Num: [10/600]
Discriminator Loss: 0.7494, Generator Loss: 2.8890
D(x): 0.8333, D(G(z)): 0.2766
Epoch: [2/20], Batch Num: [11/600]
Discriminator Loss: 0.9091, Generator Loss: 2.9574
D(x): 0.7742, D(G(z)): 0.2987
Epoch: [2/20], Batch Num: [12/600]
Discriminator Loss: 0.7866, Generator Loss: 2.9150
D(x): 0.8161, D(G(z)): 0.2889
Epoch: [2/20], Batch Num: [13/600]
Discriminator Loss: 1.5388, Generator Loss: 2.2699
D(x): 0.5891, D(G(z)): 0.2921
Epoch: [2/20], Batch Num: [14/600]
Discriminator Loss: 1.3289, Generator Loss: 2.2625
D(x): 0.7915, D(G(z)): 0.4682
Epoch: [2/20], Batch Num: [15/600]
Discriminator Loss: 1.3794, Generator Loss: 2.4113
D(x): 0.6656, D(G(z)): 0.4248
Epoch: [2/20], Batch Num: [16/600]
Discriminator Loss: 1.7118, Generator Loss: 1.7444
D(x): 0.5970, D(G(z)): 0.3747
Epoch: [2/20], Batch Num: [17/600]
Discriminator Loss: 1.8264, Generator Loss: 1.6033
D(x): 0.6480, D(G(z)): 0.5244
Epoch: [2/20], Batch Num: [18/600]
Discriminator Loss: 2.2045, Generator Loss: 1.3117
D(x): 0.5985, D(G(z)): 0.5229
Epoch: [2/20], Batch Num: [19/600]
Discriminator Loss: 2.1623, Generator Loss: 1.5729
D(x): 0.6181, D(G(z)): 0.5950
Epoch: [2/20], Batch Num: [20/600]
Discriminator Loss: 2.4625, Generator Loss: 0.9298
D(x): 0.4772, D(G(z)): 0.4948
Epoch: [2/20], Batch Num: [21/600]
Discriminator Loss: 2.3170, Generator Loss: 0.9521
D(x): 0.6465, D(G(z)): 0.6779
Epoch: [2/20], Batch Num: [22/600]
Discriminator Loss: 2.2959, Generator Loss: 1.0593
D(x): 0.5990, D(G(z)): 0.6134
Epoch: [2/20], Batch Num: [23/600]
Discriminator Loss: 2.2351, Generator Loss: 1.2667
D(x): 0.5851, D(G(z)): 0.6243
Epoch: [2/20], Batch Num: [24/600]
Discriminator Loss: 2.2503, Generator Loss: 1.1655
D(x): 0.4940, D(G(z)): 0.5046
Epoch: [2/20], Batch Num: [25/600]
Discriminator Loss: 2.3790, Generator Loss: 0.6502
D(x): 0.5054, D(G(z)): 0.5414
Epoch: [2/20], Batch Num: [26/600]
Discriminator Loss: 2.1488, Generator Loss: 0.5906
D(x): 0.6580, D(G(z)): 0.6762
Epoch: [2/20], Batch Num: [27/600]
Discriminator Loss: 2.1077, Generator Loss: 1.0245
D(x): 0.7063, D(G(z)): 0.7195
Epoch: [2/20], Batch Num: [28/600]
Discriminator Loss: 2.1797, Generator Loss: 1.0598
D(x): 0.5220, D(G(z)): 0.5559
Epoch: [2/20], Batch Num: [29/600]
Discriminator Loss: 2.0926, Generator Loss: 1.1624
D(x): 0.4890, D(G(z)): 0.5009
Epoch: [2/20], Batch Num: [30/600]
Discriminator Loss: 1.9854, Generator Loss: 0.7021
D(x): 0.4959, D(G(z)): 0.5323
Epoch: [2/20], Batch Num: [31/600]
Discriminator Loss: 2.1161, Generator Loss: 0.5165
D(x): 0.5638, D(G(z)): 0.6408
Epoch: [2/20], Batch Num: [32/600]
Discriminator Loss: 2.0502, Generator Loss: 0.5998
D(x): 0.6658, D(G(z)): 0.7125
Epoch: [2/20], Batch Num: [33/600]
Discriminator Loss: 1.8771, Generator Loss: 0.7940
D(x): 0.6344, D(G(z)): 0.6501
Epoch: [2/20], Batch Num: [34/600]
Discriminator Loss: 2.0866, Generator Loss: 0.9782
D(x): 0.5242, D(G(z)): 0.5903
Epoch: [2/20], Batch Num: [35/600]
Discriminator Loss: 1.7921, Generator Loss: 0.9615
D(x): 0.5372, D(G(z)): 0.5319
Epoch: [2/20], Batch Num: [36/600]
Discriminator Loss: 1.9022, Generator Loss: 0.7502
D(x): 0.5276, D(G(z)): 0.5578
Epoch: [2/20], Batch Num: [37/600]
Discriminator Loss: 1.8531, Generator Loss: 0.7357
D(x): 0.5742, D(G(z)): 0.6140
Epoch: [2/20], Batch Num: [38/600]
Discriminator Loss: 1.8592, Generator Loss: 0.6477
D(x): 0.5724, D(G(z)): 0.6193
Epoch: [2/20], Batch Num: [39/600]
Discriminator Loss: 1.6378, Generator Loss: 0.6440
D(x): 0.6739, D(G(z)): 0.6423
Epoch: [2/20], Batch Num: [40/600]
Discriminator Loss: 1.6886, Generator Loss: 0.6758
D(x): 0.6310, D(G(z)): 0.6247
Epoch: [2/20], Batch Num: [41/600]
Discriminator Loss: 1.5680, Generator Loss: 0.8043
D(x): 0.6019, D(G(z)): 0.5816
Epoch: [2/20], Batch Num: [42/600]
Discriminator Loss: 1.6167, Generator Loss: 0.8472
D(x): 0.5840, D(G(z)): 0.5522
Epoch: [2/20], Batch Num: [43/600]
Discriminator Loss: 1.5010, Generator Loss: 0.8871
D(x): 0.6091, D(G(z)): 0.5345
Epoch: [2/20], Batch Num: [44/600]
Discriminator Loss: 1.4255, Generator Loss: 0.9391
D(x): 0.5954, D(G(z)): 0.5158
Epoch: [2/20], Batch Num: [45/600]
Discriminator Loss: 1.4404, Generator Loss: 0.8065
D(x): 0.5563, D(G(z)): 0.4865
Epoch: [2/20], Batch Num: [46/600]
Discriminator Loss: 1.4216, Generator Loss: 0.8289
D(x): 0.6137, D(G(z)): 0.5399
Epoch: [2/20], Batch Num: [47/600]
Discriminator Loss: 1.4742, Generator Loss: 0.7746
D(x): 0.6433, D(G(z)): 0.5805
Epoch: [2/20], Batch Num: [48/600]
Discriminator Loss: 1.2597, Generator Loss: 0.7544
D(x): 0.6789, D(G(z)): 0.5191
Epoch: [2/20], Batch Num: [49/600]
Discriminator Loss: 1.1480, Generator Loss: 0.9563
D(x): 0.7209, D(G(z)): 0.5032
Epoch: [2/20], Batch Num: [50/600]
Discriminator Loss: 1.1351, Generator Loss: 0.9869
D(x): 0.7086, D(G(z)): 0.4929
Epoch: [2/20], Batch Num: [51/600]
Discriminator Loss: 1.1555, Generator Loss: 1.0298
D(x): 0.6566, D(G(z)): 0.4571
Epoch: [2/20], Batch Num: [52/600]
Discriminator Loss: 1.0188, Generator Loss: 1.2253
D(x): 0.6628, D(G(z)): 0.4093
Epoch: [2/20], Batch Num: [53/600]
Discriminator Loss: 1.0286, Generator Loss: 1.2673
D(x): 0.6429, D(G(z)): 0.3847
Epoch: [2/20], Batch Num: [54/600]
Discriminator Loss: 1.0324, Generator Loss: 1.2969
D(x): 0.6274, D(G(z)): 0.3835
Epoch: [2/20], Batch Num: [55/600]
Discriminator Loss: 1.0601, Generator Loss: 1.2184
D(x): 0.6196, D(G(z)): 0.3807
Epoch: [2/20], Batch Num: [56/600]
Discriminator Loss: 1.0099, Generator Loss: 1.1287
D(x): 0.6566, D(G(z)): 0.3918
Epoch: [2/20], Batch Num: [57/600]
Discriminator Loss: 1.0451, Generator Loss: 1.0364
D(x): 0.6783, D(G(z)): 0.4235
Epoch: [2/20], Batch Num: [58/600]
Discriminator Loss: 0.9386, Generator Loss: 1.0445
D(x): 0.6994, D(G(z)): 0.4033
Epoch: [2/20], Batch Num: [59/600]
Discriminator Loss: 0.9210, Generator Loss: 1.1644
D(x): 0.7190, D(G(z)): 0.4110
Epoch: [2/20], Batch Num: [60/600]
Discriminator Loss: 0.9394, Generator Loss: 1.1187
D(x): 0.7301, D(G(z)): 0.4258
Epoch: [2/20], Batch Num: [61/600]
Discriminator Loss: 0.9094, Generator Loss: 1.1834
D(x): 0.7297, D(G(z)): 0.4094
Epoch: [2/20], Batch Num: [62/600]
Discriminator Loss: 0.8457, Generator Loss: 1.4128
D(x): 0.7280, D(G(z)): 0.3725
Epoch: [2/20], Batch Num: [63/600]
Discriminator Loss: 0.8934, Generator Loss: 1.4670
D(x): 0.7016, D(G(z)): 0.3674
Epoch: [2/20], Batch Num: [64/600]
Discriminator Loss: 0.8812, Generator Loss: 1.4788
D(x): 0.6759, D(G(z)): 0.3448
Epoch: [2/20], Batch Num: [65/600]
Discriminator Loss: 0.8059, Generator Loss: 1.4597
D(x): 0.7019, D(G(z)): 0.3172
Epoch: [2/20], Batch Num: [66/600]
Discriminator Loss: 0.8473, Generator Loss: 1.5327
D(x): 0.6980, D(G(z)): 0.3373
Epoch: [2/20], Batch Num: [67/600]
Discriminator Loss: 0.7726, Generator Loss: 1.5668
D(x): 0.7060, D(G(z)): 0.3154
Epoch: [2/20], Batch Num: [68/600]
Discriminator Loss: 0.7704, Generator Loss: 1.7068
D(x): 0.7156, D(G(z)): 0.3162
Epoch: [2/20], Batch Num: [69/600]
Discriminator Loss: 0.8234, Generator Loss: 1.5448
D(x): 0.7032, D(G(z)): 0.3340
Epoch: [2/20], Batch Num: [70/600]
Discriminator Loss: 0.8392, Generator Loss: 1.7735
D(x): 0.6896, D(G(z)): 0.3349
Epoch: [2/20], Batch Num: [71/600]
Discriminator Loss: 0.8074, Generator Loss: 1.7651
D(x): 0.6964, D(G(z)): 0.3217
Epoch: [2/20], Batch Num: [72/600]
Discriminator Loss: 0.7541, Generator Loss: 1.6608
D(x): 0.7117, D(G(z)): 0.3093
Epoch: [2/20], Batch Num: [73/600]
Discriminator Loss: 0.8469, Generator Loss: 1.7540
D(x): 0.6810, D(G(z)): 0.3147
Epoch: [2/20], Batch Num: [74/600]
Discriminator Loss: 0.8751, Generator Loss: 1.9914
D(x): 0.6774, D(G(z)): 0.3351
Epoch: [2/20], Batch Num: [75/600]
Discriminator Loss: 0.9011, Generator Loss: 1.9427
D(x): 0.6698, D(G(z)): 0.3441
Epoch: [2/20], Batch Num: [76/600]
Discriminator Loss: 0.8586, Generator Loss: 1.8935
D(x): 0.6717, D(G(z)): 0.3169
Epoch: [2/20], Batch Num: [77/600]
Discriminator Loss: 0.8927, Generator Loss: 1.9809
D(x): 0.6493, D(G(z)): 0.3198
Epoch: [2/20], Batch Num: [78/600]
Discriminator Loss: 1.0494, Generator Loss: 1.8814
D(x): 0.6144, D(G(z)): 0.3487
Epoch: [2/20], Batch Num: [79/600]
Discriminator Loss: 0.9756, Generator Loss: 1.9533
D(x): 0.6305, D(G(z)): 0.3467
Epoch: [2/20], Batch Num: [80/600]
Discriminator Loss: 1.1091, Generator Loss: 1.8548
D(x): 0.6368, D(G(z)): 0.3975
Epoch: [2/20], Batch Num: [81/600]
Discriminator Loss: 1.1429, Generator Loss: 1.4397
D(x): 0.6038, D(G(z)): 0.3885
Epoch: [2/20], Batch Num: [82/600]
Discriminator Loss: 1.2640, Generator Loss: 1.4754
D(x): 0.5732, D(G(z)): 0.4182
Epoch: [2/20], Batch Num: [83/600]
Discriminator Loss: 1.3534, Generator Loss: 1.5234
D(x): 0.5695, D(G(z)): 0.4555
Epoch: [2/20], Batch Num: [84/600]
Discriminator Loss: 1.4308, Generator Loss: 1.5920
D(x): 0.5753, D(G(z)): 0.4631
Epoch: [2/20], Batch Num: [85/600]
Discriminator Loss: 1.4678, Generator Loss: 1.3137
D(x): 0.5352, D(G(z)): 0.4553
Epoch: [2/20], Batch Num: [86/600]
Discriminator Loss: 1.5004, Generator Loss: 1.3786
D(x): 0.5419, D(G(z)): 0.4565
Epoch: [2/20], Batch Num: [87/600]
Discriminator Loss: 1.5793, Generator Loss: 1.2503
D(x): 0.5418, D(G(z)): 0.4944
Epoch: [2/20], Batch Num: [88/600]
Discriminator Loss: 1.3885, Generator Loss: 1.4485
D(x): 0.6343, D(G(z)): 0.5198
Epoch: [2/20], Batch Num: [89/600]
Discriminator Loss: 1.4015, Generator Loss: 1.7728
D(x): 0.6035, D(G(z)): 0.4699
Epoch: [2/20], Batch Num: [90/600]
Discriminator Loss: 1.4890, Generator Loss: 1.6567
D(x): 0.4975, D(G(z)): 0.3872
Epoch: [2/20], Batch Num: [91/600]
Discriminator Loss: 1.4446, Generator Loss: 1.5962
D(x): 0.5028, D(G(z)): 0.3870
Epoch: [2/20], Batch Num: [92/600]
Discriminator Loss: 1.3911, Generator Loss: 1.5209
D(x): 0.5377, D(G(z)): 0.4079
Epoch: [2/20], Batch Num: [93/600]
Discriminator Loss: 1.3891, Generator Loss: 1.4846
D(x): 0.5701, D(G(z)): 0.4261
Epoch: [2/20], Batch Num: [94/600]
Discriminator Loss: 1.2111, Generator Loss: 1.4932
D(x): 0.6709, D(G(z)): 0.4682
Epoch: [2/20], Batch Num: [95/600]
Discriminator Loss: 1.2302, Generator Loss: 1.4972
D(x): 0.6458, D(G(z)): 0.4216
Epoch: [2/20], Batch Num: [96/600]
Discriminator Loss: 1.1821, Generator Loss: 1.6850
D(x): 0.6089, D(G(z)): 0.3935
Epoch: [2/20], Batch Num: [97/600]
Discriminator Loss: 1.2368, Generator Loss: 1.8061
D(x): 0.5912, D(G(z)): 0.3629
Epoch: [2/20], Batch Num: [98/600]
Discriminator Loss: 1.1611, Generator Loss: 1.5892
D(x): 0.5889, D(G(z)): 0.3493
Epoch: [2/20], Batch Num: [99/600]
Discriminator Loss: 1.2364, Generator Loss: 1.8703
D(x): 0.5973, D(G(z)): 0.3945
Epoch: 2, Batch Num: [100/600]

Epoch: [2/20], Batch Num: [100/600]
Discriminator Loss: 1.1596, Generator Loss: 1.6338
D(x): 0.5914, D(G(z)): 0.3578
Epoch: [2/20], Batch Num: [101/600]
Discriminator Loss: 1.1299, Generator Loss: 1.5753
D(x): 0.6411, D(G(z)): 0.3990
Epoch: [2/20], Batch Num: [102/600]
Discriminator Loss: 1.2569, Generator Loss: 1.6689
D(x): 0.6680, D(G(z)): 0.4576
Epoch: [2/20], Batch Num: [103/600]
Discriminator Loss: 1.2973, Generator Loss: 1.6585
D(x): 0.6086, D(G(z)): 0.4495
Epoch: [2/20], Batch Num: [104/600]
Discriminator Loss: 1.2633, Generator Loss: 1.5719
D(x): 0.5810, D(G(z)): 0.3941
Epoch: [2/20], Batch Num: [105/600]
Discriminator Loss: 1.4159, Generator Loss: 1.4184
D(x): 0.5291, D(G(z)): 0.4014
Epoch: [2/20], Batch Num: [106/600]
Discriminator Loss: 1.4213, Generator Loss: 1.4331
D(x): 0.5488, D(G(z)): 0.4313
Epoch: [2/20], Batch Num: [107/600]
Discriminator Loss: 1.4790, Generator Loss: 1.3004
D(x): 0.6097, D(G(z)): 0.4812
Epoch: [2/20], Batch Num: [108/600]
Discriminator Loss: 1.3824, Generator Loss: 1.4026
D(x): 0.6513, D(G(z)): 0.5147
Epoch: [2/20], Batch Num: [109/600]
Discriminator Loss: 1.5662, Generator Loss: 1.3202
D(x): 0.6175, D(G(z)): 0.5306
Epoch: [2/20], Batch Num: [110/600]
Discriminator Loss: 1.7521, Generator Loss: 1.4062
D(x): 0.5274, D(G(z)): 0.4596
Epoch: [2/20], Batch Num: [111/600]
Discriminator Loss: 1.5249, Generator Loss: 1.3372
D(x): 0.5681, D(G(z)): 0.4730
Epoch: [2/20], Batch Num: [112/600]
Discriminator Loss: 1.3032, Generator Loss: 1.2986
D(x): 0.6233, D(G(z)): 0.4442
Epoch: [2/20], Batch Num: [113/600]
Discriminator Loss: 1.4096, Generator Loss: 1.3708
D(x): 0.5861, D(G(z)): 0.4401
Epoch: [2/20], Batch Num: [114/600]
Discriminator Loss: 1.3504, Generator Loss: 1.3540
D(x): 0.5807, D(G(z)): 0.4213
Epoch: [2/20], Batch Num: [115/600]
Discriminator Loss: 1.0233, Generator Loss: 1.3550
D(x): 0.6767, D(G(z)): 0.3858
Epoch: [2/20], Batch Num: [116/600]
Discriminator Loss: 1.1938, Generator Loss: 1.6392
D(x): 0.6298, D(G(z)): 0.3959
Epoch: [2/20], Batch Num: [117/600]
Discriminator Loss: 1.0927, Generator Loss: 1.5693
D(x): 0.6408, D(G(z)): 0.3620
Epoch: [2/20], Batch Num: [118/600]
Discriminator Loss: 1.1060, Generator Loss: 1.8312
D(x): 0.6274, D(G(z)): 0.3579
Epoch: [2/20], Batch Num: [119/600]
Discriminator Loss: 0.8017, Generator Loss: 1.7183
D(x): 0.6890, D(G(z)): 0.2705
Epoch: [2/20], Batch Num: [120/600]
Discriminator Loss: 1.0658, Generator Loss: 1.6590
D(x): 0.6305, D(G(z)): 0.3343
Epoch: [2/20], Batch Num: [121/600]
Discriminator Loss: 0.7824, Generator Loss: 1.8488
D(x): 0.7170, D(G(z)): 0.2936
Epoch: [2/20], Batch Num: [122/600]
Discriminator Loss: 0.7937, Generator Loss: 1.8934
D(x): 0.7371, D(G(z)): 0.3269
Epoch: [2/20], Batch Num: [123/600]
Discriminator Loss: 0.7826, Generator Loss: 2.1326
D(x): 0.7173, D(G(z)): 0.2701
Epoch: [2/20], Batch Num: [124/600]
Discriminator Loss: 0.7705, Generator Loss: 2.5403
D(x): 0.7302, D(G(z)): 0.2482
Epoch: [2/20], Batch Num: [125/600]
Discriminator Loss: 0.8829, Generator Loss: 2.1459
D(x): 0.6768, D(G(z)): 0.2546
Epoch: [2/20], Batch Num: [126/600]
Discriminator Loss: 0.7983, Generator Loss: 2.1202
D(x): 0.7064, D(G(z)): 0.2394
Epoch: [2/20], Batch Num: [127/600]
Discriminator Loss: 0.8108, Generator Loss: 1.9246
D(x): 0.7522, D(G(z)): 0.3031
Epoch: [2/20], Batch Num: [128/600]
Discriminator Loss: 0.7294, Generator Loss: 2.1843
D(x): 0.7846, D(G(z)): 0.3094
Epoch: [2/20], Batch Num: [129/600]
Discriminator Loss: 0.8306, Generator Loss: 2.1449
D(x): 0.7288, D(G(z)): 0.2837
Epoch: [2/20], Batch Num: [130/600]
Discriminator Loss: 0.8522, Generator Loss: 2.3994
D(x): 0.7270, D(G(z)): 0.3010
Epoch: [2/20], Batch Num: [131/600]
Discriminator Loss: 0.9287, Generator Loss: 2.1278
D(x): 0.6873, D(G(z)): 0.2912
Epoch: [2/20], Batch Num: [132/600]
Discriminator Loss: 0.8194, Generator Loss: 1.7450
D(x): 0.7296, D(G(z)): 0.2612
Epoch: [2/20], Batch Num: [133/600]
Discriminator Loss: 0.9164, Generator Loss: 1.7812
D(x): 0.7177, D(G(z)): 0.3362
Epoch: [2/20], Batch Num: [134/600]
Discriminator Loss: 0.9350, Generator Loss: 1.3594
D(x): 0.7078, D(G(z)): 0.3113
Epoch: [2/20], Batch Num: [135/600]
Discriminator Loss: 1.0940, Generator Loss: 1.3523
D(x): 0.7236, D(G(z)): 0.4099
Epoch: [2/20], Batch Num: [136/600]
Discriminator Loss: 1.0625, Generator Loss: 1.6920
D(x): 0.7455, D(G(z)): 0.4109
Epoch: [2/20], Batch Num: [137/600]
Discriminator Loss: 1.0479, Generator Loss: 1.7081
D(x): 0.6941, D(G(z)): 0.3590
Epoch: [2/20], Batch Num: [138/600]
Discriminator Loss: 1.0030, Generator Loss: 1.6778
D(x): 0.6784, D(G(z)): 0.3296
Epoch: [2/20], Batch Num: [139/600]
Discriminator Loss: 0.8105, Generator Loss: 1.6238
D(x): 0.7310, D(G(z)): 0.3099
Epoch: [2/20], Batch Num: [140/600]
Discriminator Loss: 0.9371, Generator Loss: 1.6616
D(x): 0.6974, D(G(z)): 0.3285
Epoch: [2/20], Batch Num: [141/600]
Discriminator Loss: 0.9200, Generator Loss: 1.4729
D(x): 0.7357, D(G(z)): 0.3720
Epoch: [2/20], Batch Num: [142/600]
Discriminator Loss: 0.8410, Generator Loss: 1.7147
D(x): 0.7793, D(G(z)): 0.3742
Epoch: [2/20], Batch Num: [143/600]
Discriminator Loss: 0.8587, Generator Loss: 1.9233
D(x): 0.7399, D(G(z)): 0.3350
Epoch: [2/20], Batch Num: [144/600]
Discriminator Loss: 0.8517, Generator Loss: 1.7507
D(x): 0.6599, D(G(z)): 0.2318
Epoch: [2/20], Batch Num: [145/600]
Discriminator Loss: 0.8073, Generator Loss: 1.5989
D(x): 0.7246, D(G(z)): 0.2812
Epoch: [2/20], Batch Num: [146/600]
Discriminator Loss: 0.8702, Generator Loss: 1.5290
D(x): 0.7377, D(G(z)): 0.3070
Epoch: [2/20], Batch Num: [147/600]
Discriminator Loss: 0.7994, Generator Loss: 1.4857
D(x): 0.7900, D(G(z)): 0.3561
Epoch: [2/20], Batch Num: [148/600]
Discriminator Loss: 0.6976, Generator Loss: 1.5359
D(x): 0.8172, D(G(z)): 0.3383
Epoch: [2/20], Batch Num: [149/600]
Discriminator Loss: 0.7322, Generator Loss: 1.6676
D(x): 0.7795, D(G(z)): 0.3136
Epoch: [2/20], Batch Num: [150/600]
Discriminator Loss: 0.7256, Generator Loss: 2.0858
D(x): 0.7371, D(G(z)): 0.2729
Epoch: [2/20], Batch Num: [151/600]
Discriminator Loss: 0.7405, Generator Loss: 2.0263
D(x): 0.7187, D(G(z)): 0.2639
Epoch: [2/20], Batch Num: [152/600]
Discriminator Loss: 0.8105, Generator Loss: 2.0129
D(x): 0.6992, D(G(z)): 0.2627
Epoch: [2/20], Batch Num: [153/600]
Discriminator Loss: 0.7154, Generator Loss: 1.6309
D(x): 0.7451, D(G(z)): 0.2644
Epoch: [2/20], Batch Num: [154/600]
Discriminator Loss: 0.7806, Generator Loss: 1.4792
D(x): 0.7814, D(G(z)): 0.3229
Epoch: [2/20], Batch Num: [155/600]
Discriminator Loss: 0.8531, Generator Loss: 1.4594
D(x): 0.7386, D(G(z)): 0.3309
Epoch: [2/20], Batch Num: [156/600]
Discriminator Loss: 0.7969, Generator Loss: 1.6487
D(x): 0.7824, D(G(z)): 0.3351
Epoch: [2/20], Batch Num: [157/600]
Discriminator Loss: 0.9412, Generator Loss: 1.6547
D(x): 0.7503, D(G(z)): 0.3764
Epoch: [2/20], Batch Num: [158/600]
Discriminator Loss: 0.8226, Generator Loss: 1.8665
D(x): 0.7569, D(G(z)): 0.3243
Epoch: [2/20], Batch Num: [159/600]
Discriminator Loss: 0.7820, Generator Loss: 1.8500
D(x): 0.7422, D(G(z)): 0.2854
Epoch: [2/20], Batch Num: [160/600]
Discriminator Loss: 0.8171, Generator Loss: 2.0058
D(x): 0.7048, D(G(z)): 0.2681
Epoch: [2/20], Batch Num: [161/600]
Discriminator Loss: 0.7674, Generator Loss: 1.7151
D(x): 0.7143, D(G(z)): 0.2647
Epoch: [2/20], Batch Num: [162/600]
Discriminator Loss: 0.7618, Generator Loss: 1.6789
D(x): 0.7484, D(G(z)): 0.2968
Epoch: [2/20], Batch Num: [163/600]
Discriminator Loss: 0.8007, Generator Loss: 2.0145
D(x): 0.7775, D(G(z)): 0.3387
Epoch: [2/20], Batch Num: [164/600]
Discriminator Loss: 0.8123, Generator Loss: 1.9268
D(x): 0.7567, D(G(z)): 0.3419
Epoch: [2/20], Batch Num: [165/600]
Discriminator Loss: 0.7858, Generator Loss: 1.9711
D(x): 0.7438, D(G(z)): 0.2946
Epoch: [2/20], Batch Num: [166/600]
Discriminator Loss: 0.8210, Generator Loss: 1.9154
D(x): 0.7269, D(G(z)): 0.2676
Epoch: [2/20], Batch Num: [167/600]
Discriminator Loss: 0.8691, Generator Loss: 1.7828
D(x): 0.6942, D(G(z)): 0.2835
Epoch: [2/20], Batch Num: [168/600]
Discriminator Loss: 0.8974, Generator Loss: 1.6877
D(x): 0.7077, D(G(z)): 0.3122
Epoch: [2/20], Batch Num: [169/600]
Discriminator Loss: 0.9393, Generator Loss: 1.5578
D(x): 0.7074, D(G(z)): 0.3472
Epoch: [2/20], Batch Num: [170/600]
Discriminator Loss: 1.0212, Generator Loss: 1.4170
D(x): 0.6968, D(G(z)): 0.3675
Epoch: [2/20], Batch Num: [171/600]
Discriminator Loss: 1.0158, Generator Loss: 1.4972
D(x): 0.7420, D(G(z)): 0.4074
Epoch: [2/20], Batch Num: [172/600]
Discriminator Loss: 1.0815, Generator Loss: 1.5257
D(x): 0.7209, D(G(z)): 0.4247
Epoch: [2/20], Batch Num: [173/600]
Discriminator Loss: 1.1945, Generator Loss: 1.5440
D(x): 0.6277, D(G(z)): 0.3786
Epoch: [2/20], Batch Num: [174/600]
Discriminator Loss: 1.3077, Generator Loss: 1.5420
D(x): 0.6167, D(G(z)): 0.3821
Epoch: [2/20], Batch Num: [175/600]
Discriminator Loss: 1.1811, Generator Loss: 1.4048
D(x): 0.6397, D(G(z)): 0.4034
Epoch: [2/20], Batch Num: [176/600]
Discriminator Loss: 1.2686, Generator Loss: 1.3251
D(x): 0.6340, D(G(z)): 0.4402
Epoch: [2/20], Batch Num: [177/600]
Discriminator Loss: 1.3130, Generator Loss: 1.1276
D(x): 0.6578, D(G(z)): 0.4714
Epoch: [2/20], Batch Num: [178/600]
Discriminator Loss: 1.2780, Generator Loss: 1.2504
D(x): 0.6666, D(G(z)): 0.4770
Epoch: [2/20], Batch Num: [179/600]
Discriminator Loss: 1.3397, Generator Loss: 1.3410
D(x): 0.6836, D(G(z)): 0.5133
Epoch: [2/20], Batch Num: [180/600]
Discriminator Loss: 1.4525, Generator Loss: 1.6120
D(x): 0.6188, D(G(z)): 0.4765
Epoch: [2/20], Batch Num: [181/600]
Discriminator Loss: 1.3853, Generator Loss: 1.5314
D(x): 0.5533, D(G(z)): 0.3735
Epoch: [2/20], Batch Num: [182/600]
Discriminator Loss: 1.5378, Generator Loss: 1.3203
D(x): 0.5631, D(G(z)): 0.4729
Epoch: [2/20], Batch Num: [183/600]
Discriminator Loss: 1.6121, Generator Loss: 0.9641
D(x): 0.5833, D(G(z)): 0.4857
Epoch: [2/20], Batch Num: [184/600]
Discriminator Loss: 1.5474, Generator Loss: 0.9109
D(x): 0.6401, D(G(z)): 0.5360
Epoch: [2/20], Batch Num: [185/600]
Discriminator Loss: 1.6315, Generator Loss: 0.9052
D(x): 0.6581, D(G(z)): 0.6036
Epoch: [2/20], Batch Num: [186/600]
Discriminator Loss: 1.6086, Generator Loss: 1.1498
D(x): 0.6274, D(G(z)): 0.5657
Epoch: [2/20], Batch Num: [187/600]
Discriminator Loss: 1.6802, Generator Loss: 1.1530
D(x): 0.5728, D(G(z)): 0.5414
Epoch: [2/20], Batch Num: [188/600]
Discriminator Loss: 2.0813, Generator Loss: 0.8799
D(x): 0.5046, D(G(z)): 0.5819
Epoch: [2/20], Batch Num: [189/600]
Discriminator Loss: 1.8708, Generator Loss: 0.8495
D(x): 0.5006, D(G(z)): 0.5276
Epoch: [2/20], Batch Num: [190/600]
Discriminator Loss: 1.7551, Generator Loss: 0.7131
D(x): 0.5633, D(G(z)): 0.5531
Epoch: [2/20], Batch Num: [191/600]
Discriminator Loss: 1.7628, Generator Loss: 0.6111
D(x): 0.6196, D(G(z)): 0.6184
Epoch: [2/20], Batch Num: [192/600]
Discriminator Loss: 1.7101, Generator Loss: 0.7144
D(x): 0.6658, D(G(z)): 0.6248
Epoch: [2/20], Batch Num: [193/600]
Discriminator Loss: 1.7699, Generator Loss: 0.8368
D(x): 0.6499, D(G(z)): 0.6411
Epoch: [2/20], Batch Num: [194/600]
Discriminator Loss: 1.6705, Generator Loss: 0.8582
D(x): 0.6421, D(G(z)): 0.6246
Epoch: [2/20], Batch Num: [195/600]
Discriminator Loss: 1.6599, Generator Loss: 0.9661
D(x): 0.6169, D(G(z)): 0.5878
Epoch: [2/20], Batch Num: [196/600]
Discriminator Loss: 1.7230, Generator Loss: 0.9762
D(x): 0.5332, D(G(z)): 0.5550
Epoch: [2/20], Batch Num: [197/600]
Discriminator Loss: 1.4609, Generator Loss: 1.1374
D(x): 0.5762, D(G(z)): 0.4992
Epoch: [2/20], Batch Num: [198/600]
Discriminator Loss: 1.4002, Generator Loss: 1.1451
D(x): 0.5688, D(G(z)): 0.4671
Epoch: [2/20], Batch Num: [199/600]
Discriminator Loss: 1.3443, Generator Loss: 0.9925
D(x): 0.5864, D(G(z)): 0.4817
Epoch: 2, Batch Num: [200/600]

Epoch: [2/20], Batch Num: [200/600]
Discriminator Loss: 1.4261, Generator Loss: 0.9632
D(x): 0.5930, D(G(z)): 0.4967
Epoch: [2/20], Batch Num: [201/600]
Discriminator Loss: 1.2394, Generator Loss: 1.0489
D(x): 0.6301, D(G(z)): 0.4753
Epoch: [2/20], Batch Num: [202/600]
Discriminator Loss: 1.1750, Generator Loss: 1.1082
D(x): 0.6525, D(G(z)): 0.4550
Epoch: [2/20], Batch Num: [203/600]
Discriminator Loss: 1.1290, Generator Loss: 1.1444
D(x): 0.6792, D(G(z)): 0.4717
Epoch: [2/20], Batch Num: [204/600]
Discriminator Loss: 0.9256, Generator Loss: 1.4243
D(x): 0.7180, D(G(z)): 0.3962
Epoch: [2/20], Batch Num: [205/600]
Discriminator Loss: 0.9139, Generator Loss: 1.4302
D(x): 0.6912, D(G(z)): 0.3746
Epoch: [2/20], Batch Num: [206/600]
Discriminator Loss: 0.7973, Generator Loss: 1.7956
D(x): 0.7075, D(G(z)): 0.3109
Epoch: [2/20], Batch Num: [207/600]
Discriminator Loss: 0.7089, Generator Loss: 2.0060
D(x): 0.6889, D(G(z)): 0.2437
Epoch: [2/20], Batch Num: [208/600]
Discriminator Loss: 0.5989, Generator Loss: 2.3132
D(x): 0.7237, D(G(z)): 0.2058
Epoch: [2/20], Batch Num: [209/600]
Discriminator Loss: 0.5588, Generator Loss: 2.6978
D(x): 0.7544, D(G(z)): 0.2102
Epoch: [2/20], Batch Num: [210/600]
Discriminator Loss: 0.5487, Generator Loss: 2.6275
D(x): 0.7292, D(G(z)): 0.1756
Epoch: [2/20], Batch Num: [211/600]
Discriminator Loss: 0.5462, Generator Loss: 2.7728
D(x): 0.7491, D(G(z)): 0.1909
Epoch: [2/20], Batch Num: [212/600]
Discriminator Loss: 0.4837, Generator Loss: 2.6659
D(x): 0.7729, D(G(z)): 0.1726
Epoch: [2/20], Batch Num: [213/600]
Discriminator Loss: 0.4763, Generator Loss: 2.6402
D(x): 0.7824, D(G(z)): 0.1715
Epoch: [2/20], Batch Num: [214/600]
Discriminator Loss: 0.4380, Generator Loss: 2.7057
D(x): 0.8042, D(G(z)): 0.1705
Epoch: [2/20], Batch Num: [215/600]
Discriminator Loss: 0.3757, Generator Loss: 2.9660
D(x): 0.8326, D(G(z)): 0.1544
Epoch: [2/20], Batch Num: [216/600]
Discriminator Loss: 0.4119, Generator Loss: 2.8270
D(x): 0.8248, D(G(z)): 0.1689
Epoch: [2/20], Batch Num: [217/600]
Discriminator Loss: 0.3568, Generator Loss: 3.1514
D(x): 0.8493, D(G(z)): 0.1550
Epoch: [2/20], Batch Num: [218/600]
Discriminator Loss: 0.3775, Generator Loss: 2.9785
D(x): 0.8266, D(G(z)): 0.1442
Epoch: [2/20], Batch Num: [219/600]
Discriminator Loss: 0.3892, Generator Loss: 3.1918
D(x): 0.8270, D(G(z)): 0.1536
Epoch: [2/20], Batch Num: [220/600]
Discriminator Loss: 0.3725, Generator Loss: 2.9769
D(x): 0.8126, D(G(z)): 0.1284
Epoch: [2/20], Batch Num: [221/600]
Discriminator Loss: 0.3941, Generator Loss: 3.0446
D(x): 0.8260, D(G(z)): 0.1571
Epoch: [2/20], Batch Num: [222/600]
Discriminator Loss: 0.4547, Generator Loss: 3.1784
D(x): 0.7888, D(G(z)): 0.1583
Epoch: [2/20], Batch Num: [223/600]
Discriminator Loss: 0.4599, Generator Loss: 2.9096
D(x): 0.7931, D(G(z)): 0.1550
Epoch: [2/20], Batch Num: [224/600]
Discriminator Loss: 0.5707, Generator Loss: 2.6627
D(x): 0.7570, D(G(z)): 0.1945
Epoch: [2/20], Batch Num: [225/600]
Discriminator Loss: 0.6349, Generator Loss: 2.7054
D(x): 0.7407, D(G(z)): 0.2204
Epoch: [2/20], Batch Num: [226/600]
Discriminator Loss: 0.5760, Generator Loss: 2.4994
D(x): 0.7892, D(G(z)): 0.2306
Epoch: [2/20], Batch Num: [227/600]
Discriminator Loss: 0.8135, Generator Loss: 2.1325
D(x): 0.7099, D(G(z)): 0.2727
Epoch: [2/20], Batch Num: [228/600]
Discriminator Loss: 0.8168, Generator Loss: 2.1529
D(x): 0.7240, D(G(z)): 0.2901
Epoch: [2/20], Batch Num: [229/600]
Discriminator Loss: 0.9440, Generator Loss: 1.9893
D(x): 0.7154, D(G(z)): 0.3457
Epoch: [2/20], Batch Num: [230/600]
Discriminator Loss: 0.9196, Generator Loss: 2.1040
D(x): 0.7014, D(G(z)): 0.3492
Epoch: [2/20], Batch Num: [231/600]
Discriminator Loss: 1.1244, Generator Loss: 2.0967
D(x): 0.6409, D(G(z)): 0.3583
Epoch: [2/20], Batch Num: [232/600]
Discriminator Loss: 1.1547, Generator Loss: 1.8127
D(x): 0.6086, D(G(z)): 0.3237
Epoch: [2/20], Batch Num: [233/600]
Discriminator Loss: 1.1322, Generator Loss: 1.7432
D(x): 0.7167, D(G(z)): 0.4195
Epoch: [2/20], Batch Num: [234/600]
Discriminator Loss: 1.1359, Generator Loss: 1.7022
D(x): 0.6685, D(G(z)): 0.3981
Epoch: [2/20], Batch Num: [235/600]
Discriminator Loss: 1.1640, Generator Loss: 1.7127
D(x): 0.6964, D(G(z)): 0.4460
Epoch: [2/20], Batch Num: [236/600]
Discriminator Loss: 1.1827, Generator Loss: 1.7944
D(x): 0.6102, D(G(z)): 0.3701
Epoch: [2/20], Batch Num: [237/600]
Discriminator Loss: 1.3219, Generator Loss: 1.8784
D(x): 0.5765, D(G(z)): 0.3767
Epoch: [2/20], Batch Num: [238/600]
Discriminator Loss: 1.3621, Generator Loss: 1.3377
D(x): 0.6228, D(G(z)): 0.4390
Epoch: [2/20], Batch Num: [239/600]
Discriminator Loss: 1.3330, Generator Loss: 1.3532
D(x): 0.6264, D(G(z)): 0.4424
Epoch: [2/20], Batch Num: [240/600]
Discriminator Loss: 1.3337, Generator Loss: 1.5376
D(x): 0.6946, D(G(z)): 0.4963
Epoch: [2/20], Batch Num: [241/600]
Discriminator Loss: 1.3087, Generator Loss: 1.6700
D(x): 0.5756, D(G(z)): 0.4163
Epoch: [2/20], Batch Num: [242/600]
Discriminator Loss: 1.4873, Generator Loss: 1.5634
D(x): 0.5591, D(G(z)): 0.4291
Epoch: [2/20], Batch Num: [243/600]
Discriminator Loss: 1.4555, Generator Loss: 1.3088
D(x): 0.6050, D(G(z)): 0.4591
Epoch: [2/20], Batch Num: [244/600]
Discriminator Loss: 1.5853, Generator Loss: 1.1379
D(x): 0.5454, D(G(z)): 0.4510
Epoch: [2/20], Batch Num: [245/600]
Discriminator Loss: 1.7168, Generator Loss: 1.0702
D(x): 0.5747, D(G(z)): 0.5306
Epoch: [2/20], Batch Num: [246/600]
Discriminator Loss: 1.9206, Generator Loss: 1.0287
D(x): 0.6063, D(G(z)): 0.6106
Epoch: [2/20], Batch Num: [247/600]
Discriminator Loss: 1.7553, Generator Loss: 1.0866
D(x): 0.5733, D(G(z)): 0.5344
Epoch: [2/20], Batch Num: [248/600]
Discriminator Loss: 1.6805, Generator Loss: 1.1127
D(x): 0.5881, D(G(z)): 0.5270
Epoch: [2/20], Batch Num: [249/600]
Discriminator Loss: 1.8872, Generator Loss: 1.2448
D(x): 0.5159, D(G(z)): 0.5180
Epoch: [2/20], Batch Num: [250/600]
Discriminator Loss: 2.1262, Generator Loss: 0.9949
D(x): 0.4549, D(G(z)): 0.5583
Epoch: [2/20], Batch Num: [251/600]
Discriminator Loss: 2.1483, Generator Loss: 0.7428
D(x): 0.4818, D(G(z)): 0.5469
Epoch: [2/20], Batch Num: [252/600]
Discriminator Loss: 2.0045, Generator Loss: 0.6580
D(x): 0.5619, D(G(z)): 0.6044
Epoch: [2/20], Batch Num: [253/600]
Discriminator Loss: 2.0077, Generator Loss: 0.7338
D(x): 0.6487, D(G(z)): 0.6703
Epoch: [2/20], Batch Num: [254/600]
Discriminator Loss: 1.8604, Generator Loss: 0.9103
D(x): 0.5471, D(G(z)): 0.5727
Epoch: [2/20], Batch Num: [255/600]
Discriminator Loss: 2.0755, Generator Loss: 1.0252
D(x): 0.4908, D(G(z)): 0.5875
Epoch: [2/20], Batch Num: [256/600]
Discriminator Loss: 2.1586, Generator Loss: 0.8466
D(x): 0.4655, D(G(z)): 0.5592
Epoch: [2/20], Batch Num: [257/600]
Discriminator Loss: 2.1421, Generator Loss: 0.8338
D(x): 0.4629, D(G(z)): 0.5860
Epoch: [2/20], Batch Num: [258/600]
Discriminator Loss: 1.8072, Generator Loss: 0.8122
D(x): 0.5807, D(G(z)): 0.6109
Epoch: [2/20], Batch Num: [259/600]
Discriminator Loss: 1.8529, Generator Loss: 0.6964
D(x): 0.5378, D(G(z)): 0.5898
Epoch: [2/20], Batch Num: [260/600]
Discriminator Loss: 1.9330, Generator Loss: 0.6182
D(x): 0.5550, D(G(z)): 0.6454
Epoch: [2/20], Batch Num: [261/600]
Discriminator Loss: 1.7176, Generator Loss: 0.8147
D(x): 0.5804, D(G(z)): 0.5773
Epoch: [2/20], Batch Num: [262/600]
Discriminator Loss: 1.7588, Generator Loss: 0.8283
D(x): 0.5883, D(G(z)): 0.5943
Epoch: [2/20], Batch Num: [263/600]
Discriminator Loss: 1.4326, Generator Loss: 0.9973
D(x): 0.6176, D(G(z)): 0.5392
Epoch: [2/20], Batch Num: [264/600]
Discriminator Loss: 1.5051, Generator Loss: 0.9051
D(x): 0.5732, D(G(z)): 0.5156
Epoch: [2/20], Batch Num: [265/600]
Discriminator Loss: 1.5012, Generator Loss: 1.1323
D(x): 0.5745, D(G(z)): 0.4972
Epoch: [2/20], Batch Num: [266/600]
Discriminator Loss: 1.3118, Generator Loss: 1.0754
D(x): 0.5703, D(G(z)): 0.4126
Epoch: [2/20], Batch Num: [267/600]
Discriminator Loss: 1.5299, Generator Loss: 0.9888
D(x): 0.5221, D(G(z)): 0.4481
Epoch: [2/20], Batch Num: [268/600]
Discriminator Loss: 1.2689, Generator Loss: 0.8987
D(x): 0.6159, D(G(z)): 0.4558
Epoch: [2/20], Batch Num: [269/600]
Discriminator Loss: 1.2184, Generator Loss: 0.8025
D(x): 0.6705, D(G(z)): 0.4909
Epoch: [2/20], Batch Num: [270/600]
Discriminator Loss: 1.1773, Generator Loss: 1.0154
D(x): 0.6519, D(G(z)): 0.4600
Epoch: [2/20], Batch Num: [271/600]
Discriminator Loss: 1.0355, Generator Loss: 1.0584
D(x): 0.7044, D(G(z)): 0.4355
Epoch: [2/20], Batch Num: [272/600]
Discriminator Loss: 1.1049, Generator Loss: 1.2162
D(x): 0.6401, D(G(z)): 0.3886
Epoch: [2/20], Batch Num: [273/600]
Discriminator Loss: 1.0107, Generator Loss: 1.2534
D(x): 0.6433, D(G(z)): 0.3735
Epoch: [2/20], Batch Num: [274/600]
Discriminator Loss: 1.0265, Generator Loss: 1.2856
D(x): 0.6518, D(G(z)): 0.3887
Epoch: [2/20], Batch Num: [275/600]
Discriminator Loss: 0.9262, Generator Loss: 1.3964
D(x): 0.6538, D(G(z)): 0.3198
Epoch: [2/20], Batch Num: [276/600]
Discriminator Loss: 0.8339, Generator Loss: 1.5697
D(x): 0.6712, D(G(z)): 0.2964
Epoch: [2/20], Batch Num: [277/600]
Discriminator Loss: 0.8954, Generator Loss: 1.3509
D(x): 0.6576, D(G(z)): 0.3122
Epoch: [2/20], Batch Num: [278/600]
Discriminator Loss: 0.7843, Generator Loss: 1.3570
D(x): 0.7097, D(G(z)): 0.2966
Epoch: [2/20], Batch Num: [279/600]
Discriminator Loss: 0.7445, Generator Loss: 1.4095
D(x): 0.7530, D(G(z)): 0.3224
Epoch: [2/20], Batch Num: [280/600]
Discriminator Loss: 0.7133, Generator Loss: 1.5117
D(x): 0.7589, D(G(z)): 0.3147
Epoch: [2/20], Batch Num: [281/600]
Discriminator Loss: 0.7047, Generator Loss: 1.6562
D(x): 0.7335, D(G(z)): 0.2823
Epoch: [2/20], Batch Num: [282/600]
Discriminator Loss: 0.6808, Generator Loss: 1.8087
D(x): 0.7470, D(G(z)): 0.2775
Epoch: [2/20], Batch Num: [283/600]
Discriminator Loss: 0.6416, Generator Loss: 1.9126
D(x): 0.7726, D(G(z)): 0.2795
Epoch: [2/20], Batch Num: [284/600]
Discriminator Loss: 0.6327, Generator Loss: 2.0916
D(x): 0.7303, D(G(z)): 0.2301
Epoch: [2/20], Batch Num: [285/600]
Discriminator Loss: 0.6013, Generator Loss: 1.9606
D(x): 0.7427, D(G(z)): 0.2120
Epoch: [2/20], Batch Num: [286/600]
Discriminator Loss: 0.5641, Generator Loss: 1.8240
D(x): 0.7493, D(G(z)): 0.2016
Epoch: [2/20], Batch Num: [287/600]
Discriminator Loss: 0.5781, Generator Loss: 2.0232
D(x): 0.7485, D(G(z)): 0.2100
Epoch: [2/20], Batch Num: [288/600]
Discriminator Loss: 0.5460, Generator Loss: 1.8787
D(x): 0.7731, D(G(z)): 0.2200
Epoch: [2/20], Batch Num: [289/600]
Discriminator Loss: 0.5175, Generator Loss: 1.8814
D(x): 0.7946, D(G(z)): 0.2015
Epoch: [2/20], Batch Num: [290/600]
Discriminator Loss: 0.5699, Generator Loss: 1.9270
D(x): 0.7692, D(G(z)): 0.2151
Epoch: [2/20], Batch Num: [291/600]
Discriminator Loss: 0.4979, Generator Loss: 2.0373
D(x): 0.7973, D(G(z)): 0.2098
Epoch: [2/20], Batch Num: [292/600]
Discriminator Loss: 0.5170, Generator Loss: 2.1221
D(x): 0.7755, D(G(z)): 0.1749
Epoch: [2/20], Batch Num: [293/600]
Discriminator Loss: 0.5119, Generator Loss: 2.1822
D(x): 0.7890, D(G(z)): 0.2051
Epoch: [2/20], Batch Num: [294/600]
Discriminator Loss: 0.5237, Generator Loss: 2.1963
D(x): 0.7781, D(G(z)): 0.2018
Epoch: [2/20], Batch Num: [295/600]
Discriminator Loss: 0.5187, Generator Loss: 2.2603
D(x): 0.7857, D(G(z)): 0.2111
Epoch: [2/20], Batch Num: [296/600]
Discriminator Loss: 0.5857, Generator Loss: 2.0685
D(x): 0.7708, D(G(z)): 0.2316
Epoch: [2/20], Batch Num: [297/600]
Discriminator Loss: 0.5768, Generator Loss: 2.0682
D(x): 0.7630, D(G(z)): 0.2123
Epoch: [2/20], Batch Num: [298/600]
Discriminator Loss: 0.6187, Generator Loss: 1.9450
D(x): 0.7189, D(G(z)): 0.1974
Epoch: [2/20], Batch Num: [299/600]
Discriminator Loss: 0.6011, Generator Loss: 1.9603
D(x): 0.7550, D(G(z)): 0.2261
Epoch: 2, Batch Num: [300/600]

Epoch: [2/20], Batch Num: [300/600]
Discriminator Loss: 0.6291, Generator Loss: 1.7361
D(x): 0.7398, D(G(z)): 0.2309
Epoch: [2/20], Batch Num: [301/600]
Discriminator Loss: 0.6346, Generator Loss: 1.7763
D(x): 0.7597, D(G(z)): 0.2516
Epoch: [2/20], Batch Num: [302/600]
Discriminator Loss: 0.6542, Generator Loss: 1.7841
D(x): 0.7587, D(G(z)): 0.2619
Epoch: [2/20], Batch Num: [303/600]
Discriminator Loss: 0.7111, Generator Loss: 1.6555
D(x): 0.7541, D(G(z)): 0.2781
Epoch: [2/20], Batch Num: [304/600]
Discriminator Loss: 0.8046, Generator Loss: 1.8072
D(x): 0.7106, D(G(z)): 0.3008
Epoch: [2/20], Batch Num: [305/600]
Discriminator Loss: 0.8128, Generator Loss: 1.8384
D(x): 0.7073, D(G(z)): 0.3184
Epoch: [2/20], Batch Num: [306/600]
Discriminator Loss: 0.8071, Generator Loss: 1.8892
D(x): 0.6885, D(G(z)): 0.2835
Epoch: [2/20], Batch Num: [307/600]
Discriminator Loss: 1.0246, Generator Loss: 1.6783
D(x): 0.6188, D(G(z)): 0.2862
Epoch: [2/20], Batch Num: [308/600]
Discriminator Loss: 0.8756, Generator Loss: 1.4865
D(x): 0.6794, D(G(z)): 0.3069
Epoch: [2/20], Batch Num: [309/600]
Discriminator Loss: 0.9681, Generator Loss: 1.2381
D(x): 0.6769, D(G(z)): 0.3695
Epoch: [2/20], Batch Num: [310/600]
Discriminator Loss: 0.8632, Generator Loss: 1.3262
D(x): 0.7488, D(G(z)): 0.3717
Epoch: [2/20], Batch Num: [311/600]
Discriminator Loss: 1.1089, Generator Loss: 1.1464
D(x): 0.6629, D(G(z)): 0.4233
Epoch: [2/20], Batch Num: [312/600]
Discriminator Loss: 1.1443, Generator Loss: 1.3345
D(x): 0.6476, D(G(z)): 0.4229
Epoch: [2/20], Batch Num: [313/600]
Discriminator Loss: 1.1075, Generator Loss: 1.2591
D(x): 0.6302, D(G(z)): 0.3946
Epoch: [2/20], Batch Num: [314/600]
Discriminator Loss: 1.1686, Generator Loss: 1.2358
D(x): 0.6328, D(G(z)): 0.4048
Epoch: [2/20], Batch Num: [315/600]
Discriminator Loss: 1.3809, Generator Loss: 1.0008
D(x): 0.5473, D(G(z)): 0.4218
Epoch: [2/20], Batch Num: [316/600]
Discriminator Loss: 1.3529, Generator Loss: 0.8889
D(x): 0.5885, D(G(z)): 0.4633
Epoch: [2/20], Batch Num: [317/600]
Discriminator Loss: 1.4361, Generator Loss: 0.7320
D(x): 0.6199, D(G(z)): 0.5148
Epoch: [2/20], Batch Num: [318/600]
Discriminator Loss: 1.4616, Generator Loss: 0.7235
D(x): 0.6299, D(G(z)): 0.5539
Epoch: [2/20], Batch Num: [319/600]
Discriminator Loss: 1.5375, Generator Loss: 0.8445
D(x): 0.6131, D(G(z)): 0.5666
Epoch: [2/20], Batch Num: [320/600]
Discriminator Loss: 1.5967, Generator Loss: 0.8230
D(x): 0.5395, D(G(z)): 0.5228
Epoch: [2/20], Batch Num: [321/600]
Discriminator Loss: 1.5599, Generator Loss: 0.7921
D(x): 0.5895, D(G(z)): 0.5415
Epoch: [2/20], Batch Num: [322/600]
Discriminator Loss: 1.7170, Generator Loss: 0.8444
D(x): 0.5096, D(G(z)): 0.5168
Epoch: [2/20], Batch Num: [323/600]
Discriminator Loss: 1.4930, Generator Loss: 0.7905
D(x): 0.6103, D(G(z)): 0.5507
Epoch: [2/20], Batch Num: [324/600]
Discriminator Loss: 1.7259, Generator Loss: 0.7557
D(x): 0.5665, D(G(z)): 0.5874
Epoch: [2/20], Batch Num: [325/600]
Discriminator Loss: 1.6458, Generator Loss: 0.6810
D(x): 0.5956, D(G(z)): 0.5952
Epoch: [2/20], Batch Num: [326/600]
Discriminator Loss: 1.5834, Generator Loss: 0.7151
D(x): 0.5951, D(G(z)): 0.5643
Epoch: [2/20], Batch Num: [327/600]
Discriminator Loss: 1.4623, Generator Loss: 0.7780
D(x): 0.6127, D(G(z)): 0.5561
Epoch: [2/20], Batch Num: [328/600]
Discriminator Loss: 1.5115, Generator Loss: 0.6979
D(x): 0.5851, D(G(z)): 0.5382
Epoch: [2/20], Batch Num: [329/600]
Discriminator Loss: 1.4455, Generator Loss: 0.8530
D(x): 0.5990, D(G(z)): 0.5336
Epoch: [2/20], Batch Num: [330/600]
Discriminator Loss: 1.3846, Generator Loss: 0.7839
D(x): 0.5922, D(G(z)): 0.5073
Epoch: [2/20], Batch Num: [331/600]
Discriminator Loss: 1.2801, Generator Loss: 0.9062
D(x): 0.6320, D(G(z)): 0.4866
Epoch: [2/20], Batch Num: [332/600]
Discriminator Loss: 1.4011, Generator Loss: 0.7994
D(x): 0.5865, D(G(z)): 0.5091
Epoch: [2/20], Batch Num: [333/600]
Discriminator Loss: 1.2932, Generator Loss: 1.0054
D(x): 0.6123, D(G(z)): 0.4809
Epoch: [2/20], Batch Num: [334/600]
Discriminator Loss: 1.4113, Generator Loss: 1.0430
D(x): 0.5509, D(G(z)): 0.4494
Epoch: [2/20], Batch Num: [335/600]
Discriminator Loss: 1.1673, Generator Loss: 1.0352
D(x): 0.6326, D(G(z)): 0.4494
Epoch: [2/20], Batch Num: [336/600]
Discriminator Loss: 1.1692, Generator Loss: 1.0440
D(x): 0.6373, D(G(z)): 0.4563
Epoch: [2/20], Batch Num: [337/600]
Discriminator Loss: 1.1204, Generator Loss: 0.9980
D(x): 0.6319, D(G(z)): 0.4241
Epoch: [2/20], Batch Num: [338/600]
Discriminator Loss: 1.2521, Generator Loss: 0.9934
D(x): 0.5851, D(G(z)): 0.4291
Epoch: [2/20], Batch Num: [339/600]
Discriminator Loss: 1.1591, Generator Loss: 0.9418
D(x): 0.6485, D(G(z)): 0.4543
Epoch: [2/20], Batch Num: [340/600]
Discriminator Loss: 1.0699, Generator Loss: 1.0783
D(x): 0.6657, D(G(z)): 0.4369
Epoch: [2/20], Batch Num: [341/600]
Discriminator Loss: 1.0225, Generator Loss: 1.0862
D(x): 0.6703, D(G(z)): 0.4024
Epoch: [2/20], Batch Num: [342/600]
Discriminator Loss: 1.1183, Generator Loss: 1.0717
D(x): 0.6466, D(G(z)): 0.4131
Epoch: [2/20], Batch Num: [343/600]
Discriminator Loss: 0.9546, Generator Loss: 1.0663
D(x): 0.7092, D(G(z)): 0.4061
Epoch: [2/20], Batch Num: [344/600]
Discriminator Loss: 0.9778, Generator Loss: 1.1244
D(x): 0.6717, D(G(z)): 0.3720
Epoch: [2/20], Batch Num: [345/600]
Discriminator Loss: 0.9698, Generator Loss: 1.2096
D(x): 0.6665, D(G(z)): 0.3596
Epoch: [2/20], Batch Num: [346/600]
Discriminator Loss: 0.9636, Generator Loss: 1.1243
D(x): 0.6804, D(G(z)): 0.3735
Epoch: [2/20], Batch Num: [347/600]
Discriminator Loss: 0.9642, Generator Loss: 1.2048
D(x): 0.6770, D(G(z)): 0.3750
Epoch: [2/20], Batch Num: [348/600]
Discriminator Loss: 0.9087, Generator Loss: 1.2084
D(x): 0.7176, D(G(z)): 0.3902
Epoch: [2/20], Batch Num: [349/600]
Discriminator Loss: 0.9788, Generator Loss: 1.2602
D(x): 0.6832, D(G(z)): 0.3796
Epoch: [2/20], Batch Num: [350/600]
Discriminator Loss: 0.9312, Generator Loss: 1.1832
D(x): 0.7077, D(G(z)): 0.3855
Epoch: [2/20], Batch Num: [351/600]
Discriminator Loss: 0.9366, Generator Loss: 1.3058
D(x): 0.6855, D(G(z)): 0.3607
Epoch: [2/20], Batch Num: [352/600]
Discriminator Loss: 0.9447, Generator Loss: 1.2722
D(x): 0.6806, D(G(z)): 0.3678
Epoch: [2/20], Batch Num: [353/600]
Discriminator Loss: 1.0095, Generator Loss: 1.2247
D(x): 0.6628, D(G(z)): 0.3809
Epoch: [2/20], Batch Num: [354/600]
Discriminator Loss: 0.8604, Generator Loss: 1.4224
D(x): 0.7071, D(G(z)): 0.3425
Epoch: [2/20], Batch Num: [355/600]
Discriminator Loss: 0.8182, Generator Loss: 1.2408
D(x): 0.7636, D(G(z)): 0.3664
Epoch: [2/20], Batch Num: [356/600]
Discriminator Loss: 0.8487, Generator Loss: 1.4133
D(x): 0.7144, D(G(z)): 0.3350
Epoch: [2/20], Batch Num: [357/600]
Discriminator Loss: 0.8371, Generator Loss: 1.4276
D(x): 0.6815, D(G(z)): 0.3026
Epoch: [2/20], Batch Num: [358/600]
Discriminator Loss: 0.6925, Generator Loss: 1.3303
D(x): 0.7634, D(G(z)): 0.3074
Epoch: [2/20], Batch Num: [359/600]
Discriminator Loss: 0.8944, Generator Loss: 1.6390
D(x): 0.7081, D(G(z)): 0.3568
Epoch: [2/20], Batch Num: [360/600]
Discriminator Loss: 0.8816, Generator Loss: 1.3648
D(x): 0.6598, D(G(z)): 0.2936
Epoch: [2/20], Batch Num: [361/600]
Discriminator Loss: 0.7575, Generator Loss: 1.3520
D(x): 0.7321, D(G(z)): 0.3032
Epoch: [2/20], Batch Num: [362/600]
Discriminator Loss: 0.7845, Generator Loss: 1.3834
D(x): 0.7500, D(G(z)): 0.3344
Epoch: [2/20], Batch Num: [363/600]
Discriminator Loss: 0.7552, Generator Loss: 1.4128
D(x): 0.7621, D(G(z)): 0.3321
Epoch: [2/20], Batch Num: [364/600]
Discriminator Loss: 0.7800, Generator Loss: 1.4548
D(x): 0.7593, D(G(z)): 0.3331
Epoch: [2/20], Batch Num: [365/600]
Discriminator Loss: 0.8266, Generator Loss: 1.5008
D(x): 0.7342, D(G(z)): 0.3322
Epoch: [2/20], Batch Num: [366/600]
Discriminator Loss: 0.8882, Generator Loss: 1.5368
D(x): 0.7439, D(G(z)): 0.3833
Epoch: [2/20], Batch Num: [367/600]
Discriminator Loss: 0.8342, Generator Loss: 1.5107
D(x): 0.7332, D(G(z)): 0.3467
Epoch: [2/20], Batch Num: [368/600]
Discriminator Loss: 0.7611, Generator Loss: 1.4657
D(x): 0.7538, D(G(z)): 0.3224
Epoch: [2/20], Batch Num: [369/600]
Discriminator Loss: 0.7071, Generator Loss: 1.4951
D(x): 0.7676, D(G(z)): 0.3113
Epoch: [2/20], Batch Num: [370/600]
Discriminator Loss: 0.8656, Generator Loss: 1.5833
D(x): 0.6902, D(G(z)): 0.3022
Epoch: [2/20], Batch Num: [371/600]
Discriminator Loss: 1.0097, Generator Loss: 1.2396
D(x): 0.6450, D(G(z)): 0.3185
Epoch: [2/20], Batch Num: [372/600]
Discriminator Loss: 0.8337, Generator Loss: 1.1628
D(x): 0.7557, D(G(z)): 0.3475
Epoch: [2/20], Batch Num: [373/600]
Discriminator Loss: 0.8875, Generator Loss: 1.2544
D(x): 0.7712, D(G(z)): 0.4021
Epoch: [2/20], Batch Num: [374/600]
Discriminator Loss: 0.8489, Generator Loss: 1.1858
D(x): 0.7846, D(G(z)): 0.4038
Epoch: [2/20], Batch Num: [375/600]
Discriminator Loss: 0.8875, Generator Loss: 1.4198
D(x): 0.7858, D(G(z)): 0.4200
Epoch: [2/20], Batch Num: [376/600]
Discriminator Loss: 0.8043, Generator Loss: 1.4791
D(x): 0.7643, D(G(z)): 0.3528
Epoch: [2/20], Batch Num: [377/600]
Discriminator Loss: 0.9316, Generator Loss: 1.6865
D(x): 0.6721, D(G(z)): 0.3234
Epoch: [2/20], Batch Num: [378/600]
Discriminator Loss: 0.9630, Generator Loss: 1.5169
D(x): 0.6716, D(G(z)): 0.3036
Epoch: [2/20], Batch Num: [379/600]
Discriminator Loss: 0.8930, Generator Loss: 1.3235
D(x): 0.6777, D(G(z)): 0.3014
Epoch: [2/20], Batch Num: [380/600]
Discriminator Loss: 0.9016, Generator Loss: 1.0703
D(x): 0.7598, D(G(z)): 0.3975
Epoch: [2/20], Batch Num: [381/600]
Discriminator Loss: 0.9102, Generator Loss: 1.1593
D(x): 0.7563, D(G(z)): 0.3970
Epoch: [2/20], Batch Num: [382/600]
Discriminator Loss: 0.9498, Generator Loss: 1.2211
D(x): 0.7526, D(G(z)): 0.4190
Epoch: [2/20], Batch Num: [383/600]
Discriminator Loss: 0.9739, Generator Loss: 1.2495
D(x): 0.7704, D(G(z)): 0.4258
Epoch: [2/20], Batch Num: [384/600]
Discriminator Loss: 0.9893, Generator Loss: 1.4269
D(x): 0.7426, D(G(z)): 0.4271
Epoch: [2/20], Batch Num: [385/600]
Discriminator Loss: 1.0256, Generator Loss: 1.5944
D(x): 0.6806, D(G(z)): 0.3753
Epoch: [2/20], Batch Num: [386/600]
Discriminator Loss: 1.1032, Generator Loss: 1.3649
D(x): 0.6389, D(G(z)): 0.3645
Epoch: [2/20], Batch Num: [387/600]
Discriminator Loss: 1.0521, Generator Loss: 1.3265
D(x): 0.6417, D(G(z)): 0.3478
Epoch: [2/20], Batch Num: [388/600]
Discriminator Loss: 1.0501, Generator Loss: 1.0461
D(x): 0.7251, D(G(z)): 0.4344
Epoch: [2/20], Batch Num: [389/600]
Discriminator Loss: 1.1246, Generator Loss: 1.0725
D(x): 0.7239, D(G(z)): 0.4774
Epoch: [2/20], Batch Num: [390/600]
Discriminator Loss: 1.0343, Generator Loss: 1.0959
D(x): 0.7631, D(G(z)): 0.4726
Epoch: [2/20], Batch Num: [391/600]
Discriminator Loss: 1.1670, Generator Loss: 1.1215
D(x): 0.7113, D(G(z)): 0.4645
Epoch: [2/20], Batch Num: [392/600]
Discriminator Loss: 1.0551, Generator Loss: 1.2334
D(x): 0.7175, D(G(z)): 0.4162
Epoch: [2/20], Batch Num: [393/600]
Discriminator Loss: 1.0953, Generator Loss: 1.1827
D(x): 0.6931, D(G(z)): 0.4138
Epoch: [2/20], Batch Num: [394/600]
Discriminator Loss: 1.0505, Generator Loss: 1.2023
D(x): 0.6690, D(G(z)): 0.3707
Epoch: [2/20], Batch Num: [395/600]
Discriminator Loss: 1.2225, Generator Loss: 0.9646
D(x): 0.6302, D(G(z)): 0.4247
Epoch: [2/20], Batch Num: [396/600]
Discriminator Loss: 1.3443, Generator Loss: 0.9494
D(x): 0.6786, D(G(z)): 0.5124
Epoch: [2/20], Batch Num: [397/600]
Discriminator Loss: 1.2014, Generator Loss: 0.8476
D(x): 0.7258, D(G(z)): 0.5076
Epoch: [2/20], Batch Num: [398/600]
Discriminator Loss: 1.0869, Generator Loss: 0.9510
D(x): 0.7917, D(G(z)): 0.5165
Epoch: [2/20], Batch Num: [399/600]
Discriminator Loss: 1.0697, Generator Loss: 1.0097
D(x): 0.7359, D(G(z)): 0.4740
Epoch: 2, Batch Num: [400/600]

Epoch: [2/20], Batch Num: [400/600]
Discriminator Loss: 1.1870, Generator Loss: 1.0130
D(x): 0.7014, D(G(z)): 0.4730
Epoch: [2/20], Batch Num: [401/600]
Discriminator Loss: 1.0862, Generator Loss: 1.0395
D(x): 0.7126, D(G(z)): 0.4604
Epoch: [2/20], Batch Num: [402/600]
Discriminator Loss: 1.0733, Generator Loss: 1.0099
D(x): 0.6953, D(G(z)): 0.4273
Epoch: [2/20], Batch Num: [403/600]
Discriminator Loss: 1.1475, Generator Loss: 0.9640
D(x): 0.6432, D(G(z)): 0.4067
Epoch: [2/20], Batch Num: [404/600]
Discriminator Loss: 1.1758, Generator Loss: 0.9123
D(x): 0.6790, D(G(z)): 0.4564
Epoch: [2/20], Batch Num: [405/600]
Discriminator Loss: 1.0537, Generator Loss: 0.9130
D(x): 0.7410, D(G(z)): 0.4612
Epoch: [2/20], Batch Num: [406/600]
Discriminator Loss: 1.0562, Generator Loss: 0.8211
D(x): 0.7782, D(G(z)): 0.4989
Epoch: [2/20], Batch Num: [407/600]
Discriminator Loss: 1.0561, Generator Loss: 0.8825
D(x): 0.7747, D(G(z)): 0.5035
Epoch: [2/20], Batch Num: [408/600]
Discriminator Loss: 0.9464, Generator Loss: 1.0675
D(x): 0.7582, D(G(z)): 0.4332
Epoch: [2/20], Batch Num: [409/600]
Discriminator Loss: 0.9676, Generator Loss: 1.1693
D(x): 0.7408, D(G(z)): 0.4168
Epoch: [2/20], Batch Num: [410/600]
Discriminator Loss: 0.9095, Generator Loss: 1.1945
D(x): 0.7015, D(G(z)): 0.3743
Epoch: [2/20], Batch Num: [411/600]
Discriminator Loss: 0.8520, Generator Loss: 1.1568
D(x): 0.7443, D(G(z)): 0.3774
Epoch: [2/20], Batch Num: [412/600]
Discriminator Loss: 0.8929, Generator Loss: 1.1204
D(x): 0.7418, D(G(z)): 0.3910
Epoch: [2/20], Batch Num: [413/600]
Discriminator Loss: 0.7839, Generator Loss: 1.1739
D(x): 0.7988, D(G(z)): 0.3955
Epoch: [2/20], Batch Num: [414/600]
Discriminator Loss: 0.8398, Generator Loss: 1.1236
D(x): 0.7730, D(G(z)): 0.3878
Epoch: [2/20], Batch Num: [415/600]
Discriminator Loss: 0.8134, Generator Loss: 1.2126
D(x): 0.7590, D(G(z)): 0.3545
Epoch: [2/20], Batch Num: [416/600]
Discriminator Loss: 0.7417, Generator Loss: 1.1797
D(x): 0.7994, D(G(z)): 0.3672
Epoch: [2/20], Batch Num: [417/600]
Discriminator Loss: 0.7718, Generator Loss: 1.1481
D(x): 0.7903, D(G(z)): 0.3593
Epoch: [2/20], Batch Num: [418/600]
Discriminator Loss: 0.7904, Generator Loss: 1.3022
D(x): 0.7861, D(G(z)): 0.3706
Epoch: [2/20], Batch Num: [419/600]
Discriminator Loss: 0.6842, Generator Loss: 1.3966
D(x): 0.8403, D(G(z)): 0.3565
Epoch: [2/20], Batch Num: [420/600]
Discriminator Loss: 0.5685, Generator Loss: 1.3694
D(x): 0.8311, D(G(z)): 0.2865
Epoch: [2/20], Batch Num: [421/600]
Discriminator Loss: 0.5527, Generator Loss: 1.5730
D(x): 0.8301, D(G(z)): 0.2752
Epoch: [2/20], Batch Num: [422/600]
Discriminator Loss: 0.7201, Generator Loss: 1.5960
D(x): 0.7574, D(G(z)): 0.2860
Epoch: [2/20], Batch Num: [423/600]
Discriminator Loss: 0.6525, Generator Loss: 1.4804
D(x): 0.7910, D(G(z)): 0.2786
Epoch: [2/20], Batch Num: [424/600]
Discriminator Loss: 0.5707, Generator Loss: 1.6337
D(x): 0.8373, D(G(z)): 0.2791
Epoch: [2/20], Batch Num: [425/600]
Discriminator Loss: 0.6312, Generator Loss: 1.4971
D(x): 0.8501, D(G(z)): 0.3065
Epoch: [2/20], Batch Num: [426/600]
Discriminator Loss: 0.5209, Generator Loss: 1.7054
D(x): 0.8916, D(G(z)): 0.2995
Epoch: [2/20], Batch Num: [427/600]
Discriminator Loss: 0.5812, Generator Loss: 1.8716
D(x): 0.8312, D(G(z)): 0.2565
Epoch: [2/20], Batch Num: [428/600]
Discriminator Loss: 0.5586, Generator Loss: 1.8535
D(x): 0.8014, D(G(z)): 0.2325
Epoch: [2/20], Batch Num: [429/600]
Discriminator Loss: 0.5179, Generator Loss: 1.9004
D(x): 0.8184, D(G(z)): 0.2081
Epoch: [2/20], Batch Num: [430/600]
Discriminator Loss: 0.5927, Generator Loss: 1.9747
D(x): 0.8171, D(G(z)): 0.2260
Epoch: [2/20], Batch Num: [431/600]
Discriminator Loss: 0.4885, Generator Loss: 1.7781
D(x): 0.8691, D(G(z)): 0.2438
Epoch: [2/20], Batch Num: [432/600]
Discriminator Loss: 0.4435, Generator Loss: 1.9938
D(x): 0.8850, D(G(z)): 0.2318
Epoch: [2/20], Batch Num: [433/600]
Discriminator Loss: 0.3854, Generator Loss: 2.2409
D(x): 0.8610, D(G(z)): 0.1858
Epoch: [2/20], Batch Num: [434/600]
Discriminator Loss: 0.5204, Generator Loss: 2.3758
D(x): 0.8276, D(G(z)): 0.1910
Epoch: [2/20], Batch Num: [435/600]
Discriminator Loss: 0.4920, Generator Loss: 1.9747
D(x): 0.8137, D(G(z)): 0.1708
Epoch: [2/20], Batch Num: [436/600]
Discriminator Loss: 0.4148, Generator Loss: 2.0851
D(x): 0.8839, D(G(z)): 0.2079
Epoch: [2/20], Batch Num: [437/600]
Discriminator Loss: 0.4984, Generator Loss: 2.1033
D(x): 0.8760, D(G(z)): 0.2522
Epoch: [2/20], Batch Num: [438/600]
Discriminator Loss: 0.4068, Generator Loss: 2.3395
D(x): 0.8663, D(G(z)): 0.1860
Epoch: [2/20], Batch Num: [439/600]
Discriminator Loss: 0.4614, Generator Loss: 2.4611
D(x): 0.8327, D(G(z)): 0.1737
Epoch: [2/20], Batch Num: [440/600]
Discriminator Loss: 0.5672, Generator Loss: 2.2878
D(x): 0.8062, D(G(z)): 0.1946
Epoch: [2/20], Batch Num: [441/600]
Discriminator Loss: 0.5789, Generator Loss: 2.3076
D(x): 0.8432, D(G(z)): 0.2537
Epoch: [2/20], Batch Num: [442/600]
Discriminator Loss: 0.7085, Generator Loss: 2.5104
D(x): 0.7969, D(G(z)): 0.2542
Epoch: [2/20], Batch Num: [443/600]
Discriminator Loss: 0.7041, Generator Loss: 2.4165
D(x): 0.8003, D(G(z)): 0.2447
Epoch: [2/20], Batch Num: [444/600]
Discriminator Loss: 0.6892, Generator Loss: 2.3024
D(x): 0.7612, D(G(z)): 0.1918
Epoch: [2/20], Batch Num: [445/600]
Discriminator Loss: 0.8916, Generator Loss: 1.9941
D(x): 0.7465, D(G(z)): 0.2647
Epoch: [2/20], Batch Num: [446/600]
Discriminator Loss: 1.1299, Generator Loss: 1.8155
D(x): 0.7261, D(G(z)): 0.3668
Epoch: [2/20], Batch Num: [447/600]
Discriminator Loss: 1.1162, Generator Loss: 2.2858
D(x): 0.7539, D(G(z)): 0.3591
Epoch: [2/20], Batch Num: [448/600]
Discriminator Loss: 1.4029, Generator Loss: 1.9879
D(x): 0.6347, D(G(z)): 0.3361
Epoch: [2/20], Batch Num: [449/600]
Discriminator Loss: 1.4950, Generator Loss: 1.7651
D(x): 0.5810, D(G(z)): 0.3695
Epoch: [2/20], Batch Num: [450/600]
Discriminator Loss: 1.3670, Generator Loss: 1.5121
D(x): 0.6503, D(G(z)): 0.3869
Epoch: [2/20], Batch Num: [451/600]
Discriminator Loss: 1.5856, Generator Loss: 1.7284
D(x): 0.6428, D(G(z)): 0.4370
Epoch: [2/20], Batch Num: [452/600]
Discriminator Loss: 1.5138, Generator Loss: 1.7111
D(x): 0.5887, D(G(z)): 0.4180
Epoch: [2/20], Batch Num: [453/600]
Discriminator Loss: 1.7574, Generator Loss: 1.5359
D(x): 0.5231, D(G(z)): 0.3887
Epoch: [2/20], Batch Num: [454/600]
Discriminator Loss: 1.7171, Generator Loss: 1.5660
D(x): 0.5558, D(G(z)): 0.4504
Epoch: [2/20], Batch Num: [455/600]
Discriminator Loss: 2.2362, Generator Loss: 1.3267
D(x): 0.4625, D(G(z)): 0.4626
Epoch: [2/20], Batch Num: [456/600]
Discriminator Loss: 1.7129, Generator Loss: 1.4239
D(x): 0.5238, D(G(z)): 0.4458
Epoch: [2/20], Batch Num: [457/600]
Discriminator Loss: 1.9662, Generator Loss: 1.3488
D(x): 0.5154, D(G(z)): 0.4557
Epoch: [2/20], Batch Num: [458/600]
Discriminator Loss: 1.7513, Generator Loss: 1.6238
D(x): 0.5819, D(G(z)): 0.4913
Epoch: [2/20], Batch Num: [459/600]
Discriminator Loss: 1.6220, Generator Loss: 2.0302
D(x): 0.5452, D(G(z)): 0.4211
Epoch: [2/20], Batch Num: [460/600]
Discriminator Loss: 1.6476, Generator Loss: 1.6065
D(x): 0.4629, D(G(z)): 0.2926
Epoch: [2/20], Batch Num: [461/600]
Discriminator Loss: 1.2727, Generator Loss: 1.4864
D(x): 0.5910, D(G(z)): 0.3140
Epoch: [2/20], Batch Num: [462/600]
Discriminator Loss: 1.1292, Generator Loss: 1.4118
D(x): 0.6840, D(G(z)): 0.3772
Epoch: [2/20], Batch Num: [463/600]
Discriminator Loss: 1.1493, Generator Loss: 1.9496
D(x): 0.7159, D(G(z)): 0.4239
Epoch: [2/20], Batch Num: [464/600]
Discriminator Loss: 0.9893, Generator Loss: 2.2813
D(x): 0.6952, D(G(z)): 0.3100
Epoch: [2/20], Batch Num: [465/600]
Discriminator Loss: 0.9276, Generator Loss: 2.5962
D(x): 0.6344, D(G(z)): 0.1832
Epoch: [2/20], Batch Num: [466/600]
Discriminator Loss: 0.7483, Generator Loss: 2.6471
D(x): 0.6703, D(G(z)): 0.1664
Epoch: [2/20], Batch Num: [467/600]
Discriminator Loss: 0.6156, Generator Loss: 2.3805
D(x): 0.7360, D(G(z)): 0.1466
Epoch: [2/20], Batch Num: [468/600]
Discriminator Loss: 0.3931, Generator Loss: 2.2527
D(x): 0.8907, D(G(z)): 0.2000
Epoch: [2/20], Batch Num: [469/600]
Discriminator Loss: 0.3712, Generator Loss: 2.7253
D(x): 0.8940, D(G(z)): 0.1918
Epoch: [2/20], Batch Num: [470/600]
Discriminator Loss: 0.3337, Generator Loss: 3.4772
D(x): 0.8728, D(G(z)): 0.1341
Epoch: [2/20], Batch Num: [471/600]
Discriminator Loss: 0.2461, Generator Loss: 3.9060
D(x): 0.8889, D(G(z)): 0.0950
Epoch: [2/20], Batch Num: [472/600]
Discriminator Loss: 0.2926, Generator Loss: 4.3667
D(x): 0.8427, D(G(z)): 0.0603
Epoch: [2/20], Batch Num: [473/600]
Discriminator Loss: 0.1862, Generator Loss: 4.6651
D(x): 0.9085, D(G(z)): 0.0665
Epoch: [2/20], Batch Num: [474/600]
Discriminator Loss: 0.2467, Generator Loss: 4.5824
D(x): 0.8541, D(G(z)): 0.0371
Epoch: [2/20], Batch Num: [475/600]
Discriminator Loss: 0.1942, Generator Loss: 4.5876
D(x): 0.9041, D(G(z)): 0.0596
Epoch: [2/20], Batch Num: [476/600]
Discriminator Loss: 0.2352, Generator Loss: 4.2811
D(x): 0.9089, D(G(z)): 0.0770
Epoch: [2/20], Batch Num: [477/600]
Discriminator Loss: 0.2224, Generator Loss: 4.1087
D(x): 0.9305, D(G(z)): 0.0947
Epoch: [2/20], Batch Num: [478/600]
Discriminator Loss: 0.1813, Generator Loss: 4.1088
D(x): 0.9426, D(G(z)): 0.0891
Epoch: [2/20], Batch Num: [479/600]
Discriminator Loss: 0.2625, Generator Loss: 4.2616
D(x): 0.9419, D(G(z)): 0.1425
Epoch: [2/20], Batch Num: [480/600]
Discriminator Loss: 0.2183, Generator Loss: 4.7844
D(x): 0.9368, D(G(z)): 0.1064
Epoch: [2/20], Batch Num: [481/600]
Discriminator Loss: 0.3251, Generator Loss: 4.5340
D(x): 0.8478, D(G(z)): 0.0625
Epoch: [2/20], Batch Num: [482/600]
Discriminator Loss: 0.4811, Generator Loss: 3.4476
D(x): 0.7935, D(G(z)): 0.0906
Epoch: [2/20], Batch Num: [483/600]
Discriminator Loss: 0.3885, Generator Loss: 2.8447
D(x): 0.8540, D(G(z)): 0.1447
Epoch: [2/20], Batch Num: [484/600]
Discriminator Loss: 0.6408, Generator Loss: 2.9422
D(x): 0.8640, D(G(z)): 0.2802
Epoch: [2/20], Batch Num: [485/600]
Discriminator Loss: 0.6961, Generator Loss: 2.9475
D(x): 0.8475, D(G(z)): 0.2908
Epoch: [2/20], Batch Num: [486/600]
Discriminator Loss: 0.8693, Generator Loss: 3.0533
D(x): 0.7582, D(G(z)): 0.2369
Epoch: [2/20], Batch Num: [487/600]
Discriminator Loss: 0.7742, Generator Loss: 2.5942
D(x): 0.7010, D(G(z)): 0.1769
Epoch: [2/20], Batch Num: [488/600]
Discriminator Loss: 0.8951, Generator Loss: 1.7452
D(x): 0.7238, D(G(z)): 0.2378
Epoch: [2/20], Batch Num: [489/600]
Discriminator Loss: 1.2141, Generator Loss: 1.7526
D(x): 0.8274, D(G(z)): 0.5011
Epoch: [2/20], Batch Num: [490/600]
Discriminator Loss: 1.1615, Generator Loss: 2.1738
D(x): 0.7189, D(G(z)): 0.3887
Epoch: [2/20], Batch Num: [491/600]
Discriminator Loss: 1.5318, Generator Loss: 2.1772
D(x): 0.6127, D(G(z)): 0.3428
Epoch: [2/20], Batch Num: [492/600]
Discriminator Loss: 1.6492, Generator Loss: 1.5787
D(x): 0.5412, D(G(z)): 0.3562
Epoch: [2/20], Batch Num: [493/600]
Discriminator Loss: 1.5099, Generator Loss: 1.1380
D(x): 0.6396, D(G(z)): 0.4524
Epoch: [2/20], Batch Num: [494/600]
Discriminator Loss: 1.7517, Generator Loss: 1.0239
D(x): 0.7249, D(G(z)): 0.5484
Epoch: [2/20], Batch Num: [495/600]
Discriminator Loss: 1.5005, Generator Loss: 1.7590
D(x): 0.7485, D(G(z)): 0.5121
Epoch: [2/20], Batch Num: [496/600]
Discriminator Loss: 1.5942, Generator Loss: 2.0729
D(x): 0.5862, D(G(z)): 0.4353
Epoch: [2/20], Batch Num: [497/600]
Discriminator Loss: 1.6100, Generator Loss: 1.8254
D(x): 0.5774, D(G(z)): 0.3483
Epoch: [2/20], Batch Num: [498/600]
Discriminator Loss: 1.5286, Generator Loss: 1.3242
D(x): 0.5734, D(G(z)): 0.3923
Epoch: [2/20], Batch Num: [499/600]
Discriminator Loss: 1.5298, Generator Loss: 1.1042
D(x): 0.7326, D(G(z)): 0.5223
Epoch: 2, Batch Num: [500/600]

Epoch: [2/20], Batch Num: [500/600]
Discriminator Loss: 1.5758, Generator Loss: 1.1615
D(x): 0.7716, D(G(z)): 0.5983
Epoch: [2/20], Batch Num: [501/600]
Discriminator Loss: 1.2731, Generator Loss: 1.5648
D(x): 0.7611, D(G(z)): 0.5029
Epoch: [2/20], Batch Num: [502/600]
Discriminator Loss: 1.3638, Generator Loss: 1.4750
D(x): 0.5938, D(G(z)): 0.3862
Epoch: [2/20], Batch Num: [503/600]
Discriminator Loss: 1.2590, Generator Loss: 1.4242
D(x): 0.5987, D(G(z)): 0.3291
Epoch: [2/20], Batch Num: [504/600]
Discriminator Loss: 1.3726, Generator Loss: 0.9464
D(x): 0.6124, D(G(z)): 0.4109
Epoch: [2/20], Batch Num: [505/600]
Discriminator Loss: 1.3263, Generator Loss: 0.8463
D(x): 0.7730, D(G(z)): 0.4999
Epoch: [2/20], Batch Num: [506/600]
Discriminator Loss: 1.2707, Generator Loss: 0.9819
D(x): 0.8287, D(G(z)): 0.5270
Epoch: [2/20], Batch Num: [507/600]
Discriminator Loss: 1.1285, Generator Loss: 1.3307
D(x): 0.7647, D(G(z)): 0.4519
Epoch: [2/20], Batch Num: [508/600]
Discriminator Loss: 1.2323, Generator Loss: 1.6300
D(x): 0.6909, D(G(z)): 0.4315
Epoch: [2/20], Batch Num: [509/600]
Discriminator Loss: 1.1411, Generator Loss: 1.6418
D(x): 0.6490, D(G(z)): 0.3702
Epoch: [2/20], Batch Num: [510/600]
Discriminator Loss: 1.4284, Generator Loss: 1.3500
D(x): 0.5831, D(G(z)): 0.3839
Epoch: [2/20], Batch Num: [511/600]
Discriminator Loss: 1.2982, Generator Loss: 1.1551
D(x): 0.5910, D(G(z)): 0.3885
Epoch: [2/20], Batch Num: [512/600]
Discriminator Loss: 1.1586, Generator Loss: 0.8841
D(x): 0.6935, D(G(z)): 0.4085
Epoch: [2/20], Batch Num: [513/600]
Discriminator Loss: 1.3448, Generator Loss: 0.7277
D(x): 0.7540, D(G(z)): 0.5516
Epoch: [2/20], Batch Num: [514/600]
Discriminator Loss: 1.1562, Generator Loss: 0.8884
D(x): 0.7760, D(G(z)): 0.5037
Epoch: [2/20], Batch Num: [515/600]
Discriminator Loss: 1.3345, Generator Loss: 1.2100
D(x): 0.7116, D(G(z)): 0.5251
Epoch: [2/20], Batch Num: [516/600]
Discriminator Loss: 1.2276, Generator Loss: 1.1549
D(x): 0.6503, D(G(z)): 0.4314
Epoch: [2/20], Batch Num: [517/600]
Discriminator Loss: 1.3051, Generator Loss: 1.2932
D(x): 0.5732, D(G(z)): 0.3802
Epoch: [2/20], Batch Num: [518/600]
Discriminator Loss: 1.1435, Generator Loss: 1.1412
D(x): 0.6687, D(G(z)): 0.4187
Epoch: [2/20], Batch Num: [519/600]
Discriminator Loss: 1.2668, Generator Loss: 1.0269
D(x): 0.6040, D(G(z)): 0.4299
Epoch: [2/20], Batch Num: [520/600]
Discriminator Loss: 1.4175, Generator Loss: 0.8408
D(x): 0.6501, D(G(z)): 0.4994
Epoch: [2/20], Batch Num: [521/600]
Discriminator Loss: 1.3148, Generator Loss: 0.8340
D(x): 0.6828, D(G(z)): 0.5155
Epoch: [2/20], Batch Num: [522/600]
Discriminator Loss: 1.2081, Generator Loss: 0.7988
D(x): 0.6972, D(G(z)): 0.5006
Epoch: [2/20], Batch Num: [523/600]
Discriminator Loss: 1.2871, Generator Loss: 0.7856
D(x): 0.6774, D(G(z)): 0.5254
Epoch: [2/20], Batch Num: [524/600]
Discriminator Loss: 1.2701, Generator Loss: 0.8995
D(x): 0.6878, D(G(z)): 0.5226
Epoch: [2/20], Batch Num: [525/600]
Discriminator Loss: 1.1694, Generator Loss: 0.9912
D(x): 0.6542, D(G(z)): 0.4500
Epoch: [2/20], Batch Num: [526/600]
Discriminator Loss: 1.1287, Generator Loss: 1.1060
D(x): 0.6316, D(G(z)): 0.4155
Epoch: [2/20], Batch Num: [527/600]
Discriminator Loss: 1.1817, Generator Loss: 0.9921
D(x): 0.5923, D(G(z)): 0.4113
Epoch: [2/20], Batch Num: [528/600]
Discriminator Loss: 1.1478, Generator Loss: 0.9514
D(x): 0.5967, D(G(z)): 0.3778
Epoch: [2/20], Batch Num: [529/600]
Discriminator Loss: 1.0410, Generator Loss: 0.9128
D(x): 0.6696, D(G(z)): 0.4209
Epoch: [2/20], Batch Num: [530/600]
Discriminator Loss: 1.0378, Generator Loss: 1.0226
D(x): 0.6798, D(G(z)): 0.4333
Epoch: [2/20], Batch Num: [531/600]
Discriminator Loss: 0.9603, Generator Loss: 0.9646
D(x): 0.7328, D(G(z)): 0.4431
Epoch: [2/20], Batch Num: [532/600]
Discriminator Loss: 0.9393, Generator Loss: 1.0906
D(x): 0.7129, D(G(z)): 0.4032
Epoch: [2/20], Batch Num: [533/600]
Discriminator Loss: 0.8675, Generator Loss: 1.2049
D(x): 0.7332, D(G(z)): 0.3900
Epoch: [2/20], Batch Num: [534/600]
Discriminator Loss: 0.9574, Generator Loss: 1.2526
D(x): 0.6587, D(G(z)): 0.3659
Epoch: [2/20], Batch Num: [535/600]
Discriminator Loss: 0.7624, Generator Loss: 1.3450
D(x): 0.7210, D(G(z)): 0.3201
Epoch: [2/20], Batch Num: [536/600]
Discriminator Loss: 0.6752, Generator Loss: 1.4280
D(x): 0.7433, D(G(z)): 0.2831
Epoch: [2/20], Batch Num: [537/600]
Discriminator Loss: 0.7110, Generator Loss: 1.3652
D(x): 0.7407, D(G(z)): 0.3042
Epoch: [2/20], Batch Num: [538/600]
Discriminator Loss: 0.6394, Generator Loss: 1.4992
D(x): 0.7691, D(G(z)): 0.2799
Epoch: [2/20], Batch Num: [539/600]
Discriminator Loss: 0.5823, Generator Loss: 1.4923
D(x): 0.7851, D(G(z)): 0.2689
Epoch: [2/20], Batch Num: [540/600]
Discriminator Loss: 0.5353, Generator Loss: 1.6014
D(x): 0.8239, D(G(z)): 0.2711
Epoch: [2/20], Batch Num: [541/600]
Discriminator Loss: 0.4842, Generator Loss: 1.7752
D(x): 0.8269, D(G(z)): 0.2383
Epoch: [2/20], Batch Num: [542/600]
Discriminator Loss: 0.4667, Generator Loss: 1.8015
D(x): 0.8224, D(G(z)): 0.2201
Epoch: [2/20], Batch Num: [543/600]
Discriminator Loss: 0.4335, Generator Loss: 1.9926
D(x): 0.8204, D(G(z)): 0.1949
Epoch: [2/20], Batch Num: [544/600]
Discriminator Loss: 0.4282, Generator Loss: 2.1122
D(x): 0.8245, D(G(z)): 0.1862
Epoch: [2/20], Batch Num: [545/600]
Discriminator Loss: 0.4058, Generator Loss: 2.0505
D(x): 0.8186, D(G(z)): 0.1635
Epoch: [2/20], Batch Num: [546/600]
Discriminator Loss: 0.3141, Generator Loss: 2.1264
D(x): 0.8787, D(G(z)): 0.1564
Epoch: [2/20], Batch Num: [547/600]
Discriminator Loss: 0.3774, Generator Loss: 2.0828
D(x): 0.8466, D(G(z)): 0.1704
Epoch: [2/20], Batch Num: [548/600]
Discriminator Loss: 0.3512, Generator Loss: 2.0957
D(x): 0.8606, D(G(z)): 0.1618
Epoch: [2/20], Batch Num: [549/600]
Discriminator Loss: 0.3385, Generator Loss: 2.1022
D(x): 0.8822, D(G(z)): 0.1756
Epoch: [2/20], Batch Num: [550/600]
Discriminator Loss: 0.3434, Generator Loss: 1.9226
D(x): 0.8765, D(G(z)): 0.1703
Epoch: [2/20], Batch Num: [551/600]
Discriminator Loss: 0.3040, Generator Loss: 1.9776
D(x): 0.9088, D(G(z)): 0.1756
Epoch: [2/20], Batch Num: [552/600]
Discriminator Loss: 0.3710, Generator Loss: 1.8852
D(x): 0.8878, D(G(z)): 0.2008
Epoch: [2/20], Batch Num: [553/600]
Discriminator Loss: 0.3733, Generator Loss: 1.9512
D(x): 0.8623, D(G(z)): 0.1778
Epoch: [2/20], Batch Num: [554/600]
Discriminator Loss: 0.3979, Generator Loss: 1.8673
D(x): 0.8744, D(G(z)): 0.1997
Epoch: [2/20], Batch Num: [555/600]
Discriminator Loss: 0.4579, Generator Loss: 1.8822
D(x): 0.8753, D(G(z)): 0.2477
Epoch: [2/20], Batch Num: [556/600]
Discriminator Loss: 0.5175, Generator Loss: 1.8943
D(x): 0.8337, D(G(z)): 0.2429
Epoch: [2/20], Batch Num: [557/600]
Discriminator Loss: 0.5744, Generator Loss: 1.7107
D(x): 0.8236, D(G(z)): 0.2531
Epoch: [2/20], Batch Num: [558/600]
Discriminator Loss: 0.5649, Generator Loss: 1.6894
D(x): 0.8507, D(G(z)): 0.2871
Epoch: [2/20], Batch Num: [559/600]
Discriminator Loss: 0.6684, Generator Loss: 1.6256
D(x): 0.7802, D(G(z)): 0.2686
Epoch: [2/20], Batch Num: [560/600]
Discriminator Loss: 0.8516, Generator Loss: 1.5338
D(x): 0.7434, D(G(z)): 0.3195
Epoch: [2/20], Batch Num: [561/600]
Discriminator Loss: 0.9630, Generator Loss: 1.2576
D(x): 0.7553, D(G(z)): 0.3900
Epoch: [2/20], Batch Num: [562/600]
Discriminator Loss: 1.2145, Generator Loss: 1.2807
D(x): 0.6971, D(G(z)): 0.4388
Epoch: [2/20], Batch Num: [563/600]
Discriminator Loss: 1.4317, Generator Loss: 1.0001
D(x): 0.6548, D(G(z)): 0.4651
Epoch: [2/20], Batch Num: [564/600]
Discriminator Loss: 1.6550, Generator Loss: 0.9721
D(x): 0.5644, D(G(z)): 0.4877
Epoch: [2/20], Batch Num: [565/600]
Discriminator Loss: 1.9686, Generator Loss: 0.8175
D(x): 0.5933, D(G(z)): 0.6124
Epoch: [2/20], Batch Num: [566/600]
Discriminator Loss: 2.1011, Generator Loss: 0.7103
D(x): 0.4694, D(G(z)): 0.5572
Epoch: [2/20], Batch Num: [567/600]
Discriminator Loss: 2.5155, Generator Loss: 0.4803
D(x): 0.5147, D(G(z)): 0.6753
Epoch: [2/20], Batch Num: [568/600]
Discriminator Loss: 2.7921, Generator Loss: 0.4487
D(x): 0.4891, D(G(z)): 0.7361
Epoch: [2/20], Batch Num: [569/600]
Discriminator Loss: 3.0945, Generator Loss: 0.3742
D(x): 0.4281, D(G(z)): 0.7599
Epoch: [2/20], Batch Num: [570/600]
Discriminator Loss: 3.5556, Generator Loss: 0.3049
D(x): 0.3321, D(G(z)): 0.7772
Epoch: [2/20], Batch Num: [571/600]
Discriminator Loss: 3.5802, Generator Loss: 0.2839
D(x): 0.3454, D(G(z)): 0.8060
Epoch: [2/20], Batch Num: [572/600]
Discriminator Loss: 3.4813, Generator Loss: 0.2574
D(x): 0.3812, D(G(z)): 0.8306
Epoch: [2/20], Batch Num: [573/600]
Discriminator Loss: 3.5452, Generator Loss: 0.2225
D(x): 0.3975, D(G(z)): 0.8587
Epoch: [2/20], Batch Num: [574/600]
Discriminator Loss: 3.4656, Generator Loss: 0.2086
D(x): 0.4238, D(G(z)): 0.8533
Epoch: [2/20], Batch Num: [575/600]
Discriminator Loss: 3.0563, Generator Loss: 0.2396
D(x): 0.4917, D(G(z)): 0.8542
Epoch: [2/20], Batch Num: [576/600]
Discriminator Loss: 3.1137, Generator Loss: 0.2615
D(x): 0.4521, D(G(z)): 0.8479
Epoch: [2/20], Batch Num: [577/600]
Discriminator Loss: 2.8796, Generator Loss: 0.2981
D(x): 0.4354, D(G(z)): 0.8213
Epoch: [2/20], Batch Num: [578/600]
Discriminator Loss: 2.8211, Generator Loss: 0.3173
D(x): 0.4020, D(G(z)): 0.7966
Epoch: [2/20], Batch Num: [579/600]
Discriminator Loss: 2.5940, Generator Loss: 0.3585
D(x): 0.3856, D(G(z)): 0.7399
Epoch: [2/20], Batch Num: [580/600]
Discriminator Loss: 2.6864, Generator Loss: 0.3934
D(x): 0.3464, D(G(z)): 0.7445
Epoch: [2/20], Batch Num: [581/600]
Discriminator Loss: 2.4583, Generator Loss: 0.4341
D(x): 0.3892, D(G(z)): 0.7298
Epoch: [2/20], Batch Num: [582/600]
Discriminator Loss: 2.2958, Generator Loss: 0.4437
D(x): 0.3899, D(G(z)): 0.6889
Epoch: [2/20], Batch Num: [583/600]
Discriminator Loss: 2.1089, Generator Loss: 0.4767
D(x): 0.3994, D(G(z)): 0.6564
Epoch: [2/20], Batch Num: [584/600]
Discriminator Loss: 2.0286, Generator Loss: 0.5322
D(x): 0.3901, D(G(z)): 0.6260
Epoch: [2/20], Batch Num: [585/600]
Discriminator Loss: 1.9939, Generator Loss: 0.5164
D(x): 0.4064, D(G(z)): 0.6308
Epoch: [2/20], Batch Num: [586/600]
Discriminator Loss: 1.9026, Generator Loss: 0.5162
D(x): 0.4256, D(G(z)): 0.6223
Epoch: [2/20], Batch Num: [587/600]
Discriminator Loss: 1.7600, Generator Loss: 0.5475
D(x): 0.4582, D(G(z)): 0.6011
Epoch: [2/20], Batch Num: [588/600]
Discriminator Loss: 1.6767, Generator Loss: 0.5430
D(x): 0.4836, D(G(z)): 0.5925
Epoch: [2/20], Batch Num: [589/600]
Discriminator Loss: 1.6703, Generator Loss: 0.5486
D(x): 0.4987, D(G(z)): 0.6037
Epoch: [2/20], Batch Num: [590/600]
Discriminator Loss: 1.5334, Generator Loss: 0.5456
D(x): 0.5354, D(G(z)): 0.5832
Epoch: [2/20], Batch Num: [591/600]
Discriminator Loss: 1.4836, Generator Loss: 0.5851
D(x): 0.5413, D(G(z)): 0.5664
Epoch: [2/20], Batch Num: [592/600]
Discriminator Loss: 1.4486, Generator Loss: 0.6049
D(x): 0.5528, D(G(z)): 0.5622
Epoch: [2/20], Batch Num: [593/600]
Discriminator Loss: 1.4284, Generator Loss: 0.5902
D(x): 0.5608, D(G(z)): 0.5625
Epoch: [2/20], Batch Num: [594/600]
Discriminator Loss: 1.3661, Generator Loss: 0.6446
D(x): 0.5713, D(G(z)): 0.5417
Epoch: [2/20], Batch Num: [595/600]
Discriminator Loss: 1.3383, Generator Loss: 0.6720
D(x): 0.5669, D(G(z)): 0.5280
Epoch: [2/20], Batch Num: [596/600]
Discriminator Loss: 1.3352, Generator Loss: 0.6967
D(x): 0.5761, D(G(z)): 0.5342
Epoch: [2/20], Batch Num: [597/600]
Discriminator Loss: 1.2331, Generator Loss: 0.7549
D(x): 0.5939, D(G(z)): 0.5009
Epoch: [2/20], Batch Num: [598/600]
Discriminator Loss: 1.1910, Generator Loss: 0.7900
D(x): 0.5766, D(G(z)): 0.4650
Epoch: [2/20], Batch Num: [599/600]
Discriminator Loss: 1.1565, Generator Loss: 0.8331
D(x): 0.5982, D(G(z)): 0.4651
Epoch: 3, Batch Num: [0/600]

Epoch: [3/20], Batch Num: [0/600]
Discriminator Loss: 1.1294, Generator Loss: 0.8685
D(x): 0.5903, D(G(z)): 0.4428
Epoch: [3/20], Batch Num: [1/600]
Discriminator Loss: 1.0655, Generator Loss: 0.9874
D(x): 0.5989, D(G(z)): 0.4139
Epoch: [3/20], Batch Num: [2/600]
Discriminator Loss: 1.0341, Generator Loss: 1.0876
D(x): 0.5986, D(G(z)): 0.3947
Epoch: [3/20], Batch Num: [3/600]
Discriminator Loss: 0.9795, Generator Loss: 1.1210
D(x): 0.6077, D(G(z)): 0.3696
Epoch: [3/20], Batch Num: [4/600]
Discriminator Loss: 0.9447, Generator Loss: 1.2584
D(x): 0.5993, D(G(z)): 0.3381
Epoch: [3/20], Batch Num: [5/600]
Discriminator Loss: 0.8746, Generator Loss: 1.3311
D(x): 0.6238, D(G(z)): 0.3177
Epoch: [3/20], Batch Num: [6/600]
Discriminator Loss: 0.8733, Generator Loss: 1.5905
D(x): 0.6060, D(G(z)): 0.2951
Epoch: [3/20], Batch Num: [7/600]
Discriminator Loss: 0.7741, Generator Loss: 1.6693
D(x): 0.6261, D(G(z)): 0.2502
Epoch: [3/20], Batch Num: [8/600]
Discriminator Loss: 0.7679, Generator Loss: 1.7163
D(x): 0.6245, D(G(z)): 0.2452
Epoch: [3/20], Batch Num: [9/600]
Discriminator Loss: 0.7573, Generator Loss: 1.9245
D(x): 0.6122, D(G(z)): 0.2188
Epoch: [3/20], Batch Num: [10/600]
Discriminator Loss: 0.7318, Generator Loss: 2.1972
D(x): 0.6135, D(G(z)): 0.2002
Epoch: [3/20], Batch Num: [11/600]
Discriminator Loss: 0.7211, Generator Loss: 2.2038
D(x): 0.6085, D(G(z)): 0.1826
Epoch: [3/20], Batch Num: [12/600]
Discriminator Loss: 0.7143, Generator Loss: 2.2826
D(x): 0.6167, D(G(z)): 0.1856
Epoch: [3/20], Batch Num: [13/600]
Discriminator Loss: 0.7123, Generator Loss: 2.0849
D(x): 0.6128, D(G(z)): 0.1769
Epoch: [3/20], Batch Num: [14/600]
Discriminator Loss: 0.7233, Generator Loss: 2.2976
D(x): 0.6051, D(G(z)): 0.1798
Epoch: [3/20], Batch Num: [15/600]
Discriminator Loss: 0.7210, Generator Loss: 2.0877
D(x): 0.6115, D(G(z)): 0.1835
Epoch: [3/20], Batch Num: [16/600]
Discriminator Loss: 0.7351, Generator Loss: 1.9035
D(x): 0.6098, D(G(z)): 0.1860
Epoch: [3/20], Batch Num: [17/600]
Discriminator Loss: 0.7373, Generator Loss: 1.9597
D(x): 0.6252, D(G(z)): 0.2133
Epoch: [3/20], Batch Num: [18/600]
Discriminator Loss: 0.7999, Generator Loss: 1.7046
D(x): 0.6042, D(G(z)): 0.2089
Epoch: [3/20], Batch Num: [19/600]
Discriminator Loss: 0.8095, Generator Loss: 1.5888
D(x): 0.6371, D(G(z)): 0.2665
Epoch: [3/20], Batch Num: [20/600]
Discriminator Loss: 0.8073, Generator Loss: 1.3806
D(x): 0.6499, D(G(z)): 0.2830
Epoch: [3/20], Batch Num: [21/600]
Discriminator Loss: 0.8546, Generator Loss: 1.3959
D(x): 0.6575, D(G(z)): 0.3157
Epoch: [3/20], Batch Num: [22/600]
Discriminator Loss: 0.8235, Generator Loss: 1.3814
D(x): 0.6865, D(G(z)): 0.3380
Epoch: [3/20], Batch Num: [23/600]
Discriminator Loss: 0.8587, Generator Loss: 1.3391
D(x): 0.6614, D(G(z)): 0.3297
Epoch: [3/20], Batch Num: [24/600]
Discriminator Loss: 0.9220, Generator Loss: 1.4201
D(x): 0.6451, D(G(z)): 0.3353
Epoch: [3/20], Batch Num: [25/600]
Discriminator Loss: 0.9819, Generator Loss: 1.4864
D(x): 0.5914, D(G(z)): 0.3005
Epoch: [3/20], Batch Num: [26/600]
Discriminator Loss: 1.0155, Generator Loss: 1.3032
D(x): 0.6070, D(G(z)): 0.3526
Epoch: [3/20], Batch Num: [27/600]
Discriminator Loss: 1.0625, Generator Loss: 1.1443
D(x): 0.6047, D(G(z)): 0.3517
Epoch: [3/20], Batch Num: [28/600]
Discriminator Loss: 1.1367, Generator Loss: 1.0261
D(x): 0.6195, D(G(z)): 0.4201
Epoch: [3/20], Batch Num: [29/600]
Discriminator Loss: 1.1332, Generator Loss: 0.8763
D(x): 0.6553, D(G(z)): 0.4645
Epoch: [3/20], Batch Num: [30/600]
Discriminator Loss: 1.1607, Generator Loss: 0.8438
D(x): 0.6510, D(G(z)): 0.4714
Epoch: [3/20], Batch Num: [31/600]
Discriminator Loss: 1.3018, Generator Loss: 0.7836
D(x): 0.6206, D(G(z)): 0.5010
Epoch: [3/20], Batch Num: [32/600]
Discriminator Loss: 1.3334, Generator Loss: 0.7034
D(x): 0.5854, D(G(z)): 0.4927
Epoch: [3/20], Batch Num: [33/600]
Discriminator Loss: 1.2988, Generator Loss: 0.7045
D(x): 0.6581, D(G(z)): 0.5280
Epoch: [3/20], Batch Num: [34/600]
Discriminator Loss: 1.4733, Generator Loss: 0.6905
D(x): 0.6102, D(G(z)): 0.5658
Epoch: [3/20], Batch Num: [35/600]
Discriminator Loss: 1.3748, Generator Loss: 0.7278
D(x): 0.6216, D(G(z)): 0.5383
Epoch: [3/20], Batch Num: [36/600]
Discriminator Loss: 1.4991, Generator Loss: 0.6290
D(x): 0.5645, D(G(z)): 0.5300
Epoch: [3/20], Batch Num: [37/600]
Discriminator Loss: 1.4640, Generator Loss: 0.6331
D(x): 0.5999, D(G(z)): 0.5522
Epoch: [3/20], Batch Num: [38/600]
Discriminator Loss: 1.5384, Generator Loss: 0.6515
D(x): 0.6083, D(G(z)): 0.5917
Epoch: [3/20], Batch Num: [39/600]
Discriminator Loss: 1.6137, Generator Loss: 0.6218
D(x): 0.5673, D(G(z)): 0.5853
Epoch: [3/20], Batch Num: [40/600]
Discriminator Loss: 1.7357, Generator Loss: 0.5718
D(x): 0.5172, D(G(z)): 0.6032
Epoch: [3/20], Batch Num: [41/600]
Discriminator Loss: 1.4950, Generator Loss: 0.5509
D(x): 0.6352, D(G(z)): 0.6050
Epoch: [3/20], Batch Num: [42/600]
Discriminator Loss: 1.4781, Generator Loss: 0.5476
D(x): 0.6433, D(G(z)): 0.5951
Epoch: [3/20], Batch Num: [43/600]
Discriminator Loss: 1.5675, Generator Loss: 0.6573
D(x): 0.5785, D(G(z)): 0.5922
Epoch: [3/20], Batch Num: [44/600]
Discriminator Loss: 1.4507, Generator Loss: 0.6780
D(x): 0.6060, D(G(z)): 0.5639
Epoch: [3/20], Batch Num: [45/600]
Discriminator Loss: 1.4635, Generator Loss: 0.6706
D(x): 0.5587, D(G(z)): 0.5335
Epoch: [3/20], Batch Num: [46/600]
Discriminator Loss: 1.5090, Generator Loss: 0.6954
D(x): 0.5365, D(G(z)): 0.5299
Epoch: [3/20], Batch Num: [47/600]
Discriminator Loss: 1.5303, Generator Loss: 0.6492
D(x): 0.5085, D(G(z)): 0.5224
Epoch: [3/20], Batch Num: [48/600]
Discriminator Loss: 1.4783, Generator Loss: 0.6516
D(x): 0.5437, D(G(z)): 0.5275
Epoch: [3/20], Batch Num: [49/600]
Discriminator Loss: 1.4255, Generator Loss: 0.6754
D(x): 0.5565, D(G(z)): 0.5192
Epoch: [3/20], Batch Num: [50/600]
Discriminator Loss: 1.5535, Generator Loss: 0.5911
D(x): 0.5451, D(G(z)): 0.5681
Epoch: [3/20], Batch Num: [51/600]
Discriminator Loss: 1.4256, Generator Loss: 0.6036
D(x): 0.6130, D(G(z)): 0.5783
Epoch: [3/20], Batch Num: [52/600]
Discriminator Loss: 1.4176, Generator Loss: 0.6371
D(x): 0.6140, D(G(z)): 0.5647
Epoch: [3/20], Batch Num: [53/600]
Discriminator Loss: 1.4965, Generator Loss: 0.7148
D(x): 0.5665, D(G(z)): 0.5618
Epoch: [3/20], Batch Num: [54/600]
Discriminator Loss: 1.3829, Generator Loss: 0.6816
D(x): 0.5659, D(G(z)): 0.5090
Epoch: [3/20], Batch Num: [55/600]
Discriminator Loss: 1.4490, Generator Loss: 0.7461
D(x): 0.5546, D(G(z)): 0.5298
Epoch: [3/20], Batch Num: [56/600]
Discriminator Loss: 1.3772, Generator Loss: 0.8246
D(x): 0.5623, D(G(z)): 0.5112
Epoch: [3/20], Batch Num: [57/600]
Discriminator Loss: 1.3960, Generator Loss: 0.7903
D(x): 0.5523, D(G(z)): 0.5018
Epoch: [3/20], Batch Num: [58/600]
Discriminator Loss: 1.4295, Generator Loss: 0.7671
D(x): 0.5043, D(G(z)): 0.4860
Epoch: [3/20], Batch Num: [59/600]
Discriminator Loss: 1.3779, Generator Loss: 0.7566
D(x): 0.5500, D(G(z)): 0.4897
Epoch: [3/20], Batch Num: [60/600]
Discriminator Loss: 1.3717, Generator Loss: 0.7200
D(x): 0.5613, D(G(z)): 0.5107
Epoch: [3/20], Batch Num: [61/600]
Discriminator Loss: 1.2657, Generator Loss: 0.7243
D(x): 0.5888, D(G(z)): 0.4848
Epoch: [3/20], Batch Num: [62/600]
Discriminator Loss: 1.3277, Generator Loss: 0.6959
D(x): 0.5791, D(G(z)): 0.5069
Epoch: [3/20], Batch Num: [63/600]
Discriminator Loss: 1.3297, Generator Loss: 0.7015
D(x): 0.5953, D(G(z)): 0.5171
Epoch: [3/20], Batch Num: [64/600]
Discriminator Loss: 1.2859, Generator Loss: 0.7514
D(x): 0.6101, D(G(z)): 0.5125
Epoch: [3/20], Batch Num: [65/600]
Discriminator Loss: 1.2384, Generator Loss: 0.7534
D(x): 0.6218, D(G(z)): 0.4962
Epoch: [3/20], Batch Num: [66/600]
Discriminator Loss: 1.2077, Generator Loss: 0.8452
D(x): 0.6083, D(G(z)): 0.4699
Epoch: [3/20], Batch Num: [67/600]
Discriminator Loss: 1.2693, Generator Loss: 0.8390
D(x): 0.5762, D(G(z)): 0.4766
Epoch: [3/20], Batch Num: [68/600]
Discriminator Loss: 1.2364, Generator Loss: 0.8839
D(x): 0.5640, D(G(z)): 0.4422
Epoch: [3/20], Batch Num: [69/600]
Discriminator Loss: 1.2357, Generator Loss: 0.8349
D(x): 0.5685, D(G(z)): 0.4455
Epoch: [3/20], Batch Num: [70/600]
Discriminator Loss: 1.3003, Generator Loss: 0.9294
D(x): 0.5518, D(G(z)): 0.4480
Epoch: [3/20], Batch Num: [71/600]
Discriminator Loss: 1.2776, Generator Loss: 0.9067
D(x): 0.5523, D(G(z)): 0.4449
Epoch: [3/20], Batch Num: [72/600]
Discriminator Loss: 1.1396, Generator Loss: 0.8303
D(x): 0.6073, D(G(z)): 0.4381
Epoch: [3/20], Batch Num: [73/600]
Discriminator Loss: 1.2809, Generator Loss: 0.8382
D(x): 0.5456, D(G(z)): 0.4539
Epoch: [3/20], Batch Num: [74/600]
Discriminator Loss: 1.2018, Generator Loss: 0.8098
D(x): 0.5908, D(G(z)): 0.4489
Epoch: [3/20], Batch Num: [75/600]
Discriminator Loss: 1.2481, Generator Loss: 0.8465
D(x): 0.6064, D(G(z)): 0.4870
Epoch: [3/20], Batch Num: [76/600]
Discriminator Loss: 1.2208, Generator Loss: 0.8673
D(x): 0.6004, D(G(z)): 0.4727
Epoch: [3/20], Batch Num: [77/600]
Discriminator Loss: 1.2449, Generator Loss: 0.7820
D(x): 0.5772, D(G(z)): 0.4626
Epoch: [3/20], Batch Num: [78/600]
Discriminator Loss: 1.2136, Generator Loss: 0.8706
D(x): 0.5997, D(G(z)): 0.4646
Epoch: [3/20], Batch Num: [79/600]
Discriminator Loss: 1.2029, Generator Loss: 0.8421
D(x): 0.5953, D(G(z)): 0.4557
Epoch: [3/20], Batch Num: [80/600]
Discriminator Loss: 1.1941, Generator Loss: 0.8964
D(x): 0.5985, D(G(z)): 0.4598
Epoch: [3/20], Batch Num: [81/600]
Discriminator Loss: 1.1610, Generator Loss: 0.8643
D(x): 0.5835, D(G(z)): 0.4284
Epoch: [3/20], Batch Num: [82/600]
Discriminator Loss: 1.1106, Generator Loss: 0.8933
D(x): 0.6302, D(G(z)): 0.4420
Epoch: [3/20], Batch Num: [83/600]
Discriminator Loss: 1.2641, Generator Loss: 0.8621
D(x): 0.5744, D(G(z)): 0.4602
Epoch: [3/20], Batch Num: [84/600]
Discriminator Loss: 1.1757, Generator Loss: 0.8617
D(x): 0.6074, D(G(z)): 0.4539
Epoch: [3/20], Batch Num: [85/600]
Discriminator Loss: 1.2258, Generator Loss: 0.8763
D(x): 0.6050, D(G(z)): 0.4701
Epoch: [3/20], Batch Num: [86/600]
Discriminator Loss: 1.1471, Generator Loss: 0.8635
D(x): 0.6280, D(G(z)): 0.4605
Epoch: [3/20], Batch Num: [87/600]
Discriminator Loss: 1.1479, Generator Loss: 0.8778
D(x): 0.5856, D(G(z)): 0.4184
Epoch: [3/20], Batch Num: [88/600]
Discriminator Loss: 1.1451, Generator Loss: 0.8561
D(x): 0.6000, D(G(z)): 0.4308
Epoch: [3/20], Batch Num: [89/600]
Discriminator Loss: 1.0911, Generator Loss: 0.8506
D(x): 0.6303, D(G(z)): 0.4325
Epoch: [3/20], Batch Num: [90/600]
Discriminator Loss: 1.1330, Generator Loss: 0.8877
D(x): 0.6170, D(G(z)): 0.4421
Epoch: [3/20], Batch Num: [91/600]
Discriminator Loss: 1.1972, Generator Loss: 0.8686
D(x): 0.5823, D(G(z)): 0.4385
Epoch: [3/20], Batch Num: [92/600]
Discriminator Loss: 1.2307, Generator Loss: 0.8737
D(x): 0.6080, D(G(z)): 0.4747
Epoch: [3/20], Batch Num: [93/600]
Discriminator Loss: 1.1034, Generator Loss: 0.8701
D(x): 0.6458, D(G(z)): 0.4554
Epoch: [3/20], Batch Num: [94/600]
Discriminator Loss: 1.1251, Generator Loss: 0.8543
D(x): 0.6275, D(G(z)): 0.4450
Epoch: [3/20], Batch Num: [95/600]
Discriminator Loss: 1.1491, Generator Loss: 0.8574
D(x): 0.6166, D(G(z)): 0.4441
Epoch: [3/20], Batch Num: [96/600]
Discriminator Loss: 1.2001, Generator Loss: 0.8123
D(x): 0.5947, D(G(z)): 0.4546
Epoch: [3/20], Batch Num: [97/600]
Discriminator Loss: 1.0962, Generator Loss: 0.8568
D(x): 0.6307, D(G(z)): 0.4358
Epoch: [3/20], Batch Num: [98/600]
Discriminator Loss: 1.1567, Generator Loss: 0.8781
D(x): 0.6090, D(G(z)): 0.4429
Epoch: [3/20], Batch Num: [99/600]
Discriminator Loss: 1.1859, Generator Loss: 0.8543
D(x): 0.5899, D(G(z)): 0.4404
Epoch: 3, Batch Num: [100/600]

Epoch: [3/20], Batch Num: [100/600]
Discriminator Loss: 1.1400, Generator Loss: 0.8804
D(x): 0.6210, D(G(z)): 0.4492
Epoch: [3/20], Batch Num: [101/600]
Discriminator Loss: 1.2250, Generator Loss: 0.8623
D(x): 0.5832, D(G(z)): 0.4471
Epoch: [3/20], Batch Num: [102/600]
Discriminator Loss: 1.1010, Generator Loss: 0.8727
D(x): 0.6369, D(G(z)): 0.4403
Epoch: [3/20], Batch Num: [103/600]
Discriminator Loss: 1.1231, Generator Loss: 0.9387
D(x): 0.6284, D(G(z)): 0.4432
Epoch: [3/20], Batch Num: [104/600]
Discriminator Loss: 1.1692, Generator Loss: 0.8798
D(x): 0.6039, D(G(z)): 0.4401
Epoch: [3/20], Batch Num: [105/600]
Discriminator Loss: 1.1362, Generator Loss: 0.9332
D(x): 0.6048, D(G(z)): 0.4306
Epoch: [3/20], Batch Num: [106/600]
Discriminator Loss: 1.1469, Generator Loss: 0.8596
D(x): 0.6200, D(G(z)): 0.4489
Epoch: [3/20], Batch Num: [107/600]
Discriminator Loss: 1.1521, Generator Loss: 0.9016
D(x): 0.6201, D(G(z)): 0.4440
Epoch: [3/20], Batch Num: [108/600]
Discriminator Loss: 1.1872, Generator Loss: 0.9055
D(x): 0.6093, D(G(z)): 0.4345
Epoch: [3/20], Batch Num: [109/600]
Discriminator Loss: 1.1610, Generator Loss: 0.8832
D(x): 0.6015, D(G(z)): 0.4379
Epoch: [3/20], Batch Num: [110/600]
Discriminator Loss: 1.2614, Generator Loss: 0.8220
D(x): 0.5784, D(G(z)): 0.4447
Epoch: [3/20], Batch Num: [111/600]
Discriminator Loss: 1.1482, Generator Loss: 0.8607
D(x): 0.6229, D(G(z)): 0.4523
Epoch: [3/20], Batch Num: [112/600]
Discriminator Loss: 1.1283, Generator Loss: 0.8425
D(x): 0.6381, D(G(z)): 0.4470
Epoch: [3/20], Batch Num: [113/600]
Discriminator Loss: 1.1604, Generator Loss: 0.8257
D(x): 0.6311, D(G(z)): 0.4604
Epoch: [3/20], Batch Num: [114/600]
Discriminator Loss: 1.1912, Generator Loss: 0.8414
D(x): 0.6309, D(G(z)): 0.4591
Epoch: [3/20], Batch Num: [115/600]
Discriminator Loss: 1.1527, Generator Loss: 0.8833
D(x): 0.6341, D(G(z)): 0.4454
Epoch: [3/20], Batch Num: [116/600]
Discriminator Loss: 1.1538, Generator Loss: 0.8934
D(x): 0.6235, D(G(z)): 0.4320
Epoch: [3/20], Batch Num: [117/600]
Discriminator Loss: 1.1625, Generator Loss: 0.9933
D(x): 0.6226, D(G(z)): 0.4379
Epoch: [3/20], Batch Num: [118/600]
Discriminator Loss: 1.1051, Generator Loss: 0.9949
D(x): 0.6343, D(G(z)): 0.4287
Epoch: [3/20], Batch Num: [119/600]
Discriminator Loss: 1.2444, Generator Loss: 0.9366
D(x): 0.5485, D(G(z)): 0.4173
Epoch: [3/20], Batch Num: [120/600]
Discriminator Loss: 1.0922, Generator Loss: 0.9371
D(x): 0.6290, D(G(z)): 0.4223
Epoch: [3/20], Batch Num: [121/600]
Discriminator Loss: 1.1184, Generator Loss: 0.8844
D(x): 0.6500, D(G(z)): 0.4431
Epoch: [3/20], Batch Num: [122/600]
Discriminator Loss: 1.1245, Generator Loss: 0.8719
D(x): 0.6393, D(G(z)): 0.4449
Epoch: [3/20], Batch Num: [123/600]
Discriminator Loss: 1.1147, Generator Loss: 0.8602
D(x): 0.6543, D(G(z)): 0.4436
Epoch: [3/20], Batch Num: [124/600]
Discriminator Loss: 1.1407, Generator Loss: 0.9234
D(x): 0.6173, D(G(z)): 0.4402
Epoch: [3/20], Batch Num: [125/600]
Discriminator Loss: 1.1542, Generator Loss: 0.9132
D(x): 0.6267, D(G(z)): 0.4434
Epoch: [3/20], Batch Num: [126/600]
Discriminator Loss: 1.1632, Generator Loss: 0.8972
D(x): 0.6188, D(G(z)): 0.4440
Epoch: [3/20], Batch Num: [127/600]
Discriminator Loss: 1.1526, Generator Loss: 0.9843
D(x): 0.6099, D(G(z)): 0.4208
Epoch: [3/20], Batch Num: [128/600]
Discriminator Loss: 1.0087, Generator Loss: 0.9761
D(x): 0.6437, D(G(z)): 0.3808
Epoch: [3/20], Batch Num: [129/600]
Discriminator Loss: 1.1140, Generator Loss: 0.9242
D(x): 0.6022, D(G(z)): 0.3985
Epoch: [3/20], Batch Num: [130/600]
Discriminator Loss: 1.0476, Generator Loss: 0.9422
D(x): 0.6767, D(G(z)): 0.4370
Epoch: [3/20], Batch Num: [131/600]
Discriminator Loss: 1.1043, Generator Loss: 0.9190
D(x): 0.6303, D(G(z)): 0.4115
Epoch: [3/20], Batch Num: [132/600]
Discriminator Loss: 1.0011, Generator Loss: 0.9810
D(x): 0.6690, D(G(z)): 0.4039
Epoch: [3/20], Batch Num: [133/600]
Discriminator Loss: 0.9269, Generator Loss: 1.0948
D(x): 0.6937, D(G(z)): 0.3834
Epoch: [3/20], Batch Num: [134/600]
Discriminator Loss: 0.9403, Generator Loss: 1.1935
D(x): 0.6703, D(G(z)): 0.3722
Epoch: [3/20], Batch Num: [135/600]
Discriminator Loss: 0.8407, Generator Loss: 1.1715
D(x): 0.6920, D(G(z)): 0.3266
Epoch: [3/20], Batch Num: [136/600]
Discriminator Loss: 0.8719, Generator Loss: 1.1629
D(x): 0.6728, D(G(z)): 0.3310
Epoch: [3/20], Batch Num: [137/600]
Discriminator Loss: 0.8764, Generator Loss: 1.2090
D(x): 0.6896, D(G(z)): 0.3531
Epoch: [3/20], Batch Num: [138/600]
Discriminator Loss: 0.7609, Generator Loss: 1.2965
D(x): 0.7457, D(G(z)): 0.3386
Epoch: [3/20], Batch Num: [139/600]
Discriminator Loss: 0.7198, Generator Loss: 1.3987
D(x): 0.7590, D(G(z)): 0.3299
Epoch: [3/20], Batch Num: [140/600]
Discriminator Loss: 0.7042, Generator Loss: 1.5380
D(x): 0.7467, D(G(z)): 0.2990
Epoch: [3/20], Batch Num: [141/600]
Discriminator Loss: 0.6820, Generator Loss: 1.5456
D(x): 0.7409, D(G(z)): 0.2855
Epoch: [3/20], Batch Num: [142/600]
Discriminator Loss: 0.6356, Generator Loss: 1.6326
D(x): 0.7478, D(G(z)): 0.2408
Epoch: [3/20], Batch Num: [143/600]
Discriminator Loss: 0.6063, Generator Loss: 1.7723
D(x): 0.7582, D(G(z)): 0.2455
Epoch: [3/20], Batch Num: [144/600]
Discriminator Loss: 0.5137, Generator Loss: 1.8356
D(x): 0.7941, D(G(z)): 0.2096
Epoch: [3/20], Batch Num: [145/600]
Discriminator Loss: 0.4495, Generator Loss: 1.8180
D(x): 0.8307, D(G(z)): 0.1994
Epoch: [3/20], Batch Num: [146/600]
Discriminator Loss: 0.4782, Generator Loss: 1.9803
D(x): 0.8173, D(G(z)): 0.2096
Epoch: [3/20], Batch Num: [147/600]
Discriminator Loss: 0.4910, Generator Loss: 1.9528
D(x): 0.8076, D(G(z)): 0.2024
Epoch: [3/20], Batch Num: [148/600]
Discriminator Loss: 0.4894, Generator Loss: 2.2011
D(x): 0.8059, D(G(z)): 0.2037
Epoch: [3/20], Batch Num: [149/600]
Discriminator Loss: 0.4574, Generator Loss: 2.3104
D(x): 0.8178, D(G(z)): 0.1991
Epoch: [3/20], Batch Num: [150/600]
Discriminator Loss: 0.4861, Generator Loss: 2.2539
D(x): 0.7913, D(G(z)): 0.1741
Epoch: [3/20], Batch Num: [151/600]
Discriminator Loss: 0.4994, Generator Loss: 2.2195
D(x): 0.8268, D(G(z)): 0.2250
Epoch: [3/20], Batch Num: [152/600]
Discriminator Loss: 0.5530, Generator Loss: 2.1220
D(x): 0.7609, D(G(z)): 0.1888
Epoch: [3/20], Batch Num: [153/600]
Discriminator Loss: 0.5011, Generator Loss: 1.8511
D(x): 0.8173, D(G(z)): 0.2183
Epoch: [3/20], Batch Num: [154/600]
Discriminator Loss: 0.7135, Generator Loss: 1.7699
D(x): 0.7824, D(G(z)): 0.2768
Epoch: [3/20], Batch Num: [155/600]
Discriminator Loss: 0.6626, Generator Loss: 1.8227
D(x): 0.7842, D(G(z)): 0.2656
Epoch: [3/20], Batch Num: [156/600]
Discriminator Loss: 0.9679, Generator Loss: 1.5552
D(x): 0.6951, D(G(z)): 0.3314
Epoch: [3/20], Batch Num: [157/600]
Discriminator Loss: 0.8772, Generator Loss: 1.7379
D(x): 0.7482, D(G(z)): 0.3499
Epoch: [3/20], Batch Num: [158/600]
Discriminator Loss: 1.1572, Generator Loss: 1.4778
D(x): 0.6430, D(G(z)): 0.3818
Epoch: [3/20], Batch Num: [159/600]
Discriminator Loss: 1.1848, Generator Loss: 1.1739
D(x): 0.6459, D(G(z)): 0.3706
Epoch: [3/20], Batch Num: [160/600]
Discriminator Loss: 1.3581, Generator Loss: 1.0535
D(x): 0.6330, D(G(z)): 0.4699
Epoch: [3/20], Batch Num: [161/600]
Discriminator Loss: 1.5666, Generator Loss: 1.0915
D(x): 0.5762, D(G(z)): 0.4887
Epoch: [3/20], Batch Num: [162/600]
Discriminator Loss: 1.7499, Generator Loss: 0.9945
D(x): 0.5153, D(G(z)): 0.4831
Epoch: [3/20], Batch Num: [163/600]
Discriminator Loss: 1.9007, Generator Loss: 0.8800
D(x): 0.5181, D(G(z)): 0.5680
Epoch: [3/20], Batch Num: [164/600]
Discriminator Loss: 2.1391, Generator Loss: 0.6848
D(x): 0.4900, D(G(z)): 0.5764
Epoch: [3/20], Batch Num: [165/600]
Discriminator Loss: 2.2186, Generator Loss: 0.7070
D(x): 0.4621, D(G(z)): 0.6368
Epoch: [3/20], Batch Num: [166/600]
Discriminator Loss: 2.5214, Generator Loss: 0.6463
D(x): 0.3954, D(G(z)): 0.6199
Epoch: [3/20], Batch Num: [167/600]
Discriminator Loss: 2.5383, Generator Loss: 0.5504
D(x): 0.3845, D(G(z)): 0.6510
Epoch: [3/20], Batch Num: [168/600]
Discriminator Loss: 2.3782, Generator Loss: 0.5402
D(x): 0.4222, D(G(z)): 0.6802
Epoch: [3/20], Batch Num: [169/600]
Discriminator Loss: 2.3221, Generator Loss: 0.5865
D(x): 0.4338, D(G(z)): 0.6688
Epoch: [3/20], Batch Num: [170/600]
Discriminator Loss: 2.3789, Generator Loss: 0.6054
D(x): 0.3756, D(G(z)): 0.6427
Epoch: [3/20], Batch Num: [171/600]
Discriminator Loss: 2.0637, Generator Loss: 0.5987
D(x): 0.4424, D(G(z)): 0.6065
Epoch: [3/20], Batch Num: [172/600]
Discriminator Loss: 1.9904, Generator Loss: 0.6524
D(x): 0.4312, D(G(z)): 0.5933
Epoch: [3/20], Batch Num: [173/600]
Discriminator Loss: 2.1111, Generator Loss: 0.5373
D(x): 0.4027, D(G(z)): 0.6019
Epoch: [3/20], Batch Num: [174/600]
Discriminator Loss: 1.9186, Generator Loss: 0.5196
D(x): 0.4568, D(G(z)): 0.5896
Epoch: [3/20], Batch Num: [175/600]
Discriminator Loss: 1.7160, Generator Loss: 0.5948
D(x): 0.5120, D(G(z)): 0.5989
Epoch: [3/20], Batch Num: [176/600]
Discriminator Loss: 1.7868, Generator Loss: 0.5497
D(x): 0.5157, D(G(z)): 0.6237
Epoch: [3/20], Batch Num: [177/600]
Discriminator Loss: 1.6934, Generator Loss: 0.5942
D(x): 0.5131, D(G(z)): 0.5969
Epoch: [3/20], Batch Num: [178/600]
Discriminator Loss: 1.5990, Generator Loss: 0.6445
D(x): 0.5063, D(G(z)): 0.5633
Epoch: [3/20], Batch Num: [179/600]
Discriminator Loss: 1.5571, Generator Loss: 0.6957
D(x): 0.5206, D(G(z)): 0.5430
Epoch: [3/20], Batch Num: [180/600]
Discriminator Loss: 1.5449, Generator Loss: 0.7020
D(x): 0.4984, D(G(z)): 0.5169
Epoch: [3/20], Batch Num: [181/600]
Discriminator Loss: 1.5922, Generator Loss: 0.6864
D(x): 0.4839, D(G(z)): 0.5118
Epoch: [3/20], Batch Num: [182/600]
Discriminator Loss: 1.6165, Generator Loss: 0.6896
D(x): 0.4693, D(G(z)): 0.5082
Epoch: [3/20], Batch Num: [183/600]
Discriminator Loss: 1.5601, Generator Loss: 0.6144
D(x): 0.5015, D(G(z)): 0.5267
Epoch: [3/20], Batch Num: [184/600]
Discriminator Loss: 1.4212, Generator Loss: 0.6698
D(x): 0.5611, D(G(z)): 0.5312
Epoch: [3/20], Batch Num: [185/600]
Discriminator Loss: 1.4511, Generator Loss: 0.6739
D(x): 0.5532, D(G(z)): 0.5334
Epoch: [3/20], Batch Num: [186/600]
Discriminator Loss: 1.4127, Generator Loss: 0.7456
D(x): 0.5808, D(G(z)): 0.5322
Epoch: [3/20], Batch Num: [187/600]
Discriminator Loss: 1.3509, Generator Loss: 0.8134
D(x): 0.5810, D(G(z)): 0.5177
Epoch: [3/20], Batch Num: [188/600]
Discriminator Loss: 1.3440, Generator Loss: 0.8138
D(x): 0.5612, D(G(z)): 0.4876
Epoch: [3/20], Batch Num: [189/600]
Discriminator Loss: 1.3947, Generator Loss: 0.8807
D(x): 0.5332, D(G(z)): 0.4925
Epoch: [3/20], Batch Num: [190/600]
Discriminator Loss: 1.4070, Generator Loss: 0.8739
D(x): 0.5033, D(G(z)): 0.4633
Epoch: [3/20], Batch Num: [191/600]
Discriminator Loss: 1.3636, Generator Loss: 0.9574
D(x): 0.5087, D(G(z)): 0.4466
Epoch: [3/20], Batch Num: [192/600]
Discriminator Loss: 1.2920, Generator Loss: 0.9149
D(x): 0.5381, D(G(z)): 0.4383
Epoch: [3/20], Batch Num: [193/600]
Discriminator Loss: 1.1891, Generator Loss: 0.9005
D(x): 0.5412, D(G(z)): 0.3905
Epoch: [3/20], Batch Num: [194/600]
Discriminator Loss: 1.1856, Generator Loss: 0.8752
D(x): 0.5648, D(G(z)): 0.4154
Epoch: [3/20], Batch Num: [195/600]
Discriminator Loss: 1.1563, Generator Loss: 0.9226
D(x): 0.5695, D(G(z)): 0.4105
Epoch: [3/20], Batch Num: [196/600]
Discriminator Loss: 1.1537, Generator Loss: 0.9525
D(x): 0.5991, D(G(z)): 0.4297
Epoch: [3/20], Batch Num: [197/600]
Discriminator Loss: 1.0405, Generator Loss: 0.9742
D(x): 0.6311, D(G(z)): 0.4064
Epoch: [3/20], Batch Num: [198/600]
Discriminator Loss: 1.0543, Generator Loss: 1.0033
D(x): 0.6301, D(G(z)): 0.4058
Epoch: [3/20], Batch Num: [199/600]
Discriminator Loss: 0.9979, Generator Loss: 1.1131
D(x): 0.6366, D(G(z)): 0.3868
Epoch: 3, Batch Num: [200/600]

Epoch: [3/20], Batch Num: [200/600]
Discriminator Loss: 1.0064, Generator Loss: 1.1971
D(x): 0.6132, D(G(z)): 0.3614
Epoch: [3/20], Batch Num: [201/600]
Discriminator Loss: 0.9805, Generator Loss: 1.1977
D(x): 0.5983, D(G(z)): 0.3261
Epoch: [3/20], Batch Num: [202/600]
Discriminator Loss: 0.9730, Generator Loss: 1.2646
D(x): 0.5984, D(G(z)): 0.3239
Epoch: [3/20], Batch Num: [203/600]
Discriminator Loss: 0.9294, Generator Loss: 1.3167
D(x): 0.6205, D(G(z)): 0.3210
Epoch: [3/20], Batch Num: [204/600]
Discriminator Loss: 0.8712, Generator Loss: 1.4044
D(x): 0.6640, D(G(z)): 0.3311
Epoch: [3/20], Batch Num: [205/600]
Discriminator Loss: 0.8060, Generator Loss: 1.3671
D(x): 0.6747, D(G(z)): 0.3028
Epoch: [3/20], Batch Num: [206/600]
Discriminator Loss: 0.7815, Generator Loss: 1.4358
D(x): 0.6936, D(G(z)): 0.2946
Epoch: [3/20], Batch Num: [207/600]
Discriminator Loss: 0.8357, Generator Loss: 1.4066
D(x): 0.6607, D(G(z)): 0.3046
Epoch: [3/20], Batch Num: [208/600]
Discriminator Loss: 0.7718, Generator Loss: 1.4222
D(x): 0.6939, D(G(z)): 0.2896
Epoch: [3/20], Batch Num: [209/600]
Discriminator Loss: 0.7775, Generator Loss: 1.4239
D(x): 0.6900, D(G(z)): 0.2864
Epoch: [3/20], Batch Num: [210/600]
Discriminator Loss: 0.7360, Generator Loss: 1.5600
D(x): 0.7138, D(G(z)): 0.2842
Epoch: [3/20], Batch Num: [211/600]
Discriminator Loss: 0.6644, Generator Loss: 1.6110
D(x): 0.7477, D(G(z)): 0.2737
Epoch: [3/20], Batch Num: [212/600]
Discriminator Loss: 0.6514, Generator Loss: 1.5766
D(x): 0.7227, D(G(z)): 0.2334
Epoch: [3/20], Batch Num: [213/600]
Discriminator Loss: 0.6880, Generator Loss: 1.5549
D(x): 0.7379, D(G(z)): 0.2603
Epoch: [3/20], Batch Num: [214/600]
Discriminator Loss: 0.6184, Generator Loss: 1.6473
D(x): 0.7629, D(G(z)): 0.2454
Epoch: [3/20], Batch Num: [215/600]
Discriminator Loss: 0.6783, Generator Loss: 1.6992
D(x): 0.7592, D(G(z)): 0.2795
Epoch: [3/20], Batch Num: [216/600]
Discriminator Loss: 0.6028, Generator Loss: 1.7428
D(x): 0.7575, D(G(z)): 0.2400
Epoch: [3/20], Batch Num: [217/600]
Discriminator Loss: 0.6240, Generator Loss: 1.4891
D(x): 0.7558, D(G(z)): 0.2436
Epoch: [3/20], Batch Num: [218/600]
Discriminator Loss: 0.6267, Generator Loss: 1.6384
D(x): 0.7797, D(G(z)): 0.2736
Epoch: [3/20], Batch Num: [219/600]
Discriminator Loss: 0.6066, Generator Loss: 1.7972
D(x): 0.7990, D(G(z)): 0.2683
Epoch: [3/20], Batch Num: [220/600]
Discriminator Loss: 0.6318, Generator Loss: 1.8124
D(x): 0.7708, D(G(z)): 0.2630
Epoch: [3/20], Batch Num: [221/600]
Discriminator Loss: 0.5196, Generator Loss: 1.8574
D(x): 0.7822, D(G(z)): 0.1913
Epoch: [3/20], Batch Num: [222/600]
Discriminator Loss: 0.6955, Generator Loss: 1.7171
D(x): 0.7089, D(G(z)): 0.2152
Epoch: [3/20], Batch Num: [223/600]
Discriminator Loss: 0.6431, Generator Loss: 1.6482
D(x): 0.7538, D(G(z)): 0.2313
Epoch: [3/20], Batch Num: [224/600]
Discriminator Loss: 0.6475, Generator Loss: 1.4768
D(x): 0.7980, D(G(z)): 0.2786
Epoch: [3/20], Batch Num: [225/600]
Discriminator Loss: 0.6583, Generator Loss: 1.5431
D(x): 0.8165, D(G(z)): 0.3095
Epoch: [3/20], Batch Num: [226/600]
Discriminator Loss: 0.5939, Generator Loss: 1.6789
D(x): 0.8242, D(G(z)): 0.2976
Epoch: [3/20], Batch Num: [227/600]
Discriminator Loss: 0.7094, Generator Loss: 1.7000
D(x): 0.7248, D(G(z)): 0.2522
Epoch: [3/20], Batch Num: [228/600]
Discriminator Loss: 0.6153, Generator Loss: 1.6007
D(x): 0.7680, D(G(z)): 0.2528
Epoch: [3/20], Batch Num: [229/600]
Discriminator Loss: 0.6102, Generator Loss: 1.7212
D(x): 0.8038, D(G(z)): 0.2785
Epoch: [3/20], Batch Num: [230/600]
Discriminator Loss: 0.6256, Generator Loss: 1.7633
D(x): 0.7690, D(G(z)): 0.2507
Epoch: [3/20], Batch Num: [231/600]
Discriminator Loss: 0.7272, Generator Loss: 1.5790
D(x): 0.7110, D(G(z)): 0.2404
Epoch: [3/20], Batch Num: [232/600]
Discriminator Loss: 0.8098, Generator Loss: 1.2499
D(x): 0.7351, D(G(z)): 0.3027
Epoch: [3/20], Batch Num: [233/600]
Discriminator Loss: 0.7833, Generator Loss: 1.3494
D(x): 0.7764, D(G(z)): 0.3437
Epoch: [3/20], Batch Num: [234/600]
Discriminator Loss: 0.8234, Generator Loss: 1.2657
D(x): 0.7578, D(G(z)): 0.3429
Epoch: [3/20], Batch Num: [235/600]
Discriminator Loss: 0.8424, Generator Loss: 1.3028
D(x): 0.7535, D(G(z)): 0.3392
Epoch: [3/20], Batch Num: [236/600]
Discriminator Loss: 0.8187, Generator Loss: 1.3241
D(x): 0.7311, D(G(z)): 0.3320
Epoch: [3/20], Batch Num: [237/600]
Discriminator Loss: 1.0691, Generator Loss: 1.1436
D(x): 0.6280, D(G(z)): 0.3344
Epoch: [3/20], Batch Num: [238/600]
Discriminator Loss: 0.9060, Generator Loss: 1.1299
D(x): 0.7475, D(G(z)): 0.3900
Epoch: [3/20], Batch Num: [239/600]
Discriminator Loss: 1.0876, Generator Loss: 1.0513
D(x): 0.7066, D(G(z)): 0.4425
Epoch: [3/20], Batch Num: [240/600]
Discriminator Loss: 1.0684, Generator Loss: 1.0261
D(x): 0.6932, D(G(z)): 0.4103
Epoch: [3/20], Batch Num: [241/600]
Discriminator Loss: 1.0299, Generator Loss: 1.0582
D(x): 0.7180, D(G(z)): 0.4336
Epoch: [3/20], Batch Num: [242/600]
Discriminator Loss: 1.1071, Generator Loss: 1.0782
D(x): 0.6595, D(G(z)): 0.4278
Epoch: [3/20], Batch Num: [243/600]
Discriminator Loss: 1.3148, Generator Loss: 0.9432
D(x): 0.6138, D(G(z)): 0.4291
Epoch: [3/20], Batch Num: [244/600]
Discriminator Loss: 1.0612, Generator Loss: 0.9361
D(x): 0.6985, D(G(z)): 0.4488
Epoch: [3/20], Batch Num: [245/600]
Discriminator Loss: 1.2024, Generator Loss: 0.8872
D(x): 0.6627, D(G(z)): 0.4671
Epoch: [3/20], Batch Num: [246/600]
Discriminator Loss: 1.2084, Generator Loss: 0.8221
D(x): 0.6576, D(G(z)): 0.4764
Epoch: [3/20], Batch Num: [247/600]
Discriminator Loss: 1.2504, Generator Loss: 0.7704
D(x): 0.6690, D(G(z)): 0.5114
Epoch: [3/20], Batch Num: [248/600]
Discriminator Loss: 1.2349, Generator Loss: 0.7862
D(x): 0.6550, D(G(z)): 0.4829
Epoch: [3/20], Batch Num: [249/600]
Discriminator Loss: 1.2915, Generator Loss: 0.7765
D(x): 0.6392, D(G(z)): 0.4896
Epoch: [3/20], Batch Num: [250/600]
Discriminator Loss: 1.2261, Generator Loss: 0.7894
D(x): 0.7036, D(G(z)): 0.5274
Epoch: [3/20], Batch Num: [251/600]
Discriminator Loss: 1.4509, Generator Loss: 0.8164
D(x): 0.6086, D(G(z)): 0.5223
Epoch: [3/20], Batch Num: [252/600]
Discriminator Loss: 1.2666, Generator Loss: 0.8296
D(x): 0.6521, D(G(z)): 0.4963
Epoch: [3/20], Batch Num: [253/600]
Discriminator Loss: 1.3635, Generator Loss: 0.7988
D(x): 0.6122, D(G(z)): 0.5010
Epoch: [3/20], Batch Num: [254/600]
Discriminator Loss: 1.3702, Generator Loss: 0.7724
D(x): 0.6310, D(G(z)): 0.5149
Epoch: [3/20], Batch Num: [255/600]
Discriminator Loss: 1.4056, Generator Loss: 0.7693
D(x): 0.6114, D(G(z)): 0.5260
Epoch: [3/20], Batch Num: [256/600]
Discriminator Loss: 1.4450, Generator Loss: 0.7218
D(x): 0.6018, D(G(z)): 0.5278
Epoch: [3/20], Batch Num: [257/600]
Discriminator Loss: 1.3972, Generator Loss: 0.7123
D(x): 0.6256, D(G(z)): 0.5395
Epoch: [3/20], Batch Num: [258/600]
Discriminator Loss: 1.3185, Generator Loss: 0.7359
D(x): 0.6548, D(G(z)): 0.5245
Epoch: [3/20], Batch Num: [259/600]
Discriminator Loss: 1.3892, Generator Loss: 0.8072
D(x): 0.6168, D(G(z)): 0.5372
Epoch: [3/20], Batch Num: [260/600]
Discriminator Loss: 1.3386, Generator Loss: 0.8143
D(x): 0.6139, D(G(z)): 0.5068
Epoch: [3/20], Batch Num: [261/600]
Discriminator Loss: 1.2507, Generator Loss: 0.8276
D(x): 0.6308, D(G(z)): 0.4831
Epoch: [3/20], Batch Num: [262/600]
Discriminator Loss: 1.3029, Generator Loss: 0.8105
D(x): 0.6047, D(G(z)): 0.4805
Epoch: [3/20], Batch Num: [263/600]
Discriminator Loss: 1.1811, Generator Loss: 0.8443
D(x): 0.6590, D(G(z)): 0.4714
Epoch: [3/20], Batch Num: [264/600]
Discriminator Loss: 1.3161, Generator Loss: 0.9130
D(x): 0.6149, D(G(z)): 0.4824
Epoch: [3/20], Batch Num: [265/600]
Discriminator Loss: 1.1969, Generator Loss: 0.9358
D(x): 0.6395, D(G(z)): 0.4583
Epoch: [3/20], Batch Num: [266/600]
Discriminator Loss: 1.1530, Generator Loss: 0.9144
D(x): 0.6475, D(G(z)): 0.4526
Epoch: [3/20], Batch Num: [267/600]
Discriminator Loss: 1.1658, Generator Loss: 0.9781
D(x): 0.6420, D(G(z)): 0.4495
Epoch: [3/20], Batch Num: [268/600]
Discriminator Loss: 1.1780, Generator Loss: 1.0016
D(x): 0.6143, D(G(z)): 0.4321
Epoch: [3/20], Batch Num: [269/600]
Discriminator Loss: 1.0802, Generator Loss: 0.9240
D(x): 0.6748, D(G(z)): 0.4157
Epoch: [3/20], Batch Num: [270/600]
Discriminator Loss: 1.0781, Generator Loss: 1.0676
D(x): 0.6705, D(G(z)): 0.4114
Epoch: [3/20], Batch Num: [271/600]
Discriminator Loss: 1.0729, Generator Loss: 1.0115
D(x): 0.6324, D(G(z)): 0.3891
Epoch: [3/20], Batch Num: [272/600]
Discriminator Loss: 1.1080, Generator Loss: 1.1370
D(x): 0.6476, D(G(z)): 0.4100
Epoch: [3/20], Batch Num: [273/600]
Discriminator Loss: 1.1936, Generator Loss: 1.0660
D(x): 0.6187, D(G(z)): 0.4004
Epoch: [3/20], Batch Num: [274/600]
Discriminator Loss: 0.9677, Generator Loss: 0.9880
D(x): 0.7076, D(G(z)): 0.3862
Epoch: [3/20], Batch Num: [275/600]
Discriminator Loss: 0.9717, Generator Loss: 1.0283
D(x): 0.6943, D(G(z)): 0.3906
Epoch: [3/20], Batch Num: [276/600]
Discriminator Loss: 0.9677, Generator Loss: 1.1235
D(x): 0.7078, D(G(z)): 0.4082
Epoch: [3/20], Batch Num: [277/600]
Discriminator Loss: 0.9292, Generator Loss: 1.1936
D(x): 0.7098, D(G(z)): 0.3875
Epoch: [3/20], Batch Num: [278/600]
Discriminator Loss: 1.0308, Generator Loss: 1.2602
D(x): 0.6699, D(G(z)): 0.3725
Epoch: [3/20], Batch Num: [279/600]
Discriminator Loss: 0.8320, Generator Loss: 1.1044
D(x): 0.7252, D(G(z)): 0.3367
Epoch: [3/20], Batch Num: [280/600]
Discriminator Loss: 0.8923, Generator Loss: 1.3451
D(x): 0.7128, D(G(z)): 0.3414
Epoch: [3/20], Batch Num: [281/600]
Discriminator Loss: 0.7413, Generator Loss: 1.4743
D(x): 0.7479, D(G(z)): 0.3028
Epoch: [3/20], Batch Num: [282/600]
Discriminator Loss: 0.8590, Generator Loss: 1.3986
D(x): 0.7142, D(G(z)): 0.3088
Epoch: [3/20], Batch Num: [283/600]
Discriminator Loss: 0.8434, Generator Loss: 1.4037
D(x): 0.7096, D(G(z)): 0.3214
Epoch: [3/20], Batch Num: [284/600]
Discriminator Loss: 0.7447, Generator Loss: 1.4452
D(x): 0.7439, D(G(z)): 0.2774
Epoch: [3/20], Batch Num: [285/600]
Discriminator Loss: 0.7452, Generator Loss: 1.3932
D(x): 0.7537, D(G(z)): 0.3065
Epoch: [3/20], Batch Num: [286/600]
Discriminator Loss: 0.7311, Generator Loss: 1.4574
D(x): 0.7647, D(G(z)): 0.3102
Epoch: [3/20], Batch Num: [287/600]
Discriminator Loss: 0.7745, Generator Loss: 1.4643
D(x): 0.7834, D(G(z)): 0.3462
Epoch: [3/20], Batch Num: [288/600]
Discriminator Loss: 0.8149, Generator Loss: 1.6127
D(x): 0.7136, D(G(z)): 0.2803
Epoch: [3/20], Batch Num: [289/600]
Discriminator Loss: 0.7957, Generator Loss: 1.6202
D(x): 0.7254, D(G(z)): 0.2806
Epoch: [3/20], Batch Num: [290/600]
Discriminator Loss: 0.8833, Generator Loss: 1.5238
D(x): 0.7231, D(G(z)): 0.3077
Epoch: [3/20], Batch Num: [291/600]
Discriminator Loss: 0.7122, Generator Loss: 1.4817
D(x): 0.7967, D(G(z)): 0.3082
Epoch: [3/20], Batch Num: [292/600]
Discriminator Loss: 0.6750, Generator Loss: 1.6831
D(x): 0.7993, D(G(z)): 0.2855
Epoch: [3/20], Batch Num: [293/600]
Discriminator Loss: 0.7698, Generator Loss: 1.6830
D(x): 0.7678, D(G(z)): 0.2987
Epoch: [3/20], Batch Num: [294/600]
Discriminator Loss: 0.8578, Generator Loss: 1.7196
D(x): 0.7534, D(G(z)): 0.3155
Epoch: [3/20], Batch Num: [295/600]
Discriminator Loss: 0.9235, Generator Loss: 1.5943
D(x): 0.7030, D(G(z)): 0.3022
Epoch: [3/20], Batch Num: [296/600]
Discriminator Loss: 0.8268, Generator Loss: 1.4497
D(x): 0.7631, D(G(z)): 0.3317
Epoch: [3/20], Batch Num: [297/600]
Discriminator Loss: 0.7961, Generator Loss: 1.3575
D(x): 0.7967, D(G(z)): 0.3405
Epoch: [3/20], Batch Num: [298/600]
Discriminator Loss: 0.8499, Generator Loss: 1.4860
D(x): 0.7608, D(G(z)): 0.3419
Epoch: [3/20], Batch Num: [299/600]
Discriminator Loss: 0.8458, Generator Loss: 1.4704
D(x): 0.7916, D(G(z)): 0.3684
Epoch: 3, Batch Num: [300/600]

Epoch: [3/20], Batch Num: [300/600]
Discriminator Loss: 0.9302, Generator Loss: 1.5201
D(x): 0.7411, D(G(z)): 0.3525
Epoch: [3/20], Batch Num: [301/600]
Discriminator Loss: 0.9451, Generator Loss: 1.5589
D(x): 0.7323, D(G(z)): 0.3256
Epoch: [3/20], Batch Num: [302/600]
Discriminator Loss: 0.9975, Generator Loss: 1.3921
D(x): 0.7032, D(G(z)): 0.3412
Epoch: [3/20], Batch Num: [303/600]
Discriminator Loss: 1.0231, Generator Loss: 1.2806
D(x): 0.7462, D(G(z)): 0.3999
Epoch: [3/20], Batch Num: [304/600]
Discriminator Loss: 1.0938, Generator Loss: 1.0814
D(x): 0.7060, D(G(z)): 0.4007
Epoch: [3/20], Batch Num: [305/600]
Discriminator Loss: 1.0892, Generator Loss: 1.1863
D(x): 0.7380, D(G(z)): 0.4165
Epoch: [3/20], Batch Num: [306/600]
Discriminator Loss: 0.9947, Generator Loss: 1.2766
D(x): 0.7440, D(G(z)): 0.4172
Epoch: [3/20], Batch Num: [307/600]
Discriminator Loss: 1.0212, Generator Loss: 1.3019
D(x): 0.7277, D(G(z)): 0.3963
Epoch: [3/20], Batch Num: [308/600]
Discriminator Loss: 1.0288, Generator Loss: 1.2686
D(x): 0.7203, D(G(z)): 0.3615
Epoch: [3/20], Batch Num: [309/600]
Discriminator Loss: 1.0720, Generator Loss: 1.2520
D(x): 0.6674, D(G(z)): 0.3553
Epoch: [3/20], Batch Num: [310/600]
Discriminator Loss: 1.0116, Generator Loss: 1.1041
D(x): 0.7068, D(G(z)): 0.3980
Epoch: [3/20], Batch Num: [311/600]
Discriminator Loss: 0.8694, Generator Loss: 1.1235
D(x): 0.7805, D(G(z)): 0.3974
Epoch: [3/20], Batch Num: [312/600]
Discriminator Loss: 0.9413, Generator Loss: 1.1566
D(x): 0.7841, D(G(z)): 0.4204
Epoch: [3/20], Batch Num: [313/600]
Discriminator Loss: 1.0014, Generator Loss: 1.4528
D(x): 0.7103, D(G(z)): 0.3831
Epoch: [3/20], Batch Num: [314/600]
Discriminator Loss: 0.7664, Generator Loss: 1.6680
D(x): 0.7573, D(G(z)): 0.3028
Epoch: [3/20], Batch Num: [315/600]
Discriminator Loss: 0.8926, Generator Loss: 1.6319
D(x): 0.6659, D(G(z)): 0.2860
Epoch: [3/20], Batch Num: [316/600]
Discriminator Loss: 0.7302, Generator Loss: 1.6759
D(x): 0.7608, D(G(z)): 0.2919
Epoch: [3/20], Batch Num: [317/600]
Discriminator Loss: 0.7946, Generator Loss: 1.7285
D(x): 0.7902, D(G(z)): 0.3172
Epoch: [3/20], Batch Num: [318/600]
Discriminator Loss: 0.6349, Generator Loss: 1.8137
D(x): 0.7834, D(G(z)): 0.2534
Epoch: [3/20], Batch Num: [319/600]
Discriminator Loss: 0.6397, Generator Loss: 2.0551
D(x): 0.7958, D(G(z)): 0.2488
Epoch: [3/20], Batch Num: [320/600]
Discriminator Loss: 0.6224, Generator Loss: 2.4041
D(x): 0.8195, D(G(z)): 0.2564
Epoch: [3/20], Batch Num: [321/600]
Discriminator Loss: 0.4418, Generator Loss: 2.9372
D(x): 0.8278, D(G(z)): 0.1620
Epoch: [3/20], Batch Num: [322/600]
Discriminator Loss: 0.5418, Generator Loss: 2.6392
D(x): 0.7981, D(G(z)): 0.1601
Epoch: [3/20], Batch Num: [323/600]
Discriminator Loss: 0.3586, Generator Loss: 2.6122
D(x): 0.8596, D(G(z)): 0.1401
Epoch: [3/20], Batch Num: [324/600]
Discriminator Loss: 0.5037, Generator Loss: 2.4560
D(x): 0.7896, D(G(z)): 0.1577
Epoch: [3/20], Batch Num: [325/600]
Discriminator Loss: 0.4026, Generator Loss: 2.3742
D(x): 0.8886, D(G(z)): 0.1836
Epoch: [3/20], Batch Num: [326/600]
Discriminator Loss: 0.5775, Generator Loss: 2.6474
D(x): 0.8126, D(G(z)): 0.1823
Epoch: [3/20], Batch Num: [327/600]
Discriminator Loss: 0.3590, Generator Loss: 2.3944
D(x): 0.9114, D(G(z)): 0.1879
Epoch: [3/20], Batch Num: [328/600]
Discriminator Loss: 0.4154, Generator Loss: 3.2349
D(x): 0.8977, D(G(z)): 0.2086
Epoch: [3/20], Batch Num: [329/600]
Discriminator Loss: 0.4816, Generator Loss: 3.6640
D(x): 0.8342, D(G(z)): 0.1632
Epoch: [3/20], Batch Num: [330/600]
Discriminator Loss: 0.5682, Generator Loss: 3.3065
D(x): 0.7662, D(G(z)): 0.1171
Epoch: [3/20], Batch Num: [331/600]
Discriminator Loss: 0.5981, Generator Loss: 2.2100
D(x): 0.7682, D(G(z)): 0.1219
Epoch: [3/20], Batch Num: [332/600]
Discriminator Loss: 0.6974, Generator Loss: 1.8891
D(x): 0.8362, D(G(z)): 0.2746
Epoch: [3/20], Batch Num: [333/600]
Discriminator Loss: 0.7544, Generator Loss: 2.3966
D(x): 0.8972, D(G(z)): 0.3545
Epoch: [3/20], Batch Num: [334/600]
Discriminator Loss: 0.7646, Generator Loss: 2.7540
D(x): 0.7921, D(G(z)): 0.2681
Epoch: [3/20], Batch Num: [335/600]
Discriminator Loss: 0.9551, Generator Loss: 2.4569
D(x): 0.6916, D(G(z)): 0.1990
Epoch: [3/20], Batch Num: [336/600]
Discriminator Loss: 0.8833, Generator Loss: 2.1565
D(x): 0.7300, D(G(z)): 0.2281
Epoch: [3/20], Batch Num: [337/600]
Discriminator Loss: 0.9891, Generator Loss: 1.7510
D(x): 0.7646, D(G(z)): 0.3215
Epoch: [3/20], Batch Num: [338/600]
Discriminator Loss: 1.0932, Generator Loss: 1.9176
D(x): 0.7286, D(G(z)): 0.3144
Epoch: [3/20], Batch Num: [339/600]
Discriminator Loss: 1.2709, Generator Loss: 2.0895
D(x): 0.7161, D(G(z)): 0.3943
Epoch: [3/20], Batch Num: [340/600]
Discriminator Loss: 1.2461, Generator Loss: 1.7494
D(x): 0.6086, D(G(z)): 0.2804
Epoch: [3/20], Batch Num: [341/600]
Discriminator Loss: 1.2924, Generator Loss: 1.4658
D(x): 0.6643, D(G(z)): 0.3348
Epoch: [3/20], Batch Num: [342/600]
Discriminator Loss: 1.1671, Generator Loss: 1.6473
D(x): 0.7263, D(G(z)): 0.3997
Epoch: [3/20], Batch Num: [343/600]
Discriminator Loss: 1.1515, Generator Loss: 2.5419
D(x): 0.7451, D(G(z)): 0.4035
Epoch: [3/20], Batch Num: [344/600]
Discriminator Loss: 1.4541, Generator Loss: 2.2821
D(x): 0.5320, D(G(z)): 0.2513
Epoch: [3/20], Batch Num: [345/600]
Discriminator Loss: 0.9810, Generator Loss: 1.7570
D(x): 0.6233, D(G(z)): 0.1712
Epoch: [3/20], Batch Num: [346/600]
Discriminator Loss: 0.9629, Generator Loss: 1.3793
D(x): 0.7176, D(G(z)): 0.2668
Epoch: [3/20], Batch Num: [347/600]
Discriminator Loss: 0.8260, Generator Loss: 1.8128
D(x): 0.8417, D(G(z)): 0.3689
Epoch: [3/20], Batch Num: [348/600]
Discriminator Loss: 0.7031, Generator Loss: 2.8392
D(x): 0.8403, D(G(z)): 0.3103
Epoch: [3/20], Batch Num: [349/600]
Discriminator Loss: 0.6126, Generator Loss: 3.5670
D(x): 0.7527, D(G(z)): 0.1548
Epoch: [3/20], Batch Num: [350/600]
Discriminator Loss: 0.5210, Generator Loss: 3.5288
D(x): 0.7447, D(G(z)): 0.0803
Epoch: [3/20], Batch Num: [351/600]
Discriminator Loss: 0.5817, Generator Loss: 3.0761
D(x): 0.7340, D(G(z)): 0.0846
Epoch: [3/20], Batch Num: [352/600]
Discriminator Loss: 0.3911, Generator Loss: 2.5366
D(x): 0.8065, D(G(z)): 0.0840
Epoch: [3/20], Batch Num: [353/600]
Discriminator Loss: 0.3667, Generator Loss: 2.4686
D(x): 0.9222, D(G(z)): 0.1899
Epoch: [3/20], Batch Num: [354/600]
Discriminator Loss: 0.3953, Generator Loss: 2.9988
D(x): 0.9544, D(G(z)): 0.2421
Epoch: [3/20], Batch Num: [355/600]
Discriminator Loss: 0.2956, Generator Loss: 3.5394
D(x): 0.9323, D(G(z)): 0.1611
Epoch: [3/20], Batch Num: [356/600]
Discriminator Loss: 0.3200, Generator Loss: 4.4167
D(x): 0.8564, D(G(z)): 0.0963
Epoch: [3/20], Batch Num: [357/600]
Discriminator Loss: 0.3246, Generator Loss: 4.3130
D(x): 0.8235, D(G(z)): 0.0503
Epoch: [3/20], Batch Num: [358/600]
Discriminator Loss: 0.2528, Generator Loss: 4.0062
D(x): 0.8637, D(G(z)): 0.0421
Epoch: [3/20], Batch Num: [359/600]
Discriminator Loss: 0.2941, Generator Loss: 3.1975
D(x): 0.8638, D(G(z)): 0.0619
Epoch: [3/20], Batch Num: [360/600]
Discriminator Loss: 0.2547, Generator Loss: 3.1523
D(x): 0.9227, D(G(z)): 0.1060
Epoch: [3/20], Batch Num: [361/600]
Discriminator Loss: 0.1978, Generator Loss: 2.8146
D(x): 0.9482, D(G(z)): 0.1177
Epoch: [3/20], Batch Num: [362/600]
Discriminator Loss: 0.2736, Generator Loss: 2.8576
D(x): 0.9462, D(G(z)): 0.1607
Epoch: [3/20], Batch Num: [363/600]
Discriminator Loss: 0.2946, Generator Loss: 3.0772
D(x): 0.9282, D(G(z)): 0.1583
Epoch: [3/20], Batch Num: [364/600]
Discriminator Loss: 0.3076, Generator Loss: 3.4220
D(x): 0.9004, D(G(z)): 0.1199
Epoch: [3/20], Batch Num: [365/600]
Discriminator Loss: 0.2644, Generator Loss: 3.4789
D(x): 0.9201, D(G(z)): 0.1291
Epoch: [3/20], Batch Num: [366/600]
Discriminator Loss: 0.5190, Generator Loss: 3.0914
D(x): 0.7766, D(G(z)): 0.0910
Epoch: [3/20], Batch Num: [367/600]
Discriminator Loss: 0.4157, Generator Loss: 2.6712
D(x): 0.8556, D(G(z)): 0.1336
Epoch: [3/20], Batch Num: [368/600]
Discriminator Loss: 0.3935, Generator Loss: 2.4204
D(x): 0.9072, D(G(z)): 0.2070
Epoch: [3/20], Batch Num: [369/600]
Discriminator Loss: 0.5737, Generator Loss: 2.1832
D(x): 0.8244, D(G(z)): 0.2013
Epoch: [3/20], Batch Num: [370/600]
Discriminator Loss: 0.5149, Generator Loss: 2.3032
D(x): 0.8710, D(G(z)): 0.2550
Epoch: [3/20], Batch Num: [371/600]
Discriminator Loss: 0.8350, Generator Loss: 2.3413
D(x): 0.7901, D(G(z)): 0.3061
Epoch: [3/20], Batch Num: [372/600]
Discriminator Loss: 0.9966, Generator Loss: 1.9117
D(x): 0.7036, D(G(z)): 0.2732
Epoch: [3/20], Batch Num: [373/600]
Discriminator Loss: 1.0406, Generator Loss: 1.5277
D(x): 0.7107, D(G(z)): 0.3094
Epoch: [3/20], Batch Num: [374/600]
Discriminator Loss: 1.1371, Generator Loss: 1.1137
D(x): 0.7171, D(G(z)): 0.3743
Epoch: [3/20], Batch Num: [375/600]
Discriminator Loss: 1.2710, Generator Loss: 1.3456
D(x): 0.7687, D(G(z)): 0.4885
Epoch: [3/20], Batch Num: [376/600]
Discriminator Loss: 1.2022, Generator Loss: 1.6617
D(x): 0.7578, D(G(z)): 0.4374
Epoch: [3/20], Batch Num: [377/600]
Discriminator Loss: 1.4932, Generator Loss: 1.4848
D(x): 0.6058, D(G(z)): 0.3912
Epoch: [3/20], Batch Num: [378/600]
Discriminator Loss: 1.4784, Generator Loss: 1.2486
D(x): 0.5967, D(G(z)): 0.3916
Epoch: [3/20], Batch Num: [379/600]
Discriminator Loss: 1.6723, Generator Loss: 0.8037
D(x): 0.5926, D(G(z)): 0.4569
Epoch: [3/20], Batch Num: [380/600]
Discriminator Loss: 1.6210, Generator Loss: 0.7210
D(x): 0.7024, D(G(z)): 0.6107
Epoch: [3/20], Batch Num: [381/600]
Discriminator Loss: 1.5484, Generator Loss: 1.1046
D(x): 0.7267, D(G(z)): 0.5884
Epoch: [3/20], Batch Num: [382/600]
Discriminator Loss: 1.5492, Generator Loss: 1.4057
D(x): 0.6504, D(G(z)): 0.4958
Epoch: [3/20], Batch Num: [383/600]
Discriminator Loss: 1.5471, Generator Loss: 1.2922
D(x): 0.5181, D(G(z)): 0.3513
Epoch: [3/20], Batch Num: [384/600]
Discriminator Loss: 1.0572, Generator Loss: 1.0854
D(x): 0.7215, D(G(z)): 0.3676
Epoch: [3/20], Batch Num: [385/600]
Discriminator Loss: 1.1691, Generator Loss: 1.1569
D(x): 0.6853, D(G(z)): 0.4079
Epoch: [3/20], Batch Num: [386/600]
Discriminator Loss: 1.0135, Generator Loss: 1.3529
D(x): 0.7312, D(G(z)): 0.4023
Epoch: [3/20], Batch Num: [387/600]
Discriminator Loss: 0.8540, Generator Loss: 1.6001
D(x): 0.7691, D(G(z)): 0.3460
Epoch: [3/20], Batch Num: [388/600]
Discriminator Loss: 0.5620, Generator Loss: 1.8916
D(x): 0.8597, D(G(z)): 0.2712
Epoch: [3/20], Batch Num: [389/600]
Discriminator Loss: 0.5945, Generator Loss: 2.2756
D(x): 0.7773, D(G(z)): 0.1958
Epoch: [3/20], Batch Num: [390/600]
Discriminator Loss: 0.4282, Generator Loss: 2.4446
D(x): 0.8364, D(G(z)): 0.1560
Epoch: [3/20], Batch Num: [391/600]
Discriminator Loss: 0.3917, Generator Loss: 2.8070
D(x): 0.8252, D(G(z)): 0.1170
Epoch: [3/20], Batch Num: [392/600]
Discriminator Loss: 0.2374, Generator Loss: 2.6922
D(x): 0.8809, D(G(z)): 0.0847
Epoch: [3/20], Batch Num: [393/600]
Discriminator Loss: 0.2532, Generator Loss: 2.7460
D(x): 0.8949, D(G(z)): 0.1050
Epoch: [3/20], Batch Num: [394/600]
Discriminator Loss: 0.2563, Generator Loss: 2.6173
D(x): 0.9001, D(G(z)): 0.1136
Epoch: [3/20], Batch Num: [395/600]
Discriminator Loss: 0.2257, Generator Loss: 2.6612
D(x): 0.9164, D(G(z)): 0.1104
Epoch: [3/20], Batch Num: [396/600]
Discriminator Loss: 0.1748, Generator Loss: 2.7947
D(x): 0.9535, D(G(z)): 0.1112
Epoch: [3/20], Batch Num: [397/600]
Discriminator Loss: 0.1665, Generator Loss: 3.0649
D(x): 0.9480, D(G(z)): 0.0995
Epoch: [3/20], Batch Num: [398/600]
Discriminator Loss: 0.2402, Generator Loss: 3.1160
D(x): 0.9234, D(G(z)): 0.1114
Epoch: [3/20], Batch Num: [399/600]
Discriminator Loss: 0.1822, Generator Loss: 3.1515
D(x): 0.9300, D(G(z)): 0.0816
Epoch: 3, Batch Num: [400/600]

Epoch: [3/20], Batch Num: [400/600]
Discriminator Loss: 0.1608, Generator Loss: 3.2403
D(x): 0.9411, D(G(z)): 0.0800
Epoch: [3/20], Batch Num: [401/600]
Discriminator Loss: 0.1818, Generator Loss: 2.9349
D(x): 0.9337, D(G(z)): 0.0874
Epoch: [3/20], Batch Num: [402/600]
Discriminator Loss: 0.1967, Generator Loss: 3.1465
D(x): 0.9213, D(G(z)): 0.0872
Epoch: [3/20], Batch Num: [403/600]
Discriminator Loss: 0.1768, Generator Loss: 2.8488
D(x): 0.9355, D(G(z)): 0.0971
Epoch: [3/20], Batch Num: [404/600]
Discriminator Loss: 0.2494, Generator Loss: 2.8692
D(x): 0.9266, D(G(z)): 0.1275
Epoch: [3/20], Batch Num: [405/600]
Discriminator Loss: 0.2601, Generator Loss: 2.6095
D(x): 0.9079, D(G(z)): 0.1329
Epoch: [3/20], Batch Num: [406/600]
Discriminator Loss: 0.2512, Generator Loss: 2.5498
D(x): 0.9131, D(G(z)): 0.1310
Epoch: [3/20], Batch Num: [407/600]
Discriminator Loss: 0.3031, Generator Loss: 2.3644
D(x): 0.8867, D(G(z)): 0.1374
Epoch: [3/20], Batch Num: [408/600]
Discriminator Loss: 0.3529, Generator Loss: 2.3288
D(x): 0.9125, D(G(z)): 0.2053
Epoch: [3/20], Batch Num: [409/600]
Discriminator Loss: 0.3940, Generator Loss: 1.9575
D(x): 0.8778, D(G(z)): 0.1921
Epoch: [3/20], Batch Num: [410/600]
Discriminator Loss: 0.6650, Generator Loss: 1.7757
D(x): 0.8010, D(G(z)): 0.2566
Epoch: [3/20], Batch Num: [411/600]
Discriminator Loss: 0.7406, Generator Loss: 1.5178
D(x): 0.8134, D(G(z)): 0.3256
Epoch: [3/20], Batch Num: [412/600]
Discriminator Loss: 0.7357, Generator Loss: 1.5644
D(x): 0.8514, D(G(z)): 0.3677
Epoch: [3/20], Batch Num: [413/600]
Discriminator Loss: 0.8252, Generator Loss: 1.5036
D(x): 0.8011, D(G(z)): 0.3631
Epoch: [3/20], Batch Num: [414/600]
Discriminator Loss: 1.1773, Generator Loss: 1.3871
D(x): 0.7210, D(G(z)): 0.3752
Epoch: [3/20], Batch Num: [415/600]
Discriminator Loss: 1.4634, Generator Loss: 1.1227
D(x): 0.6543, D(G(z)): 0.4053
Epoch: [3/20], Batch Num: [416/600]
Discriminator Loss: 1.6352, Generator Loss: 0.7545
D(x): 0.7139, D(G(z)): 0.5925
Epoch: [3/20], Batch Num: [417/600]
Discriminator Loss: 1.6575, Generator Loss: 0.6227
D(x): 0.7436, D(G(z)): 0.6392
Epoch: [3/20], Batch Num: [418/600]
Discriminator Loss: 1.6590, Generator Loss: 0.7558
D(x): 0.7352, D(G(z)): 0.6436
Epoch: [3/20], Batch Num: [419/600]
Discriminator Loss: 1.7870, Generator Loss: 0.8046
D(x): 0.6478, D(G(z)): 0.6178
Epoch: [3/20], Batch Num: [420/600]
Discriminator Loss: 1.9453, Generator Loss: 0.7373
D(x): 0.5493, D(G(z)): 0.5876
Epoch: [3/20], Batch Num: [421/600]
Discriminator Loss: 2.2048, Generator Loss: 0.6210
D(x): 0.5580, D(G(z)): 0.6163
Epoch: [3/20], Batch Num: [422/600]
Discriminator Loss: 2.3239, Generator Loss: 0.3864
D(x): 0.5453, D(G(z)): 0.6795
Epoch: [3/20], Batch Num: [423/600]
Discriminator Loss: 2.0247, Generator Loss: 0.3152
D(x): 0.6666, D(G(z)): 0.7236
Epoch: [3/20], Batch Num: [424/600]
Discriminator Loss: 2.1914, Generator Loss: 0.3109
D(x): 0.6374, D(G(z)): 0.7631
Epoch: [3/20], Batch Num: [425/600]
Discriminator Loss: 1.9219, Generator Loss: 0.3337
D(x): 0.7739, D(G(z)): 0.7738
Epoch: [3/20], Batch Num: [426/600]
Discriminator Loss: 1.7261, Generator Loss: 0.4598
D(x): 0.7760, D(G(z)): 0.7387
Epoch: [3/20], Batch Num: [427/600]
Discriminator Loss: 1.6152, Generator Loss: 0.6122
D(x): 0.7124, D(G(z)): 0.6721
Epoch: [3/20], Batch Num: [428/600]
Discriminator Loss: 1.2790, Generator Loss: 0.7689
D(x): 0.7315, D(G(z)): 0.5578
Epoch: [3/20], Batch Num: [429/600]
Discriminator Loss: 1.3488, Generator Loss: 0.9638
D(x): 0.6343, D(G(z)): 0.4948
Epoch: [3/20], Batch Num: [430/600]
Discriminator Loss: 1.2928, Generator Loss: 1.0164
D(x): 0.6153, D(G(z)): 0.4513
Epoch: [3/20], Batch Num: [431/600]
Discriminator Loss: 1.2137, Generator Loss: 1.0077
D(x): 0.6089, D(G(z)): 0.4121
Epoch: [3/20], Batch Num: [432/600]
Discriminator Loss: 1.1250, Generator Loss: 0.9321
D(x): 0.6250, D(G(z)): 0.3764
Epoch: [3/20], Batch Num: [433/600]
Discriminator Loss: 1.0942, Generator Loss: 0.9425
D(x): 0.6894, D(G(z)): 0.4539
Epoch: [3/20], Batch Num: [434/600]
Discriminator Loss: 0.9155, Generator Loss: 0.8757
D(x): 0.7621, D(G(z)): 0.4264
Epoch: [3/20], Batch Num: [435/600]
Discriminator Loss: 0.9270, Generator Loss: 0.9689
D(x): 0.7777, D(G(z)): 0.4475
Epoch: [3/20], Batch Num: [436/600]
Discriminator Loss: 0.8058, Generator Loss: 1.0687
D(x): 0.8344, D(G(z)): 0.4337
Epoch: [3/20], Batch Num: [437/600]
Discriminator Loss: 0.7344, Generator Loss: 1.2797
D(x): 0.8334, D(G(z)): 0.3913
Epoch: [3/20], Batch Num: [438/600]
Discriminator Loss: 0.6585, Generator Loss: 1.6277
D(x): 0.8274, D(G(z)): 0.3465
Epoch: [3/20], Batch Num: [439/600]
Discriminator Loss: 0.5466, Generator Loss: 2.0015
D(x): 0.8051, D(G(z)): 0.2415
Epoch: [3/20], Batch Num: [440/600]
Discriminator Loss: 0.5474, Generator Loss: 2.3687
D(x): 0.7473, D(G(z)): 0.1716
Epoch: [3/20], Batch Num: [441/600]
Discriminator Loss: 0.6821, Generator Loss: 2.4461
D(x): 0.6928, D(G(z)): 0.1485
Epoch: [3/20], Batch Num: [442/600]
Discriminator Loss: 0.4853, Generator Loss: 2.5373
D(x): 0.7603, D(G(z)): 0.1260
Epoch: [3/20], Batch Num: [443/600]
Discriminator Loss: 0.4025, Generator Loss: 2.4405
D(x): 0.8091, D(G(z)): 0.1321
Epoch: [3/20], Batch Num: [444/600]
Discriminator Loss: 0.4296, Generator Loss: 2.5466
D(x): 0.8036, D(G(z)): 0.1403
Epoch: [3/20], Batch Num: [445/600]
Discriminator Loss: 0.3591, Generator Loss: 2.3073
D(x): 0.8658, D(G(z)): 0.1694
Epoch: [3/20], Batch Num: [446/600]
Discriminator Loss: 0.3727, Generator Loss: 2.1920
D(x): 0.8648, D(G(z)): 0.1717
Epoch: [3/20], Batch Num: [447/600]
Discriminator Loss: 0.3411, Generator Loss: 2.3967
D(x): 0.8960, D(G(z)): 0.1845
Epoch: [3/20], Batch Num: [448/600]
Discriminator Loss: 0.2881, Generator Loss: 2.4763
D(x): 0.8999, D(G(z)): 0.1516
Epoch: [3/20], Batch Num: [449/600]
Discriminator Loss: 0.3164, Generator Loss: 2.6663
D(x): 0.8847, D(G(z)): 0.1445
Epoch: [3/20], Batch Num: [450/600]
Discriminator Loss: 0.3334, Generator Loss: 2.9837
D(x): 0.8802, D(G(z)): 0.1508
Epoch: [3/20], Batch Num: [451/600]
Discriminator Loss: 0.2612, Generator Loss: 2.9985
D(x): 0.8849, D(G(z)): 0.1052
Epoch: [3/20], Batch Num: [452/600]
Discriminator Loss: 0.3474, Generator Loss: 3.0135
D(x): 0.8266, D(G(z)): 0.0929
Epoch: [3/20], Batch Num: [453/600]
Discriminator Loss: 0.3896, Generator Loss: 2.7267
D(x): 0.8206, D(G(z)): 0.1122
Epoch: [3/20], Batch Num: [454/600]
Discriminator Loss: 0.4206, Generator Loss: 2.4335
D(x): 0.8065, D(G(z)): 0.1260
Epoch: [3/20], Batch Num: [455/600]
Discriminator Loss: 0.4703, Generator Loss: 2.1162
D(x): 0.8549, D(G(z)): 0.2003
Epoch: [3/20], Batch Num: [456/600]
Discriminator Loss: 0.5303, Generator Loss: 1.8390
D(x): 0.8687, D(G(z)): 0.2532
Epoch: [3/20], Batch Num: [457/600]
Discriminator Loss: 0.5683, Generator Loss: 2.0289
D(x): 0.8801, D(G(z)): 0.3087
Epoch: [3/20], Batch Num: [458/600]
Discriminator Loss: 0.4708, Generator Loss: 2.1289
D(x): 0.8788, D(G(z)): 0.2598
Epoch: [3/20], Batch Num: [459/600]
Discriminator Loss: 0.6334, Generator Loss: 2.3785
D(x): 0.7960, D(G(z)): 0.2238
Epoch: [3/20], Batch Num: [460/600]
Discriminator Loss: 0.7465, Generator Loss: 2.0566
D(x): 0.7168, D(G(z)): 0.2033
Epoch: [3/20], Batch Num: [461/600]
Discriminator Loss: 0.8959, Generator Loss: 1.4718
D(x): 0.7144, D(G(z)): 0.2586
Epoch: [3/20], Batch Num: [462/600]
Discriminator Loss: 1.0538, Generator Loss: 1.0854
D(x): 0.7474, D(G(z)): 0.3931
Epoch: [3/20], Batch Num: [463/600]
Discriminator Loss: 1.1991, Generator Loss: 0.9259
D(x): 0.7838, D(G(z)): 0.5116
Epoch: [3/20], Batch Num: [464/600]
Discriminator Loss: 1.1784, Generator Loss: 1.2285
D(x): 0.7775, D(G(z)): 0.5304
Epoch: [3/20], Batch Num: [465/600]
Discriminator Loss: 1.1987, Generator Loss: 1.4576
D(x): 0.7232, D(G(z)): 0.4257
Epoch: [3/20], Batch Num: [466/600]
Discriminator Loss: 1.3574, Generator Loss: 1.5922
D(x): 0.6325, D(G(z)): 0.3633
Epoch: [3/20], Batch Num: [467/600]
Discriminator Loss: 1.1640, Generator Loss: 1.4310
D(x): 0.6587, D(G(z)): 0.3205
Epoch: [3/20], Batch Num: [468/600]
Discriminator Loss: 0.9074, Generator Loss: 1.4340
D(x): 0.7337, D(G(z)): 0.3292
Epoch: [3/20], Batch Num: [469/600]
Discriminator Loss: 0.7242, Generator Loss: 1.8711
D(x): 0.8426, D(G(z)): 0.3465
Epoch: [3/20], Batch Num: [470/600]
Discriminator Loss: 0.6906, Generator Loss: 2.3769
D(x): 0.8147, D(G(z)): 0.2587
Epoch: [3/20], Batch Num: [471/600]
Discriminator Loss: 0.7095, Generator Loss: 2.3209
D(x): 0.7213, D(G(z)): 0.1953
Epoch: [3/20], Batch Num: [472/600]
Discriminator Loss: 0.5790, Generator Loss: 2.4460
D(x): 0.7724, D(G(z)): 0.1585
Epoch: [3/20], Batch Num: [473/600]
Discriminator Loss: 0.3655, Generator Loss: 3.0551
D(x): 0.8933, D(G(z)): 0.1494
Epoch: [3/20], Batch Num: [474/600]
Discriminator Loss: 0.2884, Generator Loss: 3.2353
D(x): 0.8985, D(G(z)): 0.1184
Epoch: [3/20], Batch Num: [475/600]
Discriminator Loss: 0.3008, Generator Loss: 3.1442
D(x): 0.8809, D(G(z)): 0.1230
Epoch: [3/20], Batch Num: [476/600]
Discriminator Loss: 0.3644, Generator Loss: 3.3112
D(x): 0.8539, D(G(z)): 0.1090
Epoch: [3/20], Batch Num: [477/600]
Discriminator Loss: 0.3439, Generator Loss: 3.2066
D(x): 0.8711, D(G(z)): 0.1027
Epoch: [3/20], Batch Num: [478/600]
Discriminator Loss: 0.3934, Generator Loss: 3.2035
D(x): 0.8591, D(G(z)): 0.1003
Epoch: [3/20], Batch Num: [479/600]
Discriminator Loss: 0.3350, Generator Loss: 3.0448
D(x): 0.8775, D(G(z)): 0.1249
Epoch: [3/20], Batch Num: [480/600]
Discriminator Loss: 0.3748, Generator Loss: 2.7403
D(x): 0.8469, D(G(z)): 0.1059
Epoch: [3/20], Batch Num: [481/600]
Discriminator Loss: 0.6108, Generator Loss: 2.4082
D(x): 0.7965, D(G(z)): 0.1929
Epoch: [3/20], Batch Num: [482/600]
Discriminator Loss: 0.5274, Generator Loss: 2.3835
D(x): 0.8241, D(G(z)): 0.1911
Epoch: [3/20], Batch Num: [483/600]
Discriminator Loss: 0.5723, Generator Loss: 2.5495
D(x): 0.8402, D(G(z)): 0.2509
Epoch: [3/20], Batch Num: [484/600]
Discriminator Loss: 0.5739, Generator Loss: 2.6888
D(x): 0.8262, D(G(z)): 0.2124
Epoch: [3/20], Batch Num: [485/600]
Discriminator Loss: 0.7876, Generator Loss: 2.2970
D(x): 0.7286, D(G(z)): 0.2020
Epoch: [3/20], Batch Num: [486/600]
Discriminator Loss: 1.2029, Generator Loss: 1.8380
D(x): 0.6512, D(G(z)): 0.2677
Epoch: [3/20], Batch Num: [487/600]
Discriminator Loss: 1.1067, Generator Loss: 1.2100
D(x): 0.7297, D(G(z)): 0.3697
Epoch: [3/20], Batch Num: [488/600]
Discriminator Loss: 1.5453, Generator Loss: 1.2746
D(x): 0.6696, D(G(z)): 0.4836
Epoch: [3/20], Batch Num: [489/600]
Discriminator Loss: 1.5692, Generator Loss: 1.8788
D(x): 0.6949, D(G(z)): 0.5039
Epoch: [3/20], Batch Num: [490/600]
Discriminator Loss: 1.7295, Generator Loss: 1.9555
D(x): 0.5713, D(G(z)): 0.3945
Epoch: [3/20], Batch Num: [491/600]
Discriminator Loss: 2.0399, Generator Loss: 1.1543
D(x): 0.4351, D(G(z)): 0.2783
Epoch: [3/20], Batch Num: [492/600]
Discriminator Loss: 2.2605, Generator Loss: 0.7762
D(x): 0.5636, D(G(z)): 0.5577
Epoch: [3/20], Batch Num: [493/600]
Discriminator Loss: 2.3106, Generator Loss: 0.7541
D(x): 0.5536, D(G(z)): 0.5901
Epoch: [3/20], Batch Num: [494/600]
Discriminator Loss: 2.2376, Generator Loss: 0.8953
D(x): 0.6036, D(G(z)): 0.6309
Epoch: [3/20], Batch Num: [495/600]
Discriminator Loss: 2.4942, Generator Loss: 1.2070
D(x): 0.4631, D(G(z)): 0.5220
Epoch: [3/20], Batch Num: [496/600]
Discriminator Loss: 2.4780, Generator Loss: 1.0843
D(x): 0.4435, D(G(z)): 0.4487
Epoch: [3/20], Batch Num: [497/600]
Discriminator Loss: 2.1236, Generator Loss: 0.8070
D(x): 0.5230, D(G(z)): 0.4759
Epoch: [3/20], Batch Num: [498/600]
Discriminator Loss: 1.9861, Generator Loss: 0.7145
D(x): 0.5321, D(G(z)): 0.5225
Epoch: [3/20], Batch Num: [499/600]
Discriminator Loss: 1.7042, Generator Loss: 0.7620
D(x): 0.5850, D(G(z)): 0.5157
Epoch: 3, Batch Num: [500/600]

Epoch: [3/20], Batch Num: [500/600]
Discriminator Loss: 1.6079, Generator Loss: 0.8807
D(x): 0.6508, D(G(z)): 0.5389
Epoch: [3/20], Batch Num: [501/600]
Discriminator Loss: 1.3683, Generator Loss: 1.0046
D(x): 0.6661, D(G(z)): 0.4877
Epoch: [3/20], Batch Num: [502/600]
Discriminator Loss: 1.1391, Generator Loss: 1.3360
D(x): 0.6704, D(G(z)): 0.4046
Epoch: [3/20], Batch Num: [503/600]
Discriminator Loss: 1.0845, Generator Loss: 1.4771
D(x): 0.6606, D(G(z)): 0.3330
Epoch: [3/20], Batch Num: [504/600]
Discriminator Loss: 0.9799, Generator Loss: 1.7270
D(x): 0.6268, D(G(z)): 0.2447
Epoch: [3/20], Batch Num: [505/600]
Discriminator Loss: 0.8007, Generator Loss: 1.7561
D(x): 0.6847, D(G(z)): 0.2326
Epoch: [3/20], Batch Num: [506/600]
Discriminator Loss: 0.6766, Generator Loss: 1.9916
D(x): 0.7267, D(G(z)): 0.2085
Epoch: [3/20], Batch Num: [507/600]
Discriminator Loss: 0.6079, Generator Loss: 2.0023
D(x): 0.7397, D(G(z)): 0.1714
Epoch: [3/20], Batch Num: [508/600]
Discriminator Loss: 0.5200, Generator Loss: 2.0018
D(x): 0.7701, D(G(z)): 0.1664
Epoch: [3/20], Batch Num: [509/600]
Discriminator Loss: 0.4312, Generator Loss: 2.1094
D(x): 0.8232, D(G(z)): 0.1552
Epoch: [3/20], Batch Num: [510/600]
Discriminator Loss: 0.4297, Generator Loss: 2.3095
D(x): 0.8255, D(G(z)): 0.1636
Epoch: [3/20], Batch Num: [511/600]
Discriminator Loss: 0.4087, Generator Loss: 2.4512
D(x): 0.8428, D(G(z)): 0.1645
Epoch: [3/20], Batch Num: [512/600]
Discriminator Loss: 0.3738, Generator Loss: 2.4926
D(x): 0.8551, D(G(z)): 0.1621
Epoch: [3/20], Batch Num: [513/600]
Discriminator Loss: 0.2951, Generator Loss: 2.6741
D(x): 0.8912, D(G(z)): 0.1451
Epoch: [3/20], Batch Num: [514/600]
Discriminator Loss: 0.3427, Generator Loss: 2.7820
D(x): 0.8427, D(G(z)): 0.1094
Epoch: [3/20], Batch Num: [515/600]
Discriminator Loss: 0.2528, Generator Loss: 2.7715
D(x): 0.8861, D(G(z)): 0.1001
Epoch: [3/20], Batch Num: [516/600]
Discriminator Loss: 0.3053, Generator Loss: 2.8549
D(x): 0.8699, D(G(z)): 0.1081
Epoch: [3/20], Batch Num: [517/600]
Discriminator Loss: 0.3267, Generator Loss: 2.9171
D(x): 0.8486, D(G(z)): 0.1125
Epoch: [3/20], Batch Num: [518/600]
Discriminator Loss: 0.3131, Generator Loss: 2.5373
D(x): 0.8487, D(G(z)): 0.1062
Epoch: [3/20], Batch Num: [519/600]
Discriminator Loss: 0.3001, Generator Loss: 2.5800
D(x): 0.8794, D(G(z)): 0.1352
Epoch: [3/20], Batch Num: [520/600]
Discriminator Loss: 0.3269, Generator Loss: 2.3409
D(x): 0.8520, D(G(z)): 0.1224
Epoch: [3/20], Batch Num: [521/600]
Discriminator Loss: 0.2840, Generator Loss: 2.4878
D(x): 0.9009, D(G(z)): 0.1477
Epoch: [3/20], Batch Num: [522/600]
Discriminator Loss: 0.3533, Generator Loss: 2.3975
D(x): 0.8736, D(G(z)): 0.1695
Epoch: [3/20], Batch Num: [523/600]
Discriminator Loss: 0.3721, Generator Loss: 2.3802
D(x): 0.8716, D(G(z)): 0.1682
Epoch: [3/20], Batch Num: [524/600]
Discriminator Loss: 0.4024, Generator Loss: 2.2721
D(x): 0.8387, D(G(z)): 0.1638
Epoch: [3/20], Batch Num: [525/600]
Discriminator Loss: 0.3366, Generator Loss: 2.4093
D(x): 0.8728, D(G(z)): 0.1490
Epoch: [3/20], Batch Num: [526/600]
Discriminator Loss: 0.5215, Generator Loss: 2.3159
D(x): 0.7870, D(G(z)): 0.1779
Epoch: [3/20], Batch Num: [527/600]
Discriminator Loss: 0.5506, Generator Loss: 2.1231
D(x): 0.8034, D(G(z)): 0.2013
Epoch: [3/20], Batch Num: [528/600]
Discriminator Loss: 0.5275, Generator Loss: 1.9573
D(x): 0.8189, D(G(z)): 0.2285
Epoch: [3/20], Batch Num: [529/600]
Discriminator Loss: 0.5000, Generator Loss: 1.9161
D(x): 0.8381, D(G(z)): 0.2289
Epoch: [3/20], Batch Num: [530/600]
Discriminator Loss: 0.6359, Generator Loss: 1.8192
D(x): 0.8059, D(G(z)): 0.2525
Epoch: [3/20], Batch Num: [531/600]
Discriminator Loss: 0.6426, Generator Loss: 1.6927
D(x): 0.7908, D(G(z)): 0.2511
Epoch: [3/20], Batch Num: [532/600]
Discriminator Loss: 0.9610, Generator Loss: 1.5857
D(x): 0.7193, D(G(z)): 0.3074
Epoch: [3/20], Batch Num: [533/600]
Discriminator Loss: 0.8730, Generator Loss: 1.5837
D(x): 0.7503, D(G(z)): 0.3441
Epoch: [3/20], Batch Num: [534/600]
Discriminator Loss: 0.8470, Generator Loss: 1.4452
D(x): 0.7647, D(G(z)): 0.3287
Epoch: [3/20], Batch Num: [535/600]
Discriminator Loss: 0.8668, Generator Loss: 1.4131
D(x): 0.7345, D(G(z)): 0.3047
Epoch: [3/20], Batch Num: [536/600]
Discriminator Loss: 1.1033, Generator Loss: 1.2133
D(x): 0.6766, D(G(z)): 0.3618
Epoch: [3/20], Batch Num: [537/600]
Discriminator Loss: 1.0658, Generator Loss: 1.2004
D(x): 0.6939, D(G(z)): 0.3569
Epoch: [3/20], Batch Num: [538/600]
Discriminator Loss: 1.2782, Generator Loss: 1.0368
D(x): 0.6622, D(G(z)): 0.3900
Epoch: [3/20], Batch Num: [539/600]
Discriminator Loss: 1.1464, Generator Loss: 1.2233
D(x): 0.7262, D(G(z)): 0.4244
Epoch: [3/20], Batch Num: [540/600]
Discriminator Loss: 1.4079, Generator Loss: 1.2912
D(x): 0.6157, D(G(z)): 0.4186
Epoch: [3/20], Batch Num: [541/600]
Discriminator Loss: 1.3020, Generator Loss: 1.2947
D(x): 0.6307, D(G(z)): 0.3954
Epoch: [3/20], Batch Num: [542/600]
Discriminator Loss: 1.3694, Generator Loss: 1.3770
D(x): 0.6543, D(G(z)): 0.4305
Epoch: [3/20], Batch Num: [543/600]
Discriminator Loss: 1.3038, Generator Loss: 1.1787
D(x): 0.6007, D(G(z)): 0.3400
Epoch: [3/20], Batch Num: [544/600]
Discriminator Loss: 1.2885, Generator Loss: 1.0464
D(x): 0.6759, D(G(z)): 0.3918
Epoch: [3/20], Batch Num: [545/600]
Discriminator Loss: 1.3592, Generator Loss: 1.1195
D(x): 0.6763, D(G(z)): 0.4762
Epoch: [3/20], Batch Num: [546/600]
Discriminator Loss: 1.2487, Generator Loss: 1.2692
D(x): 0.6870, D(G(z)): 0.4281
Epoch: [3/20], Batch Num: [547/600]
Discriminator Loss: 1.1157, Generator Loss: 1.4884
D(x): 0.6655, D(G(z)): 0.3409
Epoch: [3/20], Batch Num: [548/600]
Discriminator Loss: 1.3853, Generator Loss: 1.3385
D(x): 0.5717, D(G(z)): 0.3457
Epoch: [3/20], Batch Num: [549/600]
Discriminator Loss: 1.3971, Generator Loss: 1.1265
D(x): 0.5751, D(G(z)): 0.3246
Epoch: [3/20], Batch Num: [550/600]
Discriminator Loss: 1.1217, Generator Loss: 1.1189
D(x): 0.6975, D(G(z)): 0.4198
Epoch: [3/20], Batch Num: [551/600]
Discriminator Loss: 1.0746, Generator Loss: 1.1288
D(x): 0.6965, D(G(z)): 0.4076
Epoch: [3/20], Batch Num: [552/600]
Discriminator Loss: 1.0810, Generator Loss: 1.2885
D(x): 0.6784, D(G(z)): 0.4109
Epoch: [3/20], Batch Num: [553/600]
Discriminator Loss: 0.9263, Generator Loss: 1.3941
D(x): 0.7166, D(G(z)): 0.3323
Epoch: [3/20], Batch Num: [554/600]
Discriminator Loss: 0.9891, Generator Loss: 1.5965
D(x): 0.6857, D(G(z)): 0.3388
Epoch: [3/20], Batch Num: [555/600]
Discriminator Loss: 1.0248, Generator Loss: 1.8133
D(x): 0.6424, D(G(z)): 0.2846
Epoch: [3/20], Batch Num: [556/600]
Discriminator Loss: 0.8386, Generator Loss: 1.8096
D(x): 0.6907, D(G(z)): 0.2414
Epoch: [3/20], Batch Num: [557/600]
Discriminator Loss: 0.7414, Generator Loss: 1.8292
D(x): 0.7569, D(G(z)): 0.2233
Epoch: [3/20], Batch Num: [558/600]
Discriminator Loss: 0.8862, Generator Loss: 1.7419
D(x): 0.6902, D(G(z)): 0.2371
Epoch: [3/20], Batch Num: [559/600]
Discriminator Loss: 0.7229, Generator Loss: 1.7041
D(x): 0.7705, D(G(z)): 0.2610
Epoch: [3/20], Batch Num: [560/600]
Discriminator Loss: 0.6135, Generator Loss: 1.9027
D(x): 0.8233, D(G(z)): 0.2754
Epoch: [3/20], Batch Num: [561/600]
Discriminator Loss: 0.6198, Generator Loss: 2.1497
D(x): 0.8164, D(G(z)): 0.2484
Epoch: [3/20], Batch Num: [562/600]
Discriminator Loss: 0.5312, Generator Loss: 2.4136
D(x): 0.8191, D(G(z)): 0.1967
Epoch: [3/20], Batch Num: [563/600]
Discriminator Loss: 0.4787, Generator Loss: 2.8236
D(x): 0.8367, D(G(z)): 0.1828
Epoch: [3/20], Batch Num: [564/600]
Discriminator Loss: 0.5221, Generator Loss: 2.8735
D(x): 0.7780, D(G(z)): 0.1187
Epoch: [3/20], Batch Num: [565/600]
Discriminator Loss: 0.4333, Generator Loss: 2.7719
D(x): 0.8042, D(G(z)): 0.1276
Epoch: [3/20], Batch Num: [566/600]
Discriminator Loss: 0.4363, Generator Loss: 2.6311
D(x): 0.8025, D(G(z)): 0.1125
Epoch: [3/20], Batch Num: [567/600]
Discriminator Loss: 0.4825, Generator Loss: 2.4418
D(x): 0.8389, D(G(z)): 0.1672
Epoch: [3/20], Batch Num: [568/600]
Discriminator Loss: 0.4631, Generator Loss: 2.0675
D(x): 0.8161, D(G(z)): 0.1572
Epoch: [3/20], Batch Num: [569/600]
Discriminator Loss: 0.4736, Generator Loss: 2.1163
D(x): 0.8774, D(G(z)): 0.2080
Epoch: [3/20], Batch Num: [570/600]
Discriminator Loss: 0.5009, Generator Loss: 2.1772
D(x): 0.8641, D(G(z)): 0.2361
Epoch: [3/20], Batch Num: [571/600]
Discriminator Loss: 0.4383, Generator Loss: 2.4598
D(x): 0.8694, D(G(z)): 0.1966
Epoch: [3/20], Batch Num: [572/600]
Discriminator Loss: 0.4920, Generator Loss: 2.9517
D(x): 0.8434, D(G(z)): 0.1937
Epoch: [3/20], Batch Num: [573/600]
Discriminator Loss: 0.5249, Generator Loss: 2.8128
D(x): 0.8043, D(G(z)): 0.1572
Epoch: [3/20], Batch Num: [574/600]
Discriminator Loss: 0.4464, Generator Loss: 2.8586
D(x): 0.8256, D(G(z)): 0.1108
Epoch: [3/20], Batch Num: [575/600]
Discriminator Loss: 0.5156, Generator Loss: 2.5066
D(x): 0.8262, D(G(z)): 0.1387
Epoch: [3/20], Batch Num: [576/600]
Discriminator Loss: 0.3219, Generator Loss: 2.3109
D(x): 0.8935, D(G(z)): 0.1492
Epoch: [3/20], Batch Num: [577/600]
Discriminator Loss: 0.4264, Generator Loss: 2.3593
D(x): 0.8821, D(G(z)): 0.1932
Epoch: [3/20], Batch Num: [578/600]
Discriminator Loss: 0.5582, Generator Loss: 2.0637
D(x): 0.8469, D(G(z)): 0.2361
Epoch: [3/20], Batch Num: [579/600]
Discriminator Loss: 0.4189, Generator Loss: 2.6209
D(x): 0.8975, D(G(z)): 0.2071
Epoch: [3/20], Batch Num: [580/600]
Discriminator Loss: 0.4480, Generator Loss: 2.4934
D(x): 0.8795, D(G(z)): 0.1612
Epoch: [3/20], Batch Num: [581/600]
Discriminator Loss: 0.4181, Generator Loss: 2.7143
D(x): 0.8652, D(G(z)): 0.1486
Epoch: [3/20], Batch Num: [582/600]
Discriminator Loss: 0.4933, Generator Loss: 2.5059
D(x): 0.8165, D(G(z)): 0.1254
Epoch: [3/20], Batch Num: [583/600]
Discriminator Loss: 0.4229, Generator Loss: 2.6113
D(x): 0.8835, D(G(z)): 0.1779
Epoch: [3/20], Batch Num: [584/600]
Discriminator Loss: 0.4064, Generator Loss: 2.3356
D(x): 0.8564, D(G(z)): 0.1431
Epoch: [3/20], Batch Num: [585/600]
Discriminator Loss: 0.4078, Generator Loss: 2.3424
D(x): 0.8636, D(G(z)): 0.1491
Epoch: [3/20], Batch Num: [586/600]
Discriminator Loss: 0.5026, Generator Loss: 2.2270
D(x): 0.8684, D(G(z)): 0.2111
Epoch: [3/20], Batch Num: [587/600]
Discriminator Loss: 0.5229, Generator Loss: 2.1509
D(x): 0.8808, D(G(z)): 0.2242
Epoch: [3/20], Batch Num: [588/600]
Discriminator Loss: 0.3924, Generator Loss: 2.4170
D(x): 0.8904, D(G(z)): 0.1905
Epoch: [3/20], Batch Num: [589/600]
Discriminator Loss: 0.5117, Generator Loss: 2.5263
D(x): 0.8400, D(G(z)): 0.1727
Epoch: [3/20], Batch Num: [590/600]
Discriminator Loss: 0.4568, Generator Loss: 2.7059
D(x): 0.8426, D(G(z)): 0.1630
Epoch: [3/20], Batch Num: [591/600]
Discriminator Loss: 0.3575, Generator Loss: 2.6809
D(x): 0.8935, D(G(z)): 0.1296
Epoch: [3/20], Batch Num: [592/600]
Discriminator Loss: 0.4898, Generator Loss: 2.3115
D(x): 0.8701, D(G(z)): 0.1945
Epoch: [3/20], Batch Num: [593/600]
Discriminator Loss: 0.4982, Generator Loss: 2.4024
D(x): 0.8538, D(G(z)): 0.1843
Epoch: [3/20], Batch Num: [594/600]
Discriminator Loss: 0.3590, Generator Loss: 2.5136
D(x): 0.8954, D(G(z)): 0.1615
Epoch: [3/20], Batch Num: [595/600]
Discriminator Loss: 0.3777, Generator Loss: 2.4947
D(x): 0.8933, D(G(z)): 0.1689
Epoch: [3/20], Batch Num: [596/600]
Discriminator Loss: 0.3802, Generator Loss: 2.8424
D(x): 0.8876, D(G(z)): 0.1560
Epoch: [3/20], Batch Num: [597/600]
Discriminator Loss: 0.4442, Generator Loss: 2.8341
D(x): 0.8543, D(G(z)): 0.1622
Epoch: [3/20], Batch Num: [598/600]
Discriminator Loss: 0.2471, Generator Loss: 2.6380
D(x): 0.9131, D(G(z)): 0.1134
Epoch: [3/20], Batch Num: [599/600]
Discriminator Loss: 0.3303, Generator Loss: 2.8134
D(x): 0.9145, D(G(z)): 0.1348
Epoch: 4, Batch Num: [0/600]

Epoch: [4/20], Batch Num: [0/600]
Discriminator Loss: 0.4060, Generator Loss: 2.5524
D(x): 0.8747, D(G(z)): 0.1370
Epoch: [4/20], Batch Num: [1/600]
Discriminator Loss: 0.4005, Generator Loss: 2.8931
D(x): 0.8957, D(G(z)): 0.1601
Epoch: [4/20], Batch Num: [2/600]
Discriminator Loss: 0.3188, Generator Loss: 2.8174
D(x): 0.9344, D(G(z)): 0.1717
Epoch: [4/20], Batch Num: [3/600]
Discriminator Loss: 0.3292, Generator Loss: 2.8893
D(x): 0.9049, D(G(z)): 0.1620
Epoch: [4/20], Batch Num: [4/600]
Discriminator Loss: 0.4939, Generator Loss: 2.9301
D(x): 0.8408, D(G(z)): 0.1343
Epoch: [4/20], Batch Num: [5/600]
Discriminator Loss: 0.4142, Generator Loss: 2.5989
D(x): 0.8399, D(G(z)): 0.1310
Epoch: [4/20], Batch Num: [6/600]
Discriminator Loss: 0.3689, Generator Loss: 2.6180
D(x): 0.9166, D(G(z)): 0.1715
Epoch: [4/20], Batch Num: [7/600]
Discriminator Loss: 0.5670, Generator Loss: 2.4005
D(x): 0.8348, D(G(z)): 0.1867
Epoch: [4/20], Batch Num: [8/600]
Discriminator Loss: 0.5991, Generator Loss: 2.0083
D(x): 0.8232, D(G(z)): 0.2110
Epoch: [4/20], Batch Num: [9/600]
Discriminator Loss: 0.4858, Generator Loss: 2.2023
D(x): 0.8817, D(G(z)): 0.2126
Epoch: [4/20], Batch Num: [10/600]
Discriminator Loss: 0.5467, Generator Loss: 2.4343
D(x): 0.8864, D(G(z)): 0.2561
Epoch: [4/20], Batch Num: [11/600]
Discriminator Loss: 0.6960, Generator Loss: 2.5981
D(x): 0.8002, D(G(z)): 0.2109
Epoch: [4/20], Batch Num: [12/600]
Discriminator Loss: 0.7080, Generator Loss: 2.4357
D(x): 0.7892, D(G(z)): 0.1929
Epoch: [4/20], Batch Num: [13/600]
Discriminator Loss: 0.6076, Generator Loss: 2.3207
D(x): 0.8096, D(G(z)): 0.1881
Epoch: [4/20], Batch Num: [14/600]
Discriminator Loss: 0.8869, Generator Loss: 1.9288
D(x): 0.7753, D(G(z)): 0.2696
Epoch: [4/20], Batch Num: [15/600]
Discriminator Loss: 0.6496, Generator Loss: 2.0200
D(x): 0.8265, D(G(z)): 0.2553
Epoch: [4/20], Batch Num: [16/600]
Discriminator Loss: 0.9054, Generator Loss: 2.0551
D(x): 0.7959, D(G(z)): 0.3572
Epoch: [4/20], Batch Num: [17/600]
Discriminator Loss: 0.9113, Generator Loss: 2.3876
D(x): 0.7702, D(G(z)): 0.2827
Epoch: [4/20], Batch Num: [18/600]
Discriminator Loss: 0.9166, Generator Loss: 2.3574
D(x): 0.7549, D(G(z)): 0.2144
Epoch: [4/20], Batch Num: [19/600]
Discriminator Loss: 0.8287, Generator Loss: 2.1857
D(x): 0.7543, D(G(z)): 0.2291
Epoch: [4/20], Batch Num: [20/600]
Discriminator Loss: 0.9822, Generator Loss: 1.7439
D(x): 0.7458, D(G(z)): 0.2911
Epoch: [4/20], Batch Num: [21/600]
Discriminator Loss: 0.8953, Generator Loss: 1.8658
D(x): 0.8249, D(G(z)): 0.3607
Epoch: [4/20], Batch Num: [22/600]
Discriminator Loss: 0.9584, Generator Loss: 2.1011
D(x): 0.7442, D(G(z)): 0.3069
Epoch: [4/20], Batch Num: [23/600]
Discriminator Loss: 0.8820, Generator Loss: 2.1118
D(x): 0.7533, D(G(z)): 0.2253
Epoch: [4/20], Batch Num: [24/600]
Discriminator Loss: 0.8309, Generator Loss: 1.7859
D(x): 0.7630, D(G(z)): 0.2160
Epoch: [4/20], Batch Num: [25/600]
Discriminator Loss: 0.9380, Generator Loss: 1.8391
D(x): 0.7727, D(G(z)): 0.2796
Epoch: [4/20], Batch Num: [26/600]
Discriminator Loss: 0.6189, Generator Loss: 2.0211
D(x): 0.8518, D(G(z)): 0.2620
Epoch: [4/20], Batch Num: [27/600]
Discriminator Loss: 0.5453, Generator Loss: 2.5463
D(x): 0.8414, D(G(z)): 0.1801
Epoch: [4/20], Batch Num: [28/600]
Discriminator Loss: 0.4709, Generator Loss: 2.3146
D(x): 0.8762, D(G(z)): 0.1905
Epoch: [4/20], Batch Num: [29/600]
Discriminator Loss: 0.5348, Generator Loss: 3.1495
D(x): 0.8482, D(G(z)): 0.2012
Epoch: [4/20], Batch Num: [30/600]
Discriminator Loss: 0.4687, Generator Loss: 3.2118
D(x): 0.8318, D(G(z)): 0.1418
Epoch: [4/20], Batch Num: [31/600]
Discriminator Loss: 0.5241, Generator Loss: 3.3031
D(x): 0.8299, D(G(z)): 0.1745
Epoch: [4/20], Batch Num: [32/600]
Discriminator Loss: 0.4251, Generator Loss: 2.8917
D(x): 0.8817, D(G(z)): 0.1444
Epoch: [4/20], Batch Num: [33/600]
Discriminator Loss: 0.4778, Generator Loss: 2.9496
D(x): 0.8745, D(G(z)): 0.1664
Epoch: [4/20], Batch Num: [34/600]
Discriminator Loss: 0.5435, Generator Loss: 2.6186
D(x): 0.8936, D(G(z)): 0.2244
Epoch: [4/20], Batch Num: [35/600]
Discriminator Loss: 0.5906, Generator Loss: 2.9062
D(x): 0.8343, D(G(z)): 0.1934
Epoch: [4/20], Batch Num: [36/600]
Discriminator Loss: 0.4723, Generator Loss: 3.4450
D(x): 0.9144, D(G(z)): 0.2263
Epoch: [4/20], Batch Num: [37/600]
Discriminator Loss: 0.4924, Generator Loss: 3.2847
D(x): 0.8784, D(G(z)): 0.1905
Epoch: [4/20], Batch Num: [38/600]
Discriminator Loss: 0.5437, Generator Loss: 4.0449
D(x): 0.8878, D(G(z)): 0.1965
Epoch: [4/20], Batch Num: [39/600]
Discriminator Loss: 0.6109, Generator Loss: 3.8794
D(x): 0.8292, D(G(z)): 0.2007
Epoch: [4/20], Batch Num: [40/600]
Discriminator Loss: 0.6776, Generator Loss: 3.7801
D(x): 0.8027, D(G(z)): 0.1910
Epoch: [4/20], Batch Num: [41/600]
Discriminator Loss: 0.6639, Generator Loss: 3.5049
D(x): 0.8418, D(G(z)): 0.2263
Epoch: [4/20], Batch Num: [42/600]
Discriminator Loss: 0.6292, Generator Loss: 3.5813
D(x): 0.8592, D(G(z)): 0.2491
Epoch: [4/20], Batch Num: [43/600]
Discriminator Loss: 0.5766, Generator Loss: 3.0323
D(x): 0.8851, D(G(z)): 0.2276
Epoch: [4/20], Batch Num: [44/600]
Discriminator Loss: 0.5992, Generator Loss: 3.3798
D(x): 0.8733, D(G(z)): 0.2278
Epoch: [4/20], Batch Num: [45/600]
Discriminator Loss: 0.9306, Generator Loss: 3.0566
D(x): 0.8022, D(G(z)): 0.2711
Epoch: [4/20], Batch Num: [46/600]
Discriminator Loss: 0.8233, Generator Loss: 3.4619
D(x): 0.7998, D(G(z)): 0.2476
Epoch: [4/20], Batch Num: [47/600]
Discriminator Loss: 1.0605, Generator Loss: 3.5710
D(x): 0.7471, D(G(z)): 0.2580
Epoch: [4/20], Batch Num: [48/600]
Discriminator Loss: 1.3114, Generator Loss: 2.7797
D(x): 0.7020, D(G(z)): 0.3343
Epoch: [4/20], Batch Num: [49/600]
Discriminator Loss: 1.2314, Generator Loss: 2.4445
D(x): 0.7397, D(G(z)): 0.3379
Epoch: [4/20], Batch Num: [50/600]
Discriminator Loss: 1.5196, Generator Loss: 2.2480
D(x): 0.7016, D(G(z)): 0.3937
Epoch: [4/20], Batch Num: [51/600]
Discriminator Loss: 1.3288, Generator Loss: 1.7646
D(x): 0.6970, D(G(z)): 0.3785
Epoch: [4/20], Batch Num: [52/600]
Discriminator Loss: 1.2647, Generator Loss: 2.1498
D(x): 0.7008, D(G(z)): 0.3888
Epoch: [4/20], Batch Num: [53/600]
Discriminator Loss: 1.3669, Generator Loss: 2.4520
D(x): 0.6636, D(G(z)): 0.3456
Epoch: [4/20], Batch Num: [54/600]
Discriminator Loss: 1.1242, Generator Loss: 2.4190
D(x): 0.6716, D(G(z)): 0.3014
Epoch: [4/20], Batch Num: [55/600]
Discriminator Loss: 1.1088, Generator Loss: 2.3754
D(x): 0.6754, D(G(z)): 0.2662
Epoch: [4/20], Batch Num: [56/600]
Discriminator Loss: 1.6154, Generator Loss: 1.8335
D(x): 0.5809, D(G(z)): 0.2630
Epoch: [4/20], Batch Num: [57/600]
Discriminator Loss: 1.2033, Generator Loss: 1.5776
D(x): 0.6611, D(G(z)): 0.3243
Epoch: [4/20], Batch Num: [58/600]
Discriminator Loss: 1.1196, Generator Loss: 1.7349
D(x): 0.7121, D(G(z)): 0.3526
Epoch: [4/20], Batch Num: [59/600]
Discriminator Loss: 0.9003, Generator Loss: 2.2330
D(x): 0.8165, D(G(z)): 0.3689
Epoch: [4/20], Batch Num: [60/600]
Discriminator Loss: 1.0585, Generator Loss: 2.8074
D(x): 0.7676, D(G(z)): 0.3086
Epoch: [4/20], Batch Num: [61/600]
Discriminator Loss: 0.7240, Generator Loss: 2.8988
D(x): 0.7257, D(G(z)): 0.1331
Epoch: [4/20], Batch Num: [62/600]
Discriminator Loss: 0.7907, Generator Loss: 3.2224
D(x): 0.7010, D(G(z)): 0.1446
Epoch: [4/20], Batch Num: [63/600]
Discriminator Loss: 0.8263, Generator Loss: 2.8006
D(x): 0.6753, D(G(z)): 0.1416
Epoch: [4/20], Batch Num: [64/600]
Discriminator Loss: 0.6115, Generator Loss: 2.2523
D(x): 0.7922, D(G(z)): 0.1665
Epoch: [4/20], Batch Num: [65/600]
Discriminator Loss: 0.6237, Generator Loss: 2.2516
D(x): 0.7866, D(G(z)): 0.2035
Epoch: [4/20], Batch Num: [66/600]
Discriminator Loss: 0.6341, Generator Loss: 2.3118
D(x): 0.8036, D(G(z)): 0.2299
Epoch: [4/20], Batch Num: [67/600]
Discriminator Loss: 0.5855, Generator Loss: 2.1777
D(x): 0.8249, D(G(z)): 0.2409
Epoch: [4/20], Batch Num: [68/600]
Discriminator Loss: 0.5469, Generator Loss: 2.1291
D(x): 0.8505, D(G(z)): 0.2448
Epoch: [4/20], Batch Num: [69/600]
Discriminator Loss: 0.4966, Generator Loss: 2.8380
D(x): 0.8583, D(G(z)): 0.2072
Epoch: [4/20], Batch Num: [70/600]
Discriminator Loss: 0.4030, Generator Loss: 2.6841
D(x): 0.8389, D(G(z)): 0.1513
Epoch: [4/20], Batch Num: [71/600]
Discriminator Loss: 0.5494, Generator Loss: 2.7189
D(x): 0.7770, D(G(z)): 0.1321
Epoch: [4/20], Batch Num: [72/600]
Discriminator Loss: 0.5551, Generator Loss: 2.5982
D(x): 0.8095, D(G(z)): 0.1656
Epoch: [4/20], Batch Num: [73/600]
Discriminator Loss: 0.6499, Generator Loss: 2.4173
D(x): 0.7340, D(G(z)): 0.1520
Epoch: [4/20], Batch Num: [74/600]
Discriminator Loss: 0.6650, Generator Loss: 1.9708
D(x): 0.7553, D(G(z)): 0.2030
Epoch: [4/20], Batch Num: [75/600]
Discriminator Loss: 0.7329, Generator Loss: 1.7806
D(x): 0.7929, D(G(z)): 0.2780
Epoch: [4/20], Batch Num: [76/600]
Discriminator Loss: 0.8439, Generator Loss: 1.7758
D(x): 0.7987, D(G(z)): 0.3474
Epoch: [4/20], Batch Num: [77/600]
Discriminator Loss: 0.7301, Generator Loss: 2.0220
D(x): 0.8357, D(G(z)): 0.3384
Epoch: [4/20], Batch Num: [78/600]
Discriminator Loss: 0.7964, Generator Loss: 2.4395
D(x): 0.7545, D(G(z)): 0.2351
Epoch: [4/20], Batch Num: [79/600]
Discriminator Loss: 0.7949, Generator Loss: 2.2324
D(x): 0.7334, D(G(z)): 0.1813
Epoch: [4/20], Batch Num: [80/600]
Discriminator Loss: 0.8938, Generator Loss: 2.0442
D(x): 0.6934, D(G(z)): 0.2070
Epoch: [4/20], Batch Num: [81/600]
Discriminator Loss: 0.8193, Generator Loss: 1.7248
D(x): 0.7550, D(G(z)): 0.2570
Epoch: [4/20], Batch Num: [82/600]
Discriminator Loss: 0.8544, Generator Loss: 1.6753
D(x): 0.7360, D(G(z)): 0.2781
Epoch: [4/20], Batch Num: [83/600]
Discriminator Loss: 1.1570, Generator Loss: 1.6190
D(x): 0.7028, D(G(z)): 0.3209
Epoch: [4/20], Batch Num: [84/600]
Discriminator Loss: 1.1277, Generator Loss: 1.4959
D(x): 0.7000, D(G(z)): 0.3398
Epoch: [4/20], Batch Num: [85/600]
Discriminator Loss: 1.1168, Generator Loss: 1.6413
D(x): 0.7225, D(G(z)): 0.3614
Epoch: [4/20], Batch Num: [86/600]
Discriminator Loss: 1.0778, Generator Loss: 1.8064
D(x): 0.7190, D(G(z)): 0.3214
Epoch: [4/20], Batch Num: [87/600]
Discriminator Loss: 1.3657, Generator Loss: 1.6999
D(x): 0.6101, D(G(z)): 0.2911
Epoch: [4/20], Batch Num: [88/600]
Discriminator Loss: 1.1532, Generator Loss: 1.5292
D(x): 0.6693, D(G(z)): 0.2940
Epoch: [4/20], Batch Num: [89/600]
Discriminator Loss: 1.1041, Generator Loss: 1.6227
D(x): 0.7024, D(G(z)): 0.3371
Epoch: [4/20], Batch Num: [90/600]
Discriminator Loss: 1.1751, Generator Loss: 1.5221
D(x): 0.6996, D(G(z)): 0.3742
Epoch: [4/20], Batch Num: [91/600]
Discriminator Loss: 1.2925, Generator Loss: 1.5048
D(x): 0.6320, D(G(z)): 0.3318
Epoch: [4/20], Batch Num: [92/600]
Discriminator Loss: 1.1627, Generator Loss: 1.5077
D(x): 0.6546, D(G(z)): 0.3122
Epoch: [4/20], Batch Num: [93/600]
Discriminator Loss: 0.9019, Generator Loss: 1.6068
D(x): 0.7797, D(G(z)): 0.3315
Epoch: [4/20], Batch Num: [94/600]
Discriminator Loss: 0.9630, Generator Loss: 1.6847
D(x): 0.7364, D(G(z)): 0.3380
Epoch: [4/20], Batch Num: [95/600]
Discriminator Loss: 0.8208, Generator Loss: 1.9319
D(x): 0.7198, D(G(z)): 0.2531
Epoch: [4/20], Batch Num: [96/600]
Discriminator Loss: 0.8741, Generator Loss: 2.0525
D(x): 0.6997, D(G(z)): 0.2276
Epoch: [4/20], Batch Num: [97/600]
Discriminator Loss: 0.6461, Generator Loss: 2.0544
D(x): 0.7828, D(G(z)): 0.2069
Epoch: [4/20], Batch Num: [98/600]
Discriminator Loss: 0.7573, Generator Loss: 2.1430
D(x): 0.7518, D(G(z)): 0.2256
Epoch: [4/20], Batch Num: [99/600]
Discriminator Loss: 0.7445, Generator Loss: 2.0152
D(x): 0.7408, D(G(z)): 0.2137
Epoch: 4, Batch Num: [100/600]

Epoch: [4/20], Batch Num: [100/600]
Discriminator Loss: 0.5353, Generator Loss: 2.0367
D(x): 0.8476, D(G(z)): 0.2245
Epoch: [4/20], Batch Num: [101/600]
Discriminator Loss: 0.6495, Generator Loss: 2.1879
D(x): 0.8133, D(G(z)): 0.2505
Epoch: [4/20], Batch Num: [102/600]
Discriminator Loss: 0.5685, Generator Loss: 2.1324
D(x): 0.8268, D(G(z)): 0.2269
Epoch: [4/20], Batch Num: [103/600]
Discriminator Loss: 0.5470, Generator Loss: 2.3026
D(x): 0.8257, D(G(z)): 0.1876
Epoch: [4/20], Batch Num: [104/600]
Discriminator Loss: 0.5208, Generator Loss: 2.2978
D(x): 0.8126, D(G(z)): 0.1614
Epoch: [4/20], Batch Num: [105/600]
Discriminator Loss: 0.5772, Generator Loss: 2.3150
D(x): 0.8349, D(G(z)): 0.1856
Epoch: [4/20], Batch Num: [106/600]
Discriminator Loss: 0.5748, Generator Loss: 2.4676
D(x): 0.7882, D(G(z)): 0.1658
Epoch: [4/20], Batch Num: [107/600]
Discriminator Loss: 0.5520, Generator Loss: 2.4102
D(x): 0.8087, D(G(z)): 0.1707
Epoch: [4/20], Batch Num: [108/600]
Discriminator Loss: 0.4689, Generator Loss: 2.1225
D(x): 0.8451, D(G(z)): 0.1783
Epoch: [4/20], Batch Num: [109/600]
Discriminator Loss: 0.4854, Generator Loss: 2.1683
D(x): 0.8585, D(G(z)): 0.1779
Epoch: [4/20], Batch Num: [110/600]
Discriminator Loss: 0.6116, Generator Loss: 2.0760
D(x): 0.7742, D(G(z)): 0.1746
Epoch: [4/20], Batch Num: [111/600]
Discriminator Loss: 0.6864, Generator Loss: 1.9597
D(x): 0.8033, D(G(z)): 0.2216
Epoch: [4/20], Batch Num: [112/600]
Discriminator Loss: 0.5757, Generator Loss: 1.9623
D(x): 0.8509, D(G(z)): 0.2448
Epoch: [4/20], Batch Num: [113/600]
Discriminator Loss: 0.5465, Generator Loss: 2.1217
D(x): 0.8782, D(G(z)): 0.2734
Epoch: [4/20], Batch Num: [114/600]
Discriminator Loss: 0.5871, Generator Loss: 2.2186
D(x): 0.8267, D(G(z)): 0.2262
Epoch: [4/20], Batch Num: [115/600]
Discriminator Loss: 0.4191, Generator Loss: 2.4645
D(x): 0.8906, D(G(z)): 0.2011
Epoch: [4/20], Batch Num: [116/600]
Discriminator Loss: 0.4664, Generator Loss: 2.7811
D(x): 0.8356, D(G(z)): 0.1332
Epoch: [4/20], Batch Num: [117/600]
Discriminator Loss: 0.3782, Generator Loss: 2.7524
D(x): 0.8518, D(G(z)): 0.1024
Epoch: [4/20], Batch Num: [118/600]
Discriminator Loss: 0.4281, Generator Loss: 2.6413
D(x): 0.8473, D(G(z)): 0.1232
Epoch: [4/20], Batch Num: [119/600]
Discriminator Loss: 0.5104, Generator Loss: 2.6894
D(x): 0.8247, D(G(z)): 0.1549
Epoch: [4/20], Batch Num: [120/600]
Discriminator Loss: 0.3325, Generator Loss: 2.3191
D(x): 0.8992, D(G(z)): 0.1480
Epoch: [4/20], Batch Num: [121/600]
Discriminator Loss: 0.3839, Generator Loss: 2.5137
D(x): 0.8866, D(G(z)): 0.1531
Epoch: [4/20], Batch Num: [122/600]
Discriminator Loss: 0.4256, Generator Loss: 2.4550
D(x): 0.8824, D(G(z)): 0.1748
Epoch: [4/20], Batch Num: [123/600]
Discriminator Loss: 0.5010, Generator Loss: 2.6115
D(x): 0.8909, D(G(z)): 0.2332
Epoch: [4/20], Batch Num: [124/600]
Discriminator Loss: 0.3653, Generator Loss: 2.6082
D(x): 0.9084, D(G(z)): 0.1735
Epoch: [4/20], Batch Num: [125/600]
Discriminator Loss: 0.4490, Generator Loss: 2.8387
D(x): 0.8813, D(G(z)): 0.2045
Epoch: [4/20], Batch Num: [126/600]
Discriminator Loss: 0.4041, Generator Loss: 3.2363
D(x): 0.8752, D(G(z)): 0.1526
Epoch: [4/20], Batch Num: [127/600]
Discriminator Loss: 0.5296, Generator Loss: 3.0431
D(x): 0.8205, D(G(z)): 0.1498
Epoch: [4/20], Batch Num: [128/600]
Discriminator Loss: 0.4584, Generator Loss: 2.8624
D(x): 0.8744, D(G(z)): 0.1607
Epoch: [4/20], Batch Num: [129/600]
Discriminator Loss: 0.5350, Generator Loss: 2.6755
D(x): 0.8302, D(G(z)): 0.1554
Epoch: [4/20], Batch Num: [130/600]
Discriminator Loss: 0.4599, Generator Loss: 2.7584
D(x): 0.8392, D(G(z)): 0.1436
Epoch: [4/20], Batch Num: [131/600]
Discriminator Loss: 0.5134, Generator Loss: 2.2034
D(x): 0.8295, D(G(z)): 0.1687
Epoch: [4/20], Batch Num: [132/600]
Discriminator Loss: 0.5917, Generator Loss: 2.0570
D(x): 0.8542, D(G(z)): 0.2225
Epoch: [4/20], Batch Num: [133/600]
Discriminator Loss: 0.4278, Generator Loss: 2.0602
D(x): 0.9290, D(G(z)): 0.2489
Epoch: [4/20], Batch Num: [134/600]
Discriminator Loss: 0.6368, Generator Loss: 2.1749
D(x): 0.8579, D(G(z)): 0.2610
Epoch: [4/20], Batch Num: [135/600]
Discriminator Loss: 0.6903, Generator Loss: 2.4526
D(x): 0.8326, D(G(z)): 0.2562
Epoch: [4/20], Batch Num: [136/600]
Discriminator Loss: 0.7028, Generator Loss: 2.4300
D(x): 0.7936, D(G(z)): 0.2021
Epoch: [4/20], Batch Num: [137/600]
Discriminator Loss: 0.5747, Generator Loss: 2.3913
D(x): 0.8120, D(G(z)): 0.1821
Epoch: [4/20], Batch Num: [138/600]
Discriminator Loss: 0.5317, Generator Loss: 2.2775
D(x): 0.8380, D(G(z)): 0.1959
Epoch: [4/20], Batch Num: [139/600]
Discriminator Loss: 0.7524, Generator Loss: 1.8715
D(x): 0.7669, D(G(z)): 0.2151
Epoch: [4/20], Batch Num: [140/600]
Discriminator Loss: 0.6250, Generator Loss: 1.9637
D(x): 0.8605, D(G(z)): 0.2537
Epoch: [4/20], Batch Num: [141/600]
Discriminator Loss: 0.6310, Generator Loss: 1.6493
D(x): 0.8389, D(G(z)): 0.2611
Epoch: [4/20], Batch Num: [142/600]
Discriminator Loss: 0.5819, Generator Loss: 1.9594
D(x): 0.8669, D(G(z)): 0.2677
Epoch: [4/20], Batch Num: [143/600]
Discriminator Loss: 0.5348, Generator Loss: 2.2844
D(x): 0.8739, D(G(z)): 0.2562
Epoch: [4/20], Batch Num: [144/600]
Discriminator Loss: 0.6084, Generator Loss: 2.3673
D(x): 0.8106, D(G(z)): 0.1883
Epoch: [4/20], Batch Num: [145/600]
Discriminator Loss: 0.8858, Generator Loss: 2.1949
D(x): 0.6940, D(G(z)): 0.1828
Epoch: [4/20], Batch Num: [146/600]
Discriminator Loss: 0.5965, Generator Loss: 1.7365
D(x): 0.8210, D(G(z)): 0.1982
Epoch: [4/20], Batch Num: [147/600]
Discriminator Loss: 0.6035, Generator Loss: 1.7473
D(x): 0.8709, D(G(z)): 0.2586
Epoch: [4/20], Batch Num: [148/600]
Discriminator Loss: 0.7371, Generator Loss: 1.6417
D(x): 0.8102, D(G(z)): 0.2671
Epoch: [4/20], Batch Num: [149/600]
Discriminator Loss: 0.5687, Generator Loss: 1.6668
D(x): 0.8618, D(G(z)): 0.2511
Epoch: [4/20], Batch Num: [150/600]
Discriminator Loss: 0.7725, Generator Loss: 1.7915
D(x): 0.7805, D(G(z)): 0.2693
Epoch: [4/20], Batch Num: [151/600]
Discriminator Loss: 0.5722, Generator Loss: 2.0210
D(x): 0.8641, D(G(z)): 0.2606
Epoch: [4/20], Batch Num: [152/600]
Discriminator Loss: 0.4814, Generator Loss: 2.1347
D(x): 0.8760, D(G(z)): 0.2195
Epoch: [4/20], Batch Num: [153/600]
Discriminator Loss: 0.5591, Generator Loss: 2.1563
D(x): 0.8369, D(G(z)): 0.1832
Epoch: [4/20], Batch Num: [154/600]
Discriminator Loss: 0.6564, Generator Loss: 2.0649
D(x): 0.7990, D(G(z)): 0.1697
Epoch: [4/20], Batch Num: [155/600]
Discriminator Loss: 0.6275, Generator Loss: 1.7977
D(x): 0.8032, D(G(z)): 0.1634
Epoch: [4/20], Batch Num: [156/600]
Discriminator Loss: 0.5727, Generator Loss: 1.6616
D(x): 0.8484, D(G(z)): 0.2122
Epoch: [4/20], Batch Num: [157/600]
Discriminator Loss: 0.8216, Generator Loss: 1.4746
D(x): 0.7683, D(G(z)): 0.2765
Epoch: [4/20], Batch Num: [158/600]
Discriminator Loss: 0.7157, Generator Loss: 1.4628
D(x): 0.8483, D(G(z)): 0.3030
Epoch: [4/20], Batch Num: [159/600]
Discriminator Loss: 0.6046, Generator Loss: 2.0432
D(x): 0.9043, D(G(z)): 0.3278
Epoch: [4/20], Batch Num: [160/600]
Discriminator Loss: 0.5895, Generator Loss: 2.4259
D(x): 0.8415, D(G(z)): 0.2189
Epoch: [4/20], Batch Num: [161/600]
Discriminator Loss: 0.6661, Generator Loss: 2.7241
D(x): 0.8250, D(G(z)): 0.1497
Epoch: [4/20], Batch Num: [162/600]
Discriminator Loss: 0.8017, Generator Loss: 2.5841
D(x): 0.7372, D(G(z)): 0.1355
Epoch: [4/20], Batch Num: [163/600]
Discriminator Loss: 0.8714, Generator Loss: 2.1747
D(x): 0.7026, D(G(z)): 0.1556
Epoch: [4/20], Batch Num: [164/600]
Discriminator Loss: 0.7139, Generator Loss: 1.4481
D(x): 0.7600, D(G(z)): 0.1785
Epoch: [4/20], Batch Num: [165/600]
Discriminator Loss: 0.7627, Generator Loss: 1.2305
D(x): 0.8540, D(G(z)): 0.3135
Epoch: [4/20], Batch Num: [166/600]
Discriminator Loss: 0.8618, Generator Loss: 1.7235
D(x): 0.8694, D(G(z)): 0.3844
Epoch: [4/20], Batch Num: [167/600]
Discriminator Loss: 0.8407, Generator Loss: 2.2605
D(x): 0.8573, D(G(z)): 0.3219
Epoch: [4/20], Batch Num: [168/600]
Discriminator Loss: 0.7631, Generator Loss: 2.4893
D(x): 0.7692, D(G(z)): 0.1689
Epoch: [4/20], Batch Num: [169/600]
Discriminator Loss: 0.7646, Generator Loss: 2.6068
D(x): 0.7241, D(G(z)): 0.1417
Epoch: [4/20], Batch Num: [170/600]
Discriminator Loss: 0.7477, Generator Loss: 2.3811
D(x): 0.7258, D(G(z)): 0.1282
Epoch: [4/20], Batch Num: [171/600]
Discriminator Loss: 0.6111, Generator Loss: 2.0542
D(x): 0.8254, D(G(z)): 0.1888
Epoch: [4/20], Batch Num: [172/600]
Discriminator Loss: 0.7299, Generator Loss: 1.6571
D(x): 0.7694, D(G(z)): 0.2176
Epoch: [4/20], Batch Num: [173/600]
Discriminator Loss: 0.6923, Generator Loss: 1.4876
D(x): 0.8058, D(G(z)): 0.2440
Epoch: [4/20], Batch Num: [174/600]
Discriminator Loss: 0.6572, Generator Loss: 1.5752
D(x): 0.9153, D(G(z)): 0.3414
Epoch: [4/20], Batch Num: [175/600]
Discriminator Loss: 0.7194, Generator Loss: 1.7747
D(x): 0.8651, D(G(z)): 0.3150
Epoch: [4/20], Batch Num: [176/600]
Discriminator Loss: 0.6546, Generator Loss: 2.0626
D(x): 0.8597, D(G(z)): 0.2677
Epoch: [4/20], Batch Num: [177/600]
Discriminator Loss: 0.5279, Generator Loss: 2.4704
D(x): 0.8389, D(G(z)): 0.1710
Epoch: [4/20], Batch Num: [178/600]
Discriminator Loss: 0.5834, Generator Loss: 2.4160
D(x): 0.7913, D(G(z)): 0.1524
Epoch: [4/20], Batch Num: [179/600]
Discriminator Loss: 0.4184, Generator Loss: 2.5613
D(x): 0.8739, D(G(z)): 0.1447
Epoch: [4/20], Batch Num: [180/600]
Discriminator Loss: 0.4711, Generator Loss: 2.3905
D(x): 0.8415, D(G(z)): 0.1577
Epoch: [4/20], Batch Num: [181/600]
Discriminator Loss: 0.4664, Generator Loss: 2.1435
D(x): 0.8539, D(G(z)): 0.1368
Epoch: [4/20], Batch Num: [182/600]
Discriminator Loss: 0.3615, Generator Loss: 2.1071
D(x): 0.9014, D(G(z)): 0.1634
Epoch: [4/20], Batch Num: [183/600]
Discriminator Loss: 0.3650, Generator Loss: 2.0975
D(x): 0.9325, D(G(z)): 0.1996
Epoch: [4/20], Batch Num: [184/600]
Discriminator Loss: 0.3722, Generator Loss: 1.9595
D(x): 0.9059, D(G(z)): 0.1764
Epoch: [4/20], Batch Num: [185/600]
Discriminator Loss: 0.3922, Generator Loss: 2.2727
D(x): 0.9260, D(G(z)): 0.2004
Epoch: [4/20], Batch Num: [186/600]
Discriminator Loss: 0.3733, Generator Loss: 2.3029
D(x): 0.9370, D(G(z)): 0.2026
Epoch: [4/20], Batch Num: [187/600]
Discriminator Loss: 0.4191, Generator Loss: 2.5430
D(x): 0.9009, D(G(z)): 0.1835
Epoch: [4/20], Batch Num: [188/600]
Discriminator Loss: 0.3600, Generator Loss: 3.0321
D(x): 0.8830, D(G(z)): 0.1336
Epoch: [4/20], Batch Num: [189/600]
Discriminator Loss: 0.3015, Generator Loss: 3.2118
D(x): 0.8949, D(G(z)): 0.1139
Epoch: [4/20], Batch Num: [190/600]
Discriminator Loss: 0.3288, Generator Loss: 3.4060
D(x): 0.8864, D(G(z)): 0.1060
Epoch: [4/20], Batch Num: [191/600]
Discriminator Loss: 0.3473, Generator Loss: 3.4819
D(x): 0.8882, D(G(z)): 0.1043
Epoch: [4/20], Batch Num: [192/600]
Discriminator Loss: 0.4054, Generator Loss: 3.1180
D(x): 0.8822, D(G(z)): 0.1264
Epoch: [4/20], Batch Num: [193/600]
Discriminator Loss: 0.1712, Generator Loss: 3.2598
D(x): 0.9503, D(G(z)): 0.0858
Epoch: [4/20], Batch Num: [194/600]
Discriminator Loss: 0.2612, Generator Loss: 3.3709
D(x): 0.9376, D(G(z)): 0.1110
Epoch: [4/20], Batch Num: [195/600]
Discriminator Loss: 0.2691, Generator Loss: 3.2847
D(x): 0.9294, D(G(z)): 0.1119
Epoch: [4/20], Batch Num: [196/600]
Discriminator Loss: 0.2615, Generator Loss: 3.3738
D(x): 0.9276, D(G(z)): 0.1162
Epoch: [4/20], Batch Num: [197/600]
Discriminator Loss: 0.2023, Generator Loss: 3.2981
D(x): 0.9449, D(G(z)): 0.1008
Epoch: [4/20], Batch Num: [198/600]
Discriminator Loss: 0.2188, Generator Loss: 3.5744
D(x): 0.9573, D(G(z)): 0.1316
Epoch: [4/20], Batch Num: [199/600]
Discriminator Loss: 0.2097, Generator Loss: 3.6835
D(x): 0.9609, D(G(z)): 0.1235
Epoch: 4, Batch Num: [200/600]

Epoch: [4/20], Batch Num: [200/600]
Discriminator Loss: 0.1494, Generator Loss: 3.7953
D(x): 0.9596, D(G(z)): 0.0828
Epoch: [4/20], Batch Num: [201/600]
Discriminator Loss: 0.2596, Generator Loss: 4.0962
D(x): 0.9139, D(G(z)): 0.0936
Epoch: [4/20], Batch Num: [202/600]
Discriminator Loss: 0.2862, Generator Loss: 4.1589
D(x): 0.9017, D(G(z)): 0.0782
Epoch: [4/20], Batch Num: [203/600]
Discriminator Loss: 0.1413, Generator Loss: 3.9781
D(x): 0.9600, D(G(z)): 0.0762
Epoch: [4/20], Batch Num: [204/600]
Discriminator Loss: 0.2785, Generator Loss: 3.9526
D(x): 0.9059, D(G(z)): 0.0734
Epoch: [4/20], Batch Num: [205/600]
Discriminator Loss: 0.2195, Generator Loss: 3.4561
D(x): 0.9436, D(G(z)): 0.0905
Epoch: [4/20], Batch Num: [206/600]
Discriminator Loss: 0.1482, Generator Loss: 3.6127
D(x): 0.9616, D(G(z)): 0.0839
Epoch: [4/20], Batch Num: [207/600]
Discriminator Loss: 0.3103, Generator Loss: 3.3224
D(x): 0.9242, D(G(z)): 0.1140
Epoch: [4/20], Batch Num: [208/600]
Discriminator Loss: 0.1709, Generator Loss: 3.6202
D(x): 0.9748, D(G(z)): 0.1188
Epoch: [4/20], Batch Num: [209/600]
Discriminator Loss: 0.2332, Generator Loss: 3.4184
D(x): 0.9390, D(G(z)): 0.0909
Epoch: [4/20], Batch Num: [210/600]
Discriminator Loss: 0.3285, Generator Loss: 3.4677
D(x): 0.9061, D(G(z)): 0.1123
Epoch: [4/20], Batch Num: [211/600]
Discriminator Loss: 0.2583, Generator Loss: 3.7842
D(x): 0.9478, D(G(z)): 0.1393
Epoch: [4/20], Batch Num: [212/600]
Discriminator Loss: 0.2729, Generator Loss: 3.8552
D(x): 0.9417, D(G(z)): 0.1407
Epoch: [4/20], Batch Num: [213/600]
Discriminator Loss: 0.3007, Generator Loss: 3.7963
D(x): 0.9445, D(G(z)): 0.1592
Epoch: [4/20], Batch Num: [214/600]
Discriminator Loss: 0.3342, Generator Loss: 3.8099
D(x): 0.9245, D(G(z)): 0.1351
Epoch: [4/20], Batch Num: [215/600]
Discriminator Loss: 0.6233, Generator Loss: 3.2289
D(x): 0.8639, D(G(z)): 0.1963
Epoch: [4/20], Batch Num: [216/600]
Discriminator Loss: 0.4684, Generator Loss: 3.1504
D(x): 0.8890, D(G(z)): 0.1869
Epoch: [4/20], Batch Num: [217/600]
Discriminator Loss: 0.8934, Generator Loss: 2.4507
D(x): 0.8575, D(G(z)): 0.3045
Epoch: [4/20], Batch Num: [218/600]
Discriminator Loss: 0.7931, Generator Loss: 2.7251
D(x): 0.9149, D(G(z)): 0.3383
Epoch: [4/20], Batch Num: [219/600]
Discriminator Loss: 0.5959, Generator Loss: 3.3568
D(x): 0.9318, D(G(z)): 0.2708
Epoch: [4/20], Batch Num: [220/600]
Discriminator Loss: 0.9202, Generator Loss: 3.1131
D(x): 0.7750, D(G(z)): 0.2855
Epoch: [4/20], Batch Num: [221/600]
Discriminator Loss: 0.8738, Generator Loss: 2.5220
D(x): 0.7819, D(G(z)): 0.2464
Epoch: [4/20], Batch Num: [222/600]
Discriminator Loss: 0.9131, Generator Loss: 2.2916
D(x): 0.8389, D(G(z)): 0.3057
Epoch: [4/20], Batch Num: [223/600]
Discriminator Loss: 1.0076, Generator Loss: 2.1588
D(x): 0.7963, D(G(z)): 0.2920
Epoch: [4/20], Batch Num: [224/600]
Discriminator Loss: 0.7911, Generator Loss: 2.4000
D(x): 0.8260, D(G(z)): 0.2817
Epoch: [4/20], Batch Num: [225/600]
Discriminator Loss: 0.9034, Generator Loss: 2.5385
D(x): 0.7671, D(G(z)): 0.2666
Epoch: [4/20], Batch Num: [226/600]
Discriminator Loss: 0.8270, Generator Loss: 2.1412
D(x): 0.7893, D(G(z)): 0.2306
Epoch: [4/20], Batch Num: [227/600]
Discriminator Loss: 0.7200, Generator Loss: 2.1952
D(x): 0.7960, D(G(z)): 0.2187
Epoch: [4/20], Batch Num: [228/600]
Discriminator Loss: 0.6635, Generator Loss: 2.2231
D(x): 0.8114, D(G(z)): 0.2199
Epoch: [4/20], Batch Num: [229/600]
Discriminator Loss: 0.8285, Generator Loss: 2.2247
D(x): 0.7915, D(G(z)): 0.2824
Epoch: [4/20], Batch Num: [230/600]
Discriminator Loss: 0.6833, Generator Loss: 2.5030
D(x): 0.7936, D(G(z)): 0.2235
Epoch: [4/20], Batch Num: [231/600]
Discriminator Loss: 0.6393, Generator Loss: 2.0583
D(x): 0.7688, D(G(z)): 0.1718
Epoch: [4/20], Batch Num: [232/600]
Discriminator Loss: 0.6656, Generator Loss: 1.7046
D(x): 0.7832, D(G(z)): 0.1878
Epoch: [4/20], Batch Num: [233/600]
Discriminator Loss: 0.5613, Generator Loss: 1.8604
D(x): 0.8871, D(G(z)): 0.2898
Epoch: [4/20], Batch Num: [234/600]
Discriminator Loss: 0.7125, Generator Loss: 2.2000
D(x): 0.8907, D(G(z)): 0.3443
Epoch: [4/20], Batch Num: [235/600]
Discriminator Loss: 0.5853, Generator Loss: 2.7973
D(x): 0.8563, D(G(z)): 0.2392
Epoch: [4/20], Batch Num: [236/600]
Discriminator Loss: 0.5539, Generator Loss: 3.1805
D(x): 0.8303, D(G(z)): 0.1899
Epoch: [4/20], Batch Num: [237/600]
Discriminator Loss: 0.6221, Generator Loss: 3.2868
D(x): 0.7466, D(G(z)): 0.1206
Epoch: [4/20], Batch Num: [238/600]
Discriminator Loss: 0.6669, Generator Loss: 2.9335
D(x): 0.7767, D(G(z)): 0.1781
Epoch: [4/20], Batch Num: [239/600]
Discriminator Loss: 0.5545, Generator Loss: 2.6635
D(x): 0.8876, D(G(z)): 0.2468
Epoch: [4/20], Batch Num: [240/600]
Discriminator Loss: 0.6239, Generator Loss: 2.6788
D(x): 0.8780, D(G(z)): 0.2717
Epoch: [4/20], Batch Num: [241/600]
Discriminator Loss: 0.5922, Generator Loss: 3.3478
D(x): 0.8721, D(G(z)): 0.2476
Epoch: [4/20], Batch Num: [242/600]
Discriminator Loss: 0.6305, Generator Loss: 3.1887
D(x): 0.8318, D(G(z)): 0.1905
Epoch: [4/20], Batch Num: [243/600]
Discriminator Loss: 0.7785, Generator Loss: 3.2483
D(x): 0.7644, D(G(z)): 0.1680
Epoch: [4/20], Batch Num: [244/600]
Discriminator Loss: 0.8425, Generator Loss: 2.7428
D(x): 0.7637, D(G(z)): 0.1768
Epoch: [4/20], Batch Num: [245/600]
Discriminator Loss: 0.9844, Generator Loss: 2.4248
D(x): 0.8032, D(G(z)): 0.3078
Epoch: [4/20], Batch Num: [246/600]
Discriminator Loss: 0.9751, Generator Loss: 2.3906
D(x): 0.7749, D(G(z)): 0.3084
Epoch: [4/20], Batch Num: [247/600]
Discriminator Loss: 1.1637, Generator Loss: 2.2088
D(x): 0.7635, D(G(z)): 0.3434
Epoch: [4/20], Batch Num: [248/600]
Discriminator Loss: 1.1464, Generator Loss: 2.0754
D(x): 0.7352, D(G(z)): 0.3454
Epoch: [4/20], Batch Num: [249/600]
Discriminator Loss: 1.0805, Generator Loss: 2.0435
D(x): 0.7504, D(G(z)): 0.3055
Epoch: [4/20], Batch Num: [250/600]
Discriminator Loss: 1.3142, Generator Loss: 1.7733
D(x): 0.6957, D(G(z)): 0.3388
Epoch: [4/20], Batch Num: [251/600]
Discriminator Loss: 1.4152, Generator Loss: 1.8049
D(x): 0.6753, D(G(z)): 0.3653
Epoch: [4/20], Batch Num: [252/600]
Discriminator Loss: 1.3618, Generator Loss: 1.9036
D(x): 0.7282, D(G(z)): 0.4030
Epoch: [4/20], Batch Num: [253/600]
Discriminator Loss: 1.4963, Generator Loss: 1.7318
D(x): 0.5845, D(G(z)): 0.3007
Epoch: [4/20], Batch Num: [254/600]
Discriminator Loss: 1.3863, Generator Loss: 1.3901
D(x): 0.6283, D(G(z)): 0.3316
Epoch: [4/20], Batch Num: [255/600]
Discriminator Loss: 1.4322, Generator Loss: 1.4458
D(x): 0.6968, D(G(z)): 0.4034
Epoch: [4/20], Batch Num: [256/600]
Discriminator Loss: 1.4045, Generator Loss: 1.8044
D(x): 0.7020, D(G(z)): 0.4015
Epoch: [4/20], Batch Num: [257/600]
Discriminator Loss: 1.4247, Generator Loss: 1.8850
D(x): 0.6520, D(G(z)): 0.3574
Epoch: [4/20], Batch Num: [258/600]
Discriminator Loss: 0.9649, Generator Loss: 2.1860
D(x): 0.7004, D(G(z)): 0.2461
Epoch: [4/20], Batch Num: [259/600]
Discriminator Loss: 0.9932, Generator Loss: 1.9594
D(x): 0.6720, D(G(z)): 0.2253
Epoch: [4/20], Batch Num: [260/600]
Discriminator Loss: 0.8596, Generator Loss: 2.0524
D(x): 0.6939, D(G(z)): 0.2145
Epoch: [4/20], Batch Num: [261/600]
Discriminator Loss: 0.9179, Generator Loss: 1.8591
D(x): 0.6886, D(G(z)): 0.2394
Epoch: [4/20], Batch Num: [262/600]
Discriminator Loss: 0.8887, Generator Loss: 1.9391
D(x): 0.7033, D(G(z)): 0.2292
Epoch: [4/20], Batch Num: [263/600]
Discriminator Loss: 0.8564, Generator Loss: 1.8659
D(x): 0.7486, D(G(z)): 0.2909
Epoch: [4/20], Batch Num: [264/600]
Discriminator Loss: 0.8560, Generator Loss: 1.8757
D(x): 0.7418, D(G(z)): 0.2654
Epoch: [4/20], Batch Num: [265/600]
Discriminator Loss: 0.9574, Generator Loss: 2.0252
D(x): 0.7471, D(G(z)): 0.3043
Epoch: [4/20], Batch Num: [266/600]
Discriminator Loss: 0.9616, Generator Loss: 2.1662
D(x): 0.6861, D(G(z)): 0.2161
Epoch: [4/20], Batch Num: [267/600]
Discriminator Loss: 0.9604, Generator Loss: 2.2492
D(x): 0.6961, D(G(z)): 0.2474
Epoch: [4/20], Batch Num: [268/600]
Discriminator Loss: 0.9184, Generator Loss: 2.2810
D(x): 0.6857, D(G(z)): 0.2267
Epoch: [4/20], Batch Num: [269/600]
Discriminator Loss: 0.7984, Generator Loss: 2.1018
D(x): 0.7384, D(G(z)): 0.2216
Epoch: [4/20], Batch Num: [270/600]
Discriminator Loss: 0.7874, Generator Loss: 2.3204
D(x): 0.7469, D(G(z)): 0.2261
Epoch: [4/20], Batch Num: [271/600]
Discriminator Loss: 0.8304, Generator Loss: 2.0973
D(x): 0.7095, D(G(z)): 0.2007
Epoch: [4/20], Batch Num: [272/600]
Discriminator Loss: 0.9132, Generator Loss: 2.1144
D(x): 0.6882, D(G(z)): 0.2254
Epoch: [4/20], Batch Num: [273/600]
Discriminator Loss: 0.7844, Generator Loss: 2.0565
D(x): 0.7742, D(G(z)): 0.2361
Epoch: [4/20], Batch Num: [274/600]
Discriminator Loss: 0.7459, Generator Loss: 1.8763
D(x): 0.7577, D(G(z)): 0.2267
Epoch: [4/20], Batch Num: [275/600]
Discriminator Loss: 0.8017, Generator Loss: 1.9746
D(x): 0.7755, D(G(z)): 0.2706
Epoch: [4/20], Batch Num: [276/600]
Discriminator Loss: 0.8192, Generator Loss: 1.9479
D(x): 0.7580, D(G(z)): 0.2251
Epoch: [4/20], Batch Num: [277/600]
Discriminator Loss: 0.9205, Generator Loss: 1.6876
D(x): 0.7129, D(G(z)): 0.2493
Epoch: [4/20], Batch Num: [278/600]
Discriminator Loss: 0.6378, Generator Loss: 1.9553
D(x): 0.8362, D(G(z)): 0.2551
Epoch: [4/20], Batch Num: [279/600]
Discriminator Loss: 0.7818, Generator Loss: 2.0457
D(x): 0.7417, D(G(z)): 0.2441
Epoch: [4/20], Batch Num: [280/600]
Discriminator Loss: 0.6839, Generator Loss: 2.0988
D(x): 0.7848, D(G(z)): 0.2312
Epoch: [4/20], Batch Num: [281/600]
Discriminator Loss: 0.6898, Generator Loss: 2.0105
D(x): 0.7881, D(G(z)): 0.2418
Epoch: [4/20], Batch Num: [282/600]
Discriminator Loss: 0.6602, Generator Loss: 2.0981
D(x): 0.7691, D(G(z)): 0.2118
Epoch: [4/20], Batch Num: [283/600]
Discriminator Loss: 0.6147, Generator Loss: 2.1069
D(x): 0.8436, D(G(z)): 0.2364
Epoch: [4/20], Batch Num: [284/600]
Discriminator Loss: 0.7419, Generator Loss: 1.9998
D(x): 0.7442, D(G(z)): 0.2099
Epoch: [4/20], Batch Num: [285/600]
Discriminator Loss: 0.7508, Generator Loss: 1.6254
D(x): 0.7822, D(G(z)): 0.2377
Epoch: [4/20], Batch Num: [286/600]
Discriminator Loss: 0.6510, Generator Loss: 1.6797
D(x): 0.8102, D(G(z)): 0.2503
Epoch: [4/20], Batch Num: [287/600]
Discriminator Loss: 0.7873, Generator Loss: 1.7454
D(x): 0.7806, D(G(z)): 0.2444
Epoch: [4/20], Batch Num: [288/600]
Discriminator Loss: 0.7687, Generator Loss: 1.7321
D(x): 0.7971, D(G(z)): 0.3047
Epoch: [4/20], Batch Num: [289/600]
Discriminator Loss: 0.6993, Generator Loss: 1.7383
D(x): 0.8383, D(G(z)): 0.2735
Epoch: [4/20], Batch Num: [290/600]
Discriminator Loss: 0.7932, Generator Loss: 1.9629
D(x): 0.7624, D(G(z)): 0.2634
Epoch: [4/20], Batch Num: [291/600]
Discriminator Loss: 0.5427, Generator Loss: 2.0280
D(x): 0.8414, D(G(z)): 0.2201
Epoch: [4/20], Batch Num: [292/600]
Discriminator Loss: 0.7633, Generator Loss: 1.6948
D(x): 0.7350, D(G(z)): 0.2165
Epoch: [4/20], Batch Num: [293/600]
Discriminator Loss: 0.5622, Generator Loss: 1.5974
D(x): 0.8492, D(G(z)): 0.2432
Epoch: [4/20], Batch Num: [294/600]
Discriminator Loss: 0.7646, Generator Loss: 1.7102
D(x): 0.8175, D(G(z)): 0.3076
Epoch: [4/20], Batch Num: [295/600]
Discriminator Loss: 0.8820, Generator Loss: 1.5967
D(x): 0.7773, D(G(z)): 0.2740
Epoch: [4/20], Batch Num: [296/600]
Discriminator Loss: 0.7205, Generator Loss: 1.6529
D(x): 0.8122, D(G(z)): 0.2586
Epoch: [4/20], Batch Num: [297/600]
Discriminator Loss: 0.7498, Generator Loss: 1.6273
D(x): 0.8033, D(G(z)): 0.2653
Epoch: [4/20], Batch Num: [298/600]
Discriminator Loss: 0.6806, Generator Loss: 1.8160
D(x): 0.8043, D(G(z)): 0.2555
Epoch: [4/20], Batch Num: [299/600]
Discriminator Loss: 0.6789, Generator Loss: 1.8051
D(x): 0.8244, D(G(z)): 0.2703
Epoch: 4, Batch Num: [300/600]

Epoch: [4/20], Batch Num: [300/600]
Discriminator Loss: 0.7676, Generator Loss: 1.8864
D(x): 0.7888, D(G(z)): 0.2491
Epoch: [4/20], Batch Num: [301/600]
Discriminator Loss: 0.6020, Generator Loss: 1.9384
D(x): 0.8680, D(G(z)): 0.2742
Epoch: [4/20], Batch Num: [302/600]
Discriminator Loss: 0.6475, Generator Loss: 2.0322
D(x): 0.8111, D(G(z)): 0.2184
Epoch: [4/20], Batch Num: [303/600]
Discriminator Loss: 0.5732, Generator Loss: 2.3202
D(x): 0.8312, D(G(z)): 0.1958
Epoch: [4/20], Batch Num: [304/600]
Discriminator Loss: 0.5685, Generator Loss: 1.8872
D(x): 0.8000, D(G(z)): 0.1717
Epoch: [4/20], Batch Num: [305/600]
Discriminator Loss: 0.5769, Generator Loss: 2.0611
D(x): 0.8328, D(G(z)): 0.2134
Epoch: [4/20], Batch Num: [306/600]
Discriminator Loss: 0.4627, Generator Loss: 2.1373
D(x): 0.8642, D(G(z)): 0.1918
Epoch: [4/20], Batch Num: [307/600]
Discriminator Loss: 0.4180, Generator Loss: 2.1140
D(x): 0.9037, D(G(z)): 0.2046
Epoch: [4/20], Batch Num: [308/600]
Discriminator Loss: 0.4358, Generator Loss: 2.1135
D(x): 0.8776, D(G(z)): 0.1964
Epoch: [4/20], Batch Num: [309/600]
Discriminator Loss: 0.4476, Generator Loss: 2.3735
D(x): 0.8696, D(G(z)): 0.1901
Epoch: [4/20], Batch Num: [310/600]
Discriminator Loss: 0.5026, Generator Loss: 2.2876
D(x): 0.8544, D(G(z)): 0.1592
Epoch: [4/20], Batch Num: [311/600]
Discriminator Loss: 0.5662, Generator Loss: 2.3063
D(x): 0.8146, D(G(z)): 0.1534
Epoch: [4/20], Batch Num: [312/600]
Discriminator Loss: 0.5038, Generator Loss: 2.3810
D(x): 0.8351, D(G(z)): 0.1630
Epoch: [4/20], Batch Num: [313/600]
Discriminator Loss: 0.4689, Generator Loss: 2.3193
D(x): 0.8525, D(G(z)): 0.1708
Epoch: [4/20], Batch Num: [314/600]
Discriminator Loss: 0.3559, Generator Loss: 2.2233
D(x): 0.8983, D(G(z)): 0.1609
Epoch: [4/20], Batch Num: [315/600]
Discriminator Loss: 0.3615, Generator Loss: 2.1973
D(x): 0.9102, D(G(z)): 0.1736
Epoch: [4/20], Batch Num: [316/600]
Discriminator Loss: 0.3774, Generator Loss: 2.2331
D(x): 0.9061, D(G(z)): 0.1741
Epoch: [4/20], Batch Num: [317/600]
Discriminator Loss: 0.3331, Generator Loss: 2.2248
D(x): 0.9248, D(G(z)): 0.1779
Epoch: [4/20], Batch Num: [318/600]
Discriminator Loss: 0.2683, Generator Loss: 2.4551
D(x): 0.9260, D(G(z)): 0.1430
Epoch: [4/20], Batch Num: [319/600]
Discriminator Loss: 0.4922, Generator Loss: 2.5120
D(x): 0.8283, D(G(z)): 0.1352
Epoch: [4/20], Batch Num: [320/600]
Discriminator Loss: 0.2748, Generator Loss: 2.5156
D(x): 0.9235, D(G(z)): 0.1296
Epoch: [4/20], Batch Num: [321/600]
Discriminator Loss: 0.3754, Generator Loss: 2.5447
D(x): 0.8749, D(G(z)): 0.1301
Epoch: [4/20], Batch Num: [322/600]
Discriminator Loss: 0.3602, Generator Loss: 2.6520
D(x): 0.8856, D(G(z)): 0.1270
Epoch: [4/20], Batch Num: [323/600]
Discriminator Loss: 0.4487, Generator Loss: 2.3126
D(x): 0.8425, D(G(z)): 0.1232
Epoch: [4/20], Batch Num: [324/600]
Discriminator Loss: 0.4702, Generator Loss: 2.1632
D(x): 0.8286, D(G(z)): 0.1404
Epoch: [4/20], Batch Num: [325/600]
Discriminator Loss: 0.4646, Generator Loss: 1.9898
D(x): 0.8715, D(G(z)): 0.2002
Epoch: [4/20], Batch Num: [326/600]
Discriminator Loss: 0.5353, Generator Loss: 1.8097
D(x): 0.8701, D(G(z)): 0.2316
Epoch: [4/20], Batch Num: [327/600]
Discriminator Loss: 0.3720, Generator Loss: 2.0531
D(x): 0.9434, D(G(z)): 0.2363
Epoch: [4/20], Batch Num: [328/600]
Discriminator Loss: 0.4189, Generator Loss: 2.5137
D(x): 0.9120, D(G(z)): 0.1958
Epoch: [4/20], Batch Num: [329/600]
Discriminator Loss: 0.3796, Generator Loss: 2.7362
D(x): 0.8914, D(G(z)): 0.1513
Epoch: [4/20], Batch Num: [330/600]
Discriminator Loss: 0.4849, Generator Loss: 2.7833
D(x): 0.8238, D(G(z)): 0.1130
Epoch: [4/20], Batch Num: [331/600]
Discriminator Loss: 0.6433, Generator Loss: 2.4882
D(x): 0.7731, D(G(z)): 0.0957
Epoch: [4/20], Batch Num: [332/600]
Discriminator Loss: 0.5036, Generator Loss: 2.2132
D(x): 0.8317, D(G(z)): 0.1472
Epoch: [4/20], Batch Num: [333/600]
Discriminator Loss: 0.4716, Generator Loss: 1.8849
D(x): 0.8755, D(G(z)): 0.1796
Epoch: [4/20], Batch Num: [334/600]
Discriminator Loss: 0.5439, Generator Loss: 1.7809
D(x): 0.8472, D(G(z)): 0.2012
Epoch: [4/20], Batch Num: [335/600]
Discriminator Loss: 0.5708, Generator Loss: 1.9575
D(x): 0.8696, D(G(z)): 0.2267
Epoch: [4/20], Batch Num: [336/600]
Discriminator Loss: 0.4556, Generator Loss: 2.1127
D(x): 0.9322, D(G(z)): 0.2568
Epoch: [4/20], Batch Num: [337/600]
Discriminator Loss: 0.6684, Generator Loss: 2.3536
D(x): 0.8117, D(G(z)): 0.1806
Epoch: [4/20], Batch Num: [338/600]
Discriminator Loss: 0.4502, Generator Loss: 2.5267
D(x): 0.8350, D(G(z)): 0.1269
Epoch: [4/20], Batch Num: [339/600]
Discriminator Loss: 0.7778, Generator Loss: 2.2894
D(x): 0.7691, D(G(z)): 0.1565
Epoch: [4/20], Batch Num: [340/600]
Discriminator Loss: 0.8637, Generator Loss: 2.0453
D(x): 0.7537, D(G(z)): 0.1614
Epoch: [4/20], Batch Num: [341/600]
Discriminator Loss: 0.8981, Generator Loss: 1.7013
D(x): 0.7468, D(G(z)): 0.2213
Epoch: [4/20], Batch Num: [342/600]
Discriminator Loss: 0.9631, Generator Loss: 1.6292
D(x): 0.8135, D(G(z)): 0.3010
Epoch: [4/20], Batch Num: [343/600]
Discriminator Loss: 0.6914, Generator Loss: 1.8159
D(x): 0.8469, D(G(z)): 0.2881
Epoch: [4/20], Batch Num: [344/600]
Discriminator Loss: 0.7074, Generator Loss: 2.2750
D(x): 0.8908, D(G(z)): 0.3119
Epoch: [4/20], Batch Num: [345/600]
Discriminator Loss: 0.8374, Generator Loss: 2.4956
D(x): 0.7815, D(G(z)): 0.2079
Epoch: [4/20], Batch Num: [346/600]
Discriminator Loss: 1.0588, Generator Loss: 2.3950
D(x): 0.6784, D(G(z)): 0.1363
Epoch: [4/20], Batch Num: [347/600]
Discriminator Loss: 0.7469, Generator Loss: 2.1172
D(x): 0.7817, D(G(z)): 0.1435
Epoch: [4/20], Batch Num: [348/600]
Discriminator Loss: 0.8131, Generator Loss: 1.6593
D(x): 0.7341, D(G(z)): 0.1709
Epoch: [4/20], Batch Num: [349/600]
Discriminator Loss: 0.7562, Generator Loss: 1.5670
D(x): 0.8303, D(G(z)): 0.2665
Epoch: [4/20], Batch Num: [350/600]
Discriminator Loss: 0.7539, Generator Loss: 1.4814
D(x): 0.8154, D(G(z)): 0.2885
Epoch: [4/20], Batch Num: [351/600]
Discriminator Loss: 0.9708, Generator Loss: 1.6363
D(x): 0.8481, D(G(z)): 0.3860
Epoch: [4/20], Batch Num: [352/600]
Discriminator Loss: 0.7244, Generator Loss: 2.0324
D(x): 0.8423, D(G(z)): 0.2796
Epoch: [4/20], Batch Num: [353/600]
Discriminator Loss: 0.6351, Generator Loss: 2.1356
D(x): 0.8005, D(G(z)): 0.1931
Epoch: [4/20], Batch Num: [354/600]
Discriminator Loss: 0.5820, Generator Loss: 2.2268
D(x): 0.8255, D(G(z)): 0.1828
Epoch: [4/20], Batch Num: [355/600]
Discriminator Loss: 0.5993, Generator Loss: 2.2793
D(x): 0.8185, D(G(z)): 0.1499
Epoch: [4/20], Batch Num: [356/600]
Discriminator Loss: 0.8996, Generator Loss: 2.0531
D(x): 0.7156, D(G(z)): 0.1502
Epoch: [4/20], Batch Num: [357/600]
Discriminator Loss: 0.7955, Generator Loss: 1.8808
D(x): 0.7234, D(G(z)): 0.1656
Epoch: [4/20], Batch Num: [358/600]
Discriminator Loss: 0.6499, Generator Loss: 1.7974
D(x): 0.8294, D(G(z)): 0.2449
Epoch: [4/20], Batch Num: [359/600]
Discriminator Loss: 0.6998, Generator Loss: 1.6933
D(x): 0.8676, D(G(z)): 0.2701
Epoch: [4/20], Batch Num: [360/600]
Discriminator Loss: 0.6042, Generator Loss: 1.9199
D(x): 0.8764, D(G(z)): 0.2490
Epoch: [4/20], Batch Num: [361/600]
Discriminator Loss: 0.5256, Generator Loss: 2.1701
D(x): 0.8556, D(G(z)): 0.2135
Epoch: [4/20], Batch Num: [362/600]
Discriminator Loss: 0.5952, Generator Loss: 2.1286
D(x): 0.8359, D(G(z)): 0.1854
Epoch: [4/20], Batch Num: [363/600]
Discriminator Loss: 0.6548, Generator Loss: 2.2503
D(x): 0.8308, D(G(z)): 0.1818
Epoch: [4/20], Batch Num: [364/600]
Discriminator Loss: 0.5244, Generator Loss: 2.3120
D(x): 0.8400, D(G(z)): 0.1581
Epoch: [4/20], Batch Num: [365/600]
Discriminator Loss: 0.5873, Generator Loss: 2.3247
D(x): 0.7758, D(G(z)): 0.1396
Epoch: [4/20], Batch Num: [366/600]
Discriminator Loss: 0.5359, Generator Loss: 2.2223
D(x): 0.8137, D(G(z)): 0.1549
Epoch: [4/20], Batch Num: [367/600]
Discriminator Loss: 0.4368, Generator Loss: 2.3362
D(x): 0.8984, D(G(z)): 0.1836
Epoch: [4/20], Batch Num: [368/600]
Discriminator Loss: 0.4540, Generator Loss: 2.1124
D(x): 0.8796, D(G(z)): 0.1855
Epoch: [4/20], Batch Num: [369/600]
Discriminator Loss: 0.4996, Generator Loss: 2.2653
D(x): 0.8717, D(G(z)): 0.1840
Epoch: [4/20], Batch Num: [370/600]
Discriminator Loss: 0.6328, Generator Loss: 2.2649
D(x): 0.8423, D(G(z)): 0.2424
Epoch: [4/20], Batch Num: [371/600]
Discriminator Loss: 0.4605, Generator Loss: 2.5936
D(x): 0.8767, D(G(z)): 0.1704
Epoch: [4/20], Batch Num: [372/600]
Discriminator Loss: 0.5564, Generator Loss: 2.5631
D(x): 0.8368, D(G(z)): 0.1745
Epoch: [4/20], Batch Num: [373/600]
Discriminator Loss: 0.4875, Generator Loss: 2.5326
D(x): 0.8563, D(G(z)): 0.1654
Epoch: [4/20], Batch Num: [374/600]
Discriminator Loss: 0.5365, Generator Loss: 2.7518
D(x): 0.8228, D(G(z)): 0.1496
Epoch: [4/20], Batch Num: [375/600]
Discriminator Loss: 0.6134, Generator Loss: 2.2820
D(x): 0.7986, D(G(z)): 0.1326
Epoch: [4/20], Batch Num: [376/600]
Discriminator Loss: 0.3769, Generator Loss: 2.3941
D(x): 0.8979, D(G(z)): 0.1504
Epoch: [4/20], Batch Num: [377/600]
Discriminator Loss: 0.4725, Generator Loss: 2.1940
D(x): 0.8535, D(G(z)): 0.1536
Epoch: [4/20], Batch Num: [378/600]
Discriminator Loss: 0.6329, Generator Loss: 2.1546
D(x): 0.8503, D(G(z)): 0.2314
Epoch: [4/20], Batch Num: [379/600]
Discriminator Loss: 0.6517, Generator Loss: 2.3884
D(x): 0.8685, D(G(z)): 0.2139
Epoch: [4/20], Batch Num: [380/600]
Discriminator Loss: 0.4894, Generator Loss: 2.7612
D(x): 0.8986, D(G(z)): 0.2022
Epoch: [4/20], Batch Num: [381/600]
Discriminator Loss: 0.3801, Generator Loss: 2.7432
D(x): 0.8883, D(G(z)): 0.1592
Epoch: [4/20], Batch Num: [382/600]
Discriminator Loss: 0.6221, Generator Loss: 2.8833
D(x): 0.8110, D(G(z)): 0.1632
Epoch: [4/20], Batch Num: [383/600]
Discriminator Loss: 0.4410, Generator Loss: 2.8583
D(x): 0.8477, D(G(z)): 0.1249
Epoch: [4/20], Batch Num: [384/600]
Discriminator Loss: 0.4856, Generator Loss: 2.7360
D(x): 0.8480, D(G(z)): 0.1580
Epoch: [4/20], Batch Num: [385/600]
Discriminator Loss: 0.6918, Generator Loss: 2.7264
D(x): 0.7652, D(G(z)): 0.1215
Epoch: [4/20], Batch Num: [386/600]
Discriminator Loss: 0.5435, Generator Loss: 2.1875
D(x): 0.8320, D(G(z)): 0.1695
Epoch: [4/20], Batch Num: [387/600]
Discriminator Loss: 0.5543, Generator Loss: 2.3651
D(x): 0.8860, D(G(z)): 0.2339
Epoch: [4/20], Batch Num: [388/600]
Discriminator Loss: 0.5667, Generator Loss: 2.1606
D(x): 0.9022, D(G(z)): 0.2502
Epoch: [4/20], Batch Num: [389/600]
Discriminator Loss: 0.4903, Generator Loss: 2.6804
D(x): 0.8817, D(G(z)): 0.2148
Epoch: [4/20], Batch Num: [390/600]
Discriminator Loss: 0.4288, Generator Loss: 2.9049
D(x): 0.8978, D(G(z)): 0.1821
Epoch: [4/20], Batch Num: [391/600]
Discriminator Loss: 0.4994, Generator Loss: 3.2318
D(x): 0.8347, D(G(z)): 0.1543
Epoch: [4/20], Batch Num: [392/600]
Discriminator Loss: 0.5036, Generator Loss: 3.0512
D(x): 0.8122, D(G(z)): 0.1270
Epoch: [4/20], Batch Num: [393/600]
Discriminator Loss: 0.5874, Generator Loss: 2.9264
D(x): 0.7570, D(G(z)): 0.1254
Epoch: [4/20], Batch Num: [394/600]
Discriminator Loss: 0.4261, Generator Loss: 2.2606
D(x): 0.8678, D(G(z)): 0.1619
Epoch: [4/20], Batch Num: [395/600]
Discriminator Loss: 0.5454, Generator Loss: 2.0931
D(x): 0.8861, D(G(z)): 0.2718
Epoch: [4/20], Batch Num: [396/600]
Discriminator Loss: 0.6716, Generator Loss: 2.0591
D(x): 0.8729, D(G(z)): 0.2835
Epoch: [4/20], Batch Num: [397/600]
Discriminator Loss: 0.6617, Generator Loss: 2.1550
D(x): 0.8654, D(G(z)): 0.2925
Epoch: [4/20], Batch Num: [398/600]
Discriminator Loss: 0.6389, Generator Loss: 2.2387
D(x): 0.8004, D(G(z)): 0.2512
Epoch: [4/20], Batch Num: [399/600]
Discriminator Loss: 1.0733, Generator Loss: 2.0555
D(x): 0.7073, D(G(z)): 0.2150
Epoch: 4, Batch Num: [400/600]

Epoch: [4/20], Batch Num: [400/600]
Discriminator Loss: 0.9489, Generator Loss: 1.9235
D(x): 0.7359, D(G(z)): 0.2464
Epoch: [4/20], Batch Num: [401/600]
Discriminator Loss: 0.7612, Generator Loss: 1.8729
D(x): 0.7812, D(G(z)): 0.2803
Epoch: [4/20], Batch Num: [402/600]
Discriminator Loss: 0.8535, Generator Loss: 1.4715
D(x): 0.7860, D(G(z)): 0.3291
Epoch: [4/20], Batch Num: [403/600]
Discriminator Loss: 0.9627, Generator Loss: 1.6908
D(x): 0.7847, D(G(z)): 0.3500
Epoch: [4/20], Batch Num: [404/600]
Discriminator Loss: 1.0622, Generator Loss: 1.9610
D(x): 0.7172, D(G(z)): 0.3322
Epoch: [4/20], Batch Num: [405/600]
Discriminator Loss: 0.8987, Generator Loss: 1.9776
D(x): 0.7342, D(G(z)): 0.2620
Epoch: [4/20], Batch Num: [406/600]
Discriminator Loss: 0.9622, Generator Loss: 1.7850
D(x): 0.7271, D(G(z)): 0.2907
Epoch: [4/20], Batch Num: [407/600]
Discriminator Loss: 0.9171, Generator Loss: 1.8612
D(x): 0.7366, D(G(z)): 0.2787
Epoch: [4/20], Batch Num: [408/600]
Discriminator Loss: 1.0273, Generator Loss: 1.8217
D(x): 0.7179, D(G(z)): 0.3313
Epoch: [4/20], Batch Num: [409/600]
Discriminator Loss: 0.8726, Generator Loss: 1.5240
D(x): 0.7544, D(G(z)): 0.3055
Epoch: [4/20], Batch Num: [410/600]
Discriminator Loss: 0.7666, Generator Loss: 1.7656
D(x): 0.8276, D(G(z)): 0.3351
Epoch: [4/20], Batch Num: [411/600]
Discriminator Loss: 0.6774, Generator Loss: 1.8855
D(x): 0.7825, D(G(z)): 0.2644
Epoch: [4/20], Batch Num: [412/600]
Discriminator Loss: 0.7336, Generator Loss: 2.0564
D(x): 0.7398, D(G(z)): 0.2312
Epoch: [4/20], Batch Num: [413/600]
Discriminator Loss: 0.9288, Generator Loss: 1.7291
D(x): 0.7062, D(G(z)): 0.2828
Epoch: [4/20], Batch Num: [414/600]
Discriminator Loss: 0.7050, Generator Loss: 1.9626
D(x): 0.8100, D(G(z)): 0.2899
Epoch: [4/20], Batch Num: [415/600]
Discriminator Loss: 0.6984, Generator Loss: 1.9013
D(x): 0.8119, D(G(z)): 0.3051
Epoch: [4/20], Batch Num: [416/600]
Discriminator Loss: 0.7222, Generator Loss: 2.1803
D(x): 0.7877, D(G(z)): 0.2583
Epoch: [4/20], Batch Num: [417/600]
Discriminator Loss: 0.7029, Generator Loss: 2.0678
D(x): 0.7847, D(G(z)): 0.2402
Epoch: [4/20], Batch Num: [418/600]
Discriminator Loss: 0.6555, Generator Loss: 2.0260
D(x): 0.7847, D(G(z)): 0.2150
Epoch: [4/20], Batch Num: [419/600]
Discriminator Loss: 0.5466, Generator Loss: 2.4159
D(x): 0.8647, D(G(z)): 0.2470
Epoch: [4/20], Batch Num: [420/600]
Discriminator Loss: 0.5086, Generator Loss: 2.5478
D(x): 0.8407, D(G(z)): 0.2065
Epoch: [4/20], Batch Num: [421/600]
Discriminator Loss: 0.5858, Generator Loss: 2.4754
D(x): 0.8065, D(G(z)): 0.1796
Epoch: [4/20], Batch Num: [422/600]
Discriminator Loss: 0.5863, Generator Loss: 2.3105
D(x): 0.8048, D(G(z)): 0.1819
Epoch: [4/20], Batch Num: [423/600]
Discriminator Loss: 0.6653, Generator Loss: 2.0956
D(x): 0.8019, D(G(z)): 0.2034
Epoch: [4/20], Batch Num: [424/600]
Discriminator Loss: 0.6645, Generator Loss: 2.2514
D(x): 0.8104, D(G(z)): 0.2349
Epoch: [4/20], Batch Num: [425/600]
Discriminator Loss: 0.4612, Generator Loss: 2.0300
D(x): 0.8944, D(G(z)): 0.2341
Epoch: [4/20], Batch Num: [426/600]
Discriminator Loss: 0.6479, Generator Loss: 2.1352
D(x): 0.7897, D(G(z)): 0.2036
Epoch: [4/20], Batch Num: [427/600]
Discriminator Loss: 0.6754, Generator Loss: 2.1747
D(x): 0.8643, D(G(z)): 0.2506
Epoch: [4/20], Batch Num: [428/600]
Discriminator Loss: 0.5435, Generator Loss: 2.3533
D(x): 0.8513, D(G(z)): 0.2217
Epoch: [4/20], Batch Num: [429/600]
Discriminator Loss: 0.5800, Generator Loss: 2.3009
D(x): 0.8038, D(G(z)): 0.1856
Epoch: [4/20], Batch Num: [430/600]
Discriminator Loss: 0.6575, Generator Loss: 1.9536
D(x): 0.7663, D(G(z)): 0.1786
Epoch: [4/20], Batch Num: [431/600]
Discriminator Loss: 0.6423, Generator Loss: 1.7649
D(x): 0.7947, D(G(z)): 0.2053
Epoch: [4/20], Batch Num: [432/600]
Discriminator Loss: 0.7635, Generator Loss: 1.6996
D(x): 0.8210, D(G(z)): 0.2867
Epoch: [4/20], Batch Num: [433/600]
Discriminator Loss: 0.6494, Generator Loss: 1.9735
D(x): 0.8450, D(G(z)): 0.2698
Epoch: [4/20], Batch Num: [434/600]
Discriminator Loss: 0.4556, Generator Loss: 2.2350
D(x): 0.8878, D(G(z)): 0.2293
Epoch: [4/20], Batch Num: [435/600]
Discriminator Loss: 0.6164, Generator Loss: 2.3254
D(x): 0.8105, D(G(z)): 0.1630
Epoch: [4/20], Batch Num: [436/600]
Discriminator Loss: 0.6400, Generator Loss: 2.2857
D(x): 0.7839, D(G(z)): 0.1504
Epoch: [4/20], Batch Num: [437/600]
Discriminator Loss: 0.6498, Generator Loss: 1.8840
D(x): 0.7651, D(G(z)): 0.1534
Epoch: [4/20], Batch Num: [438/600]
Discriminator Loss: 0.4712, Generator Loss: 1.7295
D(x): 0.8575, D(G(z)): 0.1809
Epoch: [4/20], Batch Num: [439/600]
Discriminator Loss: 0.5651, Generator Loss: 1.8219
D(x): 0.8877, D(G(z)): 0.2848
Epoch: [4/20], Batch Num: [440/600]
Discriminator Loss: 0.6539, Generator Loss: 2.1007
D(x): 0.8550, D(G(z)): 0.2723
Epoch: [4/20], Batch Num: [441/600]
Discriminator Loss: 0.5602, Generator Loss: 2.2764
D(x): 0.8392, D(G(z)): 0.2191
Epoch: [4/20], Batch Num: [442/600]
Discriminator Loss: 0.6231, Generator Loss: 2.3370
D(x): 0.7904, D(G(z)): 0.1666
Epoch: [4/20], Batch Num: [443/600]
Discriminator Loss: 0.4770, Generator Loss: 2.5056
D(x): 0.8488, D(G(z)): 0.1549
Epoch: [4/20], Batch Num: [444/600]
Discriminator Loss: 0.6662, Generator Loss: 2.3797
D(x): 0.7817, D(G(z)): 0.1735
Epoch: [4/20], Batch Num: [445/600]
Discriminator Loss: 0.5419, Generator Loss: 2.0204
D(x): 0.8099, D(G(z)): 0.1467
Epoch: [4/20], Batch Num: [446/600]
Discriminator Loss: 0.2924, Generator Loss: 2.2739
D(x): 0.9402, D(G(z)): 0.1765
Epoch: [4/20], Batch Num: [447/600]
Discriminator Loss: 0.5395, Generator Loss: 2.3729
D(x): 0.8558, D(G(z)): 0.2211
Epoch: [4/20], Batch Num: [448/600]
Discriminator Loss: 0.4003, Generator Loss: 2.4071
D(x): 0.8600, D(G(z)): 0.1608
Epoch: [4/20], Batch Num: [449/600]
Discriminator Loss: 0.5089, Generator Loss: 2.6501
D(x): 0.8448, D(G(z)): 0.1871
Epoch: [4/20], Batch Num: [450/600]
Discriminator Loss: 0.5153, Generator Loss: 2.8022
D(x): 0.8387, D(G(z)): 0.1534
Epoch: [4/20], Batch Num: [451/600]
Discriminator Loss: 0.5634, Generator Loss: 2.5836
D(x): 0.8152, D(G(z)): 0.1332
Epoch: [4/20], Batch Num: [452/600]
Discriminator Loss: 0.4553, Generator Loss: 2.7801
D(x): 0.8825, D(G(z)): 0.1636
Epoch: [4/20], Batch Num: [453/600]
Discriminator Loss: 0.5306, Generator Loss: 2.9577
D(x): 0.8496, D(G(z)): 0.1579
Epoch: [4/20], Batch Num: [454/600]
Discriminator Loss: 0.4048, Generator Loss: 2.5488
D(x): 0.8458, D(G(z)): 0.1108
Epoch: [4/20], Batch Num: [455/600]
Discriminator Loss: 0.4984, Generator Loss: 2.7767
D(x): 0.8761, D(G(z)): 0.1746
Epoch: [4/20], Batch Num: [456/600]
Discriminator Loss: 0.3997, Generator Loss: 3.0276
D(x): 0.8797, D(G(z)): 0.1562
Epoch: [4/20], Batch Num: [457/600]
Discriminator Loss: 0.4552, Generator Loss: 3.1271
D(x): 0.8562, D(G(z)): 0.1524
Epoch: [4/20], Batch Num: [458/600]
Discriminator Loss: 0.4527, Generator Loss: 3.0652
D(x): 0.8360, D(G(z)): 0.1094
Epoch: [4/20], Batch Num: [459/600]
Discriminator Loss: 0.5715, Generator Loss: 2.9899
D(x): 0.8354, D(G(z)): 0.1514
Epoch: [4/20], Batch Num: [460/600]
Discriminator Loss: 0.5313, Generator Loss: 2.6965
D(x): 0.8484, D(G(z)): 0.1794
Epoch: [4/20], Batch Num: [461/600]
Discriminator Loss: 0.5605, Generator Loss: 3.1539
D(x): 0.8654, D(G(z)): 0.1777
Epoch: [4/20], Batch Num: [462/600]
Discriminator Loss: 0.5039, Generator Loss: 3.4491
D(x): 0.8611, D(G(z)): 0.1504
Epoch: [4/20], Batch Num: [463/600]
Discriminator Loss: 0.4822, Generator Loss: 3.6742
D(x): 0.8458, D(G(z)): 0.1202
Epoch: [4/20], Batch Num: [464/600]
Discriminator Loss: 0.5175, Generator Loss: 3.0186
D(x): 0.8098, D(G(z)): 0.1035
Epoch: [4/20], Batch Num: [465/600]
Discriminator Loss: 0.6565, Generator Loss: 2.6479
D(x): 0.8181, D(G(z)): 0.1285
Epoch: [4/20], Batch Num: [466/600]
Discriminator Loss: 0.5232, Generator Loss: 2.5182
D(x): 0.9192, D(G(z)): 0.2431
Epoch: [4/20], Batch Num: [467/600]
Discriminator Loss: 0.4418, Generator Loss: 3.2950
D(x): 0.8925, D(G(z)): 0.1466
Epoch: [4/20], Batch Num: [468/600]
Discriminator Loss: 0.6475, Generator Loss: 3.3869
D(x): 0.8409, D(G(z)): 0.1528
Epoch: [4/20], Batch Num: [469/600]
Discriminator Loss: 0.6407, Generator Loss: 3.2321
D(x): 0.8252, D(G(z)): 0.1343
Epoch: [4/20], Batch Num: [470/600]
Discriminator Loss: 0.6635, Generator Loss: 2.8813
D(x): 0.8171, D(G(z)): 0.1455
Epoch: [4/20], Batch Num: [471/600]
Discriminator Loss: 0.7428, Generator Loss: 2.4795
D(x): 0.8239, D(G(z)): 0.1749
Epoch: [4/20], Batch Num: [472/600]
Discriminator Loss: 0.7950, Generator Loss: 2.4716
D(x): 0.8191, D(G(z)): 0.2691
Epoch: [4/20], Batch Num: [473/600]
Discriminator Loss: 0.8939, Generator Loss: 2.4713
D(x): 0.7980, D(G(z)): 0.2618
Epoch: [4/20], Batch Num: [474/600]
Discriminator Loss: 0.6795, Generator Loss: 2.9661
D(x): 0.8199, D(G(z)): 0.2209
Epoch: [4/20], Batch Num: [475/600]
Discriminator Loss: 0.5749, Generator Loss: 3.1435
D(x): 0.8471, D(G(z)): 0.1798
Epoch: [4/20], Batch Num: [476/600]
Discriminator Loss: 0.8990, Generator Loss: 2.7266
D(x): 0.7323, D(G(z)): 0.1461
Epoch: [4/20], Batch Num: [477/600]
Discriminator Loss: 0.5455, Generator Loss: 2.4208
D(x): 0.8418, D(G(z)): 0.1790
Epoch: [4/20], Batch Num: [478/600]
Discriminator Loss: 0.7441, Generator Loss: 2.3182
D(x): 0.8161, D(G(z)): 0.1936
Epoch: [4/20], Batch Num: [479/600]
Discriminator Loss: 0.5808, Generator Loss: 2.2877
D(x): 0.8647, D(G(z)): 0.2366
Epoch: [4/20], Batch Num: [480/600]
Discriminator Loss: 0.6571, Generator Loss: 2.7361
D(x): 0.8297, D(G(z)): 0.2267
Epoch: [4/20], Batch Num: [481/600]
Discriminator Loss: 0.4617, Generator Loss: 3.0743
D(x): 0.8720, D(G(z)): 0.1628
Epoch: [4/20], Batch Num: [482/600]
Discriminator Loss: 0.5112, Generator Loss: 3.4299
D(x): 0.8367, D(G(z)): 0.1282
Epoch: [4/20], Batch Num: [483/600]
Discriminator Loss: 0.5709, Generator Loss: 3.3237
D(x): 0.7905, D(G(z)): 0.0919
Epoch: [4/20], Batch Num: [484/600]
Discriminator Loss: 0.5018, Generator Loss: 2.6839
D(x): 0.8245, D(G(z)): 0.1173
Epoch: [4/20], Batch Num: [485/600]
Discriminator Loss: 0.4139, Generator Loss: 2.9619
D(x): 0.8907, D(G(z)): 0.1662
Epoch: [4/20], Batch Num: [486/600]
Discriminator Loss: 0.4369, Generator Loss: 2.8644
D(x): 0.8789, D(G(z)): 0.1617
Epoch: [4/20], Batch Num: [487/600]
Discriminator Loss: 0.3666, Generator Loss: 2.7004
D(x): 0.9022, D(G(z)): 0.1562
Epoch: [4/20], Batch Num: [488/600]
Discriminator Loss: 0.3794, Generator Loss: 3.0230
D(x): 0.9035, D(G(z)): 0.1521
Epoch: [4/20], Batch Num: [489/600]
Discriminator Loss: 0.4111, Generator Loss: 3.2110
D(x): 0.8677, D(G(z)): 0.1209
Epoch: [4/20], Batch Num: [490/600]
Discriminator Loss: 0.3715, Generator Loss: 3.1172
D(x): 0.8610, D(G(z)): 0.1113
Epoch: [4/20], Batch Num: [491/600]
Discriminator Loss: 0.3145, Generator Loss: 3.2030
D(x): 0.8918, D(G(z)): 0.1220
Epoch: [4/20], Batch Num: [492/600]
Discriminator Loss: 0.3020, Generator Loss: 3.3704
D(x): 0.9224, D(G(z)): 0.1461
Epoch: [4/20], Batch Num: [493/600]
Discriminator Loss: 0.4696, Generator Loss: 3.0111
D(x): 0.8206, D(G(z)): 0.0981
Epoch: [4/20], Batch Num: [494/600]
Discriminator Loss: 0.4236, Generator Loss: 3.1504
D(x): 0.8648, D(G(z)): 0.1243
Epoch: [4/20], Batch Num: [495/600]
Discriminator Loss: 0.4327, Generator Loss: 2.8191
D(x): 0.8634, D(G(z)): 0.1461
Epoch: [4/20], Batch Num: [496/600]
Discriminator Loss: 0.4008, Generator Loss: 2.8399
D(x): 0.9152, D(G(z)): 0.1815
Epoch: [4/20], Batch Num: [497/600]
Discriminator Loss: 0.4083, Generator Loss: 2.8666
D(x): 0.8932, D(G(z)): 0.1500
Epoch: [4/20], Batch Num: [498/600]
Discriminator Loss: 0.6197, Generator Loss: 2.8938
D(x): 0.8120, D(G(z)): 0.1709
Epoch: [4/20], Batch Num: [499/600]
Discriminator Loss: 0.4732, Generator Loss: 3.0599
D(x): 0.8638, D(G(z)): 0.1474
Epoch: 4, Batch Num: [500/600]

Epoch: [4/20], Batch Num: [500/600]
Discriminator Loss: 0.6642, Generator Loss: 3.1603
D(x): 0.8142, D(G(z)): 0.1644
Epoch: [4/20], Batch Num: [501/600]
Discriminator Loss: 0.6735, Generator Loss: 2.8174
D(x): 0.8201, D(G(z)): 0.1731
Epoch: [4/20], Batch Num: [502/600]
Discriminator Loss: 0.5986, Generator Loss: 2.8425
D(x): 0.8296, D(G(z)): 0.1757
Epoch: [4/20], Batch Num: [503/600]
Discriminator Loss: 0.5887, Generator Loss: 3.0972
D(x): 0.8536, D(G(z)): 0.1905
Epoch: [4/20], Batch Num: [504/600]
Discriminator Loss: 0.4192, Generator Loss: 3.0423
D(x): 0.8877, D(G(z)): 0.1386
Epoch: [4/20], Batch Num: [505/600]
Discriminator Loss: 0.6166, Generator Loss: 2.9143
D(x): 0.8400, D(G(z)): 0.1402
Epoch: [4/20], Batch Num: [506/600]
Discriminator Loss: 0.5971, Generator Loss: 3.2017
D(x): 0.8364, D(G(z)): 0.1667
Epoch: [4/20], Batch Num: [507/600]
Discriminator Loss: 0.8743, Generator Loss: 2.5988
D(x): 0.7654, D(G(z)): 0.1920
Epoch: [4/20], Batch Num: [508/600]
Discriminator Loss: 0.7134, Generator Loss: 2.5918
D(x): 0.8212, D(G(z)): 0.1917
Epoch: [4/20], Batch Num: [509/600]
Discriminator Loss: 0.6525, Generator Loss: 2.5569
D(x): 0.8917, D(G(z)): 0.2677
Epoch: [4/20], Batch Num: [510/600]
Discriminator Loss: 0.7937, Generator Loss: 2.7541
D(x): 0.8200, D(G(z)): 0.2219
Epoch: [4/20], Batch Num: [511/600]
Discriminator Loss: 0.7329, Generator Loss: 2.5209
D(x): 0.7940, D(G(z)): 0.1539
Epoch: [4/20], Batch Num: [512/600]
Discriminator Loss: 0.7131, Generator Loss: 2.7237
D(x): 0.8331, D(G(z)): 0.1810
Epoch: [4/20], Batch Num: [513/600]
Discriminator Loss: 1.0393, Generator Loss: 2.3039
D(x): 0.7367, D(G(z)): 0.1873
Epoch: [4/20], Batch Num: [514/600]
Discriminator Loss: 0.7644, Generator Loss: 2.0373
D(x): 0.7954, D(G(z)): 0.2079
Epoch: [4/20], Batch Num: [515/600]
Discriminator Loss: 0.6255, Generator Loss: 2.1914
D(x): 0.8518, D(G(z)): 0.2420
Epoch: [4/20], Batch Num: [516/600]
Discriminator Loss: 0.8425, Generator Loss: 2.1740
D(x): 0.8157, D(G(z)): 0.2730
Epoch: [4/20], Batch Num: [517/600]
Discriminator Loss: 0.6003, Generator Loss: 2.4435
D(x): 0.8567, D(G(z)): 0.2075
Epoch: [4/20], Batch Num: [518/600]
Discriminator Loss: 0.4828, Generator Loss: 2.7038
D(x): 0.8692, D(G(z)): 0.1757
Epoch: [4/20], Batch Num: [519/600]
Discriminator Loss: 0.6094, Generator Loss: 2.6481
D(x): 0.8467, D(G(z)): 0.1409
Epoch: [4/20], Batch Num: [520/600]
Discriminator Loss: 0.5438, Generator Loss: 2.4795
D(x): 0.8228, D(G(z)): 0.1208
Epoch: [4/20], Batch Num: [521/600]
Discriminator Loss: 0.6046, Generator Loss: 2.2963
D(x): 0.8087, D(G(z)): 0.1250
Epoch: [4/20], Batch Num: [522/600]
Discriminator Loss: 0.6417, Generator Loss: 1.8811
D(x): 0.8086, D(G(z)): 0.1875
Epoch: [4/20], Batch Num: [523/600]
Discriminator Loss: 0.4634, Generator Loss: 2.0430
D(x): 0.9198, D(G(z)): 0.2501
Epoch: [4/20], Batch Num: [524/600]
Discriminator Loss: 0.4535, Generator Loss: 2.2041
D(x): 0.9050, D(G(z)): 0.2331
Epoch: [4/20], Batch Num: [525/600]
Discriminator Loss: 0.4997, Generator Loss: 2.4147
D(x): 0.8575, D(G(z)): 0.1862
Epoch: [4/20], Batch Num: [526/600]
Discriminator Loss: 0.4374, Generator Loss: 2.6934
D(x): 0.8761, D(G(z)): 0.1677
Epoch: [4/20], Batch Num: [527/600]
Discriminator Loss: 0.4605, Generator Loss: 2.5787
D(x): 0.8710, D(G(z)): 0.1422
Epoch: [4/20], Batch Num: [528/600]
Discriminator Loss: 0.4943, Generator Loss: 2.6808
D(x): 0.8289, D(G(z)): 0.1094
Epoch: [4/20], Batch Num: [529/600]
Discriminator Loss: 0.4769, Generator Loss: 2.5339
D(x): 0.8465, D(G(z)): 0.1259
Epoch: [4/20], Batch Num: [530/600]
Discriminator Loss: 0.3725, Generator Loss: 2.4950
D(x): 0.8944, D(G(z)): 0.1508
Epoch: [4/20], Batch Num: [531/600]
Discriminator Loss: 0.5584, Generator Loss: 2.3687
D(x): 0.8524, D(G(z)): 0.1797
Epoch: [4/20], Batch Num: [532/600]
Discriminator Loss: 0.4735, Generator Loss: 2.3727
D(x): 0.8567, D(G(z)): 0.1579
Epoch: [4/20], Batch Num: [533/600]
Discriminator Loss: 0.5227, Generator Loss: 2.4410
D(x): 0.8496, D(G(z)): 0.1681
Epoch: [4/20], Batch Num: [534/600]
Discriminator Loss: 0.4011, Generator Loss: 2.4088
D(x): 0.9008, D(G(z)): 0.1868
Epoch: [4/20], Batch Num: [535/600]
Discriminator Loss: 0.4066, Generator Loss: 2.5442
D(x): 0.9014, D(G(z)): 0.1910
Epoch: [4/20], Batch Num: [536/600]
Discriminator Loss: 0.4341, Generator Loss: 2.7390
D(x): 0.9022, D(G(z)): 0.1715
Epoch: [4/20], Batch Num: [537/600]
Discriminator Loss: 0.4907, Generator Loss: 2.8603
D(x): 0.8693, D(G(z)): 0.1401
Epoch: [4/20], Batch Num: [538/600]
Discriminator Loss: 0.5307, Generator Loss: 2.9903
D(x): 0.8404, D(G(z)): 0.1136
Epoch: [4/20], Batch Num: [539/600]
Discriminator Loss: 0.4036, Generator Loss: 2.7765
D(x): 0.8616, D(G(z)): 0.1157
Epoch: [4/20], Batch Num: [540/600]
Discriminator Loss: 0.3714, Generator Loss: 2.7405
D(x): 0.8650, D(G(z)): 0.0913
Epoch: [4/20], Batch Num: [541/600]
Discriminator Loss: 0.6031, Generator Loss: 2.4693
D(x): 0.8227, D(G(z)): 0.1538
Epoch: [4/20], Batch Num: [542/600]
Discriminator Loss: 0.4518, Generator Loss: 2.1873
D(x): 0.9076, D(G(z)): 0.1953
Epoch: [4/20], Batch Num: [543/600]
Discriminator Loss: 0.6369, Generator Loss: 2.2066
D(x): 0.8724, D(G(z)): 0.2241
Epoch: [4/20], Batch Num: [544/600]
Discriminator Loss: 0.5996, Generator Loss: 2.0625
D(x): 0.8643, D(G(z)): 0.2177
Epoch: [4/20], Batch Num: [545/600]
Discriminator Loss: 0.6485, Generator Loss: 2.1938
D(x): 0.8411, D(G(z)): 0.2225
Epoch: [4/20], Batch Num: [546/600]
Discriminator Loss: 0.4651, Generator Loss: 2.2444
D(x): 0.8983, D(G(z)): 0.1980
Epoch: [4/20], Batch Num: [547/600]
Discriminator Loss: 0.3916, Generator Loss: 2.5352
D(x): 0.9209, D(G(z)): 0.1938
Epoch: [4/20], Batch Num: [548/600]
Discriminator Loss: 0.7213, Generator Loss: 2.7418
D(x): 0.7853, D(G(z)): 0.1623
Epoch: [4/20], Batch Num: [549/600]
Discriminator Loss: 0.5989, Generator Loss: 2.3621
D(x): 0.8304, D(G(z)): 0.1519
Epoch: [4/20], Batch Num: [550/600]
Discriminator Loss: 0.7512, Generator Loss: 2.4612
D(x): 0.7933, D(G(z)): 0.1645
Epoch: [4/20], Batch Num: [551/600]
Discriminator Loss: 0.4071, Generator Loss: 2.2077
D(x): 0.9048, D(G(z)): 0.1787
Epoch: [4/20], Batch Num: [552/600]
Discriminator Loss: 0.6006, Generator Loss: 1.9761
D(x): 0.8426, D(G(z)): 0.1721
Epoch: [4/20], Batch Num: [553/600]
Discriminator Loss: 0.6682, Generator Loss: 1.6559
D(x): 0.8299, D(G(z)): 0.2243
Epoch: [4/20], Batch Num: [554/600]
Discriminator Loss: 0.6369, Generator Loss: 1.7997
D(x): 0.8663, D(G(z)): 0.2505
Epoch: [4/20], Batch Num: [555/600]
Discriminator Loss: 0.5539, Generator Loss: 1.8255
D(x): 0.8694, D(G(z)): 0.2609
Epoch: [4/20], Batch Num: [556/600]
Discriminator Loss: 0.5705, Generator Loss: 2.1581
D(x): 0.8803, D(G(z)): 0.2484
Epoch: [4/20], Batch Num: [557/600]
Discriminator Loss: 0.4975, Generator Loss: 2.0900
D(x): 0.8899, D(G(z)): 0.2192
Epoch: [4/20], Batch Num: [558/600]
Discriminator Loss: 0.5333, Generator Loss: 2.5916
D(x): 0.8781, D(G(z)): 0.2082
Epoch: [4/20], Batch Num: [559/600]
Discriminator Loss: 0.3874, Generator Loss: 2.5123
D(x): 0.8772, D(G(z)): 0.1209
Epoch: [4/20], Batch Num: [560/600]
Discriminator Loss: 0.6282, Generator Loss: 2.4779
D(x): 0.7947, D(G(z)): 0.1460
Epoch: [4/20], Batch Num: [561/600]
Discriminator Loss: 0.6326, Generator Loss: 2.4421
D(x): 0.8073, D(G(z)): 0.1364
Epoch: [4/20], Batch Num: [562/600]
Discriminator Loss: 0.6384, Generator Loss: 2.0137
D(x): 0.8100, D(G(z)): 0.1515
Epoch: [4/20], Batch Num: [563/600]
Discriminator Loss: 0.5586, Generator Loss: 1.9802
D(x): 0.8573, D(G(z)): 0.2169
Epoch: [4/20], Batch Num: [564/600]
Discriminator Loss: 0.7227, Generator Loss: 1.9003
D(x): 0.8833, D(G(z)): 0.3099
Epoch: [4/20], Batch Num: [565/600]
Discriminator Loss: 0.6282, Generator Loss: 1.9875
D(x): 0.8649, D(G(z)): 0.2550
Epoch: [4/20], Batch Num: [566/600]
Discriminator Loss: 0.8824, Generator Loss: 2.1370
D(x): 0.7989, D(G(z)): 0.2791
Epoch: [4/20], Batch Num: [567/600]
Discriminator Loss: 0.5267, Generator Loss: 2.3868
D(x): 0.8568, D(G(z)): 0.1906
Epoch: [4/20], Batch Num: [568/600]
Discriminator Loss: 0.6335, Generator Loss: 2.4990
D(x): 0.8020, D(G(z)): 0.1576
Epoch: [4/20], Batch Num: [569/600]
Discriminator Loss: 0.5978, Generator Loss: 2.3424
D(x): 0.8157, D(G(z)): 0.1840
Epoch: [4/20], Batch Num: [570/600]
Discriminator Loss: 0.4853, Generator Loss: 2.2556
D(x): 0.8739, D(G(z)): 0.1823
Epoch: [4/20], Batch Num: [571/600]
Discriminator Loss: 0.5635, Generator Loss: 2.0578
D(x): 0.8588, D(G(z)): 0.1873
Epoch: [4/20], Batch Num: [572/600]
Discriminator Loss: 0.7225, Generator Loss: 2.2467
D(x): 0.8104, D(G(z)): 0.2276
Epoch: [4/20], Batch Num: [573/600]
Discriminator Loss: 0.7143, Generator Loss: 2.0353
D(x): 0.8164, D(G(z)): 0.2604
Epoch: [4/20], Batch Num: [574/600]
Discriminator Loss: 0.5397, Generator Loss: 1.7531
D(x): 0.8660, D(G(z)): 0.2250
Epoch: [4/20], Batch Num: [575/600]
Discriminator Loss: 0.6073, Generator Loss: 1.9491
D(x): 0.8652, D(G(z)): 0.2350
Epoch: [4/20], Batch Num: [576/600]
Discriminator Loss: 0.5096, Generator Loss: 1.9419
D(x): 0.8872, D(G(z)): 0.2148
Epoch: [4/20], Batch Num: [577/600]
Discriminator Loss: 0.5601, Generator Loss: 2.1098
D(x): 0.8574, D(G(z)): 0.1941
Epoch: [4/20], Batch Num: [578/600]
Discriminator Loss: 0.6203, Generator Loss: 2.1363
D(x): 0.8234, D(G(z)): 0.2063
Epoch: [4/20], Batch Num: [579/600]
Discriminator Loss: 0.6246, Generator Loss: 2.1476
D(x): 0.8284, D(G(z)): 0.1860
Epoch: [4/20], Batch Num: [580/600]
Discriminator Loss: 0.6474, Generator Loss: 1.8952
D(x): 0.8153, D(G(z)): 0.2044
Epoch: [4/20], Batch Num: [581/600]
Discriminator Loss: 0.5954, Generator Loss: 1.8375
D(x): 0.8348, D(G(z)): 0.2080
Epoch: [4/20], Batch Num: [582/600]
Discriminator Loss: 0.5170, Generator Loss: 1.7605
D(x): 0.8522, D(G(z)): 0.1925
Epoch: [4/20], Batch Num: [583/600]
Discriminator Loss: 0.6059, Generator Loss: 1.8680
D(x): 0.8497, D(G(z)): 0.2301
Epoch: [4/20], Batch Num: [584/600]
Discriminator Loss: 0.5078, Generator Loss: 1.8180
D(x): 0.8798, D(G(z)): 0.2136
Epoch: [4/20], Batch Num: [585/600]
Discriminator Loss: 0.6789, Generator Loss: 1.6792
D(x): 0.8035, D(G(z)): 0.2236
Epoch: [4/20], Batch Num: [586/600]
Discriminator Loss: 0.5559, Generator Loss: 1.6985
D(x): 0.8736, D(G(z)): 0.2331
Epoch: [4/20], Batch Num: [587/600]
Discriminator Loss: 0.5398, Generator Loss: 2.0532
D(x): 0.8811, D(G(z)): 0.2427
Epoch: [4/20], Batch Num: [588/600]
Discriminator Loss: 0.4869, Generator Loss: 2.0766
D(x): 0.8739, D(G(z)): 0.1997
Epoch: [4/20], Batch Num: [589/600]
Discriminator Loss: 0.5473, Generator Loss: 2.2321
D(x): 0.8534, D(G(z)): 0.1880
Epoch: [4/20], Batch Num: [590/600]
Discriminator Loss: 0.6813, Generator Loss: 2.3590
D(x): 0.8111, D(G(z)): 0.1766
Epoch: [4/20], Batch Num: [591/600]
Discriminator Loss: 0.6189, Generator Loss: 2.2979
D(x): 0.8007, D(G(z)): 0.1498
Epoch: [4/20], Batch Num: [592/600]
Discriminator Loss: 0.6633, Generator Loss: 1.9988
D(x): 0.7978, D(G(z)): 0.1456
Epoch: [4/20], Batch Num: [593/600]
Discriminator Loss: 0.6034, Generator Loss: 1.8523
D(x): 0.8132, D(G(z)): 0.1651
Epoch: [4/20], Batch Num: [594/600]
Discriminator Loss: 0.5270, Generator Loss: 1.7349
D(x): 0.8888, D(G(z)): 0.2370
Epoch: [4/20], Batch Num: [595/600]
Discriminator Loss: 0.5445, Generator Loss: 1.6937
D(x): 0.9039, D(G(z)): 0.2526
Epoch: [4/20], Batch Num: [596/600]
Discriminator Loss: 0.3412, Generator Loss: 1.9594
D(x): 0.9446, D(G(z)): 0.2187
Epoch: [4/20], Batch Num: [597/600]
Discriminator Loss: 0.3925, Generator Loss: 2.1131
D(x): 0.9173, D(G(z)): 0.1994
Epoch: [4/20], Batch Num: [598/600]
Discriminator Loss: 0.4710, Generator Loss: 2.2659
D(x): 0.8726, D(G(z)): 0.1887
Epoch: [4/20], Batch Num: [599/600]
Discriminator Loss: 0.4335, Generator Loss: 2.5567
D(x): 0.8744, D(G(z)): 0.1462
Epoch: 5, Batch Num: [0/600]

Epoch: [5/20], Batch Num: [0/600]
Discriminator Loss: 0.4650, Generator Loss: 2.5329
D(x): 0.8676, D(G(z)): 0.1484
Epoch: [5/20], Batch Num: [1/600]
Discriminator Loss: 0.3792, Generator Loss: 2.7025
D(x): 0.8741, D(G(z)): 0.1184
Epoch: [5/20], Batch Num: [2/600]
Discriminator Loss: 0.4554, Generator Loss: 2.4232
D(x): 0.8333, D(G(z)): 0.1181
Epoch: [5/20], Batch Num: [3/600]
Discriminator Loss: 0.3947, Generator Loss: 2.1708
D(x): 0.8771, D(G(z)): 0.1216
Epoch: [5/20], Batch Num: [4/600]
Discriminator Loss: 0.2591, Generator Loss: 2.0857
D(x): 0.9416, D(G(z)): 0.1311
Epoch: [5/20], Batch Num: [5/600]
Discriminator Loss: 0.3107, Generator Loss: 2.1738
D(x): 0.8977, D(G(z)): 0.1338
Epoch: [5/20], Batch Num: [6/600]
Discriminator Loss: 0.3713, Generator Loss: 2.0106
D(x): 0.9049, D(G(z)): 0.1641
Epoch: [5/20], Batch Num: [7/600]
Discriminator Loss: 0.3729, Generator Loss: 2.1349
D(x): 0.9248, D(G(z)): 0.1965
Epoch: [5/20], Batch Num: [8/600]
Discriminator Loss: 0.3083, Generator Loss: 2.3339
D(x): 0.9371, D(G(z)): 0.1680
Epoch: [5/20], Batch Num: [9/600]
Discriminator Loss: 0.2518, Generator Loss: 2.4801
D(x): 0.9472, D(G(z)): 0.1360
Epoch: [5/20], Batch Num: [10/600]
Discriminator Loss: 0.3128, Generator Loss: 2.6258
D(x): 0.9114, D(G(z)): 0.1273
Epoch: [5/20], Batch Num: [11/600]
Discriminator Loss: 0.2188, Generator Loss: 2.7800
D(x): 0.9404, D(G(z)): 0.1042
Epoch: [5/20], Batch Num: [12/600]
Discriminator Loss: 0.2673, Generator Loss: 2.9804
D(x): 0.9108, D(G(z)): 0.0908
Epoch: [5/20], Batch Num: [13/600]
Discriminator Loss: 0.2512, Generator Loss: 2.9681
D(x): 0.9099, D(G(z)): 0.0791
Epoch: [5/20], Batch Num: [14/600]
Discriminator Loss: 0.4092, Generator Loss: 2.9456
D(x): 0.8647, D(G(z)): 0.0756
Epoch: [5/20], Batch Num: [15/600]
Discriminator Loss: 0.2318, Generator Loss: 2.7452
D(x): 0.9221, D(G(z)): 0.0947
Epoch: [5/20], Batch Num: [16/600]
Discriminator Loss: 0.3300, Generator Loss: 2.6193
D(x): 0.8963, D(G(z)): 0.0978
Epoch: [5/20], Batch Num: [17/600]
Discriminator Loss: 0.2283, Generator Loss: 2.4956
D(x): 0.9407, D(G(z)): 0.1079
Epoch: [5/20], Batch Num: [18/600]
Discriminator Loss: 0.2670, Generator Loss: 2.4553
D(x): 0.9304, D(G(z)): 0.1298
Epoch: [5/20], Batch Num: [19/600]
Discriminator Loss: 0.3008, Generator Loss: 2.5723
D(x): 0.9294, D(G(z)): 0.1438
Epoch: [5/20], Batch Num: [20/600]
Discriminator Loss: 0.2817, Generator Loss: 2.6245
D(x): 0.9399, D(G(z)): 0.1383
Epoch: [5/20], Batch Num: [21/600]
Discriminator Loss: 0.2575, Generator Loss: 2.6746
D(x): 0.9228, D(G(z)): 0.1137
Epoch: [5/20], Batch Num: [22/600]
Discriminator Loss: 0.3395, Generator Loss: 2.8034
D(x): 0.8987, D(G(z)): 0.1033
Epoch: [5/20], Batch Num: [23/600]
Discriminator Loss: 0.2466, Generator Loss: 2.8282
D(x): 0.9205, D(G(z)): 0.1033
Epoch: [5/20], Batch Num: [24/600]
Discriminator Loss: 0.2747, Generator Loss: 2.7968
D(x): 0.9280, D(G(z)): 0.1179
Epoch: [5/20], Batch Num: [25/600]
Discriminator Loss: 0.2978, Generator Loss: 2.8218
D(x): 0.9037, D(G(z)): 0.1007
Epoch: [5/20], Batch Num: [26/600]
Discriminator Loss: 0.2736, Generator Loss: 2.7701
D(x): 0.9200, D(G(z)): 0.1062
Epoch: [5/20], Batch Num: [27/600]
Discriminator Loss: 0.2859, Generator Loss: 2.6832
D(x): 0.9277, D(G(z)): 0.0856
Epoch: [5/20], Batch Num: [28/600]
Discriminator Loss: 0.2793, Generator Loss: 2.9372
D(x): 0.9301, D(G(z)): 0.1252
Epoch: [5/20], Batch Num: [29/600]
Discriminator Loss: 0.3025, Generator Loss: 3.0702
D(x): 0.9198, D(G(z)): 0.1152
Epoch: [5/20], Batch Num: [30/600]
Discriminator Loss: 0.3068, Generator Loss: 3.1433
D(x): 0.9166, D(G(z)): 0.1119
Epoch: [5/20], Batch Num: [31/600]
Discriminator Loss: 0.3509, Generator Loss: 3.1601
D(x): 0.8901, D(G(z)): 0.0999
Epoch: [5/20], Batch Num: [32/600]
Discriminator Loss: 0.4361, Generator Loss: 3.1369
D(x): 0.8598, D(G(z)): 0.0784
Epoch: [5/20], Batch Num: [33/600]
Discriminator Loss: 0.3194, Generator Loss: 2.8846
D(x): 0.9029, D(G(z)): 0.1060
Epoch: [5/20], Batch Num: [34/600]
Discriminator Loss: 0.3556, Generator Loss: 2.7663
D(x): 0.8854, D(G(z)): 0.0877
Epoch: [5/20], Batch Num: [35/600]
Discriminator Loss: 0.4314, Generator Loss: 2.6913
D(x): 0.8686, D(G(z)): 0.1087
Epoch: [5/20], Batch Num: [36/600]
Discriminator Loss: 0.3447, Generator Loss: 2.5000
D(x): 0.9256, D(G(z)): 0.1268
Epoch: [5/20], Batch Num: [37/600]
Discriminator Loss: 0.3892, Generator Loss: 2.6294
D(x): 0.9019, D(G(z)): 0.1583
Epoch: [5/20], Batch Num: [38/600]
Discriminator Loss: 0.4787, Generator Loss: 2.8654
D(x): 0.8578, D(G(z)): 0.1517
Epoch: [5/20], Batch Num: [39/600]
Discriminator Loss: 0.6206, Generator Loss: 2.7902
D(x): 0.8392, D(G(z)): 0.1288
Epoch: [5/20], Batch Num: [40/600]
Discriminator Loss: 0.4091, Generator Loss: 2.8520
D(x): 0.9090, D(G(z)): 0.1441
Epoch: [5/20], Batch Num: [41/600]
Discriminator Loss: 0.3430, Generator Loss: 3.0286
D(x): 0.9200, D(G(z)): 0.1174
Epoch: [5/20], Batch Num: [42/600]
Discriminator Loss: 0.4639, Generator Loss: 3.2025
D(x): 0.9035, D(G(z)): 0.1174
Epoch: [5/20], Batch Num: [43/600]
Discriminator Loss: 0.3097, Generator Loss: 3.2212
D(x): 0.8917, D(G(z)): 0.0814
Epoch: [5/20], Batch Num: [44/600]
Discriminator Loss: 0.4113, Generator Loss: 3.1395
D(x): 0.8562, D(G(z)): 0.0849
Epoch: [5/20], Batch Num: [45/600]
Discriminator Loss: 0.4268, Generator Loss: 2.9409
D(x): 0.8848, D(G(z)): 0.1351
Epoch: [5/20], Batch Num: [46/600]
Discriminator Loss: 0.3552, Generator Loss: 2.9882
D(x): 0.8903, D(G(z)): 0.0995
Epoch: [5/20], Batch Num: [47/600]
Discriminator Loss: 0.5427, Generator Loss: 2.7123
D(x): 0.8678, D(G(z)): 0.1243
Epoch: [5/20], Batch Num: [48/600]
Discriminator Loss: 0.3773, Generator Loss: 2.6667
D(x): 0.9076, D(G(z)): 0.1599
Epoch: [5/20], Batch Num: [49/600]
Discriminator Loss: 0.5613, Generator Loss: 3.0704
D(x): 0.8772, D(G(z)): 0.1860
Epoch: [5/20], Batch Num: [50/600]
Discriminator Loss: 0.4643, Generator Loss: 2.9122
D(x): 0.8835, D(G(z)): 0.1387
Epoch: [5/20], Batch Num: [51/600]
Discriminator Loss: 0.6854, Generator Loss: 3.1182
D(x): 0.8217, D(G(z)): 0.1185
Epoch: [5/20], Batch Num: [52/600]
Discriminator Loss: 0.4682, Generator Loss: 3.2249
D(x): 0.8710, D(G(z)): 0.1207
Epoch: [5/20], Batch Num: [53/600]
Discriminator Loss: 0.3711, Generator Loss: 3.1023
D(x): 0.8583, D(G(z)): 0.0832
Epoch: [5/20], Batch Num: [54/600]
Discriminator Loss: 0.3453, Generator Loss: 2.7944
D(x): 0.8931, D(G(z)): 0.1069
Epoch: [5/20], Batch Num: [55/600]
Discriminator Loss: 0.5402, Generator Loss: 2.6337
D(x): 0.8698, D(G(z)): 0.1457
Epoch: [5/20], Batch Num: [56/600]
Discriminator Loss: 0.4131, Generator Loss: 2.5747
D(x): 0.8758, D(G(z)): 0.1188
Epoch: [5/20], Batch Num: [57/600]
Discriminator Loss: 0.3936, Generator Loss: 2.4338
D(x): 0.9272, D(G(z)): 0.1447
Epoch: [5/20], Batch Num: [58/600]
Discriminator Loss: 0.3510, Generator Loss: 2.8613
D(x): 0.9127, D(G(z)): 0.1180
Epoch: [5/20], Batch Num: [59/600]
Discriminator Loss: 0.5096, Generator Loss: 2.8851
D(x): 0.8718, D(G(z)): 0.1579
Epoch: [5/20], Batch Num: [60/600]
Discriminator Loss: 0.5960, Generator Loss: 2.9593
D(x): 0.8219, D(G(z)): 0.1161
Epoch: [5/20], Batch Num: [61/600]
Discriminator Loss: 0.4436, Generator Loss: 2.9033
D(x): 0.8589, D(G(z)): 0.0966
Epoch: [5/20], Batch Num: [62/600]
Discriminator Loss: 0.4278, Generator Loss: 2.6500
D(x): 0.8578, D(G(z)): 0.1148
Epoch: [5/20], Batch Num: [63/600]
Discriminator Loss: 0.5389, Generator Loss: 2.5053
D(x): 0.8296, D(G(z)): 0.1092
Epoch: [5/20], Batch Num: [64/600]
Discriminator Loss: 0.3522, Generator Loss: 2.3561
D(x): 0.9105, D(G(z)): 0.1348
Epoch: [5/20], Batch Num: [65/600]
Discriminator Loss: 0.3424, Generator Loss: 2.5650
D(x): 0.9015, D(G(z)): 0.1517
Epoch: [5/20], Batch Num: [66/600]
Discriminator Loss: 0.3266, Generator Loss: 2.6929
D(x): 0.9194, D(G(z)): 0.1538
Epoch: [5/20], Batch Num: [67/600]
Discriminator Loss: 0.3751, Generator Loss: 2.7186
D(x): 0.8715, D(G(z)): 0.1073
Epoch: [5/20], Batch Num: [68/600]
Discriminator Loss: 0.3231, Generator Loss: 2.9753
D(x): 0.9128, D(G(z)): 0.1171
Epoch: [5/20], Batch Num: [69/600]
Discriminator Loss: 0.3590, Generator Loss: 3.0694
D(x): 0.8883, D(G(z)): 0.0815
Epoch: [5/20], Batch Num: [70/600]
Discriminator Loss: 0.2557, Generator Loss: 3.3108
D(x): 0.9187, D(G(z)): 0.0782
Epoch: [5/20], Batch Num: [71/600]
Discriminator Loss: 0.2908, Generator Loss: 3.2854
D(x): 0.8976, D(G(z)): 0.0566
Epoch: [5/20], Batch Num: [72/600]
Discriminator Loss: 0.2544, Generator Loss: 3.2287
D(x): 0.9223, D(G(z)): 0.0667
Epoch: [5/20], Batch Num: [73/600]
Discriminator Loss: 0.3087, Generator Loss: 3.0537
D(x): 0.8965, D(G(z)): 0.0619
Epoch: [5/20], Batch Num: [74/600]
Discriminator Loss: 0.2706, Generator Loss: 2.9426
D(x): 0.9205, D(G(z)): 0.0973
Epoch: [5/20], Batch Num: [75/600]
Discriminator Loss: 0.1694, Generator Loss: 3.0602
D(x): 0.9597, D(G(z)): 0.0976
Epoch: [5/20], Batch Num: [76/600]
Discriminator Loss: 0.1352, Generator Loss: 3.0588
D(x): 0.9596, D(G(z)): 0.0655
Epoch: [5/20], Batch Num: [77/600]
Discriminator Loss: 0.2281, Generator Loss: 3.0628
D(x): 0.9349, D(G(z)): 0.0867
Epoch: [5/20], Batch Num: [78/600]
Discriminator Loss: 0.1419, Generator Loss: 3.1593
D(x): 0.9651, D(G(z)): 0.0833
Epoch: [5/20], Batch Num: [79/600]
Discriminator Loss: 0.1535, Generator Loss: 3.4850
D(x): 0.9731, D(G(z)): 0.0846
Epoch: [5/20], Batch Num: [80/600]
Discriminator Loss: 0.2009, Generator Loss: 3.4034
D(x): 0.9337, D(G(z)): 0.0722
Epoch: [5/20], Batch Num: [81/600]
Discriminator Loss: 0.2054, Generator Loss: 3.5513
D(x): 0.9231, D(G(z)): 0.0516
Epoch: [5/20], Batch Num: [82/600]
Discriminator Loss: 0.2727, Generator Loss: 3.6300
D(x): 0.9051, D(G(z)): 0.0463
Epoch: [5/20], Batch Num: [83/600]
Discriminator Loss: 0.2252, Generator Loss: 3.0671
D(x): 0.9197, D(G(z)): 0.0594
Epoch: [5/20], Batch Num: [84/600]
Discriminator Loss: 0.2692, Generator Loss: 2.8102
D(x): 0.9235, D(G(z)): 0.0825
Epoch: [5/20], Batch Num: [85/600]
Discriminator Loss: 0.1511, Generator Loss: 2.6701
D(x): 0.9781, D(G(z)): 0.1050
Epoch: [5/20], Batch Num: [86/600]
Discriminator Loss: 0.3293, Generator Loss: 2.8134
D(x): 0.9353, D(G(z)): 0.1499
Epoch: [5/20], Batch Num: [87/600]
Discriminator Loss: 0.2916, Generator Loss: 2.8954
D(x): 0.9335, D(G(z)): 0.1274
Epoch: [5/20], Batch Num: [88/600]
Discriminator Loss: 0.1924, Generator Loss: 3.2930
D(x): 0.9496, D(G(z)): 0.0899
Epoch: [5/20], Batch Num: [89/600]
Discriminator Loss: 0.3679, Generator Loss: 2.9854
D(x): 0.8838, D(G(z)): 0.0766
Epoch: [5/20], Batch Num: [90/600]
Discriminator Loss: 0.4069, Generator Loss: 2.8399
D(x): 0.8847, D(G(z)): 0.0922
Epoch: [5/20], Batch Num: [91/600]
Discriminator Loss: 0.3513, Generator Loss: 2.5245
D(x): 0.8770, D(G(z)): 0.1077
Epoch: [5/20], Batch Num: [92/600]
Discriminator Loss: 0.2965, Generator Loss: 2.3631
D(x): 0.9352, D(G(z)): 0.1254
Epoch: [5/20], Batch Num: [93/600]
Discriminator Loss: 0.4498, Generator Loss: 2.0789
D(x): 0.8955, D(G(z)): 0.1848
Epoch: [5/20], Batch Num: [94/600]
Discriminator Loss: 0.4767, Generator Loss: 2.4037
D(x): 0.9126, D(G(z)): 0.1711
Epoch: [5/20], Batch Num: [95/600]
Discriminator Loss: 0.3286, Generator Loss: 2.3967
D(x): 0.9108, D(G(z)): 0.1337
Epoch: [5/20], Batch Num: [96/600]
Discriminator Loss: 0.4997, Generator Loss: 2.3265
D(x): 0.8839, D(G(z)): 0.1741
Epoch: [5/20], Batch Num: [97/600]
Discriminator Loss: 0.6240, Generator Loss: 2.5423
D(x): 0.8450, D(G(z)): 0.1486
Epoch: [5/20], Batch Num: [98/600]
Discriminator Loss: 0.6791, Generator Loss: 2.6627
D(x): 0.8641, D(G(z)): 0.1872
Epoch: [5/20], Batch Num: [99/600]
Discriminator Loss: 0.4945, Generator Loss: 2.4350
D(x): 0.8656, D(G(z)): 0.1370
Epoch: 5, Batch Num: [100/600]

Epoch: [5/20], Batch Num: [100/600]
Discriminator Loss: 0.5534, Generator Loss: 2.4913
D(x): 0.8454, D(G(z)): 0.1347
Epoch: [5/20], Batch Num: [101/600]
Discriminator Loss: 0.4993, Generator Loss: 2.1995
D(x): 0.8665, D(G(z)): 0.1565
Epoch: [5/20], Batch Num: [102/600]
Discriminator Loss: 0.5129, Generator Loss: 2.2040
D(x): 0.8955, D(G(z)): 0.1875
Epoch: [5/20], Batch Num: [103/600]
Discriminator Loss: 0.6486, Generator Loss: 2.0342
D(x): 0.8195, D(G(z)): 0.1688
Epoch: [5/20], Batch Num: [104/600]
Discriminator Loss: 0.5681, Generator Loss: 2.1152
D(x): 0.8664, D(G(z)): 0.1973
Epoch: [5/20], Batch Num: [105/600]
Discriminator Loss: 0.5304, Generator Loss: 2.2705
D(x): 0.8840, D(G(z)): 0.1741
Epoch: [5/20], Batch Num: [106/600]
Discriminator Loss: 0.7130, Generator Loss: 2.1901
D(x): 0.8063, D(G(z)): 0.1822
Epoch: [5/20], Batch Num: [107/600]
Discriminator Loss: 0.6316, Generator Loss: 2.1023
D(x): 0.8182, D(G(z)): 0.1646
Epoch: [5/20], Batch Num: [108/600]
Discriminator Loss: 0.5499, Generator Loss: 2.0283
D(x): 0.8660, D(G(z)): 0.1938
Epoch: [5/20], Batch Num: [109/600]
Discriminator Loss: 0.5001, Generator Loss: 2.0533
D(x): 0.8531, D(G(z)): 0.1738
Epoch: [5/20], Batch Num: [110/600]
Discriminator Loss: 0.7589, Generator Loss: 1.9630
D(x): 0.8088, D(G(z)): 0.2082
Epoch: [5/20], Batch Num: [111/600]
Discriminator Loss: 0.4596, Generator Loss: 2.1413
D(x): 0.8792, D(G(z)): 0.1820
Epoch: [5/20], Batch Num: [112/600]
Discriminator Loss: 0.4416, Generator Loss: 2.2920
D(x): 0.8711, D(G(z)): 0.1560
Epoch: [5/20], Batch Num: [113/600]
Discriminator Loss: 0.5954, Generator Loss: 2.4931
D(x): 0.8570, D(G(z)): 0.1874
Epoch: [5/20], Batch Num: [114/600]
Discriminator Loss: 0.3895, Generator Loss: 2.8355
D(x): 0.8733, D(G(z)): 0.1252
Epoch: [5/20], Batch Num: [115/600]
Discriminator Loss: 0.4421, Generator Loss: 2.7973
D(x): 0.8334, D(G(z)): 0.1081
Epoch: [5/20], Batch Num: [116/600]
Discriminator Loss: 0.3009, Generator Loss: 2.3234
D(x): 0.8946, D(G(z)): 0.1145
Epoch: [5/20], Batch Num: [117/600]
Discriminator Loss: 0.3402, Generator Loss: 2.6031
D(x): 0.9260, D(G(z)): 0.1569
Epoch: [5/20], Batch Num: [118/600]
Discriminator Loss: 0.3188, Generator Loss: 2.8382
D(x): 0.9310, D(G(z)): 0.1482
Epoch: [5/20], Batch Num: [119/600]
Discriminator Loss: 0.2583, Generator Loss: 3.0694
D(x): 0.9289, D(G(z)): 0.1082
Epoch: [5/20], Batch Num: [120/600]
Discriminator Loss: 0.4125, Generator Loss: 2.9707
D(x): 0.9057, D(G(z)): 0.1310
Epoch: [5/20], Batch Num: [121/600]
Discriminator Loss: 0.5507, Generator Loss: 3.0063
D(x): 0.8189, D(G(z)): 0.0985
Epoch: [5/20], Batch Num: [122/600]
Discriminator Loss: 0.2727, Generator Loss: 2.9193
D(x): 0.9131, D(G(z)): 0.0875
Epoch: [5/20], Batch Num: [123/600]
Discriminator Loss: 0.4509, Generator Loss: 3.0572
D(x): 0.8574, D(G(z)): 0.1071
Epoch: [5/20], Batch Num: [124/600]
Discriminator Loss: 0.3981, Generator Loss: 2.6171
D(x): 0.8564, D(G(z)): 0.0978
Epoch: [5/20], Batch Num: [125/600]
Discriminator Loss: 0.5663, Generator Loss: 2.6002
D(x): 0.8650, D(G(z)): 0.1729
Epoch: [5/20], Batch Num: [126/600]
Discriminator Loss: 0.4169, Generator Loss: 2.7814
D(x): 0.8726, D(G(z)): 0.1292
Epoch: [5/20], Batch Num: [127/600]
Discriminator Loss: 0.4001, Generator Loss: 2.9988
D(x): 0.9204, D(G(z)): 0.1385
Epoch: [5/20], Batch Num: [128/600]
Discriminator Loss: 0.4376, Generator Loss: 3.2870
D(x): 0.8921, D(G(z)): 0.1364
Epoch: [5/20], Batch Num: [129/600]
Discriminator Loss: 0.6582, Generator Loss: 3.4166
D(x): 0.8187, D(G(z)): 0.1190
Epoch: [5/20], Batch Num: [130/600]
Discriminator Loss: 0.9312, Generator Loss: 3.3503
D(x): 0.7761, D(G(z)): 0.1340
Epoch: [5/20], Batch Num: [131/600]
Discriminator Loss: 0.7926, Generator Loss: 2.9023
D(x): 0.7546, D(G(z)): 0.0922
Epoch: [5/20], Batch Num: [132/600]
Discriminator Loss: 0.9326, Generator Loss: 2.5644
D(x): 0.7576, D(G(z)): 0.1856
Epoch: [5/20], Batch Num: [133/600]
Discriminator Loss: 1.0190, Generator Loss: 2.2534
D(x): 0.7803, D(G(z)): 0.2563
Epoch: [5/20], Batch Num: [134/600]
Discriminator Loss: 1.0942, Generator Loss: 2.4944
D(x): 0.8054, D(G(z)): 0.2564
Epoch: [5/20], Batch Num: [135/600]
Discriminator Loss: 1.0072, Generator Loss: 2.8412
D(x): 0.7694, D(G(z)): 0.2384
Epoch: [5/20], Batch Num: [136/600]
Discriminator Loss: 1.3333, Generator Loss: 2.7509
D(x): 0.6550, D(G(z)): 0.1946
Epoch: [5/20], Batch Num: [137/600]
Discriminator Loss: 1.3813, Generator Loss: 2.8979
D(x): 0.6843, D(G(z)): 0.2285
Epoch: [5/20], Batch Num: [138/600]
Discriminator Loss: 1.0396, Generator Loss: 2.9715
D(x): 0.7342, D(G(z)): 0.2009
Epoch: [5/20], Batch Num: [139/600]
Discriminator Loss: 0.6438, Generator Loss: 2.6446
D(x): 0.8198, D(G(z)): 0.1573
Epoch: [5/20], Batch Num: [140/600]
Discriminator Loss: 0.6169, Generator Loss: 2.3533
D(x): 0.7973, D(G(z)): 0.1398
Epoch: [5/20], Batch Num: [141/600]
Discriminator Loss: 0.7593, Generator Loss: 1.9176
D(x): 0.7530, D(G(z)): 0.1736
Epoch: [5/20], Batch Num: [142/600]
Discriminator Loss: 0.8503, Generator Loss: 1.9562
D(x): 0.7956, D(G(z)): 0.2521
Epoch: [5/20], Batch Num: [143/600]
Discriminator Loss: 0.6471, Generator Loss: 2.4337
D(x): 0.8643, D(G(z)): 0.2621
Epoch: [5/20], Batch Num: [144/600]
Discriminator Loss: 0.2974, Generator Loss: 3.1165
D(x): 0.9185, D(G(z)): 0.1522
Epoch: [5/20], Batch Num: [145/600]
Discriminator Loss: 0.3905, Generator Loss: 3.6942
D(x): 0.8698, D(G(z)): 0.1163
Epoch: [5/20], Batch Num: [146/600]
Discriminator Loss: 0.4736, Generator Loss: 3.9817
D(x): 0.8220, D(G(z)): 0.0901
Epoch: [5/20], Batch Num: [147/600]
Discriminator Loss: 0.3383, Generator Loss: 3.4091
D(x): 0.8780, D(G(z)): 0.1046
Epoch: [5/20], Batch Num: [148/600]
Discriminator Loss: 0.3915, Generator Loss: 2.9469
D(x): 0.8402, D(G(z)): 0.0846
Epoch: [5/20], Batch Num: [149/600]
Discriminator Loss: 0.3184, Generator Loss: 2.8590
D(x): 0.8988, D(G(z)): 0.1334
Epoch: [5/20], Batch Num: [150/600]
Discriminator Loss: 0.3444, Generator Loss: 2.6831
D(x): 0.9184, D(G(z)): 0.1625
Epoch: [5/20], Batch Num: [151/600]
Discriminator Loss: 0.4183, Generator Loss: 2.8840
D(x): 0.8702, D(G(z)): 0.1639
Epoch: [5/20], Batch Num: [152/600]
Discriminator Loss: 0.5825, Generator Loss: 2.5769
D(x): 0.8585, D(G(z)): 0.1991
Epoch: [5/20], Batch Num: [153/600]
Discriminator Loss: 0.4882, Generator Loss: 2.8758
D(x): 0.8843, D(G(z)): 0.2069
Epoch: [5/20], Batch Num: [154/600]
Discriminator Loss: 0.4498, Generator Loss: 2.9266
D(x): 0.8769, D(G(z)): 0.1802
Epoch: [5/20], Batch Num: [155/600]
Discriminator Loss: 0.7264, Generator Loss: 2.8772
D(x): 0.7833, D(G(z)): 0.1809
Epoch: [5/20], Batch Num: [156/600]
Discriminator Loss: 0.5184, Generator Loss: 2.6616
D(x): 0.8482, D(G(z)): 0.1747
Epoch: [5/20], Batch Num: [157/600]
Discriminator Loss: 0.5813, Generator Loss: 2.3157
D(x): 0.8224, D(G(z)): 0.1785
Epoch: [5/20], Batch Num: [158/600]
Discriminator Loss: 0.6559, Generator Loss: 2.0600
D(x): 0.7946, D(G(z)): 0.1934
Epoch: [5/20], Batch Num: [159/600]
Discriminator Loss: 0.7060, Generator Loss: 1.8461
D(x): 0.8196, D(G(z)): 0.2565
Epoch: [5/20], Batch Num: [160/600]
Discriminator Loss: 0.5671, Generator Loss: 2.0201
D(x): 0.8530, D(G(z)): 0.2399
Epoch: [5/20], Batch Num: [161/600]
Discriminator Loss: 0.5315, Generator Loss: 2.3378
D(x): 0.8656, D(G(z)): 0.2350
Epoch: [5/20], Batch Num: [162/600]
Discriminator Loss: 0.4757, Generator Loss: 2.5791
D(x): 0.8539, D(G(z)): 0.1877
Epoch: [5/20], Batch Num: [163/600]
Discriminator Loss: 0.4889, Generator Loss: 2.6181
D(x): 0.8218, D(G(z)): 0.1497
Epoch: [5/20], Batch Num: [164/600]
Discriminator Loss: 0.5406, Generator Loss: 2.2565
D(x): 0.8212, D(G(z)): 0.1750
Epoch: [5/20], Batch Num: [165/600]
Discriminator Loss: 0.6942, Generator Loss: 2.3881
D(x): 0.8170, D(G(z)): 0.1985
Epoch: [5/20], Batch Num: [166/600]
Discriminator Loss: 0.4826, Generator Loss: 2.0461
D(x): 0.8774, D(G(z)): 0.2049
Epoch: [5/20], Batch Num: [167/600]
Discriminator Loss: 0.4635, Generator Loss: 2.2615
D(x): 0.8946, D(G(z)): 0.2012
Epoch: [5/20], Batch Num: [168/600]
Discriminator Loss: 0.4489, Generator Loss: 2.3240
D(x): 0.8885, D(G(z)): 0.1900
Epoch: [5/20], Batch Num: [169/600]
Discriminator Loss: 0.5123, Generator Loss: 2.6474
D(x): 0.8673, D(G(z)): 0.1884
Epoch: [5/20], Batch Num: [170/600]
Discriminator Loss: 0.4910, Generator Loss: 2.6360
D(x): 0.8488, D(G(z)): 0.1571
Epoch: [5/20], Batch Num: [171/600]
Discriminator Loss: 0.3928, Generator Loss: 2.7992
D(x): 0.8658, D(G(z)): 0.1282
Epoch: [5/20], Batch Num: [172/600]
Discriminator Loss: 0.5371, Generator Loss: 2.4654
D(x): 0.8063, D(G(z)): 0.1376
Epoch: [5/20], Batch Num: [173/600]
Discriminator Loss: 0.4975, Generator Loss: 2.0950
D(x): 0.8370, D(G(z)): 0.1594
Epoch: [5/20], Batch Num: [174/600]
Discriminator Loss: 0.3300, Generator Loss: 2.1464
D(x): 0.9536, D(G(z)): 0.2161
Epoch: [5/20], Batch Num: [175/600]
Discriminator Loss: 0.3839, Generator Loss: 2.5160
D(x): 0.9053, D(G(z)): 0.1873
Epoch: [5/20], Batch Num: [176/600]
Discriminator Loss: 0.3497, Generator Loss: 2.7869
D(x): 0.9159, D(G(z)): 0.1768
Epoch: [5/20], Batch Num: [177/600]
Discriminator Loss: 0.3968, Generator Loss: 3.1673
D(x): 0.8900, D(G(z)): 0.1376
Epoch: [5/20], Batch Num: [178/600]
Discriminator Loss: 0.5030, Generator Loss: 2.8321
D(x): 0.8169, D(G(z)): 0.1272
Epoch: [5/20], Batch Num: [179/600]
Discriminator Loss: 0.4281, Generator Loss: 2.4349
D(x): 0.8412, D(G(z)): 0.1050
Epoch: [5/20], Batch Num: [180/600]
Discriminator Loss: 0.3372, Generator Loss: 2.6663
D(x): 0.9301, D(G(z)): 0.1754
Epoch: [5/20], Batch Num: [181/600]
Discriminator Loss: 0.3903, Generator Loss: 2.3809
D(x): 0.8900, D(G(z)): 0.1409
Epoch: [5/20], Batch Num: [182/600]
Discriminator Loss: 0.4023, Generator Loss: 2.5153
D(x): 0.8984, D(G(z)): 0.1692
Epoch: [5/20], Batch Num: [183/600]
Discriminator Loss: 0.4122, Generator Loss: 2.7285
D(x): 0.8946, D(G(z)): 0.1688
Epoch: [5/20], Batch Num: [184/600]
Discriminator Loss: 0.2865, Generator Loss: 2.8108
D(x): 0.9464, D(G(z)): 0.1508
Epoch: [5/20], Batch Num: [185/600]
Discriminator Loss: 0.4666, Generator Loss: 2.8899
D(x): 0.8798, D(G(z)): 0.1690
Epoch: [5/20], Batch Num: [186/600]
Discriminator Loss: 0.4023, Generator Loss: 3.0820
D(x): 0.8769, D(G(z)): 0.1240
Epoch: [5/20], Batch Num: [187/600]
Discriminator Loss: 0.5568, Generator Loss: 2.7486
D(x): 0.7901, D(G(z)): 0.0879
Epoch: [5/20], Batch Num: [188/600]
Discriminator Loss: 0.6679, Generator Loss: 2.3463
D(x): 0.8505, D(G(z)): 0.2073
Epoch: [5/20], Batch Num: [189/600]
Discriminator Loss: 0.5118, Generator Loss: 2.4670
D(x): 0.8988, D(G(z)): 0.1784
Epoch: [5/20], Batch Num: [190/600]
Discriminator Loss: 0.4286, Generator Loss: 2.4768
D(x): 0.9124, D(G(z)): 0.2248
Epoch: [5/20], Batch Num: [191/600]
Discriminator Loss: 0.5207, Generator Loss: 2.6841
D(x): 0.8855, D(G(z)): 0.2053
Epoch: [5/20], Batch Num: [192/600]
Discriminator Loss: 0.4136, Generator Loss: 2.6850
D(x): 0.8784, D(G(z)): 0.1468
Epoch: [5/20], Batch Num: [193/600]
Discriminator Loss: 0.6578, Generator Loss: 2.7803
D(x): 0.8245, D(G(z)): 0.1821
Epoch: [5/20], Batch Num: [194/600]
Discriminator Loss: 0.7235, Generator Loss: 2.7810
D(x): 0.7933, D(G(z)): 0.1987
Epoch: [5/20], Batch Num: [195/600]
Discriminator Loss: 0.7353, Generator Loss: 2.1873
D(x): 0.8038, D(G(z)): 0.1736
Epoch: [5/20], Batch Num: [196/600]
Discriminator Loss: 0.4809, Generator Loss: 1.9973
D(x): 0.8929, D(G(z)): 0.2029
Epoch: [5/20], Batch Num: [197/600]
Discriminator Loss: 0.6519, Generator Loss: 2.0973
D(x): 0.8417, D(G(z)): 0.2175
Epoch: [5/20], Batch Num: [198/600]
Discriminator Loss: 0.6546, Generator Loss: 2.2193
D(x): 0.8552, D(G(z)): 0.2330
Epoch: [5/20], Batch Num: [199/600]
Discriminator Loss: 0.6541, Generator Loss: 1.9846
D(x): 0.7951, D(G(z)): 0.2074
Epoch: 5, Batch Num: [200/600]

Epoch: [5/20], Batch Num: [200/600]
Discriminator Loss: 0.7595, Generator Loss: 1.9853
D(x): 0.8135, D(G(z)): 0.2672
Epoch: [5/20], Batch Num: [201/600]
Discriminator Loss: 0.6142, Generator Loss: 2.1680
D(x): 0.8522, D(G(z)): 0.2281
Epoch: [5/20], Batch Num: [202/600]
Discriminator Loss: 0.8092, Generator Loss: 1.8549
D(x): 0.7427, D(G(z)): 0.1956
Epoch: [5/20], Batch Num: [203/600]
Discriminator Loss: 0.7089, Generator Loss: 1.8754
D(x): 0.8270, D(G(z)): 0.2573
Epoch: [5/20], Batch Num: [204/600]
Discriminator Loss: 0.5595, Generator Loss: 2.0601
D(x): 0.8868, D(G(z)): 0.2361
Epoch: [5/20], Batch Num: [205/600]
Discriminator Loss: 0.8365, Generator Loss: 1.9762
D(x): 0.7863, D(G(z)): 0.2363
Epoch: [5/20], Batch Num: [206/600]
Discriminator Loss: 0.8088, Generator Loss: 2.1839
D(x): 0.7924, D(G(z)): 0.2452
Epoch: [5/20], Batch Num: [207/600]
Discriminator Loss: 0.7161, Generator Loss: 2.4522
D(x): 0.7997, D(G(z)): 0.2058
Epoch: [5/20], Batch Num: [208/600]
Discriminator Loss: 0.3904, Generator Loss: 2.6132
D(x): 0.8786, D(G(z)): 0.1668
Epoch: [5/20], Batch Num: [209/600]
Discriminator Loss: 0.4511, Generator Loss: 2.7744
D(x): 0.8750, D(G(z)): 0.1486
Epoch: [5/20], Batch Num: [210/600]
Discriminator Loss: 0.4480, Generator Loss: 2.8690
D(x): 0.8487, D(G(z)): 0.1341
Epoch: [5/20], Batch Num: [211/600]
Discriminator Loss: 0.3571, Generator Loss: 2.9955
D(x): 0.8777, D(G(z)): 0.1145
Epoch: [5/20], Batch Num: [212/600]
Discriminator Loss: 0.4047, Generator Loss: 3.0276
D(x): 0.8886, D(G(z)): 0.1525
Epoch: [5/20], Batch Num: [213/600]
Discriminator Loss: 0.3017, Generator Loss: 3.3838
D(x): 0.8971, D(G(z)): 0.1183
Epoch: [5/20], Batch Num: [214/600]
Discriminator Loss: 0.3749, Generator Loss: 3.2174
D(x): 0.8717, D(G(z)): 0.1313
Epoch: [5/20], Batch Num: [215/600]
Discriminator Loss: 0.3167, Generator Loss: 3.2120
D(x): 0.8929, D(G(z)): 0.1175
Epoch: [5/20], Batch Num: [216/600]
Discriminator Loss: 0.3470, Generator Loss: 3.2502
D(x): 0.9130, D(G(z)): 0.1575
Epoch: [5/20], Batch Num: [217/600]
Discriminator Loss: 0.4142, Generator Loss: 3.2983
D(x): 0.8935, D(G(z)): 0.1317
Epoch: [5/20], Batch Num: [218/600]
Discriminator Loss: 0.3050, Generator Loss: 3.5171
D(x): 0.8910, D(G(z)): 0.0859
Epoch: [5/20], Batch Num: [219/600]
Discriminator Loss: 0.2659, Generator Loss: 3.4175
D(x): 0.9077, D(G(z)): 0.0943
Epoch: [5/20], Batch Num: [220/600]
Discriminator Loss: 0.3335, Generator Loss: 3.4486
D(x): 0.8981, D(G(z)): 0.0988
Epoch: [5/20], Batch Num: [221/600]
Discriminator Loss: 0.3748, Generator Loss: 3.3515
D(x): 0.8825, D(G(z)): 0.1300
Epoch: [5/20], Batch Num: [222/600]
Discriminator Loss: 0.4948, Generator Loss: 3.6646
D(x): 0.8608, D(G(z)): 0.1469
Epoch: [5/20], Batch Num: [223/600]
Discriminator Loss: 0.3968, Generator Loss: 3.8251
D(x): 0.8893, D(G(z)): 0.1155
Epoch: [5/20], Batch Num: [224/600]
Discriminator Loss: 0.4480, Generator Loss: 3.6853
D(x): 0.8683, D(G(z)): 0.0954
Epoch: [5/20], Batch Num: [225/600]
Discriminator Loss: 0.5137, Generator Loss: 3.5059
D(x): 0.8398, D(G(z)): 0.0981
Epoch: [5/20], Batch Num: [226/600]
Discriminator Loss: 0.5247, Generator Loss: 3.3435
D(x): 0.8776, D(G(z)): 0.1228
Epoch: [5/20], Batch Num: [227/600]
Discriminator Loss: 0.6444, Generator Loss: 3.6185
D(x): 0.8630, D(G(z)): 0.1630
Epoch: [5/20], Batch Num: [228/600]
Discriminator Loss: 0.6351, Generator Loss: 4.2031
D(x): 0.8711, D(G(z)): 0.2281
Epoch: [5/20], Batch Num: [229/600]
Discriminator Loss: 0.7909, Generator Loss: 4.0415
D(x): 0.7641, D(G(z)): 0.0989
Epoch: [5/20], Batch Num: [230/600]
Discriminator Loss: 0.9955, Generator Loss: 3.1342
D(x): 0.7104, D(G(z)): 0.1184
Epoch: [5/20], Batch Num: [231/600]
Discriminator Loss: 0.9560, Generator Loss: 2.4674
D(x): 0.7756, D(G(z)): 0.1898
Epoch: [5/20], Batch Num: [232/600]
Discriminator Loss: 0.9028, Generator Loss: 2.9764
D(x): 0.9011, D(G(z)): 0.3602
Epoch: [5/20], Batch Num: [233/600]
Discriminator Loss: 0.7460, Generator Loss: 3.8169
D(x): 0.8481, D(G(z)): 0.2264
Epoch: [5/20], Batch Num: [234/600]
Discriminator Loss: 0.7267, Generator Loss: 3.9127
D(x): 0.7750, D(G(z)): 0.1186
Epoch: [5/20], Batch Num: [235/600]
Discriminator Loss: 1.2082, Generator Loss: 2.9563
D(x): 0.6390, D(G(z)): 0.1012
Epoch: [5/20], Batch Num: [236/600]
Discriminator Loss: 1.1898, Generator Loss: 1.8070
D(x): 0.6943, D(G(z)): 0.1429
Epoch: [5/20], Batch Num: [237/600]
Discriminator Loss: 1.0998, Generator Loss: 1.7944
D(x): 0.8473, D(G(z)): 0.3430
Epoch: [5/20], Batch Num: [238/600]
Discriminator Loss: 0.9704, Generator Loss: 2.5178
D(x): 0.8843, D(G(z)): 0.3750
Epoch: [5/20], Batch Num: [239/600]
Discriminator Loss: 0.8555, Generator Loss: 3.4013
D(x): 0.7917, D(G(z)): 0.2112
Epoch: [5/20], Batch Num: [240/600]
Discriminator Loss: 0.9588, Generator Loss: 3.3993
D(x): 0.7037, D(G(z)): 0.1161
Epoch: [5/20], Batch Num: [241/600]
Discriminator Loss: 0.9280, Generator Loss: 2.8215
D(x): 0.7031, D(G(z)): 0.0887
Epoch: [5/20], Batch Num: [242/600]
Discriminator Loss: 0.7234, Generator Loss: 2.1329
D(x): 0.7815, D(G(z)): 0.1737
Epoch: [5/20], Batch Num: [243/600]
Discriminator Loss: 0.8272, Generator Loss: 1.8110
D(x): 0.8215, D(G(z)): 0.2937
Epoch: [5/20], Batch Num: [244/600]
Discriminator Loss: 0.7880, Generator Loss: 2.3630
D(x): 0.8531, D(G(z)): 0.3044
Epoch: [5/20], Batch Num: [245/600]
Discriminator Loss: 0.5890, Generator Loss: 2.8517
D(x): 0.8657, D(G(z)): 0.2298
Epoch: [5/20], Batch Num: [246/600]
Discriminator Loss: 0.5510, Generator Loss: 2.9213
D(x): 0.8050, D(G(z)): 0.1312
Epoch: [5/20], Batch Num: [247/600]
Discriminator Loss: 0.6058, Generator Loss: 2.9266
D(x): 0.7680, D(G(z)): 0.1006
Epoch: [5/20], Batch Num: [248/600]
Discriminator Loss: 0.6204, Generator Loss: 2.7462
D(x): 0.7927, D(G(z)): 0.1364
Epoch: [5/20], Batch Num: [249/600]
Discriminator Loss: 0.5800, Generator Loss: 2.3179
D(x): 0.8083, D(G(z)): 0.1462
Epoch: [5/20], Batch Num: [250/600]
Discriminator Loss: 0.4235, Generator Loss: 2.1596
D(x): 0.9174, D(G(z)): 0.2248
Epoch: [5/20], Batch Num: [251/600]
Discriminator Loss: 0.4412, Generator Loss: 2.4302
D(x): 0.9091, D(G(z)): 0.2223
Epoch: [5/20], Batch Num: [252/600]
Discriminator Loss: 0.2644, Generator Loss: 2.8521
D(x): 0.9380, D(G(z)): 0.1457
Epoch: [5/20], Batch Num: [253/600]
Discriminator Loss: 0.3271, Generator Loss: 3.1538
D(x): 0.9277, D(G(z)): 0.1479
Epoch: [5/20], Batch Num: [254/600]
Discriminator Loss: 0.3695, Generator Loss: 3.2727
D(x): 0.8531, D(G(z)): 0.0889
Epoch: [5/20], Batch Num: [255/600]
Discriminator Loss: 0.3638, Generator Loss: 3.3174
D(x): 0.8724, D(G(z)): 0.0752
Epoch: [5/20], Batch Num: [256/600]
Discriminator Loss: 0.6080, Generator Loss: 2.9572
D(x): 0.7874, D(G(z)): 0.1015
Epoch: [5/20], Batch Num: [257/600]
Discriminator Loss: 0.2966, Generator Loss: 2.9131
D(x): 0.8778, D(G(z)): 0.0811
Epoch: [5/20], Batch Num: [258/600]
Discriminator Loss: 0.4236, Generator Loss: 2.1699
D(x): 0.8623, D(G(z)): 0.1250
Epoch: [5/20], Batch Num: [259/600]
Discriminator Loss: 0.3500, Generator Loss: 1.9476
D(x): 0.9178, D(G(z)): 0.1827
Epoch: [5/20], Batch Num: [260/600]
Discriminator Loss: 0.3550, Generator Loss: 2.0500
D(x): 0.9530, D(G(z)): 0.2089
Epoch: [5/20], Batch Num: [261/600]
Discriminator Loss: 0.4412, Generator Loss: 2.6267
D(x): 0.9136, D(G(z)): 0.2251
Epoch: [5/20], Batch Num: [262/600]
Discriminator Loss: 0.2649, Generator Loss: 3.0728
D(x): 0.9459, D(G(z)): 0.1327
Epoch: [5/20], Batch Num: [263/600]
Discriminator Loss: 0.2511, Generator Loss: 3.2780
D(x): 0.9052, D(G(z)): 0.0927
Epoch: [5/20], Batch Num: [264/600]
Discriminator Loss: 0.3738, Generator Loss: 3.5389
D(x): 0.8733, D(G(z)): 0.0866
Epoch: [5/20], Batch Num: [265/600]
Discriminator Loss: 0.5623, Generator Loss: 3.0742
D(x): 0.8152, D(G(z)): 0.0800
Epoch: [5/20], Batch Num: [266/600]
Discriminator Loss: 0.6616, Generator Loss: 2.7871
D(x): 0.7788, D(G(z)): 0.1019
Epoch: [5/20], Batch Num: [267/600]
Discriminator Loss: 0.3730, Generator Loss: 2.2971
D(x): 0.8732, D(G(z)): 0.1109
Epoch: [5/20], Batch Num: [268/600]
Discriminator Loss: 0.4173, Generator Loss: 1.9146
D(x): 0.9191, D(G(z)): 0.1963
Epoch: [5/20], Batch Num: [269/600]
Discriminator Loss: 0.4741, Generator Loss: 1.8960
D(x): 0.9143, D(G(z)): 0.2263
Epoch: [5/20], Batch Num: [270/600]
Discriminator Loss: 0.3968, Generator Loss: 2.0720
D(x): 0.9183, D(G(z)): 0.2085
Epoch: [5/20], Batch Num: [271/600]
Discriminator Loss: 0.4453, Generator Loss: 2.5610
D(x): 0.8799, D(G(z)): 0.1791
Epoch: [5/20], Batch Num: [272/600]
Discriminator Loss: 0.4930, Generator Loss: 2.6010
D(x): 0.9020, D(G(z)): 0.1982
Epoch: [5/20], Batch Num: [273/600]
Discriminator Loss: 0.5329, Generator Loss: 2.7638
D(x): 0.8058, D(G(z)): 0.1120
Epoch: [5/20], Batch Num: [274/600]
Discriminator Loss: 0.5063, Generator Loss: 2.6284
D(x): 0.8296, D(G(z)): 0.1150
Epoch: [5/20], Batch Num: [275/600]
Discriminator Loss: 0.6056, Generator Loss: 2.6432
D(x): 0.8194, D(G(z)): 0.1448
Epoch: [5/20], Batch Num: [276/600]
Discriminator Loss: 0.5418, Generator Loss: 2.2428
D(x): 0.8249, D(G(z)): 0.1329
Epoch: [5/20], Batch Num: [277/600]
Discriminator Loss: 0.7817, Generator Loss: 2.2533
D(x): 0.8204, D(G(z)): 0.2171
Epoch: [5/20], Batch Num: [278/600]
Discriminator Loss: 0.5787, Generator Loss: 2.1784
D(x): 0.8491, D(G(z)): 0.1861
Epoch: [5/20], Batch Num: [279/600]
Discriminator Loss: 0.4721, Generator Loss: 2.3698
D(x): 0.9073, D(G(z)): 0.2028
Epoch: [5/20], Batch Num: [280/600]
Discriminator Loss: 0.7035, Generator Loss: 2.4959
D(x): 0.8084, D(G(z)): 0.1977
Epoch: [5/20], Batch Num: [281/600]
Discriminator Loss: 0.5239, Generator Loss: 2.3481
D(x): 0.8341, D(G(z)): 0.1411
Epoch: [5/20], Batch Num: [282/600]
Discriminator Loss: 0.5411, Generator Loss: 2.5429
D(x): 0.8477, D(G(z)): 0.1595
Epoch: [5/20], Batch Num: [283/600]
Discriminator Loss: 0.7332, Generator Loss: 2.2632
D(x): 0.7219, D(G(z)): 0.1412
Epoch: [5/20], Batch Num: [284/600]
Discriminator Loss: 0.5191, Generator Loss: 2.0499
D(x): 0.8768, D(G(z)): 0.1781
Epoch: [5/20], Batch Num: [285/600]
Discriminator Loss: 0.6525, Generator Loss: 1.9906
D(x): 0.8394, D(G(z)): 0.2022
Epoch: [5/20], Batch Num: [286/600]
Discriminator Loss: 0.5580, Generator Loss: 1.9680
D(x): 0.8840, D(G(z)): 0.2168
Epoch: [5/20], Batch Num: [287/600]
Discriminator Loss: 0.7007, Generator Loss: 2.1898
D(x): 0.8146, D(G(z)): 0.2029
Epoch: [5/20], Batch Num: [288/600]
Discriminator Loss: 0.5256, Generator Loss: 2.2039
D(x): 0.8351, D(G(z)): 0.1690
Epoch: [5/20], Batch Num: [289/600]
Discriminator Loss: 0.4840, Generator Loss: 2.4969
D(x): 0.9017, D(G(z)): 0.1838
Epoch: [5/20], Batch Num: [290/600]
Discriminator Loss: 0.4807, Generator Loss: 2.4807
D(x): 0.8302, D(G(z)): 0.1301
Epoch: [5/20], Batch Num: [291/600]
Discriminator Loss: 0.4834, Generator Loss: 2.5365
D(x): 0.8457, D(G(z)): 0.1327
Epoch: [5/20], Batch Num: [292/600]
Discriminator Loss: 0.4418, Generator Loss: 2.4021
D(x): 0.8621, D(G(z)): 0.1348
Epoch: [5/20], Batch Num: [293/600]
Discriminator Loss: 0.3715, Generator Loss: 2.3200
D(x): 0.8856, D(G(z)): 0.1367
Epoch: [5/20], Batch Num: [294/600]
Discriminator Loss: 0.5508, Generator Loss: 2.4718
D(x): 0.8450, D(G(z)): 0.1793
Epoch: [5/20], Batch Num: [295/600]
Discriminator Loss: 0.4977, Generator Loss: 2.5597
D(x): 0.8778, D(G(z)): 0.1819
Epoch: [5/20], Batch Num: [296/600]
Discriminator Loss: 0.3703, Generator Loss: 2.5915
D(x): 0.9129, D(G(z)): 0.1725
Epoch: [5/20], Batch Num: [297/600]
Discriminator Loss: 0.4567, Generator Loss: 2.8149
D(x): 0.8665, D(G(z)): 0.1259
Epoch: [5/20], Batch Num: [298/600]
Discriminator Loss: 0.5398, Generator Loss: 2.7681
D(x): 0.8310, D(G(z)): 0.1336
Epoch: [5/20], Batch Num: [299/600]
Discriminator Loss: 0.4861, Generator Loss: 2.7619
D(x): 0.8666, D(G(z)): 0.1404
Epoch: 5, Batch Num: [300/600]

Epoch: [5/20], Batch Num: [300/600]
Discriminator Loss: 0.5822, Generator Loss: 2.5947
D(x): 0.8498, D(G(z)): 0.1632
Epoch: [5/20], Batch Num: [301/600]
Discriminator Loss: 0.5589, Generator Loss: 2.5788
D(x): 0.8080, D(G(z)): 0.1238
Epoch: [5/20], Batch Num: [302/600]
Discriminator Loss: 0.4767, Generator Loss: 2.3989
D(x): 0.8309, D(G(z)): 0.1426
Epoch: [5/20], Batch Num: [303/600]
Discriminator Loss: 0.3709, Generator Loss: 2.2450
D(x): 0.8861, D(G(z)): 0.1436
Epoch: [5/20], Batch Num: [304/600]
Discriminator Loss: 0.4352, Generator Loss: 2.2554
D(x): 0.8947, D(G(z)): 0.1828
Epoch: [5/20], Batch Num: [305/600]
Discriminator Loss: 0.6605, Generator Loss: 2.4192
D(x): 0.8799, D(G(z)): 0.2247
Epoch: [5/20], Batch Num: [306/600]
Discriminator Loss: 0.5084, Generator Loss: 2.8602
D(x): 0.8750, D(G(z)): 0.1816
Epoch: [5/20], Batch Num: [307/600]
Discriminator Loss: 0.2472, Generator Loss: 2.8721
D(x): 0.9316, D(G(z)): 0.1317
Epoch: [5/20], Batch Num: [308/600]
Discriminator Loss: 0.4019, Generator Loss: 3.0182
D(x): 0.8353, D(G(z)): 0.0730
Epoch: [5/20], Batch Num: [309/600]
Discriminator Loss: 0.6637, Generator Loss: 2.6290
D(x): 0.7515, D(G(z)): 0.0897
Epoch: [5/20], Batch Num: [310/600]
Discriminator Loss: 0.4144, Generator Loss: 2.3091
D(x): 0.8673, D(G(z)): 0.1449
Epoch: [5/20], Batch Num: [311/600]
Discriminator Loss: 0.5181, Generator Loss: 2.2096
D(x): 0.8310, D(G(z)): 0.1789
Epoch: [5/20], Batch Num: [312/600]
Discriminator Loss: 0.5578, Generator Loss: 1.9879
D(x): 0.8714, D(G(z)): 0.2261
Epoch: [5/20], Batch Num: [313/600]
Discriminator Loss: 0.5568, Generator Loss: 1.8969
D(x): 0.8978, D(G(z)): 0.2493
Epoch: [5/20], Batch Num: [314/600]
Discriminator Loss: 0.6108, Generator Loss: 2.3125
D(x): 0.8478, D(G(z)): 0.2315
Epoch: [5/20], Batch Num: [315/600]
Discriminator Loss: 0.4675, Generator Loss: 2.5329
D(x): 0.9080, D(G(z)): 0.2145
Epoch: [5/20], Batch Num: [316/600]
Discriminator Loss: 0.5459, Generator Loss: 2.6766
D(x): 0.7975, D(G(z)): 0.1340
Epoch: [5/20], Batch Num: [317/600]
Discriminator Loss: 0.7066, Generator Loss: 2.5293
D(x): 0.7804, D(G(z)): 0.1705
Epoch: [5/20], Batch Num: [318/600]
Discriminator Loss: 0.5081, Generator Loss: 2.1776
D(x): 0.8256, D(G(z)): 0.1155
Epoch: [5/20], Batch Num: [319/600]
Discriminator Loss: 0.4798, Generator Loss: 2.2048
D(x): 0.8510, D(G(z)): 0.1592
Epoch: [5/20], Batch Num: [320/600]
Discriminator Loss: 0.4727, Generator Loss: 2.1498
D(x): 0.8934, D(G(z)): 0.2183
Epoch: [5/20], Batch Num: [321/600]
Discriminator Loss: 0.6063, Generator Loss: 2.0504
D(x): 0.8197, D(G(z)): 0.2131
Epoch: [5/20], Batch Num: [322/600]
Discriminator Loss: 0.6641, Generator Loss: 2.0799
D(x): 0.8309, D(G(z)): 0.2234
Epoch: [5/20], Batch Num: [323/600]
Discriminator Loss: 0.6591, Generator Loss: 2.1577
D(x): 0.8290, D(G(z)): 0.2322
Epoch: [5/20], Batch Num: [324/600]
Discriminator Loss: 0.5105, Generator Loss: 2.4214
D(x): 0.8521, D(G(z)): 0.1812
Epoch: [5/20], Batch Num: [325/600]
Discriminator Loss: 0.5722, Generator Loss: 2.4395
D(x): 0.8127, D(G(z)): 0.1684
Epoch: [5/20], Batch Num: [326/600]
Discriminator Loss: 0.4875, Generator Loss: 2.3555
D(x): 0.8481, D(G(z)): 0.1769
Epoch: [5/20], Batch Num: [327/600]
Discriminator Loss: 0.5725, Generator Loss: 2.3566
D(x): 0.8194, D(G(z)): 0.1760
Epoch: [5/20], Batch Num: [328/600]
Discriminator Loss: 0.4435, Generator Loss: 2.2116
D(x): 0.8473, D(G(z)): 0.1567
Epoch: [5/20], Batch Num: [329/600]
Discriminator Loss: 0.5336, Generator Loss: 2.0572
D(x): 0.8469, D(G(z)): 0.1937
Epoch: [5/20], Batch Num: [330/600]
Discriminator Loss: 0.5130, Generator Loss: 2.0011
D(x): 0.8640, D(G(z)): 0.1931
Epoch: [5/20], Batch Num: [331/600]
Discriminator Loss: 0.7223, Generator Loss: 2.1813
D(x): 0.8200, D(G(z)): 0.2448
Epoch: [5/20], Batch Num: [332/600]
Discriminator Loss: 0.5050, Generator Loss: 2.0432
D(x): 0.8714, D(G(z)): 0.2147
Epoch: [5/20], Batch Num: [333/600]
Discriminator Loss: 0.4895, Generator Loss: 2.1909
D(x): 0.8557, D(G(z)): 0.1937
Epoch: [5/20], Batch Num: [334/600]
Discriminator Loss: 0.6537, Generator Loss: 2.1347
D(x): 0.7760, D(G(z)): 0.1874
Epoch: [5/20], Batch Num: [335/600]
Discriminator Loss: 0.4819, Generator Loss: 2.2359
D(x): 0.8658, D(G(z)): 0.1741
Epoch: [5/20], Batch Num: [336/600]
Discriminator Loss: 0.6448, Generator Loss: 2.0791
D(x): 0.7872, D(G(z)): 0.1936
Epoch: [5/20], Batch Num: [337/600]
Discriminator Loss: 0.4594, Generator Loss: 2.1533
D(x): 0.8777, D(G(z)): 0.1899
Epoch: [5/20], Batch Num: [338/600]
Discriminator Loss: 0.5473, Generator Loss: 2.0954
D(x): 0.8322, D(G(z)): 0.1977
Epoch: [5/20], Batch Num: [339/600]
Discriminator Loss: 0.5128, Generator Loss: 2.1689
D(x): 0.8496, D(G(z)): 0.2166
Epoch: [5/20], Batch Num: [340/600]
Discriminator Loss: 0.6093, Generator Loss: 2.4139
D(x): 0.8660, D(G(z)): 0.2344
Epoch: [5/20], Batch Num: [341/600]
Discriminator Loss: 0.5071, Generator Loss: 2.3850
D(x): 0.8453, D(G(z)): 0.1746
Epoch: [5/20], Batch Num: [342/600]
Discriminator Loss: 0.5546, Generator Loss: 2.5049
D(x): 0.8338, D(G(z)): 0.1821
Epoch: [5/20], Batch Num: [343/600]
Discriminator Loss: 0.3729, Generator Loss: 2.3811
D(x): 0.8828, D(G(z)): 0.1431
Epoch: [5/20], Batch Num: [344/600]
Discriminator Loss: 0.5365, Generator Loss: 2.3278
D(x): 0.8290, D(G(z)): 0.1562
Epoch: [5/20], Batch Num: [345/600]
Discriminator Loss: 0.5082, Generator Loss: 2.3397
D(x): 0.8371, D(G(z)): 0.1855
Epoch: [5/20], Batch Num: [346/600]
Discriminator Loss: 0.6855, Generator Loss: 2.0706
D(x): 0.8055, D(G(z)): 0.1973
Epoch: [5/20], Batch Num: [347/600]
Discriminator Loss: 0.6913, Generator Loss: 2.1968
D(x): 0.8216, D(G(z)): 0.2261
Epoch: [5/20], Batch Num: [348/600]
Discriminator Loss: 0.5243, Generator Loss: 2.0892
D(x): 0.8810, D(G(z)): 0.2233
Epoch: [5/20], Batch Num: [349/600]
Discriminator Loss: 0.6296, Generator Loss: 2.2283
D(x): 0.8097, D(G(z)): 0.1944
Epoch: [5/20], Batch Num: [350/600]
Discriminator Loss: 0.6507, Generator Loss: 2.1576
D(x): 0.8236, D(G(z)): 0.1949
Epoch: [5/20], Batch Num: [351/600]
Discriminator Loss: 0.6727, Generator Loss: 1.9400
D(x): 0.8290, D(G(z)): 0.2257
Epoch: [5/20], Batch Num: [352/600]
Discriminator Loss: 0.6555, Generator Loss: 2.1589
D(x): 0.8313, D(G(z)): 0.2390
Epoch: [5/20], Batch Num: [353/600]
Discriminator Loss: 0.5011, Generator Loss: 2.4104
D(x): 0.8490, D(G(z)): 0.1976
Epoch: [5/20], Batch Num: [354/600]
Discriminator Loss: 0.7052, Generator Loss: 2.2262
D(x): 0.7863, D(G(z)): 0.1895
Epoch: [5/20], Batch Num: [355/600]
Discriminator Loss: 0.8361, Generator Loss: 2.1001
D(x): 0.7503, D(G(z)): 0.2107
Epoch: [5/20], Batch Num: [356/600]
Discriminator Loss: 0.4764, Generator Loss: 2.3388
D(x): 0.9117, D(G(z)): 0.2574
Epoch: [5/20], Batch Num: [357/600]
Discriminator Loss: 0.6022, Generator Loss: 2.3220
D(x): 0.8473, D(G(z)): 0.2140
Epoch: [5/20], Batch Num: [358/600]
Discriminator Loss: 0.5219, Generator Loss: 2.5999
D(x): 0.8667, D(G(z)): 0.2164
Epoch: [5/20], Batch Num: [359/600]
Discriminator Loss: 0.7922, Generator Loss: 2.6396
D(x): 0.7626, D(G(z)): 0.1564
Epoch: [5/20], Batch Num: [360/600]
Discriminator Loss: 0.4736, Generator Loss: 2.6642
D(x): 0.8603, D(G(z)): 0.1423
Epoch: [5/20], Batch Num: [361/600]
Discriminator Loss: 0.5735, Generator Loss: 2.6244
D(x): 0.8278, D(G(z)): 0.1787
Epoch: [5/20], Batch Num: [362/600]
Discriminator Loss: 0.5628, Generator Loss: 2.4398
D(x): 0.8225, D(G(z)): 0.1783
Epoch: [5/20], Batch Num: [363/600]
Discriminator Loss: 0.5501, Generator Loss: 2.3830
D(x): 0.8695, D(G(z)): 0.2161
Epoch: [5/20], Batch Num: [364/600]
Discriminator Loss: 0.4989, Generator Loss: 2.3919
D(x): 0.8834, D(G(z)): 0.2086
Epoch: [5/20], Batch Num: [365/600]
Discriminator Loss: 0.4529, Generator Loss: 2.6064
D(x): 0.8759, D(G(z)): 0.1931
Epoch: [5/20], Batch Num: [366/600]
Discriminator Loss: 0.5740, Generator Loss: 2.7027
D(x): 0.8633, D(G(z)): 0.2071
Epoch: [5/20], Batch Num: [367/600]
Discriminator Loss: 0.4627, Generator Loss: 2.7824
D(x): 0.8428, D(G(z)): 0.1309
Epoch: [5/20], Batch Num: [368/600]
Discriminator Loss: 0.4003, Generator Loss: 2.3528
D(x): 0.8473, D(G(z)): 0.1178
Epoch: [5/20], Batch Num: [369/600]
Discriminator Loss: 0.4588, Generator Loss: 2.0408
D(x): 0.8586, D(G(z)): 0.1675
Epoch: [5/20], Batch Num: [370/600]
Discriminator Loss: 0.3498, Generator Loss: 2.1943
D(x): 0.9206, D(G(z)): 0.1730
Epoch: [5/20], Batch Num: [371/600]
Discriminator Loss: 0.4274, Generator Loss: 2.4112
D(x): 0.9246, D(G(z)): 0.2349
Epoch: [5/20], Batch Num: [372/600]
Discriminator Loss: 0.3380, Generator Loss: 2.5454
D(x): 0.9059, D(G(z)): 0.1460
Epoch: [5/20], Batch Num: [373/600]
Discriminator Loss: 0.2743, Generator Loss: 2.7606
D(x): 0.9220, D(G(z)): 0.1349
Epoch: [5/20], Batch Num: [374/600]
Discriminator Loss: 0.3365, Generator Loss: 2.7712
D(x): 0.9003, D(G(z)): 0.1269
Epoch: [5/20], Batch Num: [375/600]
Discriminator Loss: 0.3667, Generator Loss: 2.9547
D(x): 0.8916, D(G(z)): 0.1189
Epoch: [5/20], Batch Num: [376/600]
Discriminator Loss: 0.3805, Generator Loss: 2.6264
D(x): 0.8697, D(G(z)): 0.1086
Epoch: [5/20], Batch Num: [377/600]
Discriminator Loss: 0.2994, Generator Loss: 2.6157
D(x): 0.9287, D(G(z)): 0.1366
Epoch: [5/20], Batch Num: [378/600]
Discriminator Loss: 0.4117, Generator Loss: 2.3083
D(x): 0.8831, D(G(z)): 0.1589
Epoch: [5/20], Batch Num: [379/600]
Discriminator Loss: 0.2690, Generator Loss: 2.4013
D(x): 0.9501, D(G(z)): 0.1613
Epoch: [5/20], Batch Num: [380/600]
Discriminator Loss: 0.3965, Generator Loss: 2.3728
D(x): 0.9071, D(G(z)): 0.1662
Epoch: [5/20], Batch Num: [381/600]
Discriminator Loss: 0.2728, Generator Loss: 2.7907
D(x): 0.9413, D(G(z)): 0.1460
Epoch: [5/20], Batch Num: [382/600]
Discriminator Loss: 0.4135, Generator Loss: 2.7104
D(x): 0.8785, D(G(z)): 0.1218
Epoch: [5/20], Batch Num: [383/600]
Discriminator Loss: 0.4643, Generator Loss: 2.5545
D(x): 0.8833, D(G(z)): 0.1672
Epoch: [5/20], Batch Num: [384/600]
Discriminator Loss: 0.3252, Generator Loss: 2.6090
D(x): 0.9119, D(G(z)): 0.1391
Epoch: [5/20], Batch Num: [385/600]
Discriminator Loss: 0.5147, Generator Loss: 2.1694
D(x): 0.8440, D(G(z)): 0.1697
Epoch: [5/20], Batch Num: [386/600]
Discriminator Loss: 0.4265, Generator Loss: 2.0385
D(x): 0.9062, D(G(z)): 0.1668
Epoch: [5/20], Batch Num: [387/600]
Discriminator Loss: 0.4768, Generator Loss: 2.1945
D(x): 0.9147, D(G(z)): 0.2242
Epoch: [5/20], Batch Num: [388/600]
Discriminator Loss: 0.5597, Generator Loss: 2.1771
D(x): 0.8791, D(G(z)): 0.2101
Epoch: [5/20], Batch Num: [389/600]
Discriminator Loss: 0.4016, Generator Loss: 2.5709
D(x): 0.9035, D(G(z)): 0.1825
Epoch: [5/20], Batch Num: [390/600]
Discriminator Loss: 0.5924, Generator Loss: 2.4708
D(x): 0.8040, D(G(z)): 0.1336
Epoch: [5/20], Batch Num: [391/600]
Discriminator Loss: 0.4890, Generator Loss: 2.0581
D(x): 0.8243, D(G(z)): 0.1366
Epoch: [5/20], Batch Num: [392/600]
Discriminator Loss: 0.6380, Generator Loss: 1.8674
D(x): 0.8310, D(G(z)): 0.1939
Epoch: [5/20], Batch Num: [393/600]
Discriminator Loss: 0.7801, Generator Loss: 1.6555
D(x): 0.8375, D(G(z)): 0.2556
Epoch: [5/20], Batch Num: [394/600]
Discriminator Loss: 0.6136, Generator Loss: 1.5828
D(x): 0.9036, D(G(z)): 0.2817
Epoch: [5/20], Batch Num: [395/600]
Discriminator Loss: 0.6303, Generator Loss: 1.7869
D(x): 0.8630, D(G(z)): 0.2595
Epoch: [5/20], Batch Num: [396/600]
Discriminator Loss: 0.6183, Generator Loss: 2.2754
D(x): 0.8567, D(G(z)): 0.2207
Epoch: [5/20], Batch Num: [397/600]
Discriminator Loss: 0.5872, Generator Loss: 2.3685
D(x): 0.8588, D(G(z)): 0.1722
Epoch: [5/20], Batch Num: [398/600]
Discriminator Loss: 0.7004, Generator Loss: 2.1552
D(x): 0.7933, D(G(z)): 0.1617
Epoch: [5/20], Batch Num: [399/600]
Discriminator Loss: 0.5297, Generator Loss: 1.9264
D(x): 0.8451, D(G(z)): 0.1542
Epoch: 5, Batch Num: [400/600]

Epoch: [5/20], Batch Num: [400/600]
Discriminator Loss: 0.6211, Generator Loss: 1.8879
D(x): 0.8271, D(G(z)): 0.2094
Epoch: [5/20], Batch Num: [401/600]
Discriminator Loss: 0.6682, Generator Loss: 1.7942
D(x): 0.8560, D(G(z)): 0.2429
Epoch: [5/20], Batch Num: [402/600]
Discriminator Loss: 0.5801, Generator Loss: 1.8933
D(x): 0.8740, D(G(z)): 0.2609
Epoch: [5/20], Batch Num: [403/600]
Discriminator Loss: 0.8045, Generator Loss: 1.9186
D(x): 0.8051, D(G(z)): 0.2333
Epoch: [5/20], Batch Num: [404/600]
Discriminator Loss: 0.7438, Generator Loss: 1.9023
D(x): 0.7898, D(G(z)): 0.2289
Epoch: [5/20], Batch Num: [405/600]
Discriminator Loss: 0.6767, Generator Loss: 1.9013
D(x): 0.8043, D(G(z)): 0.2121
Epoch: [5/20], Batch Num: [406/600]
Discriminator Loss: 0.5494, Generator Loss: 1.9071
D(x): 0.8612, D(G(z)): 0.2346
Epoch: [5/20], Batch Num: [407/600]
Discriminator Loss: 0.6451, Generator Loss: 2.0617
D(x): 0.8432, D(G(z)): 0.2187
Epoch: [5/20], Batch Num: [408/600]
Discriminator Loss: 0.6400, Generator Loss: 2.1485
D(x): 0.8182, D(G(z)): 0.2193
Epoch: [5/20], Batch Num: [409/600]
Discriminator Loss: 0.8225, Generator Loss: 2.1850
D(x): 0.7510, D(G(z)): 0.2197
Epoch: [5/20], Batch Num: [410/600]
Discriminator Loss: 0.4829, Generator Loss: 2.0084
D(x): 0.8710, D(G(z)): 0.1919
Epoch: [5/20], Batch Num: [411/600]
Discriminator Loss: 0.5239, Generator Loss: 2.0435
D(x): 0.8585, D(G(z)): 0.2085
Epoch: [5/20], Batch Num: [412/600]
Discriminator Loss: 0.7129, Generator Loss: 1.8914
D(x): 0.7707, D(G(z)): 0.1734
Epoch: [5/20], Batch Num: [413/600]
Discriminator Loss: 0.5006, Generator Loss: 1.8604
D(x): 0.8898, D(G(z)): 0.2284
Epoch: [5/20], Batch Num: [414/600]
Discriminator Loss: 0.5280, Generator Loss: 1.8910
D(x): 0.8435, D(G(z)): 0.1920
Epoch: [5/20], Batch Num: [415/600]
Discriminator Loss: 0.4661, Generator Loss: 1.9974
D(x): 0.8658, D(G(z)): 0.1939
Epoch: [5/20], Batch Num: [416/600]
Discriminator Loss: 0.3157, Generator Loss: 2.2543
D(x): 0.9290, D(G(z)): 0.1722
Epoch: [5/20], Batch Num: [417/600]
Discriminator Loss: 0.4458, Generator Loss: 2.3749
D(x): 0.8738, D(G(z)): 0.1646
Epoch: [5/20], Batch Num: [418/600]
Discriminator Loss: 0.3987, Generator Loss: 2.5327
D(x): 0.8633, D(G(z)): 0.1419
Epoch: [5/20], Batch Num: [419/600]
Discriminator Loss: 0.2629, Generator Loss: 2.6066
D(x): 0.9311, D(G(z)): 0.1240
Epoch: [5/20], Batch Num: [420/600]
Discriminator Loss: 0.4501, Generator Loss: 2.5507
D(x): 0.8523, D(G(z)): 0.1377
Epoch: [5/20], Batch Num: [421/600]
Discriminator Loss: 0.2757, Generator Loss: 2.6146
D(x): 0.8999, D(G(z)): 0.0989
Epoch: [5/20], Batch Num: [422/600]
Discriminator Loss: 0.3102, Generator Loss: 2.6496
D(x): 0.9295, D(G(z)): 0.1360
Epoch: [5/20], Batch Num: [423/600]
Discriminator Loss: 0.3320, Generator Loss: 2.6962
D(x): 0.9017, D(G(z)): 0.1250
Epoch: [5/20], Batch Num: [424/600]
Discriminator Loss: 0.3244, Generator Loss: 2.8054
D(x): 0.8985, D(G(z)): 0.1194
Epoch: [5/20], Batch Num: [425/600]
Discriminator Loss: 0.2218, Generator Loss: 2.7133
D(x): 0.9500, D(G(z)): 0.1230
Epoch: [5/20], Batch Num: [426/600]
Discriminator Loss: 0.3095, Generator Loss: 2.7261
D(x): 0.9010, D(G(z)): 0.1009
Epoch: [5/20], Batch Num: [427/600]
Discriminator Loss: 0.2822, Generator Loss: 2.8529
D(x): 0.9085, D(G(z)): 0.1115
Epoch: [5/20], Batch Num: [428/600]
Discriminator Loss: 0.3441, Generator Loss: 3.0142
D(x): 0.9347, D(G(z)): 0.1579
Epoch: [5/20], Batch Num: [429/600]
Discriminator Loss: 0.3434, Generator Loss: 3.3161
D(x): 0.9175, D(G(z)): 0.1082
Epoch: [5/20], Batch Num: [430/600]
Discriminator Loss: 0.2719, Generator Loss: 3.4086
D(x): 0.9057, D(G(z)): 0.0856
Epoch: [5/20], Batch Num: [431/600]
Discriminator Loss: 0.2155, Generator Loss: 3.4427
D(x): 0.9324, D(G(z)): 0.0824
Epoch: [5/20], Batch Num: [432/600]
Discriminator Loss: 0.3838, Generator Loss: 3.1496
D(x): 0.8860, D(G(z)): 0.0923
Epoch: [5/20], Batch Num: [433/600]
Discriminator Loss: 0.2486, Generator Loss: 3.1914
D(x): 0.9374, D(G(z)): 0.0968
Epoch: [5/20], Batch Num: [434/600]
Discriminator Loss: 0.2577, Generator Loss: 3.0951
D(x): 0.9300, D(G(z)): 0.0986
Epoch: [5/20], Batch Num: [435/600]
Discriminator Loss: 0.3825, Generator Loss: 3.2421
D(x): 0.9111, D(G(z)): 0.1279
Epoch: [5/20], Batch Num: [436/600]
Discriminator Loss: 0.4042, Generator Loss: 3.2700
D(x): 0.8675, D(G(z)): 0.0961
Epoch: [5/20], Batch Num: [437/600]
Discriminator Loss: 0.3596, Generator Loss: 3.0626
D(x): 0.8978, D(G(z)): 0.1173
Epoch: [5/20], Batch Num: [438/600]
Discriminator Loss: 0.5196, Generator Loss: 2.7325
D(x): 0.8380, D(G(z)): 0.0986
Epoch: [5/20], Batch Num: [439/600]
Discriminator Loss: 0.6985, Generator Loss: 2.4572
D(x): 0.8493, D(G(z)): 0.1838
Epoch: [5/20], Batch Num: [440/600]
Discriminator Loss: 0.4678, Generator Loss: 2.8442
D(x): 0.9119, D(G(z)): 0.2097
Epoch: [5/20], Batch Num: [441/600]
Discriminator Loss: 0.4916, Generator Loss: 3.2237
D(x): 0.8805, D(G(z)): 0.1265
Epoch: [5/20], Batch Num: [442/600]
Discriminator Loss: 0.6824, Generator Loss: 2.9285
D(x): 0.7927, D(G(z)): 0.1058
Epoch: [5/20], Batch Num: [443/600]
Discriminator Loss: 0.5233, Generator Loss: 2.8090
D(x): 0.8792, D(G(z)): 0.1638
Epoch: [5/20], Batch Num: [444/600]
Discriminator Loss: 0.8781, Generator Loss: 2.6341
D(x): 0.8041, D(G(z)): 0.1839
Epoch: [5/20], Batch Num: [445/600]
Discriminator Loss: 0.7565, Generator Loss: 2.6958
D(x): 0.8674, D(G(z)): 0.2211
Epoch: [5/20], Batch Num: [446/600]
Discriminator Loss: 0.8338, Generator Loss: 2.9441
D(x): 0.8238, D(G(z)): 0.1978
Epoch: [5/20], Batch Num: [447/600]
Discriminator Loss: 0.9857, Generator Loss: 2.8648
D(x): 0.7181, D(G(z)): 0.1594
Epoch: [5/20], Batch Num: [448/600]
Discriminator Loss: 0.8107, Generator Loss: 2.6211
D(x): 0.7877, D(G(z)): 0.1767
Epoch: [5/20], Batch Num: [449/600]
Discriminator Loss: 0.9480, Generator Loss: 2.7042
D(x): 0.7563, D(G(z)): 0.2085
Epoch: [5/20], Batch Num: [450/600]
Discriminator Loss: 0.8703, Generator Loss: 2.4684
D(x): 0.7725, D(G(z)): 0.1982
Epoch: [5/20], Batch Num: [451/600]
Discriminator Loss: 0.5513, Generator Loss: 2.4725
D(x): 0.8466, D(G(z)): 0.1728
Epoch: [5/20], Batch Num: [452/600]
Discriminator Loss: 0.8800, Generator Loss: 2.4320
D(x): 0.7871, D(G(z)): 0.2031
Epoch: [5/20], Batch Num: [453/600]
Discriminator Loss: 0.8674, Generator Loss: 2.5980
D(x): 0.7907, D(G(z)): 0.2083
Epoch: [5/20], Batch Num: [454/600]
Discriminator Loss: 1.0176, Generator Loss: 2.5922
D(x): 0.7565, D(G(z)): 0.2330
Epoch: [5/20], Batch Num: [455/600]
Discriminator Loss: 0.7533, Generator Loss: 2.7414
D(x): 0.7851, D(G(z)): 0.1893
Epoch: [5/20], Batch Num: [456/600]
Discriminator Loss: 0.5390, Generator Loss: 2.8741
D(x): 0.8837, D(G(z)): 0.2115
Epoch: [5/20], Batch Num: [457/600]
Discriminator Loss: 0.7259, Generator Loss: 3.2695
D(x): 0.7866, D(G(z)): 0.1677
Epoch: [5/20], Batch Num: [458/600]
Discriminator Loss: 0.6208, Generator Loss: 2.9510
D(x): 0.8465, D(G(z)): 0.1686
Epoch: [5/20], Batch Num: [459/600]
Discriminator Loss: 0.7092, Generator Loss: 3.1185
D(x): 0.7404, D(G(z)): 0.1248
Epoch: [5/20], Batch Num: [460/600]
Discriminator Loss: 0.6703, Generator Loss: 2.3577
D(x): 0.7880, D(G(z)): 0.1745
Epoch: [5/20], Batch Num: [461/600]
Discriminator Loss: 0.7615, Generator Loss: 2.1715
D(x): 0.8318, D(G(z)): 0.2384
Epoch: [5/20], Batch Num: [462/600]
Discriminator Loss: 0.6534, Generator Loss: 2.2910
D(x): 0.8288, D(G(z)): 0.2101
Epoch: [5/20], Batch Num: [463/600]
Discriminator Loss: 0.6213, Generator Loss: 2.6873
D(x): 0.8497, D(G(z)): 0.2052
Epoch: [5/20], Batch Num: [464/600]
Discriminator Loss: 0.5739, Generator Loss: 2.6881
D(x): 0.8142, D(G(z)): 0.1723
Epoch: [5/20], Batch Num: [465/600]
Discriminator Loss: 0.8273, Generator Loss: 2.5666
D(x): 0.7709, D(G(z)): 0.1927
Epoch: [5/20], Batch Num: [466/600]
Discriminator Loss: 0.6088, Generator Loss: 2.5386
D(x): 0.7802, D(G(z)): 0.1337
Epoch: [5/20], Batch Num: [467/600]
Discriminator Loss: 0.7682, Generator Loss: 2.6329
D(x): 0.8040, D(G(z)): 0.2172
Epoch: [5/20], Batch Num: [468/600]
Discriminator Loss: 0.4880, Generator Loss: 2.4495
D(x): 0.8731, D(G(z)): 0.2038
Epoch: [5/20], Batch Num: [469/600]
Discriminator Loss: 0.7542, Generator Loss: 2.5348
D(x): 0.7485, D(G(z)): 0.1910
Epoch: [5/20], Batch Num: [470/600]
Discriminator Loss: 0.5397, Generator Loss: 2.6647
D(x): 0.8379, D(G(z)): 0.1636
Epoch: [5/20], Batch Num: [471/600]
Discriminator Loss: 0.6873, Generator Loss: 2.7054
D(x): 0.7789, D(G(z)): 0.1746
Epoch: [5/20], Batch Num: [472/600]
Discriminator Loss: 0.5404, Generator Loss: 2.5131
D(x): 0.8093, D(G(z)): 0.1334
Epoch: [5/20], Batch Num: [473/600]
Discriminator Loss: 0.4126, Generator Loss: 2.4987
D(x): 0.8791, D(G(z)): 0.1586
Epoch: [5/20], Batch Num: [474/600]
Discriminator Loss: 0.6654, Generator Loss: 2.4253
D(x): 0.8514, D(G(z)): 0.2212
Epoch: [5/20], Batch Num: [475/600]
Discriminator Loss: 0.3810, Generator Loss: 2.5949
D(x): 0.8862, D(G(z)): 0.1524
Epoch: [5/20], Batch Num: [476/600]
Discriminator Loss: 0.4429, Generator Loss: 2.3740
D(x): 0.8649, D(G(z)): 0.1608
Epoch: [5/20], Batch Num: [477/600]
Discriminator Loss: 0.4824, Generator Loss: 2.8239
D(x): 0.8476, D(G(z)): 0.1298
Epoch: [5/20], Batch Num: [478/600]
Discriminator Loss: 0.3103, Generator Loss: 2.5901
D(x): 0.9088, D(G(z)): 0.1119
Epoch: [5/20], Batch Num: [479/600]
Discriminator Loss: 0.5591, Generator Loss: 2.5470
D(x): 0.8484, D(G(z)): 0.1507
Epoch: [5/20], Batch Num: [480/600]
Discriminator Loss: 0.5311, Generator Loss: 2.5750
D(x): 0.8552, D(G(z)): 0.1568
Epoch: [5/20], Batch Num: [481/600]
Discriminator Loss: 0.4492, Generator Loss: 2.5420
D(x): 0.8818, D(G(z)): 0.1611
Epoch: [5/20], Batch Num: [482/600]
Discriminator Loss: 0.2963, Generator Loss: 2.4570
D(x): 0.9310, D(G(z)): 0.1418
Epoch: [5/20], Batch Num: [483/600]
Discriminator Loss: 0.4824, Generator Loss: 2.4931
D(x): 0.8807, D(G(z)): 0.1875
Epoch: [5/20], Batch Num: [484/600]
Discriminator Loss: 0.6235, Generator Loss: 2.6144
D(x): 0.8110, D(G(z)): 0.1534
Epoch: [5/20], Batch Num: [485/600]
Discriminator Loss: 0.5556, Generator Loss: 2.4553
D(x): 0.8303, D(G(z)): 0.1382
Epoch: [5/20], Batch Num: [486/600]
Discriminator Loss: 0.3616, Generator Loss: 2.4260
D(x): 0.8914, D(G(z)): 0.1447
Epoch: [5/20], Batch Num: [487/600]
Discriminator Loss: 0.5455, Generator Loss: 2.0424
D(x): 0.8284, D(G(z)): 0.1540
Epoch: [5/20], Batch Num: [488/600]
Discriminator Loss: 0.6036, Generator Loss: 2.1509
D(x): 0.8547, D(G(z)): 0.2156
Epoch: [5/20], Batch Num: [489/600]
Discriminator Loss: 0.6009, Generator Loss: 2.2781
D(x): 0.8610, D(G(z)): 0.2362
Epoch: [5/20], Batch Num: [490/600]
Discriminator Loss: 0.5981, Generator Loss: 2.2928
D(x): 0.8289, D(G(z)): 0.1763
Epoch: [5/20], Batch Num: [491/600]
Discriminator Loss: 0.5535, Generator Loss: 2.2839
D(x): 0.8792, D(G(z)): 0.2048
Epoch: [5/20], Batch Num: [492/600]
Discriminator Loss: 0.6667, Generator Loss: 2.5013
D(x): 0.8034, D(G(z)): 0.1777
Epoch: [5/20], Batch Num: [493/600]
Discriminator Loss: 0.5607, Generator Loss: 2.4920
D(x): 0.8305, D(G(z)): 0.1525
Epoch: [5/20], Batch Num: [494/600]
Discriminator Loss: 0.5135, Generator Loss: 2.3455
D(x): 0.8555, D(G(z)): 0.1361
Epoch: [5/20], Batch Num: [495/600]
Discriminator Loss: 0.5497, Generator Loss: 2.2124
D(x): 0.8473, D(G(z)): 0.1689
Epoch: [5/20], Batch Num: [496/600]
Discriminator Loss: 0.6624, Generator Loss: 2.2810
D(x): 0.8876, D(G(z)): 0.2255
Epoch: [5/20], Batch Num: [497/600]
Discriminator Loss: 0.5210, Generator Loss: 2.5694
D(x): 0.8804, D(G(z)): 0.2065
Epoch: [5/20], Batch Num: [498/600]
Discriminator Loss: 0.4846, Generator Loss: 2.6206
D(x): 0.8380, D(G(z)): 0.1327
Epoch: [5/20], Batch Num: [499/600]
Discriminator Loss: 0.5098, Generator Loss: 2.6077
D(x): 0.8312, D(G(z)): 0.1063
Epoch: 5, Batch Num: [500/600]

Epoch: [5/20], Batch Num: [500/600]
Discriminator Loss: 0.4909, Generator Loss: 2.0547
D(x): 0.8470, D(G(z)): 0.1407
Epoch: [5/20], Batch Num: [501/600]
Discriminator Loss: 0.4860, Generator Loss: 1.8852
D(x): 0.8637, D(G(z)): 0.1843
Epoch: [5/20], Batch Num: [502/600]
Discriminator Loss: 0.6726, Generator Loss: 1.9086
D(x): 0.8412, D(G(z)): 0.2158
Epoch: [5/20], Batch Num: [503/600]
Discriminator Loss: 0.7961, Generator Loss: 1.7752
D(x): 0.8439, D(G(z)): 0.2782
Epoch: [5/20], Batch Num: [504/600]
Discriminator Loss: 0.7190, Generator Loss: 2.0268
D(x): 0.8784, D(G(z)): 0.2738
Epoch: [5/20], Batch Num: [505/600]
Discriminator Loss: 0.5789, Generator Loss: 2.4202
D(x): 0.8548, D(G(z)): 0.1939
Epoch: [5/20], Batch Num: [506/600]
Discriminator Loss: 0.3942, Generator Loss: 2.6661
D(x): 0.8951, D(G(z)): 0.1292
Epoch: [5/20], Batch Num: [507/600]
Discriminator Loss: 0.4959, Generator Loss: 2.7746
D(x): 0.8326, D(G(z)): 0.1078
Epoch: [5/20], Batch Num: [508/600]
Discriminator Loss: 0.4659, Generator Loss: 2.7723
D(x): 0.8485, D(G(z)): 0.0939
Epoch: [5/20], Batch Num: [509/600]
Discriminator Loss: 0.5105, Generator Loss: 2.5607
D(x): 0.8462, D(G(z)): 0.1327
Epoch: [5/20], Batch Num: [510/600]
Discriminator Loss: 0.5176, Generator Loss: 2.4765
D(x): 0.8470, D(G(z)): 0.1296
Epoch: [5/20], Batch Num: [511/600]
Discriminator Loss: 0.4471, Generator Loss: 2.1464
D(x): 0.8544, D(G(z)): 0.1382
Epoch: [5/20], Batch Num: [512/600]
Discriminator Loss: 0.4193, Generator Loss: 2.1959
D(x): 0.9332, D(G(z)): 0.2043
Epoch: [5/20], Batch Num: [513/600]
Discriminator Loss: 0.3745, Generator Loss: 2.2879
D(x): 0.9330, D(G(z)): 0.1976
Epoch: [5/20], Batch Num: [514/600]
Discriminator Loss: 0.2980, Generator Loss: 2.6606
D(x): 0.9606, D(G(z)): 0.1621
Epoch: [5/20], Batch Num: [515/600]
Discriminator Loss: 0.2381, Generator Loss: 3.1043
D(x): 0.9413, D(G(z)): 0.1198
Epoch: [5/20], Batch Num: [516/600]
Discriminator Loss: 0.2923, Generator Loss: 3.0778
D(x): 0.8941, D(G(z)): 0.0820
Epoch: [5/20], Batch Num: [517/600]
Discriminator Loss: 0.2205, Generator Loss: 3.3436
D(x): 0.9226, D(G(z)): 0.0795
Epoch: [5/20], Batch Num: [518/600]
Discriminator Loss: 0.2833, Generator Loss: 3.3904
D(x): 0.8989, D(G(z)): 0.0616
Epoch: [5/20], Batch Num: [519/600]
Discriminator Loss: 0.3090, Generator Loss: 3.2916
D(x): 0.8905, D(G(z)): 0.0746
Epoch: [5/20], Batch Num: [520/600]
Discriminator Loss: 0.2259, Generator Loss: 3.0380
D(x): 0.9332, D(G(z)): 0.0912
Epoch: [5/20], Batch Num: [521/600]
Discriminator Loss: 0.2142, Generator Loss: 2.8800
D(x): 0.9485, D(G(z)): 0.1008
Epoch: [5/20], Batch Num: [522/600]
Discriminator Loss: 0.2692, Generator Loss: 2.8279
D(x): 0.9294, D(G(z)): 0.1171
Epoch: [5/20], Batch Num: [523/600]
Discriminator Loss: 0.2894, Generator Loss: 2.7321
D(x): 0.9379, D(G(z)): 0.1163
Epoch: [5/20], Batch Num: [524/600]
Discriminator Loss: 0.3608, Generator Loss: 2.7481
D(x): 0.9325, D(G(z)): 0.1535
Epoch: [5/20], Batch Num: [525/600]
Discriminator Loss: 0.1975, Generator Loss: 3.1434
D(x): 0.9777, D(G(z)): 0.1277
Epoch: [5/20], Batch Num: [526/600]
Discriminator Loss: 0.3726, Generator Loss: 3.2630
D(x): 0.9299, D(G(z)): 0.1143
Epoch: [5/20], Batch Num: [527/600]
Discriminator Loss: 0.3249, Generator Loss: 3.1794
D(x): 0.8963, D(G(z)): 0.1039
Epoch: [5/20], Batch Num: [528/600]
Discriminator Loss: 0.2399, Generator Loss: 3.2257
D(x): 0.9155, D(G(z)): 0.0684
Epoch: [5/20], Batch Num: [529/600]
Discriminator Loss: 0.3723, Generator Loss: 3.1031
D(x): 0.8842, D(G(z)): 0.0910
Epoch: [5/20], Batch Num: [530/600]
Discriminator Loss: 0.2318, Generator Loss: 2.8602
D(x): 0.9412, D(G(z)): 0.1116
Epoch: [5/20], Batch Num: [531/600]
Discriminator Loss: 0.4630, Generator Loss: 2.9228
D(x): 0.8945, D(G(z)): 0.1503
Epoch: [5/20], Batch Num: [532/600]
Discriminator Loss: 0.5443, Generator Loss: 2.6370
D(x): 0.8917, D(G(z)): 0.1654
Epoch: [5/20], Batch Num: [533/600]
Discriminator Loss: 0.4531, Generator Loss: 2.7530
D(x): 0.9107, D(G(z)): 0.1791
Epoch: [5/20], Batch Num: [534/600]
Discriminator Loss: 0.6653, Generator Loss: 2.7078
D(x): 0.8595, D(G(z)): 0.1888
Epoch: [5/20], Batch Num: [535/600]
Discriminator Loss: 0.5797, Generator Loss: 2.5734
D(x): 0.8751, D(G(z)): 0.1927
Epoch: [5/20], Batch Num: [536/600]
Discriminator Loss: 0.5781, Generator Loss: 2.8135
D(x): 0.8861, D(G(z)): 0.1811
Epoch: [5/20], Batch Num: [537/600]
Discriminator Loss: 0.6911, Generator Loss: 3.0765
D(x): 0.8712, D(G(z)): 0.2041
Epoch: [5/20], Batch Num: [538/600]
Discriminator Loss: 0.8397, Generator Loss: 2.8057
D(x): 0.7961, D(G(z)): 0.1585
Epoch: [5/20], Batch Num: [539/600]
Discriminator Loss: 0.8409, Generator Loss: 2.6670
D(x): 0.7633, D(G(z)): 0.1509
Epoch: [5/20], Batch Num: [540/600]
Discriminator Loss: 0.5364, Generator Loss: 2.3087
D(x): 0.8816, D(G(z)): 0.1732
Epoch: [5/20], Batch Num: [541/600]
Discriminator Loss: 0.7784, Generator Loss: 2.4013
D(x): 0.8764, D(G(z)): 0.2697
Epoch: [5/20], Batch Num: [542/600]
Discriminator Loss: 0.9432, Generator Loss: 2.1587
D(x): 0.8879, D(G(z)): 0.3015
Epoch: [5/20], Batch Num: [543/600]
Discriminator Loss: 0.7238, Generator Loss: 2.4958
D(x): 0.8687, D(G(z)): 0.2567
Epoch: [5/20], Batch Num: [544/600]
Discriminator Loss: 0.7826, Generator Loss: 2.5563
D(x): 0.8051, D(G(z)): 0.2225
Epoch: [5/20], Batch Num: [545/600]
Discriminator Loss: 0.7682, Generator Loss: 2.4850
D(x): 0.7859, D(G(z)): 0.1969
Epoch: [5/20], Batch Num: [546/600]
Discriminator Loss: 0.6222, Generator Loss: 2.5672
D(x): 0.8216, D(G(z)): 0.1795
Epoch: [5/20], Batch Num: [547/600]
Discriminator Loss: 0.5085, Generator Loss: 2.1126
D(x): 0.8434, D(G(z)): 0.1558
Epoch: [5/20], Batch Num: [548/600]
Discriminator Loss: 0.6042, Generator Loss: 2.2090
D(x): 0.8586, D(G(z)): 0.2417
Epoch: [5/20], Batch Num: [549/600]
Discriminator Loss: 0.6006, Generator Loss: 2.3993
D(x): 0.8430, D(G(z)): 0.2274
Epoch: [5/20], Batch Num: [550/600]
Discriminator Loss: 0.5529, Generator Loss: 2.2862
D(x): 0.8350, D(G(z)): 0.1916
Epoch: [5/20], Batch Num: [551/600]
Discriminator Loss: 0.6055, Generator Loss: 2.0455
D(x): 0.8472, D(G(z)): 0.2066
Epoch: [5/20], Batch Num: [552/600]
Discriminator Loss: 0.4958, Generator Loss: 2.3719
D(x): 0.8324, D(G(z)): 0.1673
Epoch: [5/20], Batch Num: [553/600]
Discriminator Loss: 0.4287, Generator Loss: 2.2864
D(x): 0.8700, D(G(z)): 0.1800
Epoch: [5/20], Batch Num: [554/600]
Discriminator Loss: 0.4824, Generator Loss: 2.1619
D(x): 0.8657, D(G(z)): 0.1979
Epoch: [5/20], Batch Num: [555/600]
Discriminator Loss: 0.6423, Generator Loss: 2.2605
D(x): 0.7967, D(G(z)): 0.1966
Epoch: [5/20], Batch Num: [556/600]
Discriminator Loss: 0.6243, Generator Loss: 2.3880
D(x): 0.7885, D(G(z)): 0.1876
Epoch: [5/20], Batch Num: [557/600]
Discriminator Loss: 0.5626, Generator Loss: 2.2411
D(x): 0.8257, D(G(z)): 0.1965
Epoch: [5/20], Batch Num: [558/600]
Discriminator Loss: 0.5181, Generator Loss: 1.9773
D(x): 0.8429, D(G(z)): 0.2009
Epoch: [5/20], Batch Num: [559/600]
Discriminator Loss: 0.5277, Generator Loss: 1.9736
D(x): 0.8779, D(G(z)): 0.2364
Epoch: [5/20], Batch Num: [560/600]
Discriminator Loss: 0.6589, Generator Loss: 2.2236
D(x): 0.8343, D(G(z)): 0.2488
Epoch: [5/20], Batch Num: [561/600]
Discriminator Loss: 0.4315, Generator Loss: 2.3662
D(x): 0.9029, D(G(z)): 0.2076
Epoch: [5/20], Batch Num: [562/600]
Discriminator Loss: 0.6234, Generator Loss: 2.3273
D(x): 0.7809, D(G(z)): 0.1707
Epoch: [5/20], Batch Num: [563/600]
Discriminator Loss: 0.7313, Generator Loss: 2.3976
D(x): 0.7550, D(G(z)): 0.1753
Epoch: [5/20], Batch Num: [564/600]
Discriminator Loss: 0.5981, Generator Loss: 2.1234
D(x): 0.8390, D(G(z)): 0.2195
Epoch: [5/20], Batch Num: [565/600]
Discriminator Loss: 0.6250, Generator Loss: 1.9112
D(x): 0.8217, D(G(z)): 0.2230
Epoch: [5/20], Batch Num: [566/600]
Discriminator Loss: 0.6535, Generator Loss: 2.3183
D(x): 0.7992, D(G(z)): 0.1895
Epoch: [5/20], Batch Num: [567/600]
Discriminator Loss: 0.5439, Generator Loss: 2.1859
D(x): 0.8352, D(G(z)): 0.2043
Epoch: [5/20], Batch Num: [568/600]
Discriminator Loss: 0.6677, Generator Loss: 2.4925
D(x): 0.8328, D(G(z)): 0.2370
Epoch: [5/20], Batch Num: [569/600]
Discriminator Loss: 0.5666, Generator Loss: 2.6233
D(x): 0.7932, D(G(z)): 0.1742
Epoch: [5/20], Batch Num: [570/600]
Discriminator Loss: 0.7139, Generator Loss: 2.3291
D(x): 0.7887, D(G(z)): 0.1889
Epoch: [5/20], Batch Num: [571/600]
Discriminator Loss: 0.8306, Generator Loss: 2.0621
D(x): 0.7476, D(G(z)): 0.1940
Epoch: [5/20], Batch Num: [572/600]
Discriminator Loss: 0.7063, Generator Loss: 2.0081
D(x): 0.8297, D(G(z)): 0.2428
Epoch: [5/20], Batch Num: [573/600]
Discriminator Loss: 0.8113, Generator Loss: 2.1491
D(x): 0.7758, D(G(z)): 0.2118
Epoch: [5/20], Batch Num: [574/600]
Discriminator Loss: 0.9626, Generator Loss: 1.8777
D(x): 0.7896, D(G(z)): 0.2720
Epoch: [5/20], Batch Num: [575/600]
Discriminator Loss: 0.9705, Generator Loss: 2.1060
D(x): 0.7657, D(G(z)): 0.3080
Epoch: [5/20], Batch Num: [576/600]
Discriminator Loss: 0.9335, Generator Loss: 2.2039
D(x): 0.7833, D(G(z)): 0.2623
Epoch: [5/20], Batch Num: [577/600]
Discriminator Loss: 0.7112, Generator Loss: 2.1003
D(x): 0.8217, D(G(z)): 0.2313
Epoch: [5/20], Batch Num: [578/600]
Discriminator Loss: 1.0221, Generator Loss: 2.3096
D(x): 0.7052, D(G(z)): 0.2206
Epoch: [5/20], Batch Num: [579/600]
Discriminator Loss: 0.7570, Generator Loss: 2.0719
D(x): 0.7617, D(G(z)): 0.2183
Epoch: [5/20], Batch Num: [580/600]
Discriminator Loss: 0.9256, Generator Loss: 1.9159
D(x): 0.7859, D(G(z)): 0.2656
Epoch: [5/20], Batch Num: [581/600]
Discriminator Loss: 0.9271, Generator Loss: 1.7557
D(x): 0.7444, D(G(z)): 0.2403
Epoch: [5/20], Batch Num: [582/600]
Discriminator Loss: 0.9447, Generator Loss: 1.7210
D(x): 0.7545, D(G(z)): 0.2554
Epoch: [5/20], Batch Num: [583/600]
Discriminator Loss: 0.7581, Generator Loss: 2.1276
D(x): 0.8511, D(G(z)): 0.3184
Epoch: [5/20], Batch Num: [584/600]
Discriminator Loss: 0.7131, Generator Loss: 2.0534
D(x): 0.8026, D(G(z)): 0.2455
Epoch: [5/20], Batch Num: [585/600]
Discriminator Loss: 0.8631, Generator Loss: 1.9846
D(x): 0.7325, D(G(z)): 0.1860
Epoch: [5/20], Batch Num: [586/600]
Discriminator Loss: 0.9803, Generator Loss: 2.1006
D(x): 0.6741, D(G(z)): 0.1874
Epoch: [5/20], Batch Num: [587/600]
Discriminator Loss: 0.9764, Generator Loss: 1.8383
D(x): 0.7165, D(G(z)): 0.2505
Epoch: [5/20], Batch Num: [588/600]
Discriminator Loss: 0.9216, Generator Loss: 1.7589
D(x): 0.8083, D(G(z)): 0.3182
Epoch: [5/20], Batch Num: [589/600]
Discriminator Loss: 0.8778, Generator Loss: 1.7800
D(x): 0.7695, D(G(z)): 0.3173
Epoch: [5/20], Batch Num: [590/600]
Discriminator Loss: 0.8525, Generator Loss: 1.7449
D(x): 0.7587, D(G(z)): 0.2790
Epoch: [5/20], Batch Num: [591/600]
Discriminator Loss: 0.8759, Generator Loss: 1.9025
D(x): 0.7496, D(G(z)): 0.2743
Epoch: [5/20], Batch Num: [592/600]
Discriminator Loss: 0.9093, Generator Loss: 1.6907
D(x): 0.7150, D(G(z)): 0.2808
Epoch: [5/20], Batch Num: [593/600]
Discriminator Loss: 0.8414, Generator Loss: 1.8017
D(x): 0.7400, D(G(z)): 0.2510
Epoch: [5/20], Batch Num: [594/600]
Discriminator Loss: 0.7907, Generator Loss: 1.6447
D(x): 0.7356, D(G(z)): 0.2583
Epoch: [5/20], Batch Num: [595/600]
Discriminator Loss: 0.8615, Generator Loss: 1.6440
D(x): 0.7539, D(G(z)): 0.2749
Epoch: [5/20], Batch Num: [596/600]
Discriminator Loss: 0.9179, Generator Loss: 1.7241
D(x): 0.7986, D(G(z)): 0.3295
Epoch: [5/20], Batch Num: [597/600]
Discriminator Loss: 0.7687, Generator Loss: 1.9284
D(x): 0.7887, D(G(z)): 0.2831
Epoch: [5/20], Batch Num: [598/600]
Discriminator Loss: 0.8452, Generator Loss: 2.0318
D(x): 0.7293, D(G(z)): 0.2257
Epoch: [5/20], Batch Num: [599/600]
Discriminator Loss: 0.8427, Generator Loss: 2.0306
D(x): 0.7314, D(G(z)): 0.2238
Epoch: 6, Batch Num: [0/600]

Epoch: [6/20], Batch Num: [0/600]
Discriminator Loss: 0.7458, Generator Loss: 2.1053
D(x): 0.7186, D(G(z)): 0.1803
Epoch: [6/20], Batch Num: [1/600]
Discriminator Loss: 0.8689, Generator Loss: 2.0317
D(x): 0.6958, D(G(z)): 0.1990
Epoch: [6/20], Batch Num: [2/600]
Discriminator Loss: 0.7488, Generator Loss: 1.6947
D(x): 0.7274, D(G(z)): 0.2081
Epoch: [6/20], Batch Num: [3/600]
Discriminator Loss: 0.8363, Generator Loss: 1.4972
D(x): 0.7695, D(G(z)): 0.2712
Epoch: [6/20], Batch Num: [4/600]
Discriminator Loss: 0.8681, Generator Loss: 1.5039
D(x): 0.8077, D(G(z)): 0.3375
Epoch: [6/20], Batch Num: [5/600]
Discriminator Loss: 0.5739, Generator Loss: 1.7465
D(x): 0.8586, D(G(z)): 0.2603
Epoch: [6/20], Batch Num: [6/600]
Discriminator Loss: 0.7461, Generator Loss: 1.9441
D(x): 0.7991, D(G(z)): 0.2695
Epoch: [6/20], Batch Num: [7/600]
Discriminator Loss: 0.7702, Generator Loss: 1.8800
D(x): 0.7061, D(G(z)): 0.1893
Epoch: [6/20], Batch Num: [8/600]
Discriminator Loss: 0.7670, Generator Loss: 2.0393
D(x): 0.7577, D(G(z)): 0.2342
Epoch: [6/20], Batch Num: [9/600]
Discriminator Loss: 0.6964, Generator Loss: 1.9519
D(x): 0.7522, D(G(z)): 0.1971
Epoch: [6/20], Batch Num: [10/600]
Discriminator Loss: 0.8617, Generator Loss: 1.5882
D(x): 0.6989, D(G(z)): 0.2236
Epoch: [6/20], Batch Num: [11/600]
Discriminator Loss: 0.8572, Generator Loss: 1.2872
D(x): 0.7285, D(G(z)): 0.2611
Epoch: [6/20], Batch Num: [12/600]
Discriminator Loss: 0.7619, Generator Loss: 1.5702
D(x): 0.8500, D(G(z)): 0.3557
Epoch: [6/20], Batch Num: [13/600]
Discriminator Loss: 0.7349, Generator Loss: 1.6391
D(x): 0.8075, D(G(z)): 0.2762
Epoch: [6/20], Batch Num: [14/600]
Discriminator Loss: 0.7047, Generator Loss: 1.7621
D(x): 0.8103, D(G(z)): 0.2701
Epoch: [6/20], Batch Num: [15/600]
Discriminator Loss: 0.6463, Generator Loss: 2.0247
D(x): 0.7739, D(G(z)): 0.2046
Epoch: [6/20], Batch Num: [16/600]
Discriminator Loss: 0.7269, Generator Loss: 1.8022
D(x): 0.7536, D(G(z)): 0.1997
Epoch: [6/20], Batch Num: [17/600]
Discriminator Loss: 0.7524, Generator Loss: 1.7028
D(x): 0.7211, D(G(z)): 0.2117
Epoch: [6/20], Batch Num: [18/600]
Discriminator Loss: 0.5207, Generator Loss: 1.4662
D(x): 0.8321, D(G(z)): 0.2180
Epoch: [6/20], Batch Num: [19/600]
Discriminator Loss: 0.6448, Generator Loss: 1.5949
D(x): 0.8394, D(G(z)): 0.2952
Epoch: [6/20], Batch Num: [20/600]
Discriminator Loss: 0.6881, Generator Loss: 1.6009
D(x): 0.8146, D(G(z)): 0.2975
Epoch: [6/20], Batch Num: [21/600]
Discriminator Loss: 0.7982, Generator Loss: 1.6701
D(x): 0.8058, D(G(z)): 0.3161
Epoch: [6/20], Batch Num: [22/600]
Discriminator Loss: 0.8440, Generator Loss: 1.7710
D(x): 0.7277, D(G(z)): 0.2654
Epoch: [6/20], Batch Num: [23/600]
Discriminator Loss: 0.8477, Generator Loss: 1.7098
D(x): 0.7045, D(G(z)): 0.2089
Epoch: [6/20], Batch Num: [24/600]
Discriminator Loss: 0.6736, Generator Loss: 1.6690
D(x): 0.7844, D(G(z)): 0.2380
Epoch: [6/20], Batch Num: [25/600]
Discriminator Loss: 0.6532, Generator Loss: 1.3828
D(x): 0.8065, D(G(z)): 0.2504
Epoch: [6/20], Batch Num: [26/600]
Discriminator Loss: 0.8188, Generator Loss: 1.4220
D(x): 0.7861, D(G(z)): 0.3056
Epoch: [6/20], Batch Num: [27/600]
Discriminator Loss: 0.8561, Generator Loss: 1.3183
D(x): 0.7364, D(G(z)): 0.3079
Epoch: [6/20], Batch Num: [28/600]
Discriminator Loss: 1.0081, Generator Loss: 1.5119
D(x): 0.7848, D(G(z)): 0.3691
Epoch: [6/20], Batch Num: [29/600]
Discriminator Loss: 0.8693, Generator Loss: 1.6605
D(x): 0.7564, D(G(z)): 0.2991
Epoch: [6/20], Batch Num: [30/600]
Discriminator Loss: 0.9110, Generator Loss: 1.7497
D(x): 0.7389, D(G(z)): 0.2779
Epoch: [6/20], Batch Num: [31/600]
Discriminator Loss: 0.9874, Generator Loss: 1.7430
D(x): 0.6393, D(G(z)): 0.2291
Epoch: [6/20], Batch Num: [32/600]
Discriminator Loss: 0.8225, Generator Loss: 1.4054
D(x): 0.7057, D(G(z)): 0.2279
Epoch: [6/20], Batch Num: [33/600]
Discriminator Loss: 0.8130, Generator Loss: 1.5626
D(x): 0.7985, D(G(z)): 0.3136
Epoch: [6/20], Batch Num: [34/600]
Discriminator Loss: 1.1348, Generator Loss: 1.4893
D(x): 0.7039, D(G(z)): 0.3483
Epoch: [6/20], Batch Num: [35/600]
Discriminator Loss: 0.9873, Generator Loss: 1.5229
D(x): 0.7829, D(G(z)): 0.3435
Epoch: [6/20], Batch Num: [36/600]
Discriminator Loss: 0.9119, Generator Loss: 1.6759
D(x): 0.7612, D(G(z)): 0.3058
Epoch: [6/20], Batch Num: [37/600]
Discriminator Loss: 0.9773, Generator Loss: 1.7459
D(x): 0.7050, D(G(z)): 0.2799
Epoch: [6/20], Batch Num: [38/600]
Discriminator Loss: 0.9411, Generator Loss: 1.7107
D(x): 0.6588, D(G(z)): 0.2120
Epoch: [6/20], Batch Num: [39/600]
Discriminator Loss: 0.8452, Generator Loss: 1.5543
D(x): 0.7526, D(G(z)): 0.2829
Epoch: [6/20], Batch Num: [40/600]
Discriminator Loss: 0.8103, Generator Loss: 1.4365
D(x): 0.7070, D(G(z)): 0.2422
Epoch: [6/20], Batch Num: [41/600]
Discriminator Loss: 0.9174, Generator Loss: 1.3853
D(x): 0.7473, D(G(z)): 0.3225
Epoch: [6/20], Batch Num: [42/600]
Discriminator Loss: 1.0225, Generator Loss: 1.4122
D(x): 0.7371, D(G(z)): 0.3593
Epoch: [6/20], Batch Num: [43/600]
Discriminator Loss: 0.9607, Generator Loss: 1.4459
D(x): 0.7204, D(G(z)): 0.3162
Epoch: [6/20], Batch Num: [44/600]
Discriminator Loss: 0.7809, Generator Loss: 1.4492
D(x): 0.7780, D(G(z)): 0.2936
Epoch: [6/20], Batch Num: [45/600]
Discriminator Loss: 0.8514, Generator Loss: 1.4821
D(x): 0.7119, D(G(z)): 0.2920
Epoch: [6/20], Batch Num: [46/600]
Discriminator Loss: 0.7060, Generator Loss: 1.4156
D(x): 0.7617, D(G(z)): 0.2780
Epoch: [6/20], Batch Num: [47/600]
Discriminator Loss: 0.8262, Generator Loss: 1.3500
D(x): 0.7496, D(G(z)): 0.3058
Epoch: [6/20], Batch Num: [48/600]
Discriminator Loss: 0.7178, Generator Loss: 1.4564
D(x): 0.7850, D(G(z)): 0.2807
Epoch: [6/20], Batch Num: [49/600]
Discriminator Loss: 0.8377, Generator Loss: 1.4438
D(x): 0.7018, D(G(z)): 0.2723
Epoch: [6/20], Batch Num: [50/600]
Discriminator Loss: 0.8605, Generator Loss: 1.5371
D(x): 0.7446, D(G(z)): 0.2939
Epoch: [6/20], Batch Num: [51/600]
Discriminator Loss: 0.8551, Generator Loss: 1.4937
D(x): 0.7441, D(G(z)): 0.2935
Epoch: [6/20], Batch Num: [52/600]
Discriminator Loss: 0.7864, Generator Loss: 1.4054
D(x): 0.7575, D(G(z)): 0.2863
Epoch: [6/20], Batch Num: [53/600]
Discriminator Loss: 0.8551, Generator Loss: 1.4366
D(x): 0.7565, D(G(z)): 0.3157
Epoch: [6/20], Batch Num: [54/600]
Discriminator Loss: 0.8198, Generator Loss: 1.4325
D(x): 0.7769, D(G(z)): 0.2991
Epoch: [6/20], Batch Num: [55/600]
Discriminator Loss: 0.8353, Generator Loss: 1.5044
D(x): 0.7627, D(G(z)): 0.3037
Epoch: [6/20], Batch Num: [56/600]
Discriminator Loss: 0.8595, Generator Loss: 1.4707
D(x): 0.7103, D(G(z)): 0.2763
Epoch: [6/20], Batch Num: [57/600]
Discriminator Loss: 0.9239, Generator Loss: 1.4676
D(x): 0.6800, D(G(z)): 0.2713
Epoch: [6/20], Batch Num: [58/600]
Discriminator Loss: 0.7761, Generator Loss: 1.2283
D(x): 0.7770, D(G(z)): 0.3078
Epoch: [6/20], Batch Num: [59/600]
Discriminator Loss: 0.8867, Generator Loss: 1.3285
D(x): 0.7702, D(G(z)): 0.3653
Epoch: [6/20], Batch Num: [60/600]
Discriminator Loss: 0.7324, Generator Loss: 1.3827
D(x): 0.8313, D(G(z)): 0.3323
Epoch: [6/20], Batch Num: [61/600]
Discriminator Loss: 0.6751, Generator Loss: 1.4271
D(x): 0.8089, D(G(z)): 0.2943
Epoch: [6/20], Batch Num: [62/600]
Discriminator Loss: 0.7381, Generator Loss: 1.6109
D(x): 0.8131, D(G(z)): 0.3243
Epoch: [6/20], Batch Num: [63/600]
Discriminator Loss: 0.6923, Generator Loss: 1.6505
D(x): 0.7306, D(G(z)): 0.2225
Epoch: [6/20], Batch Num: [64/600]
Discriminator Loss: 0.6757, Generator Loss: 1.6213
D(x): 0.7590, D(G(z)): 0.2264
Epoch: [6/20], Batch Num: [65/600]
Discriminator Loss: 0.6699, Generator Loss: 1.4613
D(x): 0.7870, D(G(z)): 0.2533
Epoch: [6/20], Batch Num: [66/600]
Discriminator Loss: 0.6165, Generator Loss: 1.4298
D(x): 0.8265, D(G(z)): 0.2673
Epoch: [6/20], Batch Num: [67/600]
Discriminator Loss: 0.7119, Generator Loss: 1.3751
D(x): 0.8073, D(G(z)): 0.3036
Epoch: [6/20], Batch Num: [68/600]
Discriminator Loss: 0.7686, Generator Loss: 1.5071
D(x): 0.8762, D(G(z)): 0.3816
Epoch: [6/20], Batch Num: [69/600]
Discriminator Loss: 0.6939, Generator Loss: 1.7324
D(x): 0.7941, D(G(z)): 0.2813
Epoch: [6/20], Batch Num: [70/600]
Discriminator Loss: 0.6059, Generator Loss: 1.8040
D(x): 0.7735, D(G(z)): 0.1979
Epoch: [6/20], Batch Num: [71/600]
Discriminator Loss: 0.7253, Generator Loss: 1.7087
D(x): 0.7356, D(G(z)): 0.2114
Epoch: [6/20], Batch Num: [72/600]
Discriminator Loss: 0.7286, Generator Loss: 1.5534
D(x): 0.7472, D(G(z)): 0.2357
Epoch: [6/20], Batch Num: [73/600]
Discriminator Loss: 0.6180, Generator Loss: 1.2758
D(x): 0.8081, D(G(z)): 0.2608
Epoch: [6/20], Batch Num: [74/600]
Discriminator Loss: 0.7104, Generator Loss: 1.3905
D(x): 0.8518, D(G(z)): 0.3274
Epoch: [6/20], Batch Num: [75/600]
Discriminator Loss: 0.7010, Generator Loss: 1.4898
D(x): 0.8511, D(G(z)): 0.3331
Epoch: [6/20], Batch Num: [76/600]
Discriminator Loss: 0.6873, Generator Loss: 1.5765
D(x): 0.8403, D(G(z)): 0.3248
Epoch: [6/20], Batch Num: [77/600]
Discriminator Loss: 0.6648, Generator Loss: 1.7660
D(x): 0.8197, D(G(z)): 0.2534
Epoch: [6/20], Batch Num: [78/600]
Discriminator Loss: 0.6909, Generator Loss: 1.7463
D(x): 0.7543, D(G(z)): 0.2235
Epoch: [6/20], Batch Num: [79/600]
Discriminator Loss: 0.7037, Generator Loss: 1.9540
D(x): 0.7537, D(G(z)): 0.2434
Epoch: [6/20], Batch Num: [80/600]
Discriminator Loss: 0.6306, Generator Loss: 1.6677
D(x): 0.8078, D(G(z)): 0.2421
Epoch: [6/20], Batch Num: [81/600]
Discriminator Loss: 0.5524, Generator Loss: 1.6669
D(x): 0.8524, D(G(z)): 0.2331
Epoch: [6/20], Batch Num: [82/600]
Discriminator Loss: 0.6023, Generator Loss: 1.6689
D(x): 0.8268, D(G(z)): 0.2588
Epoch: [6/20], Batch Num: [83/600]
Discriminator Loss: 0.6764, Generator Loss: 1.7044
D(x): 0.8414, D(G(z)): 0.3048
Epoch: [6/20], Batch Num: [84/600]
Discriminator Loss: 0.7063, Generator Loss: 1.8119
D(x): 0.8187, D(G(z)): 0.2871
Epoch: [6/20], Batch Num: [85/600]
Discriminator Loss: 0.6022, Generator Loss: 2.0116
D(x): 0.8107, D(G(z)): 0.2283
Epoch: [6/20], Batch Num: [86/600]
Discriminator Loss: 0.6599, Generator Loss: 2.1907
D(x): 0.7557, D(G(z)): 0.2130
Epoch: [6/20], Batch Num: [87/600]
Discriminator Loss: 0.5554, Generator Loss: 1.9471
D(x): 0.8335, D(G(z)): 0.2211
Epoch: [6/20], Batch Num: [88/600]
Discriminator Loss: 0.7255, Generator Loss: 1.6903
D(x): 0.7593, D(G(z)): 0.2262
Epoch: [6/20], Batch Num: [89/600]
Discriminator Loss: 0.6367, Generator Loss: 1.5739
D(x): 0.8312, D(G(z)): 0.2816
Epoch: [6/20], Batch Num: [90/600]
Discriminator Loss: 0.6570, Generator Loss: 1.7071
D(x): 0.8543, D(G(z)): 0.2986
Epoch: [6/20], Batch Num: [91/600]
Discriminator Loss: 0.6664, Generator Loss: 1.9500
D(x): 0.8381, D(G(z)): 0.2715
Epoch: [6/20], Batch Num: [92/600]
Discriminator Loss: 0.7946, Generator Loss: 1.9684
D(x): 0.7156, D(G(z)): 0.2223
Epoch: [6/20], Batch Num: [93/600]
Discriminator Loss: 0.7623, Generator Loss: 1.7745
D(x): 0.7424, D(G(z)): 0.2217
Epoch: [6/20], Batch Num: [94/600]
Discriminator Loss: 0.7084, Generator Loss: 1.4625
D(x): 0.7825, D(G(z)): 0.2387
Epoch: [6/20], Batch Num: [95/600]
Discriminator Loss: 0.8831, Generator Loss: 1.2799
D(x): 0.8339, D(G(z)): 0.3431
Epoch: [6/20], Batch Num: [96/600]
Discriminator Loss: 0.7754, Generator Loss: 1.8603
D(x): 0.8957, D(G(z)): 0.3867
Epoch: [6/20], Batch Num: [97/600]
Discriminator Loss: 0.6782, Generator Loss: 2.1623
D(x): 0.8202, D(G(z)): 0.2536
Epoch: [6/20], Batch Num: [98/600]
Discriminator Loss: 0.7635, Generator Loss: 2.5411
D(x): 0.7115, D(G(z)): 0.1702
Epoch: [6/20], Batch Num: [99/600]
Discriminator Loss: 0.8488, Generator Loss: 1.8471
D(x): 0.6928, D(G(z)): 0.2028
Epoch: 6, Batch Num: [100/600]

Epoch: [6/20], Batch Num: [100/600]
Discriminator Loss: 0.7633, Generator Loss: 1.6331
D(x): 0.7743, D(G(z)): 0.2564
Epoch: [6/20], Batch Num: [101/600]
Discriminator Loss: 0.7976, Generator Loss: 1.2263
D(x): 0.8070, D(G(z)): 0.3257
Epoch: [6/20], Batch Num: [102/600]
Discriminator Loss: 0.8490, Generator Loss: 1.7143
D(x): 0.8378, D(G(z)): 0.3814
Epoch: [6/20], Batch Num: [103/600]
Discriminator Loss: 0.8194, Generator Loss: 1.9472
D(x): 0.7756, D(G(z)): 0.2861
Epoch: [6/20], Batch Num: [104/600]
Discriminator Loss: 0.7348, Generator Loss: 2.1230
D(x): 0.7636, D(G(z)): 0.2259
Epoch: [6/20], Batch Num: [105/600]
Discriminator Loss: 0.8340, Generator Loss: 1.9222
D(x): 0.7041, D(G(z)): 0.1907
Epoch: [6/20], Batch Num: [106/600]
Discriminator Loss: 0.9500, Generator Loss: 1.7909
D(x): 0.6639, D(G(z)): 0.1832
Epoch: [6/20], Batch Num: [107/600]
Discriminator Loss: 0.7229, Generator Loss: 1.4051
D(x): 0.7779, D(G(z)): 0.2250
Epoch: [6/20], Batch Num: [108/600]
Discriminator Loss: 0.9375, Generator Loss: 1.3645
D(x): 0.7723, D(G(z)): 0.3364
Epoch: [6/20], Batch Num: [109/600]
Discriminator Loss: 0.8865, Generator Loss: 1.4566
D(x): 0.8166, D(G(z)): 0.3927
Epoch: [6/20], Batch Num: [110/600]
Discriminator Loss: 0.7467, Generator Loss: 1.7823
D(x): 0.8054, D(G(z)): 0.2935
Epoch: [6/20], Batch Num: [111/600]
Discriminator Loss: 0.6827, Generator Loss: 1.9376
D(x): 0.8012, D(G(z)): 0.2519
Epoch: [6/20], Batch Num: [112/600]
Discriminator Loss: 0.6572, Generator Loss: 2.1204
D(x): 0.7726, D(G(z)): 0.1938
Epoch: [6/20], Batch Num: [113/600]
Discriminator Loss: 0.9029, Generator Loss: 1.8351
D(x): 0.7040, D(G(z)): 0.2095
Epoch: [6/20], Batch Num: [114/600]
Discriminator Loss: 0.7051, Generator Loss: 1.5790
D(x): 0.7668, D(G(z)): 0.2122
Epoch: [6/20], Batch Num: [115/600]
Discriminator Loss: 0.6333, Generator Loss: 1.4781
D(x): 0.8326, D(G(z)): 0.2587
Epoch: [6/20], Batch Num: [116/600]
Discriminator Loss: 0.6035, Generator Loss: 1.5093
D(x): 0.8847, D(G(z)): 0.3105
Epoch: [6/20], Batch Num: [117/600]
Discriminator Loss: 0.8025, Generator Loss: 1.6875
D(x): 0.8000, D(G(z)): 0.3105
Epoch: [6/20], Batch Num: [118/600]
Discriminator Loss: 0.8072, Generator Loss: 2.1399
D(x): 0.7619, D(G(z)): 0.2486
Epoch: [6/20], Batch Num: [119/600]
Discriminator Loss: 0.6895, Generator Loss: 2.0727
D(x): 0.7909, D(G(z)): 0.2135
Epoch: [6/20], Batch Num: [120/600]
Discriminator Loss: 0.7617, Generator Loss: 2.0746
D(x): 0.7134, D(G(z)): 0.1620
Epoch: [6/20], Batch Num: [121/600]
Discriminator Loss: 0.5498, Generator Loss: 1.6734
D(x): 0.8254, D(G(z)): 0.1728
Epoch: [6/20], Batch Num: [122/600]
Discriminator Loss: 0.7023, Generator Loss: 1.6358
D(x): 0.8181, D(G(z)): 0.2425
Epoch: [6/20], Batch Num: [123/600]
Discriminator Loss: 0.6134, Generator Loss: 1.6260
D(x): 0.8265, D(G(z)): 0.2553
Epoch: [6/20], Batch Num: [124/600]
Discriminator Loss: 0.6532, Generator Loss: 1.7163
D(x): 0.8356, D(G(z)): 0.2904
Epoch: [6/20], Batch Num: [125/600]
Discriminator Loss: 0.5700, Generator Loss: 1.8645
D(x): 0.8615, D(G(z)): 0.2448
Epoch: [6/20], Batch Num: [126/600]
Discriminator Loss: 0.5517, Generator Loss: 2.0548
D(x): 0.8098, D(G(z)): 0.1862
Epoch: [6/20], Batch Num: [127/600]
Discriminator Loss: 0.6916, Generator Loss: 2.0993
D(x): 0.7588, D(G(z)): 0.1779
Epoch: [6/20], Batch Num: [128/600]
Discriminator Loss: 0.4514, Generator Loss: 2.0160
D(x): 0.8609, D(G(z)): 0.1706
Epoch: [6/20], Batch Num: [129/600]
Discriminator Loss: 0.6837, Generator Loss: 1.6922
D(x): 0.7937, D(G(z)): 0.2214
Epoch: [6/20], Batch Num: [130/600]
Discriminator Loss: 0.6133, Generator Loss: 1.5595
D(x): 0.8357, D(G(z)): 0.2392
Epoch: [6/20], Batch Num: [131/600]
Discriminator Loss: 0.6029, Generator Loss: 1.8712
D(x): 0.8751, D(G(z)): 0.2838
Epoch: [6/20], Batch Num: [132/600]
Discriminator Loss: 0.6173, Generator Loss: 1.7859
D(x): 0.8317, D(G(z)): 0.2409
Epoch: [6/20], Batch Num: [133/600]
Discriminator Loss: 0.5629, Generator Loss: 2.1104
D(x): 0.8441, D(G(z)): 0.2201
Epoch: [6/20], Batch Num: [134/600]
Discriminator Loss: 0.5819, Generator Loss: 2.1382
D(x): 0.8182, D(G(z)): 0.1756
Epoch: [6/20], Batch Num: [135/600]
Discriminator Loss: 0.6368, Generator Loss: 1.9761
D(x): 0.7794, D(G(z)): 0.1568
Epoch: [6/20], Batch Num: [136/600]
Discriminator Loss: 0.5424, Generator Loss: 2.0870
D(x): 0.8814, D(G(z)): 0.1988
Epoch: [6/20], Batch Num: [137/600]
Discriminator Loss: 0.6505, Generator Loss: 2.0343
D(x): 0.7889, D(G(z)): 0.1927
Epoch: [6/20], Batch Num: [138/600]
Discriminator Loss: 0.6425, Generator Loss: 1.6428
D(x): 0.7986, D(G(z)): 0.1756
Epoch: [6/20], Batch Num: [139/600]
Discriminator Loss: 0.6074, Generator Loss: 1.8664
D(x): 0.8427, D(G(z)): 0.2361
Epoch: [6/20], Batch Num: [140/600]
Discriminator Loss: 0.4968, Generator Loss: 1.8270
D(x): 0.8976, D(G(z)): 0.2484
Epoch: [6/20], Batch Num: [141/600]
Discriminator Loss: 0.5557, Generator Loss: 1.9673
D(x): 0.8325, D(G(z)): 0.2107
Epoch: [6/20], Batch Num: [142/600]
Discriminator Loss: 0.4270, Generator Loss: 2.0761
D(x): 0.8698, D(G(z)): 0.1779
Epoch: [6/20], Batch Num: [143/600]
Discriminator Loss: 0.7455, Generator Loss: 1.8846
D(x): 0.7508, D(G(z)): 0.1858
Epoch: [6/20], Batch Num: [144/600]
Discriminator Loss: 0.5568, Generator Loss: 1.6330
D(x): 0.8027, D(G(z)): 0.1820
Epoch: [6/20], Batch Num: [145/600]
Discriminator Loss: 0.6865, Generator Loss: 1.6743
D(x): 0.8366, D(G(z)): 0.2730
Epoch: [6/20], Batch Num: [146/600]
Discriminator Loss: 0.4930, Generator Loss: 1.7066
D(x): 0.8972, D(G(z)): 0.2478
Epoch: [6/20], Batch Num: [147/600]
Discriminator Loss: 0.6881, Generator Loss: 2.1100
D(x): 0.8525, D(G(z)): 0.2393
Epoch: [6/20], Batch Num: [148/600]
Discriminator Loss: 0.5861, Generator Loss: 2.2177
D(x): 0.8062, D(G(z)): 0.1604
Epoch: [6/20], Batch Num: [149/600]
Discriminator Loss: 0.5276, Generator Loss: 2.1773
D(x): 0.8089, D(G(z)): 0.1609
Epoch: [6/20], Batch Num: [150/600]
Discriminator Loss: 0.4538, Generator Loss: 2.0418
D(x): 0.8633, D(G(z)): 0.1835
Epoch: [6/20], Batch Num: [151/600]
Discriminator Loss: 0.4725, Generator Loss: 1.9011
D(x): 0.8650, D(G(z)): 0.1819
Epoch: [6/20], Batch Num: [152/600]
Discriminator Loss: 0.5494, Generator Loss: 1.8501
D(x): 0.8678, D(G(z)): 0.2274
Epoch: [6/20], Batch Num: [153/600]
Discriminator Loss: 0.4650, Generator Loss: 2.1375
D(x): 0.8844, D(G(z)): 0.1978
Epoch: [6/20], Batch Num: [154/600]
Discriminator Loss: 0.4596, Generator Loss: 2.0856
D(x): 0.8232, D(G(z)): 0.1565
Epoch: [6/20], Batch Num: [155/600]
Discriminator Loss: 0.4998, Generator Loss: 1.9339
D(x): 0.8347, D(G(z)): 0.1415
Epoch: [6/20], Batch Num: [156/600]
Discriminator Loss: 0.5225, Generator Loss: 2.1187
D(x): 0.8588, D(G(z)): 0.1692
Epoch: [6/20], Batch Num: [157/600]
Discriminator Loss: 0.4461, Generator Loss: 2.1644
D(x): 0.9095, D(G(z)): 0.2107
Epoch: [6/20], Batch Num: [158/600]
Discriminator Loss: 0.6355, Generator Loss: 2.3100
D(x): 0.8876, D(G(z)): 0.2213
Epoch: [6/20], Batch Num: [159/600]
Discriminator Loss: 0.4128, Generator Loss: 2.3669
D(x): 0.8303, D(G(z)): 0.1127
Epoch: [6/20], Batch Num: [160/600]
Discriminator Loss: 0.5270, Generator Loss: 2.4085
D(x): 0.8279, D(G(z)): 0.1291
Epoch: [6/20], Batch Num: [161/600]
Discriminator Loss: 0.4185, Generator Loss: 2.0489
D(x): 0.8616, D(G(z)): 0.1315
Epoch: [6/20], Batch Num: [162/600]
Discriminator Loss: 0.4984, Generator Loss: 1.9041
D(x): 0.8530, D(G(z)): 0.1562
Epoch: [6/20], Batch Num: [163/600]
Discriminator Loss: 0.6033, Generator Loss: 2.0969
D(x): 0.8835, D(G(z)): 0.2385
Epoch: [6/20], Batch Num: [164/600]
Discriminator Loss: 0.4723, Generator Loss: 2.2543
D(x): 0.8989, D(G(z)): 0.2013
Epoch: [6/20], Batch Num: [165/600]
Discriminator Loss: 0.5083, Generator Loss: 2.3218
D(x): 0.8254, D(G(z)): 0.1470
Epoch: [6/20], Batch Num: [166/600]
Discriminator Loss: 0.4922, Generator Loss: 2.3299
D(x): 0.8349, D(G(z)): 0.1355
Epoch: [6/20], Batch Num: [167/600]
Discriminator Loss: 0.6067, Generator Loss: 2.2196
D(x): 0.8325, D(G(z)): 0.1997
Epoch: [6/20], Batch Num: [168/600]
Discriminator Loss: 0.4332, Generator Loss: 2.3759
D(x): 0.8703, D(G(z)): 0.1680
Epoch: [6/20], Batch Num: [169/600]
Discriminator Loss: 0.4958, Generator Loss: 2.4872
D(x): 0.8581, D(G(z)): 0.1601
Epoch: [6/20], Batch Num: [170/600]
Discriminator Loss: 0.6727, Generator Loss: 2.4035
D(x): 0.8134, D(G(z)): 0.1785
Epoch: [6/20], Batch Num: [171/600]
Discriminator Loss: 0.4027, Generator Loss: 2.3560
D(x): 0.8763, D(G(z)): 0.1503
Epoch: [6/20], Batch Num: [172/600]
Discriminator Loss: 0.5591, Generator Loss: 2.3028
D(x): 0.8430, D(G(z)): 0.1891
Epoch: [6/20], Batch Num: [173/600]
Discriminator Loss: 0.7848, Generator Loss: 2.0659
D(x): 0.7841, D(G(z)): 0.1810
Epoch: [6/20], Batch Num: [174/600]
Discriminator Loss: 0.7474, Generator Loss: 1.8390
D(x): 0.8244, D(G(z)): 0.2453
Epoch: [6/20], Batch Num: [175/600]
Discriminator Loss: 0.6362, Generator Loss: 1.8378
D(x): 0.8133, D(G(z)): 0.2221
Epoch: [6/20], Batch Num: [176/600]
Discriminator Loss: 0.5814, Generator Loss: 1.9279
D(x): 0.8523, D(G(z)): 0.2266
Epoch: [6/20], Batch Num: [177/600]
Discriminator Loss: 0.7631, Generator Loss: 2.0714
D(x): 0.8005, D(G(z)): 0.2387
Epoch: [6/20], Batch Num: [178/600]
Discriminator Loss: 0.8123, Generator Loss: 2.0397
D(x): 0.7442, D(G(z)): 0.1877
Epoch: [6/20], Batch Num: [179/600]
Discriminator Loss: 0.8756, Generator Loss: 2.0078
D(x): 0.8165, D(G(z)): 0.2994
Epoch: [6/20], Batch Num: [180/600]
Discriminator Loss: 0.8880, Generator Loss: 2.0741
D(x): 0.7943, D(G(z)): 0.2702
Epoch: [6/20], Batch Num: [181/600]
Discriminator Loss: 0.7983, Generator Loss: 2.0512
D(x): 0.7407, D(G(z)): 0.2079
Epoch: [6/20], Batch Num: [182/600]
Discriminator Loss: 0.8681, Generator Loss: 1.8664
D(x): 0.7444, D(G(z)): 0.2277
Epoch: [6/20], Batch Num: [183/600]
Discriminator Loss: 0.9464, Generator Loss: 1.8323
D(x): 0.7808, D(G(z)): 0.2794
Epoch: [6/20], Batch Num: [184/600]
Discriminator Loss: 0.9583, Generator Loss: 1.8791
D(x): 0.7795, D(G(z)): 0.2867
Epoch: [6/20], Batch Num: [185/600]
Discriminator Loss: 0.6981, Generator Loss: 2.0416
D(x): 0.8108, D(G(z)): 0.2421
Epoch: [6/20], Batch Num: [186/600]
Discriminator Loss: 0.9020, Generator Loss: 1.9923
D(x): 0.7120, D(G(z)): 0.2248
Epoch: [6/20], Batch Num: [187/600]
Discriminator Loss: 0.8807, Generator Loss: 1.6628
D(x): 0.7116, D(G(z)): 0.2023
Epoch: [6/20], Batch Num: [188/600]
Discriminator Loss: 0.7086, Generator Loss: 1.4673
D(x): 0.8022, D(G(z)): 0.2523
Epoch: [6/20], Batch Num: [189/600]
Discriminator Loss: 0.8441, Generator Loss: 1.6368
D(x): 0.8575, D(G(z)): 0.3426
Epoch: [6/20], Batch Num: [190/600]
Discriminator Loss: 0.7233, Generator Loss: 2.0837
D(x): 0.8423, D(G(z)): 0.2878
Epoch: [6/20], Batch Num: [191/600]
Discriminator Loss: 0.7270, Generator Loss: 2.2159
D(x): 0.7505, D(G(z)): 0.1896
Epoch: [6/20], Batch Num: [192/600]
Discriminator Loss: 0.5498, Generator Loss: 2.0112
D(x): 0.7686, D(G(z)): 0.1372
Epoch: [6/20], Batch Num: [193/600]
Discriminator Loss: 0.6600, Generator Loss: 1.8771
D(x): 0.7856, D(G(z)): 0.1977
Epoch: [6/20], Batch Num: [194/600]
Discriminator Loss: 0.6169, Generator Loss: 1.4943
D(x): 0.8216, D(G(z)): 0.2350
Epoch: [6/20], Batch Num: [195/600]
Discriminator Loss: 0.7357, Generator Loss: 1.6925
D(x): 0.8698, D(G(z)): 0.3176
Epoch: [6/20], Batch Num: [196/600]
Discriminator Loss: 0.6655, Generator Loss: 1.9405
D(x): 0.8440, D(G(z)): 0.2663
Epoch: [6/20], Batch Num: [197/600]
Discriminator Loss: 0.5446, Generator Loss: 2.2329
D(x): 0.8650, D(G(z)): 0.2048
Epoch: [6/20], Batch Num: [198/600]
Discriminator Loss: 0.5679, Generator Loss: 2.1961
D(x): 0.8321, D(G(z)): 0.1766
Epoch: [6/20], Batch Num: [199/600]
Discriminator Loss: 0.6181, Generator Loss: 2.4212
D(x): 0.8280, D(G(z)): 0.1963
Epoch: 6, Batch Num: [200/600]

Epoch: [6/20], Batch Num: [200/600]
Discriminator Loss: 0.5636, Generator Loss: 2.1215
D(x): 0.8104, D(G(z)): 0.1497
Epoch: [6/20], Batch Num: [201/600]
Discriminator Loss: 0.6407, Generator Loss: 2.0958
D(x): 0.8112, D(G(z)): 0.2123
Epoch: [6/20], Batch Num: [202/600]
Discriminator Loss: 0.7647, Generator Loss: 1.9770
D(x): 0.8301, D(G(z)): 0.2648
Epoch: [6/20], Batch Num: [203/600]
Discriminator Loss: 0.6230, Generator Loss: 2.2716
D(x): 0.8832, D(G(z)): 0.2418
Epoch: [6/20], Batch Num: [204/600]
Discriminator Loss: 0.6183, Generator Loss: 2.3110
D(x): 0.8259, D(G(z)): 0.1937
Epoch: [6/20], Batch Num: [205/600]
Discriminator Loss: 0.5990, Generator Loss: 2.1442
D(x): 0.8011, D(G(z)): 0.1773
Epoch: [6/20], Batch Num: [206/600]
Discriminator Loss: 0.5530, Generator Loss: 2.3148
D(x): 0.8631, D(G(z)): 0.2150
Epoch: [6/20], Batch Num: [207/600]
Discriminator Loss: 0.5251, Generator Loss: 2.2929
D(x): 0.8495, D(G(z)): 0.1934
Epoch: [6/20], Batch Num: [208/600]
Discriminator Loss: 0.3710, Generator Loss: 2.3401
D(x): 0.8660, D(G(z)): 0.1412
Epoch: [6/20], Batch Num: [209/600]
Discriminator Loss: 0.4948, Generator Loss: 2.1109
D(x): 0.8412, D(G(z)): 0.1844
Epoch: [6/20], Batch Num: [210/600]
Discriminator Loss: 0.4688, Generator Loss: 2.1561
D(x): 0.8484, D(G(z)): 0.1810
Epoch: [6/20], Batch Num: [211/600]
Discriminator Loss: 0.5252, Generator Loss: 2.1427
D(x): 0.8398, D(G(z)): 0.1953
Epoch: [6/20], Batch Num: [212/600]
Discriminator Loss: 0.4173, Generator Loss: 2.2200
D(x): 0.9141, D(G(z)): 0.2348
Epoch: [6/20], Batch Num: [213/600]
Discriminator Loss: 0.4233, Generator Loss: 2.2429
D(x): 0.8638, D(G(z)): 0.1784
Epoch: [6/20], Batch Num: [214/600]
Discriminator Loss: 0.3941, Generator Loss: 2.4066
D(x): 0.8788, D(G(z)): 0.1724
Epoch: [6/20], Batch Num: [215/600]
Discriminator Loss: 0.5164, Generator Loss: 2.4113
D(x): 0.7972, D(G(z)): 0.1640
Epoch: [6/20], Batch Num: [216/600]
Discriminator Loss: 0.4029, Generator Loss: 2.1510
D(x): 0.8590, D(G(z)): 0.1614
Epoch: [6/20], Batch Num: [217/600]
Discriminator Loss: 0.4451, Generator Loss: 1.9686
D(x): 0.8910, D(G(z)): 0.2025
Epoch: [6/20], Batch Num: [218/600]
Discriminator Loss: 0.4431, Generator Loss: 2.2114
D(x): 0.8963, D(G(z)): 0.2346
Epoch: [6/20], Batch Num: [219/600]
Discriminator Loss: 0.3147, Generator Loss: 2.4194
D(x): 0.9174, D(G(z)): 0.1721
Epoch: [6/20], Batch Num: [220/600]
Discriminator Loss: 0.3524, Generator Loss: 2.6170
D(x): 0.8771, D(G(z)): 0.1420
Epoch: [6/20], Batch Num: [221/600]
Discriminator Loss: 0.4284, Generator Loss: 2.5945
D(x): 0.8407, D(G(z)): 0.1198
Epoch: [6/20], Batch Num: [222/600]
Discriminator Loss: 0.3343, Generator Loss: 2.3878
D(x): 0.8766, D(G(z)): 0.1385
Epoch: [6/20], Batch Num: [223/600]
Discriminator Loss: 0.2661, Generator Loss: 2.3371
D(x): 0.9316, D(G(z)): 0.1460
Epoch: [6/20], Batch Num: [224/600]
Discriminator Loss: 0.3260, Generator Loss: 2.1253
D(x): 0.9125, D(G(z)): 0.1789
Epoch: [6/20], Batch Num: [225/600]
Discriminator Loss: 0.3619, Generator Loss: 2.4039
D(x): 0.9010, D(G(z)): 0.1710
Epoch: [6/20], Batch Num: [226/600]
Discriminator Loss: 0.2532, Generator Loss: 2.6243
D(x): 0.9544, D(G(z)): 0.1636
Epoch: [6/20], Batch Num: [227/600]
Discriminator Loss: 0.3365, Generator Loss: 2.6916
D(x): 0.8859, D(G(z)): 0.1356
Epoch: [6/20], Batch Num: [228/600]
Discriminator Loss: 0.3840, Generator Loss: 2.4685
D(x): 0.8398, D(G(z)): 0.0982
Epoch: [6/20], Batch Num: [229/600]
Discriminator Loss: 0.3677, Generator Loss: 2.5164
D(x): 0.8780, D(G(z)): 0.1318
Epoch: [6/20], Batch Num: [230/600]
Discriminator Loss: 0.2297, Generator Loss: 2.2107
D(x): 0.9449, D(G(z)): 0.1403
Epoch: [6/20], Batch Num: [231/600]
Discriminator Loss: 0.2598, Generator Loss: 2.3596
D(x): 0.9424, D(G(z)): 0.1566
Epoch: [6/20], Batch Num: [232/600]
Discriminator Loss: 0.3563, Generator Loss: 2.8403
D(x): 0.9358, D(G(z)): 0.2057
Epoch: [6/20], Batch Num: [233/600]
Discriminator Loss: 0.3347, Generator Loss: 3.0865
D(x): 0.8781, D(G(z)): 0.1193
Epoch: [6/20], Batch Num: [234/600]
Discriminator Loss: 0.3754, Generator Loss: 2.9096
D(x): 0.8577, D(G(z)): 0.0939
Epoch: [6/20], Batch Num: [235/600]
Discriminator Loss: 0.3815, Generator Loss: 2.7971
D(x): 0.8799, D(G(z)): 0.1557
Epoch: [6/20], Batch Num: [236/600]
Discriminator Loss: 0.3776, Generator Loss: 2.5188
D(x): 0.8707, D(G(z)): 0.1301
Epoch: [6/20], Batch Num: [237/600]
Discriminator Loss: 0.3222, Generator Loss: 2.6755
D(x): 0.9362, D(G(z)): 0.1715
Epoch: [6/20], Batch Num: [238/600]
Discriminator Loss: 0.2775, Generator Loss: 2.6023
D(x): 0.9242, D(G(z)): 0.1431
Epoch: [6/20], Batch Num: [239/600]
Discriminator Loss: 0.3532, Generator Loss: 2.6193
D(x): 0.8827, D(G(z)): 0.1397
Epoch: [6/20], Batch Num: [240/600]
Discriminator Loss: 0.3846, Generator Loss: 2.5832
D(x): 0.8702, D(G(z)): 0.1266
Epoch: [6/20], Batch Num: [241/600]
Discriminator Loss: 0.4467, Generator Loss: 2.4558
D(x): 0.8851, D(G(z)): 0.1700
Epoch: [6/20], Batch Num: [242/600]
Discriminator Loss: 0.3707, Generator Loss: 2.5639
D(x): 0.9090, D(G(z)): 0.1738
Epoch: [6/20], Batch Num: [243/600]
Discriminator Loss: 0.2938, Generator Loss: 2.5866
D(x): 0.9018, D(G(z)): 0.1233
Epoch: [6/20], Batch Num: [244/600]
Discriminator Loss: 0.4331, Generator Loss: 2.7473
D(x): 0.8517, D(G(z)): 0.1458
Epoch: [6/20], Batch Num: [245/600]
Discriminator Loss: 0.4633, Generator Loss: 2.6667
D(x): 0.8636, D(G(z)): 0.1596
Epoch: [6/20], Batch Num: [246/600]
Discriminator Loss: 0.4454, Generator Loss: 2.4599
D(x): 0.8709, D(G(z)): 0.1820
Epoch: [6/20], Batch Num: [247/600]
Discriminator Loss: 0.6148, Generator Loss: 2.6262
D(x): 0.8666, D(G(z)): 0.2322
Epoch: [6/20], Batch Num: [248/600]
Discriminator Loss: 0.5174, Generator Loss: 2.6848
D(x): 0.8280, D(G(z)): 0.1529
Epoch: [6/20], Batch Num: [249/600]
Discriminator Loss: 0.5654, Generator Loss: 2.2244
D(x): 0.8328, D(G(z)): 0.1579
Epoch: [6/20], Batch Num: [250/600]
Discriminator Loss: 0.7126, Generator Loss: 1.9729
D(x): 0.8086, D(G(z)): 0.2236
Epoch: [6/20], Batch Num: [251/600]
Discriminator Loss: 0.4931, Generator Loss: 2.6890
D(x): 0.9147, D(G(z)): 0.2514
Epoch: [6/20], Batch Num: [252/600]
Discriminator Loss: 0.6145, Generator Loss: 2.6542
D(x): 0.7975, D(G(z)): 0.1572
Epoch: [6/20], Batch Num: [253/600]
Discriminator Loss: 0.6481, Generator Loss: 2.4267
D(x): 0.7768, D(G(z)): 0.1631
Epoch: [6/20], Batch Num: [254/600]
Discriminator Loss: 0.6907, Generator Loss: 1.9625
D(x): 0.8119, D(G(z)): 0.2120
Epoch: [6/20], Batch Num: [255/600]
Discriminator Loss: 0.5939, Generator Loss: 2.5821
D(x): 0.8833, D(G(z)): 0.2654
Epoch: [6/20], Batch Num: [256/600]
Discriminator Loss: 0.6485, Generator Loss: 2.8972
D(x): 0.8219, D(G(z)): 0.1343
Epoch: [6/20], Batch Num: [257/600]
Discriminator Loss: 0.8527, Generator Loss: 2.1044
D(x): 0.7099, D(G(z)): 0.1221
Epoch: [6/20], Batch Num: [258/600]
Discriminator Loss: 0.5336, Generator Loss: 2.0471
D(x): 0.8881, D(G(z)): 0.2352
Epoch: [6/20], Batch Num: [259/600]
Discriminator Loss: 0.6306, Generator Loss: 2.4611
D(x): 0.8735, D(G(z)): 0.2601
Epoch: [6/20], Batch Num: [260/600]
Discriminator Loss: 0.5068, Generator Loss: 3.1550
D(x): 0.8850, D(G(z)): 0.1931
Epoch: [6/20], Batch Num: [261/600]
Discriminator Loss: 0.6666, Generator Loss: 3.0028
D(x): 0.7559, D(G(z)): 0.1104
Epoch: [6/20], Batch Num: [262/600]
Discriminator Loss: 0.6601, Generator Loss: 2.3003
D(x): 0.7695, D(G(z)): 0.0857
Epoch: [6/20], Batch Num: [263/600]
Discriminator Loss: 0.6380, Generator Loss: 1.8092
D(x): 0.8706, D(G(z)): 0.2318
Epoch: [6/20], Batch Num: [264/600]
Discriminator Loss: 0.5558, Generator Loss: 2.6087
D(x): 0.9136, D(G(z)): 0.2603
Epoch: [6/20], Batch Num: [265/600]
Discriminator Loss: 0.5107, Generator Loss: 3.1456
D(x): 0.8679, D(G(z)): 0.1791
Epoch: [6/20], Batch Num: [266/600]
Discriminator Loss: 0.4646, Generator Loss: 3.2085
D(x): 0.8257, D(G(z)): 0.0997
Epoch: [6/20], Batch Num: [267/600]
Discriminator Loss: 0.7412, Generator Loss: 2.3543
D(x): 0.7488, D(G(z)): 0.0945
Epoch: [6/20], Batch Num: [268/600]
Discriminator Loss: 0.5928, Generator Loss: 2.1144
D(x): 0.8724, D(G(z)): 0.2266
Epoch: [6/20], Batch Num: [269/600]
Discriminator Loss: 0.7767, Generator Loss: 2.7248
D(x): 0.8831, D(G(z)): 0.2828
Epoch: [6/20], Batch Num: [270/600]
Discriminator Loss: 0.5509, Generator Loss: 3.0181
D(x): 0.8581, D(G(z)): 0.1727
Epoch: [6/20], Batch Num: [271/600]
Discriminator Loss: 0.5408, Generator Loss: 2.9272
D(x): 0.8101, D(G(z)): 0.1241
Epoch: [6/20], Batch Num: [272/600]
Discriminator Loss: 0.5927, Generator Loss: 2.5517
D(x): 0.8109, D(G(z)): 0.1232
Epoch: [6/20], Batch Num: [273/600]
Discriminator Loss: 0.3253, Generator Loss: 2.4840
D(x): 0.9023, D(G(z)): 0.1388
Epoch: [6/20], Batch Num: [274/600]
Discriminator Loss: 0.5263, Generator Loss: 2.3571
D(x): 0.8483, D(G(z)): 0.1656
Epoch: [6/20], Batch Num: [275/600]
Discriminator Loss: 0.4404, Generator Loss: 2.8821
D(x): 0.9178, D(G(z)): 0.2077
Epoch: [6/20], Batch Num: [276/600]
Discriminator Loss: 0.5202, Generator Loss: 3.2753
D(x): 0.8607, D(G(z)): 0.1544
Epoch: [6/20], Batch Num: [277/600]
Discriminator Loss: 0.4926, Generator Loss: 3.1924
D(x): 0.8288, D(G(z)): 0.0841
Epoch: [6/20], Batch Num: [278/600]
Discriminator Loss: 0.5401, Generator Loss: 2.9288
D(x): 0.8300, D(G(z)): 0.1294
Epoch: [6/20], Batch Num: [279/600]
Discriminator Loss: 0.3991, Generator Loss: 2.3591
D(x): 0.8466, D(G(z)): 0.1073
Epoch: [6/20], Batch Num: [280/600]
Discriminator Loss: 0.3162, Generator Loss: 2.5702
D(x): 0.9686, D(G(z)): 0.1984
Epoch: [6/20], Batch Num: [281/600]
Discriminator Loss: 0.2927, Generator Loss: 2.8718
D(x): 0.9235, D(G(z)): 0.1114
Epoch: [6/20], Batch Num: [282/600]
Discriminator Loss: 0.3285, Generator Loss: 3.1226
D(x): 0.9044, D(G(z)): 0.0986
Epoch: [6/20], Batch Num: [283/600]
Discriminator Loss: 0.2174, Generator Loss: 3.8646
D(x): 0.9462, D(G(z)): 0.0996
Epoch: [6/20], Batch Num: [284/600]
Discriminator Loss: 0.2146, Generator Loss: 3.6422
D(x): 0.9209, D(G(z)): 0.0704
Epoch: [6/20], Batch Num: [285/600]
Discriminator Loss: 0.2763, Generator Loss: 3.7581
D(x): 0.8945, D(G(z)): 0.0619
Epoch: [6/20], Batch Num: [286/600]
Discriminator Loss: 0.2874, Generator Loss: 3.5624
D(x): 0.8990, D(G(z)): 0.0858
Epoch: [6/20], Batch Num: [287/600]
Discriminator Loss: 0.2079, Generator Loss: 3.2956
D(x): 0.9253, D(G(z)): 0.0721
Epoch: [6/20], Batch Num: [288/600]
Discriminator Loss: 0.2582, Generator Loss: 2.8755
D(x): 0.9288, D(G(z)): 0.0929
Epoch: [6/20], Batch Num: [289/600]
Discriminator Loss: 0.2732, Generator Loss: 3.2148
D(x): 0.9513, D(G(z)): 0.1245
Epoch: [6/20], Batch Num: [290/600]
Discriminator Loss: 0.2887, Generator Loss: 3.1624
D(x): 0.9586, D(G(z)): 0.1582
Epoch: [6/20], Batch Num: [291/600]
Discriminator Loss: 0.2608, Generator Loss: 3.5718
D(x): 0.9358, D(G(z)): 0.1161
Epoch: [6/20], Batch Num: [292/600]
Discriminator Loss: 0.2612, Generator Loss: 3.7994
D(x): 0.8946, D(G(z)): 0.0728
Epoch: [6/20], Batch Num: [293/600]
Discriminator Loss: 0.1738, Generator Loss: 3.9777
D(x): 0.9321, D(G(z)): 0.0579
Epoch: [6/20], Batch Num: [294/600]
Discriminator Loss: 0.2032, Generator Loss: 3.6411
D(x): 0.9017, D(G(z)): 0.0448
Epoch: [6/20], Batch Num: [295/600]
Discriminator Loss: 0.3032, Generator Loss: 2.6892
D(x): 0.8989, D(G(z)): 0.0850
Epoch: [6/20], Batch Num: [296/600]
Discriminator Loss: 0.2767, Generator Loss: 2.8690
D(x): 0.9558, D(G(z)): 0.1516
Epoch: [6/20], Batch Num: [297/600]
Discriminator Loss: 0.4133, Generator Loss: 2.8285
D(x): 0.9148, D(G(z)): 0.1461
Epoch: [6/20], Batch Num: [298/600]
Discriminator Loss: 0.3842, Generator Loss: 3.1612
D(x): 0.8989, D(G(z)): 0.1214
Epoch: [6/20], Batch Num: [299/600]
Discriminator Loss: 0.4261, Generator Loss: 3.3667
D(x): 0.8899, D(G(z)): 0.1257
Epoch: 6, Batch Num: [300/600]

Epoch: [6/20], Batch Num: [300/600]
Discriminator Loss: 0.6353, Generator Loss: 2.8430
D(x): 0.8040, D(G(z)): 0.1432
Epoch: [6/20], Batch Num: [301/600]
Discriminator Loss: 0.6144, Generator Loss: 2.7299
D(x): 0.8351, D(G(z)): 0.1894
Epoch: [6/20], Batch Num: [302/600]
Discriminator Loss: 0.5377, Generator Loss: 2.6053
D(x): 0.8923, D(G(z)): 0.1763
Epoch: [6/20], Batch Num: [303/600]
Discriminator Loss: 0.4198, Generator Loss: 2.5419
D(x): 0.8796, D(G(z)): 0.1520
Epoch: [6/20], Batch Num: [304/600]
Discriminator Loss: 0.7332, Generator Loss: 2.3980
D(x): 0.8124, D(G(z)): 0.1594
Epoch: [6/20], Batch Num: [305/600]
Discriminator Loss: 0.7612, Generator Loss: 2.2493
D(x): 0.8301, D(G(z)): 0.2546
Epoch: [6/20], Batch Num: [306/600]
Discriminator Loss: 0.6237, Generator Loss: 2.6806
D(x): 0.8647, D(G(z)): 0.2195
Epoch: [6/20], Batch Num: [307/600]
Discriminator Loss: 0.6930, Generator Loss: 2.7798
D(x): 0.8336, D(G(z)): 0.1788
Epoch: [6/20], Batch Num: [308/600]
Discriminator Loss: 1.0239, Generator Loss: 2.3495
D(x): 0.6956, D(G(z)): 0.1761
Epoch: [6/20], Batch Num: [309/600]
Discriminator Loss: 0.9072, Generator Loss: 2.0162
D(x): 0.7458, D(G(z)): 0.1575
Epoch: [6/20], Batch Num: [310/600]
Discriminator Loss: 0.6076, Generator Loss: 1.5931
D(x): 0.8534, D(G(z)): 0.2464
Epoch: [6/20], Batch Num: [311/600]
Discriminator Loss: 0.8929, Generator Loss: 2.0418
D(x): 0.8431, D(G(z)): 0.3289
Epoch: [6/20], Batch Num: [312/600]
Discriminator Loss: 0.6270, Generator Loss: 2.3410
D(x): 0.8489, D(G(z)): 0.2392
Epoch: [6/20], Batch Num: [313/600]
Discriminator Loss: 0.9670, Generator Loss: 2.5086
D(x): 0.7026, D(G(z)): 0.1945
Epoch: [6/20], Batch Num: [314/600]
Discriminator Loss: 0.8022, Generator Loss: 2.3049
D(x): 0.7268, D(G(z)): 0.1657
Epoch: [6/20], Batch Num: [315/600]
Discriminator Loss: 0.8936, Generator Loss: 2.0871
D(x): 0.7258, D(G(z)): 0.2051
Epoch: [6/20], Batch Num: [316/600]
Discriminator Loss: 0.7824, Generator Loss: 1.8629
D(x): 0.8222, D(G(z)): 0.2575
Epoch: [6/20], Batch Num: [317/600]
Discriminator Loss: 0.7750, Generator Loss: 2.0220
D(x): 0.8074, D(G(z)): 0.2530
Epoch: [6/20], Batch Num: [318/600]
Discriminator Loss: 0.8650, Generator Loss: 2.0275
D(x): 0.7711, D(G(z)): 0.2634
Epoch: [6/20], Batch Num: [319/600]
Discriminator Loss: 0.6868, Generator Loss: 2.2763
D(x): 0.8198, D(G(z)): 0.2114
Epoch: [6/20], Batch Num: [320/600]
Discriminator Loss: 0.6104, Generator Loss: 2.5855
D(x): 0.8521, D(G(z)): 0.2145
Epoch: [6/20], Batch Num: [321/600]
Discriminator Loss: 0.6993, Generator Loss: 2.5584
D(x): 0.7457, D(G(z)): 0.1420
Epoch: [6/20], Batch Num: [322/600]
Discriminator Loss: 0.5061, Generator Loss: 2.6812
D(x): 0.8188, D(G(z)): 0.1632
Epoch: [6/20], Batch Num: [323/600]
Discriminator Loss: 0.7674, Generator Loss: 2.3769
D(x): 0.7649, D(G(z)): 0.1814
Epoch: [6/20], Batch Num: [324/600]
Discriminator Loss: 0.6676, Generator Loss: 2.5198
D(x): 0.8153, D(G(z)): 0.2330
Epoch: [6/20], Batch Num: [325/600]
Discriminator Loss: 0.8715, Generator Loss: 2.2574
D(x): 0.7570, D(G(z)): 0.2290
Epoch: [6/20], Batch Num: [326/600]
Discriminator Loss: 0.7520, Generator Loss: 2.2785
D(x): 0.7764, D(G(z)): 0.2123
Epoch: [6/20], Batch Num: [327/600]
Discriminator Loss: 0.8880, Generator Loss: 2.2472
D(x): 0.7303, D(G(z)): 0.2383
Epoch: [6/20], Batch Num: [328/600]
Discriminator Loss: 0.6414, Generator Loss: 2.3834
D(x): 0.8446, D(G(z)): 0.2590
Epoch: [6/20], Batch Num: [329/600]
Discriminator Loss: 0.7747, Generator Loss: 2.9346
D(x): 0.8131, D(G(z)): 0.2364
Epoch: [6/20], Batch Num: [330/600]
Discriminator Loss: 0.6607, Generator Loss: 3.1498
D(x): 0.7694, D(G(z)): 0.1557
Epoch: [6/20], Batch Num: [331/600]
Discriminator Loss: 0.9447, Generator Loss: 2.7825
D(x): 0.6467, D(G(z)): 0.1118
Epoch: [6/20], Batch Num: [332/600]
Discriminator Loss: 0.6901, Generator Loss: 2.1920
D(x): 0.7770, D(G(z)): 0.1754
Epoch: [6/20], Batch Num: [333/600]
Discriminator Loss: 0.7274, Generator Loss: 2.1116
D(x): 0.8235, D(G(z)): 0.2609
Epoch: [6/20], Batch Num: [334/600]
Discriminator Loss: 0.7023, Generator Loss: 2.6998
D(x): 0.8594, D(G(z)): 0.2775
Epoch: [6/20], Batch Num: [335/600]
Discriminator Loss: 0.6275, Generator Loss: 3.2630
D(x): 0.7831, D(G(z)): 0.1510
Epoch: [6/20], Batch Num: [336/600]
Discriminator Loss: 0.6618, Generator Loss: 3.0538
D(x): 0.7422, D(G(z)): 0.0963
Epoch: [6/20], Batch Num: [337/600]
Discriminator Loss: 0.9479, Generator Loss: 2.4600
D(x): 0.7156, D(G(z)): 0.1924
Epoch: [6/20], Batch Num: [338/600]
Discriminator Loss: 0.5964, Generator Loss: 2.7668
D(x): 0.8446, D(G(z)): 0.2165
Epoch: [6/20], Batch Num: [339/600]
Discriminator Loss: 0.5195, Generator Loss: 2.4497
D(x): 0.8455, D(G(z)): 0.1938
Epoch: [6/20], Batch Num: [340/600]
Discriminator Loss: 0.7115, Generator Loss: 2.8335
D(x): 0.7914, D(G(z)): 0.2017
Epoch: [6/20], Batch Num: [341/600]
Discriminator Loss: 0.5897, Generator Loss: 2.9266
D(x): 0.8277, D(G(z)): 0.1861
Epoch: [6/20], Batch Num: [342/600]
Discriminator Loss: 0.5768, Generator Loss: 2.7191
D(x): 0.7830, D(G(z)): 0.1331
Epoch: [6/20], Batch Num: [343/600]
Discriminator Loss: 0.7637, Generator Loss: 2.0420
D(x): 0.7695, D(G(z)): 0.1911
Epoch: [6/20], Batch Num: [344/600]
Discriminator Loss: 0.9128, Generator Loss: 2.3159
D(x): 0.7956, D(G(z)): 0.2962
Epoch: [6/20], Batch Num: [345/600]
Discriminator Loss: 0.8433, Generator Loss: 3.0101
D(x): 0.8330, D(G(z)): 0.3039
Epoch: [6/20], Batch Num: [346/600]
Discriminator Loss: 0.8147, Generator Loss: 2.9352
D(x): 0.7420, D(G(z)): 0.1341
Epoch: [6/20], Batch Num: [347/600]
Discriminator Loss: 1.0118, Generator Loss: 2.0992
D(x): 0.6784, D(G(z)): 0.1365
Epoch: [6/20], Batch Num: [348/600]
Discriminator Loss: 0.7292, Generator Loss: 1.8980
D(x): 0.8361, D(G(z)): 0.2809
Epoch: [6/20], Batch Num: [349/600]
Discriminator Loss: 0.7185, Generator Loss: 2.0833
D(x): 0.8512, D(G(z)): 0.2701
Epoch: [6/20], Batch Num: [350/600]
Discriminator Loss: 0.9108, Generator Loss: 2.3974
D(x): 0.7802, D(G(z)): 0.2480
Epoch: [6/20], Batch Num: [351/600]
Discriminator Loss: 0.6162, Generator Loss: 2.4645
D(x): 0.8396, D(G(z)): 0.2228
Epoch: [6/20], Batch Num: [352/600]
Discriminator Loss: 0.7434, Generator Loss: 2.7001
D(x): 0.8039, D(G(z)): 0.2124
Epoch: [6/20], Batch Num: [353/600]
Discriminator Loss: 0.9608, Generator Loss: 2.5616
D(x): 0.6877, D(G(z)): 0.1371
Epoch: [6/20], Batch Num: [354/600]
Discriminator Loss: 0.9652, Generator Loss: 1.9373
D(x): 0.7081, D(G(z)): 0.1969
Epoch: [6/20], Batch Num: [355/600]
Discriminator Loss: 0.8879, Generator Loss: 1.9298
D(x): 0.8194, D(G(z)): 0.3452
Epoch: [6/20], Batch Num: [356/600]
Discriminator Loss: 0.7060, Generator Loss: 1.8538
D(x): 0.8375, D(G(z)): 0.3020
Epoch: [6/20], Batch Num: [357/600]
Discriminator Loss: 0.5288, Generator Loss: 2.5429
D(x): 0.8801, D(G(z)): 0.2431
Epoch: [6/20], Batch Num: [358/600]
Discriminator Loss: 0.6903, Generator Loss: 2.7077
D(x): 0.7804, D(G(z)): 0.1599
Epoch: [6/20], Batch Num: [359/600]
Discriminator Loss: 0.6145, Generator Loss: 2.5886
D(x): 0.7752, D(G(z)): 0.1534
Epoch: [6/20], Batch Num: [360/600]
Discriminator Loss: 0.5614, Generator Loss: 2.3459
D(x): 0.7844, D(G(z)): 0.1617
Epoch: [6/20], Batch Num: [361/600]
Discriminator Loss: 0.5955, Generator Loss: 2.1369
D(x): 0.8313, D(G(z)): 0.2109
Epoch: [6/20], Batch Num: [362/600]
Discriminator Loss: 0.5809, Generator Loss: 1.9135
D(x): 0.8230, D(G(z)): 0.2131
Epoch: [6/20], Batch Num: [363/600]
Discriminator Loss: 0.5443, Generator Loss: 2.1568
D(x): 0.8631, D(G(z)): 0.2214
Epoch: [6/20], Batch Num: [364/600]
Discriminator Loss: 0.6217, Generator Loss: 2.2041
D(x): 0.8655, D(G(z)): 0.2553
Epoch: [6/20], Batch Num: [365/600]
Discriminator Loss: 0.5643, Generator Loss: 2.4862
D(x): 0.8402, D(G(z)): 0.2046
Epoch: [6/20], Batch Num: [366/600]
Discriminator Loss: 0.6065, Generator Loss: 2.5839
D(x): 0.8143, D(G(z)): 0.1545
Epoch: [6/20], Batch Num: [367/600]
Discriminator Loss: 0.5589, Generator Loss: 2.6906
D(x): 0.8089, D(G(z)): 0.1374
Epoch: [6/20], Batch Num: [368/600]
Discriminator Loss: 0.5953, Generator Loss: 2.4237
D(x): 0.8040, D(G(z)): 0.1642
Epoch: [6/20], Batch Num: [369/600]
Discriminator Loss: 0.5823, Generator Loss: 2.1305
D(x): 0.8061, D(G(z)): 0.1715
Epoch: [6/20], Batch Num: [370/600]
Discriminator Loss: 0.6728, Generator Loss: 2.0787
D(x): 0.8658, D(G(z)): 0.2531
Epoch: [6/20], Batch Num: [371/600]
Discriminator Loss: 0.5205, Generator Loss: 2.5075
D(x): 0.9007, D(G(z)): 0.2355
Epoch: [6/20], Batch Num: [372/600]
Discriminator Loss: 0.4924, Generator Loss: 2.9315
D(x): 0.8350, D(G(z)): 0.1527
Epoch: [6/20], Batch Num: [373/600]
Discriminator Loss: 0.4204, Generator Loss: 2.6240
D(x): 0.8620, D(G(z)): 0.1342
Epoch: [6/20], Batch Num: [374/600]
Discriminator Loss: 0.5982, Generator Loss: 2.9202
D(x): 0.8259, D(G(z)): 0.1759
Epoch: [6/20], Batch Num: [375/600]
Discriminator Loss: 0.5492, Generator Loss: 2.4982
D(x): 0.8329, D(G(z)): 0.1480
Epoch: [6/20], Batch Num: [376/600]
Discriminator Loss: 0.4747, Generator Loss: 2.3873
D(x): 0.8273, D(G(z)): 0.1385
Epoch: [6/20], Batch Num: [377/600]
Discriminator Loss: 0.5739, Generator Loss: 2.4058
D(x): 0.8625, D(G(z)): 0.2130
Epoch: [6/20], Batch Num: [378/600]
Discriminator Loss: 0.5483, Generator Loss: 2.7642
D(x): 0.8741, D(G(z)): 0.2016
Epoch: [6/20], Batch Num: [379/600]
Discriminator Loss: 0.5837, Generator Loss: 2.7602
D(x): 0.8420, D(G(z)): 0.1949
Epoch: [6/20], Batch Num: [380/600]
Discriminator Loss: 0.7370, Generator Loss: 3.2096
D(x): 0.8016, D(G(z)): 0.2098
Epoch: [6/20], Batch Num: [381/600]
Discriminator Loss: 0.5481, Generator Loss: 2.7641
D(x): 0.7905, D(G(z)): 0.1018
Epoch: [6/20], Batch Num: [382/600]
Discriminator Loss: 0.6520, Generator Loss: 2.3870
D(x): 0.7888, D(G(z)): 0.1733
Epoch: [6/20], Batch Num: [383/600]
Discriminator Loss: 0.4996, Generator Loss: 2.1301
D(x): 0.8268, D(G(z)): 0.1626
Epoch: [6/20], Batch Num: [384/600]
Discriminator Loss: 0.8703, Generator Loss: 2.1545
D(x): 0.8062, D(G(z)): 0.2841
Epoch: [6/20], Batch Num: [385/600]
Discriminator Loss: 0.8097, Generator Loss: 2.4422
D(x): 0.8331, D(G(z)): 0.2594
Epoch: [6/20], Batch Num: [386/600]
Discriminator Loss: 0.6606, Generator Loss: 2.5960
D(x): 0.8286, D(G(z)): 0.2207
Epoch: [6/20], Batch Num: [387/600]
Discriminator Loss: 0.8731, Generator Loss: 2.7151
D(x): 0.7127, D(G(z)): 0.1618
Epoch: [6/20], Batch Num: [388/600]
Discriminator Loss: 0.8400, Generator Loss: 2.7308
D(x): 0.7583, D(G(z)): 0.1783
Epoch: [6/20], Batch Num: [389/600]
Discriminator Loss: 0.9252, Generator Loss: 1.9462
D(x): 0.7236, D(G(z)): 0.2210
Epoch: [6/20], Batch Num: [390/600]
Discriminator Loss: 0.8903, Generator Loss: 1.7993
D(x): 0.7621, D(G(z)): 0.2134
Epoch: [6/20], Batch Num: [391/600]
Discriminator Loss: 0.7862, Generator Loss: 1.6580
D(x): 0.8110, D(G(z)): 0.2900
Epoch: [6/20], Batch Num: [392/600]
Discriminator Loss: 0.8547, Generator Loss: 2.1868
D(x): 0.8070, D(G(z)): 0.2708
Epoch: [6/20], Batch Num: [393/600]
Discriminator Loss: 0.7596, Generator Loss: 2.5469
D(x): 0.7980, D(G(z)): 0.2454
Epoch: [6/20], Batch Num: [394/600]
Discriminator Loss: 0.7621, Generator Loss: 2.4057
D(x): 0.7567, D(G(z)): 0.2062
Epoch: [6/20], Batch Num: [395/600]
Discriminator Loss: 0.7641, Generator Loss: 2.5332
D(x): 0.7409, D(G(z)): 0.1806
Epoch: [6/20], Batch Num: [396/600]
Discriminator Loss: 0.7356, Generator Loss: 2.4981
D(x): 0.7531, D(G(z)): 0.1950
Epoch: [6/20], Batch Num: [397/600]
Discriminator Loss: 0.7889, Generator Loss: 2.1965
D(x): 0.7618, D(G(z)): 0.1842
Epoch: [6/20], Batch Num: [398/600]
Discriminator Loss: 0.6437, Generator Loss: 2.2654
D(x): 0.8252, D(G(z)): 0.2280
Epoch: [6/20], Batch Num: [399/600]
Discriminator Loss: 0.7243, Generator Loss: 2.4052
D(x): 0.8459, D(G(z)): 0.2784
Epoch: 6, Batch Num: [400/600]

Epoch: [6/20], Batch Num: [400/600]
Discriminator Loss: 0.5369, Generator Loss: 2.5366
D(x): 0.8385, D(G(z)): 0.2005
Epoch: [6/20], Batch Num: [401/600]
Discriminator Loss: 0.5365, Generator Loss: 2.5927
D(x): 0.8358, D(G(z)): 0.1726
Epoch: [6/20], Batch Num: [402/600]
Discriminator Loss: 0.6313, Generator Loss: 2.6840
D(x): 0.7759, D(G(z)): 0.1658
Epoch: [6/20], Batch Num: [403/600]
Discriminator Loss: 0.4887, Generator Loss: 2.7471
D(x): 0.8351, D(G(z)): 0.1693
Epoch: [6/20], Batch Num: [404/600]
Discriminator Loss: 0.6202, Generator Loss: 2.3368
D(x): 0.7951, D(G(z)): 0.1576
Epoch: [6/20], Batch Num: [405/600]
Discriminator Loss: 0.4534, Generator Loss: 2.4213
D(x): 0.8373, D(G(z)): 0.1559
Epoch: [6/20], Batch Num: [406/600]
Discriminator Loss: 0.5761, Generator Loss: 2.1893
D(x): 0.8705, D(G(z)): 0.2304
Epoch: [6/20], Batch Num: [407/600]
Discriminator Loss: 0.5007, Generator Loss: 2.3879
D(x): 0.8497, D(G(z)): 0.2011
Epoch: [6/20], Batch Num: [408/600]
Discriminator Loss: 0.4882, Generator Loss: 2.4476
D(x): 0.8498, D(G(z)): 0.1810
Epoch: [6/20], Batch Num: [409/600]
Discriminator Loss: 0.5361, Generator Loss: 2.5069
D(x): 0.8202, D(G(z)): 0.1716
Epoch: [6/20], Batch Num: [410/600]
Discriminator Loss: 0.5126, Generator Loss: 2.4290
D(x): 0.8151, D(G(z)): 0.1604
Epoch: [6/20], Batch Num: [411/600]
Discriminator Loss: 0.6671, Generator Loss: 2.3370
D(x): 0.7817, D(G(z)): 0.1415
Epoch: [6/20], Batch Num: [412/600]
Discriminator Loss: 0.7166, Generator Loss: 2.0907
D(x): 0.7846, D(G(z)): 0.2010
Epoch: [6/20], Batch Num: [413/600]
Discriminator Loss: 0.6210, Generator Loss: 2.1980
D(x): 0.8483, D(G(z)): 0.2555
Epoch: [6/20], Batch Num: [414/600]
Discriminator Loss: 0.6884, Generator Loss: 2.1190
D(x): 0.8510, D(G(z)): 0.2785
Epoch: [6/20], Batch Num: [415/600]
Discriminator Loss: 0.6755, Generator Loss: 2.4039
D(x): 0.8100, D(G(z)): 0.2235
Epoch: [6/20], Batch Num: [416/600]
Discriminator Loss: 0.7861, Generator Loss: 2.4240
D(x): 0.7247, D(G(z)): 0.1597
Epoch: [6/20], Batch Num: [417/600]
Discriminator Loss: 0.7122, Generator Loss: 2.1548
D(x): 0.7635, D(G(z)): 0.1702
Epoch: [6/20], Batch Num: [418/600]
Discriminator Loss: 0.5506, Generator Loss: 1.8878
D(x): 0.8486, D(G(z)): 0.1949
Epoch: [6/20], Batch Num: [419/600]
Discriminator Loss: 0.6282, Generator Loss: 1.9741
D(x): 0.8955, D(G(z)): 0.3014
Epoch: [6/20], Batch Num: [420/600]
Discriminator Loss: 0.6427, Generator Loss: 2.4594
D(x): 0.8611, D(G(z)): 0.2603
Epoch: [6/20], Batch Num: [421/600]
Discriminator Loss: 0.4414, Generator Loss: 2.5832
D(x): 0.8434, D(G(z)): 0.1577
Epoch: [6/20], Batch Num: [422/600]
Discriminator Loss: 0.5230, Generator Loss: 2.7594
D(x): 0.8256, D(G(z)): 0.1394
Epoch: [6/20], Batch Num: [423/600]
Discriminator Loss: 0.5778, Generator Loss: 2.8840
D(x): 0.7953, D(G(z)): 0.1165
Epoch: [6/20], Batch Num: [424/600]
Discriminator Loss: 0.6120, Generator Loss: 2.3513
D(x): 0.7842, D(G(z)): 0.1631
Epoch: [6/20], Batch Num: [425/600]
Discriminator Loss: 0.5293, Generator Loss: 2.1792
D(x): 0.8384, D(G(z)): 0.2071
Epoch: [6/20], Batch Num: [426/600]
Discriminator Loss: 0.7119, Generator Loss: 2.1750
D(x): 0.8201, D(G(z)): 0.2549
Epoch: [6/20], Batch Num: [427/600]
Discriminator Loss: 0.5607, Generator Loss: 2.2777
D(x): 0.8868, D(G(z)): 0.2149
Epoch: [6/20], Batch Num: [428/600]
Discriminator Loss: 0.4859, Generator Loss: 2.3768
D(x): 0.8557, D(G(z)): 0.2037
Epoch: [6/20], Batch Num: [429/600]
Discriminator Loss: 0.5883, Generator Loss: 2.7367
D(x): 0.8590, D(G(z)): 0.2072
Epoch: [6/20], Batch Num: [430/600]
Discriminator Loss: 0.6059, Generator Loss: 2.6632
D(x): 0.7782, D(G(z)): 0.1387
Epoch: [6/20], Batch Num: [431/600]
Discriminator Loss: 0.4986, Generator Loss: 2.4250
D(x): 0.7896, D(G(z)): 0.1256
Epoch: [6/20], Batch Num: [432/600]
Discriminator Loss: 0.4804, Generator Loss: 2.2748
D(x): 0.8483, D(G(z)): 0.1858
Epoch: [6/20], Batch Num: [433/600]
Discriminator Loss: 0.4460, Generator Loss: 2.3278
D(x): 0.8852, D(G(z)): 0.1930
Epoch: [6/20], Batch Num: [434/600]
Discriminator Loss: 0.5610, Generator Loss: 2.3401
D(x): 0.8709, D(G(z)): 0.2633
Epoch: [6/20], Batch Num: [435/600]
Discriminator Loss: 0.5747, Generator Loss: 2.3917
D(x): 0.8320, D(G(z)): 0.2124
Epoch: [6/20], Batch Num: [436/600]
Discriminator Loss: 0.6262, Generator Loss: 2.5270
D(x): 0.7779, D(G(z)): 0.1961
Epoch: [6/20], Batch Num: [437/600]
Discriminator Loss: 0.5269, Generator Loss: 2.3330
D(x): 0.8591, D(G(z)): 0.2039
Epoch: [6/20], Batch Num: [438/600]
Discriminator Loss: 0.7222, Generator Loss: 2.3041
D(x): 0.8184, D(G(z)): 0.2622
Epoch: [6/20], Batch Num: [439/600]
Discriminator Loss: 0.5657, Generator Loss: 2.3820
D(x): 0.8293, D(G(z)): 0.2053
Epoch: [6/20], Batch Num: [440/600]
Discriminator Loss: 0.7806, Generator Loss: 2.0718
D(x): 0.7096, D(G(z)): 0.1815
Epoch: [6/20], Batch Num: [441/600]
Discriminator Loss: 0.6495, Generator Loss: 2.0077
D(x): 0.8752, D(G(z)): 0.2873
Epoch: [6/20], Batch Num: [442/600]
Discriminator Loss: 0.7797, Generator Loss: 2.0083
D(x): 0.7870, D(G(z)): 0.2770
Epoch: [6/20], Batch Num: [443/600]
Discriminator Loss: 0.5929, Generator Loss: 1.7922
D(x): 0.8140, D(G(z)): 0.2167
Epoch: [6/20], Batch Num: [444/600]
Discriminator Loss: 0.7844, Generator Loss: 1.8382
D(x): 0.7813, D(G(z)): 0.2413
Epoch: [6/20], Batch Num: [445/600]
Discriminator Loss: 0.6190, Generator Loss: 1.8477
D(x): 0.8545, D(G(z)): 0.2691
Epoch: [6/20], Batch Num: [446/600]
Discriminator Loss: 0.7652, Generator Loss: 1.7435
D(x): 0.7600, D(G(z)): 0.2279
Epoch: [6/20], Batch Num: [447/600]
Discriminator Loss: 0.9086, Generator Loss: 1.7248
D(x): 0.7555, D(G(z)): 0.2836
Epoch: [6/20], Batch Num: [448/600]
Discriminator Loss: 0.8735, Generator Loss: 1.9961
D(x): 0.7464, D(G(z)): 0.2749
Epoch: [6/20], Batch Num: [449/600]
Discriminator Loss: 0.8585, Generator Loss: 1.7625
D(x): 0.7427, D(G(z)): 0.2339
Epoch: [6/20], Batch Num: [450/600]
Discriminator Loss: 0.9319, Generator Loss: 2.0375
D(x): 0.7480, D(G(z)): 0.3125
Epoch: [6/20], Batch Num: [451/600]
Discriminator Loss: 0.8191, Generator Loss: 2.0085
D(x): 0.7631, D(G(z)): 0.2670
Epoch: [6/20], Batch Num: [452/600]
Discriminator Loss: 0.7877, Generator Loss: 1.9268
D(x): 0.7206, D(G(z)): 0.2328
Epoch: [6/20], Batch Num: [453/600]
Discriminator Loss: 0.8512, Generator Loss: 1.6187
D(x): 0.7561, D(G(z)): 0.2846
Epoch: [6/20], Batch Num: [454/600]
Discriminator Loss: 0.9290, Generator Loss: 2.0249
D(x): 0.7298, D(G(z)): 0.2944
Epoch: [6/20], Batch Num: [455/600]
Discriminator Loss: 0.8152, Generator Loss: 1.6139
D(x): 0.7756, D(G(z)): 0.2677
Epoch: [6/20], Batch Num: [456/600]
Discriminator Loss: 0.8104, Generator Loss: 2.0085
D(x): 0.7674, D(G(z)): 0.2695
Epoch: [6/20], Batch Num: [457/600]
Discriminator Loss: 0.7097, Generator Loss: 1.9761
D(x): 0.7486, D(G(z)): 0.2115
Epoch: [6/20], Batch Num: [458/600]
Discriminator Loss: 0.5992, Generator Loss: 1.6359
D(x): 0.8079, D(G(z)): 0.2176
Epoch: [6/20], Batch Num: [459/600]
Discriminator Loss: 0.5989, Generator Loss: 1.6929
D(x): 0.8189, D(G(z)): 0.2425
Epoch: [6/20], Batch Num: [460/600]
Discriminator Loss: 0.6052, Generator Loss: 1.9073
D(x): 0.8102, D(G(z)): 0.2290
Epoch: [6/20], Batch Num: [461/600]
Discriminator Loss: 0.7588, Generator Loss: 1.9253
D(x): 0.7626, D(G(z)): 0.2588
Epoch: [6/20], Batch Num: [462/600]
Discriminator Loss: 0.7813, Generator Loss: 1.7147
D(x): 0.7392, D(G(z)): 0.2360
Epoch: [6/20], Batch Num: [463/600]
Discriminator Loss: 0.6430, Generator Loss: 1.6494
D(x): 0.7866, D(G(z)): 0.2366
Epoch: [6/20], Batch Num: [464/600]
Discriminator Loss: 0.6666, Generator Loss: 1.7948
D(x): 0.8342, D(G(z)): 0.2899
Epoch: [6/20], Batch Num: [465/600]
Discriminator Loss: 0.6584, Generator Loss: 1.8467
D(x): 0.8269, D(G(z)): 0.2799
Epoch: [6/20], Batch Num: [466/600]
Discriminator Loss: 0.5756, Generator Loss: 1.9172
D(x): 0.8286, D(G(z)): 0.2232
Epoch: [6/20], Batch Num: [467/600]
Discriminator Loss: 0.5631, Generator Loss: 1.9987
D(x): 0.7801, D(G(z)): 0.1855
Epoch: [6/20], Batch Num: [468/600]
Discriminator Loss: 0.5809, Generator Loss: 2.0025
D(x): 0.8188, D(G(z)): 0.2005
Epoch: [6/20], Batch Num: [469/600]
Discriminator Loss: 0.5719, Generator Loss: 1.9135
D(x): 0.7881, D(G(z)): 0.1839
Epoch: [6/20], Batch Num: [470/600]
Discriminator Loss: 0.8375, Generator Loss: 1.7321
D(x): 0.7640, D(G(z)): 0.2646
Epoch: [6/20], Batch Num: [471/600]
Discriminator Loss: 0.6134, Generator Loss: 1.7499
D(x): 0.8089, D(G(z)): 0.2336
Epoch: [6/20], Batch Num: [472/600]
Discriminator Loss: 0.5228, Generator Loss: 1.6977
D(x): 0.8682, D(G(z)): 0.2373
Epoch: [6/20], Batch Num: [473/600]
Discriminator Loss: 0.6010, Generator Loss: 1.8576
D(x): 0.8291, D(G(z)): 0.2296
Epoch: [6/20], Batch Num: [474/600]
Discriminator Loss: 0.4735, Generator Loss: 2.0609
D(x): 0.8738, D(G(z)): 0.2219
Epoch: [6/20], Batch Num: [475/600]
Discriminator Loss: 0.5579, Generator Loss: 2.3063
D(x): 0.8053, D(G(z)): 0.1791
Epoch: [6/20], Batch Num: [476/600]
Discriminator Loss: 0.5213, Generator Loss: 1.9399
D(x): 0.8187, D(G(z)): 0.1763
Epoch: [6/20], Batch Num: [477/600]
Discriminator Loss: 0.4763, Generator Loss: 1.9093
D(x): 0.8399, D(G(z)): 0.1853
Epoch: [6/20], Batch Num: [478/600]
Discriminator Loss: 0.5774, Generator Loss: 1.9830
D(x): 0.8613, D(G(z)): 0.2448
Epoch: [6/20], Batch Num: [479/600]
Discriminator Loss: 0.6431, Generator Loss: 1.9991
D(x): 0.7927, D(G(z)): 0.2205
Epoch: [6/20], Batch Num: [480/600]
Discriminator Loss: 0.5571, Generator Loss: 2.0607
D(x): 0.8320, D(G(z)): 0.2059
Epoch: [6/20], Batch Num: [481/600]
Discriminator Loss: 0.7164, Generator Loss: 1.9774
D(x): 0.8071, D(G(z)): 0.2523
Epoch: [6/20], Batch Num: [482/600]
Discriminator Loss: 0.6724, Generator Loss: 2.2464
D(x): 0.8255, D(G(z)): 0.2566
Epoch: [6/20], Batch Num: [483/600]
Discriminator Loss: 0.6092, Generator Loss: 2.1946
D(x): 0.7786, D(G(z)): 0.1730
Epoch: [6/20], Batch Num: [484/600]
Discriminator Loss: 0.6432, Generator Loss: 2.0087
D(x): 0.8048, D(G(z)): 0.2040
Epoch: [6/20], Batch Num: [485/600]
Discriminator Loss: 0.5277, Generator Loss: 1.9965
D(x): 0.8394, D(G(z)): 0.1979
Epoch: [6/20], Batch Num: [486/600]
Discriminator Loss: 0.6512, Generator Loss: 2.0535
D(x): 0.8269, D(G(z)): 0.2534
Epoch: [6/20], Batch Num: [487/600]
Discriminator Loss: 0.8431, Generator Loss: 2.0042
D(x): 0.7666, D(G(z)): 0.2474
Epoch: [6/20], Batch Num: [488/600]
Discriminator Loss: 0.6454, Generator Loss: 2.2152
D(x): 0.8546, D(G(z)): 0.2585
Epoch: [6/20], Batch Num: [489/600]
Discriminator Loss: 0.5466, Generator Loss: 2.5248
D(x): 0.8449, D(G(z)): 0.1921
Epoch: [6/20], Batch Num: [490/600]
Discriminator Loss: 0.8651, Generator Loss: 2.5372
D(x): 0.6877, D(G(z)): 0.1490
Epoch: [6/20], Batch Num: [491/600]
Discriminator Loss: 0.7654, Generator Loss: 2.1189
D(x): 0.7683, D(G(z)): 0.2088
Epoch: [6/20], Batch Num: [492/600]
Discriminator Loss: 0.7309, Generator Loss: 1.7181
D(x): 0.7937, D(G(z)): 0.2095
Epoch: [6/20], Batch Num: [493/600]
Discriminator Loss: 0.5923, Generator Loss: 1.6710
D(x): 0.8883, D(G(z)): 0.2931
Epoch: [6/20], Batch Num: [494/600]
Discriminator Loss: 0.6361, Generator Loss: 2.1121
D(x): 0.8624, D(G(z)): 0.2677
Epoch: [6/20], Batch Num: [495/600]
Discriminator Loss: 0.6271, Generator Loss: 2.4541
D(x): 0.8295, D(G(z)): 0.2120
Epoch: [6/20], Batch Num: [496/600]
Discriminator Loss: 0.6680, Generator Loss: 2.5489
D(x): 0.7690, D(G(z)): 0.1695
Epoch: [6/20], Batch Num: [497/600]
Discriminator Loss: 0.5701, Generator Loss: 2.3423
D(x): 0.8104, D(G(z)): 0.1677
Epoch: [6/20], Batch Num: [498/600]
Discriminator Loss: 0.5607, Generator Loss: 2.1757
D(x): 0.8284, D(G(z)): 0.1723
Epoch: [6/20], Batch Num: [499/600]
Discriminator Loss: 0.4965, Generator Loss: 2.2507
D(x): 0.8944, D(G(z)): 0.1961
Epoch: 6, Batch Num: [500/600]

Epoch: [6/20], Batch Num: [500/600]
Discriminator Loss: 0.5709, Generator Loss: 2.3887
D(x): 0.8708, D(G(z)): 0.2168
Epoch: [6/20], Batch Num: [501/600]
Discriminator Loss: 0.7236, Generator Loss: 2.2527
D(x): 0.7970, D(G(z)): 0.2358
Epoch: [6/20], Batch Num: [502/600]
Discriminator Loss: 0.5447, Generator Loss: 2.6525
D(x): 0.8594, D(G(z)): 0.2061
Epoch: [6/20], Batch Num: [503/600]
Discriminator Loss: 0.7209, Generator Loss: 2.3679
D(x): 0.7752, D(G(z)): 0.1838
Epoch: [6/20], Batch Num: [504/600]
Discriminator Loss: 0.6061, Generator Loss: 2.3250
D(x): 0.8682, D(G(z)): 0.2051
Epoch: [6/20], Batch Num: [505/600]
Discriminator Loss: 0.6290, Generator Loss: 2.1245
D(x): 0.8104, D(G(z)): 0.1981
Epoch: [6/20], Batch Num: [506/600]
Discriminator Loss: 0.7731, Generator Loss: 2.5855
D(x): 0.8759, D(G(z)): 0.2938
Epoch: [6/20], Batch Num: [507/600]
Discriminator Loss: 0.6638, Generator Loss: 2.7294
D(x): 0.8276, D(G(z)): 0.2126
Epoch: [6/20], Batch Num: [508/600]
Discriminator Loss: 0.6118, Generator Loss: 2.7791
D(x): 0.8087, D(G(z)): 0.1735
Epoch: [6/20], Batch Num: [509/600]
Discriminator Loss: 0.7560, Generator Loss: 2.7422
D(x): 0.7586, D(G(z)): 0.1593
Epoch: [6/20], Batch Num: [510/600]
Discriminator Loss: 0.7407, Generator Loss: 2.3124
D(x): 0.8092, D(G(z)): 0.2096
Epoch: [6/20], Batch Num: [511/600]
Discriminator Loss: 0.6415, Generator Loss: 2.3526
D(x): 0.8460, D(G(z)): 0.2165
Epoch: [6/20], Batch Num: [512/600]
Discriminator Loss: 0.6197, Generator Loss: 2.3636
D(x): 0.8705, D(G(z)): 0.2506
Epoch: [6/20], Batch Num: [513/600]
Discriminator Loss: 0.6112, Generator Loss: 2.4244
D(x): 0.8319, D(G(z)): 0.2197
Epoch: [6/20], Batch Num: [514/600]
Discriminator Loss: 0.5989, Generator Loss: 2.4156
D(x): 0.8171, D(G(z)): 0.1871
Epoch: [6/20], Batch Num: [515/600]
Discriminator Loss: 0.6035, Generator Loss: 2.1007
D(x): 0.7891, D(G(z)): 0.1670
Epoch: [6/20], Batch Num: [516/600]
Discriminator Loss: 0.5723, Generator Loss: 2.0278
D(x): 0.8202, D(G(z)): 0.1815
Epoch: [6/20], Batch Num: [517/600]
Discriminator Loss: 0.5994, Generator Loss: 1.8622
D(x): 0.8544, D(G(z)): 0.2470
Epoch: [6/20], Batch Num: [518/600]
Discriminator Loss: 0.6444, Generator Loss: 2.2458
D(x): 0.8648, D(G(z)): 0.2893
Epoch: [6/20], Batch Num: [519/600]
Discriminator Loss: 0.6188, Generator Loss: 2.4697
D(x): 0.8236, D(G(z)): 0.2023
Epoch: [6/20], Batch Num: [520/600]
Discriminator Loss: 0.6323, Generator Loss: 2.7358
D(x): 0.7711, D(G(z)): 0.1405
Epoch: [6/20], Batch Num: [521/600]
Discriminator Loss: 0.6262, Generator Loss: 2.5263
D(x): 0.7758, D(G(z)): 0.1217
Epoch: [6/20], Batch Num: [522/600]
Discriminator Loss: 0.5183, Generator Loss: 2.2449
D(x): 0.8163, D(G(z)): 0.1550
Epoch: [6/20], Batch Num: [523/600]
Discriminator Loss: 0.5693, Generator Loss: 1.8466
D(x): 0.8382, D(G(z)): 0.1933
Epoch: [6/20], Batch Num: [524/600]
Discriminator Loss: 0.5625, Generator Loss: 1.9894
D(x): 0.8735, D(G(z)): 0.2484
Epoch: [6/20], Batch Num: [525/600]
Discriminator Loss: 0.6102, Generator Loss: 2.3860
D(x): 0.8809, D(G(z)): 0.2308
Epoch: [6/20], Batch Num: [526/600]
Discriminator Loss: 0.4714, Generator Loss: 2.6770
D(x): 0.8714, D(G(z)): 0.1745
Epoch: [6/20], Batch Num: [527/600]
Discriminator Loss: 0.3581, Generator Loss: 3.1962
D(x): 0.8791, D(G(z)): 0.1274
Epoch: [6/20], Batch Num: [528/600]
Discriminator Loss: 0.3695, Generator Loss: 3.0261
D(x): 0.8576, D(G(z)): 0.0951
Epoch: [6/20], Batch Num: [529/600]
Discriminator Loss: 0.5241, Generator Loss: 2.7083
D(x): 0.7761, D(G(z)): 0.1031
Epoch: [6/20], Batch Num: [530/600]
Discriminator Loss: 0.3933, Generator Loss: 2.3763
D(x): 0.8383, D(G(z)): 0.1067
Epoch: [6/20], Batch Num: [531/600]
Discriminator Loss: 0.4365, Generator Loss: 1.8916
D(x): 0.8898, D(G(z)): 0.1769
Epoch: [6/20], Batch Num: [532/600]
Discriminator Loss: 0.5717, Generator Loss: 1.9412
D(x): 0.9376, D(G(z)): 0.2969
Epoch: [6/20], Batch Num: [533/600]
Discriminator Loss: 0.5439, Generator Loss: 2.4802
D(x): 0.9128, D(G(z)): 0.2740
Epoch: [6/20], Batch Num: [534/600]
Discriminator Loss: 0.5630, Generator Loss: 3.1052
D(x): 0.8010, D(G(z)): 0.1488
Epoch: [6/20], Batch Num: [535/600]
Discriminator Loss: 0.5333, Generator Loss: 2.8017
D(x): 0.7897, D(G(z)): 0.1018
Epoch: [6/20], Batch Num: [536/600]
Discriminator Loss: 0.5141, Generator Loss: 2.2693
D(x): 0.7715, D(G(z)): 0.1026
Epoch: [6/20], Batch Num: [537/600]
Discriminator Loss: 0.6296, Generator Loss: 1.9121
D(x): 0.8154, D(G(z)): 0.1748
Epoch: [6/20], Batch Num: [538/600]
Discriminator Loss: 0.6192, Generator Loss: 1.8670
D(x): 0.8523, D(G(z)): 0.2596
Epoch: [6/20], Batch Num: [539/600]
Discriminator Loss: 0.6435, Generator Loss: 2.1314
D(x): 0.8812, D(G(z)): 0.2728
Epoch: [6/20], Batch Num: [540/600]
Discriminator Loss: 0.6301, Generator Loss: 2.5530
D(x): 0.8903, D(G(z)): 0.2290
Epoch: [6/20], Batch Num: [541/600]
Discriminator Loss: 0.6581, Generator Loss: 2.7768
D(x): 0.7837, D(G(z)): 0.1979
Epoch: [6/20], Batch Num: [542/600]
Discriminator Loss: 0.5625, Generator Loss: 2.7994
D(x): 0.7872, D(G(z)): 0.1216
Epoch: [6/20], Batch Num: [543/600]
Discriminator Loss: 0.7003, Generator Loss: 2.5841
D(x): 0.7431, D(G(z)): 0.1430
Epoch: [6/20], Batch Num: [544/600]
Discriminator Loss: 0.5367, Generator Loss: 2.2774
D(x): 0.7947, D(G(z)): 0.1416
Epoch: [6/20], Batch Num: [545/600]
Discriminator Loss: 0.5302, Generator Loss: 2.0280
D(x): 0.8444, D(G(z)): 0.1991
Epoch: [6/20], Batch Num: [546/600]
Discriminator Loss: 0.6804, Generator Loss: 1.8893
D(x): 0.8397, D(G(z)): 0.2787
Epoch: [6/20], Batch Num: [547/600]
Discriminator Loss: 0.5685, Generator Loss: 2.3452
D(x): 0.9004, D(G(z)): 0.2935
Epoch: [6/20], Batch Num: [548/600]
Discriminator Loss: 0.6163, Generator Loss: 2.3449
D(x): 0.7721, D(G(z)): 0.1710
Epoch: [6/20], Batch Num: [549/600]
Discriminator Loss: 0.5420, Generator Loss: 2.4416
D(x): 0.8016, D(G(z)): 0.1513
Epoch: [6/20], Batch Num: [550/600]
Discriminator Loss: 0.5726, Generator Loss: 2.3750
D(x): 0.7769, D(G(z)): 0.1477
Epoch: [6/20], Batch Num: [551/600]
Discriminator Loss: 0.5324, Generator Loss: 2.2363
D(x): 0.8050, D(G(z)): 0.1444
Epoch: [6/20], Batch Num: [552/600]
Discriminator Loss: 0.5862, Generator Loss: 2.0466
D(x): 0.8177, D(G(z)): 0.2157
Epoch: [6/20], Batch Num: [553/600]
Discriminator Loss: 0.5807, Generator Loss: 1.9369
D(x): 0.8157, D(G(z)): 0.2216
Epoch: [6/20], Batch Num: [554/600]
Discriminator Loss: 0.4900, Generator Loss: 2.2313
D(x): 0.8922, D(G(z)): 0.2329
Epoch: [6/20], Batch Num: [555/600]
Discriminator Loss: 0.6015, Generator Loss: 2.4081
D(x): 0.7985, D(G(z)): 0.1599
Epoch: [6/20], Batch Num: [556/600]
Discriminator Loss: 0.4637, Generator Loss: 2.2904
D(x): 0.8298, D(G(z)): 0.1383
Epoch: [6/20], Batch Num: [557/600]
Discriminator Loss: 0.4962, Generator Loss: 2.2513
D(x): 0.8344, D(G(z)): 0.1749
Epoch: [6/20], Batch Num: [558/600]
Discriminator Loss: 0.5751, Generator Loss: 2.3429
D(x): 0.8084, D(G(z)): 0.1915
Epoch: [6/20], Batch Num: [559/600]
Discriminator Loss: 0.5537, Generator Loss: 1.9891
D(x): 0.8380, D(G(z)): 0.2154
Epoch: [6/20], Batch Num: [560/600]
Discriminator Loss: 0.5331, Generator Loss: 2.2587
D(x): 0.8516, D(G(z)): 0.2284
Epoch: [6/20], Batch Num: [561/600]
Discriminator Loss: 0.4955, Generator Loss: 2.2446
D(x): 0.8418, D(G(z)): 0.1891
Epoch: [6/20], Batch Num: [562/600]
Discriminator Loss: 0.5829, Generator Loss: 2.0814
D(x): 0.8064, D(G(z)): 0.1894
Epoch: [6/20], Batch Num: [563/600]
Discriminator Loss: 0.6387, Generator Loss: 2.2639
D(x): 0.8260, D(G(z)): 0.2193
Epoch: [6/20], Batch Num: [564/600]
Discriminator Loss: 0.6815, Generator Loss: 2.4501
D(x): 0.7855, D(G(z)): 0.2049
Epoch: [6/20], Batch Num: [565/600]
Discriminator Loss: 0.6329, Generator Loss: 2.5948
D(x): 0.8049, D(G(z)): 0.1736
Epoch: [6/20], Batch Num: [566/600]
Discriminator Loss: 0.6771, Generator Loss: 2.1831
D(x): 0.7912, D(G(z)): 0.1935
Epoch: [6/20], Batch Num: [567/600]
Discriminator Loss: 0.7444, Generator Loss: 2.0499
D(x): 0.7868, D(G(z)): 0.2347
Epoch: [6/20], Batch Num: [568/600]
Discriminator Loss: 0.6823, Generator Loss: 2.3691
D(x): 0.8641, D(G(z)): 0.2521
Epoch: [6/20], Batch Num: [569/600]
Discriminator Loss: 0.5551, Generator Loss: 2.4284
D(x): 0.8296, D(G(z)): 0.1817
Epoch: [6/20], Batch Num: [570/600]
Discriminator Loss: 0.6605, Generator Loss: 2.3475
D(x): 0.7983, D(G(z)): 0.1949
Epoch: [6/20], Batch Num: [571/600]
Discriminator Loss: 0.7991, Generator Loss: 2.4593
D(x): 0.7591, D(G(z)): 0.2019
Epoch: [6/20], Batch Num: [572/600]
Discriminator Loss: 1.0924, Generator Loss: 2.1356
D(x): 0.6493, D(G(z)): 0.1750
Epoch: [6/20], Batch Num: [573/600]
Discriminator Loss: 0.7065, Generator Loss: 1.8391
D(x): 0.8039, D(G(z)): 0.2024
Epoch: [6/20], Batch Num: [574/600]
Discriminator Loss: 1.0003, Generator Loss: 2.0497
D(x): 0.7768, D(G(z)): 0.3090
Epoch: [6/20], Batch Num: [575/600]
Discriminator Loss: 0.6203, Generator Loss: 2.3975
D(x): 0.8928, D(G(z)): 0.2846
Epoch: [6/20], Batch Num: [576/600]
Discriminator Loss: 0.6387, Generator Loss: 2.8517
D(x): 0.7985, D(G(z)): 0.1858
Epoch: [6/20], Batch Num: [577/600]
Discriminator Loss: 0.6682, Generator Loss: 3.0400
D(x): 0.7599, D(G(z)): 0.1204
Epoch: [6/20], Batch Num: [578/600]
Discriminator Loss: 0.5910, Generator Loss: 2.7236
D(x): 0.7382, D(G(z)): 0.0949
Epoch: [6/20], Batch Num: [579/600]
Discriminator Loss: 0.5833, Generator Loss: 2.2451
D(x): 0.7905, D(G(z)): 0.1501
Epoch: [6/20], Batch Num: [580/600]
Discriminator Loss: 0.5353, Generator Loss: 1.9095
D(x): 0.7929, D(G(z)): 0.1561
Epoch: [6/20], Batch Num: [581/600]
Discriminator Loss: 0.6941, Generator Loss: 1.8798
D(x): 0.8385, D(G(z)): 0.2692
Epoch: [6/20], Batch Num: [582/600]
Discriminator Loss: 0.5248, Generator Loss: 2.3462
D(x): 0.9038, D(G(z)): 0.2453
Epoch: [6/20], Batch Num: [583/600]
Discriminator Loss: 0.5162, Generator Loss: 3.0041
D(x): 0.8769, D(G(z)): 0.2127
Epoch: [6/20], Batch Num: [584/600]
Discriminator Loss: 0.4635, Generator Loss: 3.2785
D(x): 0.8135, D(G(z)): 0.0903
Epoch: [6/20], Batch Num: [585/600]
Discriminator Loss: 0.6581, Generator Loss: 3.0838
D(x): 0.7555, D(G(z)): 0.0837
Epoch: [6/20], Batch Num: [586/600]
Discriminator Loss: 0.5777, Generator Loss: 2.5435
D(x): 0.7929, D(G(z)): 0.1166
Epoch: [6/20], Batch Num: [587/600]
Discriminator Loss: 0.6078, Generator Loss: 2.2855
D(x): 0.8327, D(G(z)): 0.1912
Epoch: [6/20], Batch Num: [588/600]
Discriminator Loss: 0.5860, Generator Loss: 2.3968
D(x): 0.8852, D(G(z)): 0.2380
Epoch: [6/20], Batch Num: [589/600]
Discriminator Loss: 0.6556, Generator Loss: 2.9845
D(x): 0.8672, D(G(z)): 0.2523
Epoch: [6/20], Batch Num: [590/600]
Discriminator Loss: 0.6129, Generator Loss: 3.2134
D(x): 0.8377, D(G(z)): 0.1662
Epoch: [6/20], Batch Num: [591/600]
Discriminator Loss: 0.5941, Generator Loss: 3.0478
D(x): 0.7908, D(G(z)): 0.1322
Epoch: [6/20], Batch Num: [592/600]
Discriminator Loss: 0.6771, Generator Loss: 2.7859
D(x): 0.7540, D(G(z)): 0.0931
Epoch: [6/20], Batch Num: [593/600]
Discriminator Loss: 0.6557, Generator Loss: 2.4251
D(x): 0.8002, D(G(z)): 0.1734
Epoch: [6/20], Batch Num: [594/600]
Discriminator Loss: 0.8320, Generator Loss: 1.9977
D(x): 0.8081, D(G(z)): 0.2777
Epoch: [6/20], Batch Num: [595/600]
Discriminator Loss: 0.8299, Generator Loss: 2.0921
D(x): 0.8210, D(G(z)): 0.2982
Epoch: [6/20], Batch Num: [596/600]
Discriminator Loss: 0.7701, Generator Loss: 2.1226
D(x): 0.8124, D(G(z)): 0.2652
Epoch: [6/20], Batch Num: [597/600]
Discriminator Loss: 0.8161, Generator Loss: 2.1061
D(x): 0.7874, D(G(z)): 0.2453
Epoch: [6/20], Batch Num: [598/600]
Discriminator Loss: 0.9217, Generator Loss: 2.2645
D(x): 0.6886, D(G(z)): 0.2068
Epoch: [6/20], Batch Num: [599/600]
Discriminator Loss: 0.9923, Generator Loss: 1.9197
D(x): 0.7360, D(G(z)): 0.2886
Epoch: 7, Batch Num: [0/600]

Epoch: [7/20], Batch Num: [0/600]
Discriminator Loss: 0.9578, Generator Loss: 1.8545
D(x): 0.7644, D(G(z)): 0.2909
Epoch: [7/20], Batch Num: [1/600]
Discriminator Loss: 1.0467, Generator Loss: 1.9305
D(x): 0.7278, D(G(z)): 0.3086
Epoch: [7/20], Batch Num: [2/600]
Discriminator Loss: 0.8455, Generator Loss: 1.5671
D(x): 0.7372, D(G(z)): 0.2488
Epoch: [7/20], Batch Num: [3/600]
Discriminator Loss: 0.8273, Generator Loss: 1.8711
D(x): 0.8024, D(G(z)): 0.3016
Epoch: [7/20], Batch Num: [4/600]
Discriminator Loss: 0.8965, Generator Loss: 2.0802
D(x): 0.7627, D(G(z)): 0.2780
Epoch: [7/20], Batch Num: [5/600]
Discriminator Loss: 0.8899, Generator Loss: 1.9734
D(x): 0.7119, D(G(z)): 0.2408
Epoch: [7/20], Batch Num: [6/600]
Discriminator Loss: 0.8628, Generator Loss: 1.9256
D(x): 0.7045, D(G(z)): 0.2055
Epoch: [7/20], Batch Num: [7/600]
Discriminator Loss: 0.7239, Generator Loss: 1.8714
D(x): 0.7921, D(G(z)): 0.2418
Epoch: [7/20], Batch Num: [8/600]
Discriminator Loss: 0.7245, Generator Loss: 2.0362
D(x): 0.7848, D(G(z)): 0.2365
Epoch: [7/20], Batch Num: [9/600]
Discriminator Loss: 0.6515, Generator Loss: 1.8857
D(x): 0.8132, D(G(z)): 0.2290
Epoch: [7/20], Batch Num: [10/600]
Discriminator Loss: 0.6608, Generator Loss: 2.0340
D(x): 0.8143, D(G(z)): 0.2382
Epoch: [7/20], Batch Num: [11/600]
Discriminator Loss: 0.5643, Generator Loss: 2.1264
D(x): 0.8438, D(G(z)): 0.2338
Epoch: [7/20], Batch Num: [12/600]
Discriminator Loss: 0.5079, Generator Loss: 2.3244
D(x): 0.8835, D(G(z)): 0.2122
Epoch: [7/20], Batch Num: [13/600]
Discriminator Loss: 0.6383, Generator Loss: 2.4827
D(x): 0.7982, D(G(z)): 0.2162
Epoch: [7/20], Batch Num: [14/600]
Discriminator Loss: 0.4804, Generator Loss: 2.6592
D(x): 0.8194, D(G(z)): 0.1525
Epoch: [7/20], Batch Num: [15/600]
Discriminator Loss: 0.5239, Generator Loss: 2.4745
D(x): 0.8037, D(G(z)): 0.1430
Epoch: [7/20], Batch Num: [16/600]
Discriminator Loss: 0.4607, Generator Loss: 2.4233
D(x): 0.8237, D(G(z)): 0.1410
Epoch: [7/20], Batch Num: [17/600]
Discriminator Loss: 0.4876, Generator Loss: 2.3789
D(x): 0.8452, D(G(z)): 0.1870
Epoch: [7/20], Batch Num: [18/600]
Discriminator Loss: 0.5326, Generator Loss: 2.2974
D(x): 0.8578, D(G(z)): 0.1983
Epoch: [7/20], Batch Num: [19/600]
Discriminator Loss: 0.3887, Generator Loss: 2.3510
D(x): 0.8687, D(G(z)): 0.1603
Epoch: [7/20], Batch Num: [20/600]
Discriminator Loss: 0.3988, Generator Loss: 2.4773
D(x): 0.8837, D(G(z)): 0.1801
Epoch: [7/20], Batch Num: [21/600]
Discriminator Loss: 0.3692, Generator Loss: 2.6410
D(x): 0.8941, D(G(z)): 0.1742
Epoch: [7/20], Batch Num: [22/600]
Discriminator Loss: 0.3773, Generator Loss: 2.5363
D(x): 0.8792, D(G(z)): 0.1504
Epoch: [7/20], Batch Num: [23/600]
Discriminator Loss: 0.4465, Generator Loss: 2.4615
D(x): 0.8373, D(G(z)): 0.1467
Epoch: [7/20], Batch Num: [24/600]
Discriminator Loss: 0.3253, Generator Loss: 2.5400
D(x): 0.8939, D(G(z)): 0.1410
Epoch: [7/20], Batch Num: [25/600]
Discriminator Loss: 0.4330, Generator Loss: 2.5334
D(x): 0.8425, D(G(z)): 0.1510
Epoch: [7/20], Batch Num: [26/600]
Discriminator Loss: 0.3509, Generator Loss: 2.5454
D(x): 0.9014, D(G(z)): 0.1689
Epoch: [7/20], Batch Num: [27/600]
Discriminator Loss: 0.4160, Generator Loss: 2.5717
D(x): 0.8693, D(G(z)): 0.1674
Epoch: [7/20], Batch Num: [28/600]
Discriminator Loss: 0.4069, Generator Loss: 2.7527
D(x): 0.8720, D(G(z)): 0.1681
Epoch: [7/20], Batch Num: [29/600]
Discriminator Loss: 0.4159, Generator Loss: 2.8441
D(x): 0.8680, D(G(z)): 0.1716
Epoch: [7/20], Batch Num: [30/600]
Discriminator Loss: 0.4906, Generator Loss: 2.5650
D(x): 0.8296, D(G(z)): 0.1542
Epoch: [7/20], Batch Num: [31/600]
Discriminator Loss: 0.5005, Generator Loss: 2.6603
D(x): 0.8584, D(G(z)): 0.1940
Epoch: [7/20], Batch Num: [32/600]
Discriminator Loss: 0.4523, Generator Loss: 2.3091
D(x): 0.8600, D(G(z)): 0.1615
Epoch: [7/20], Batch Num: [33/600]
Discriminator Loss: 0.6514, Generator Loss: 2.8205
D(x): 0.8099, D(G(z)): 0.2088
Epoch: [7/20], Batch Num: [34/600]
Discriminator Loss: 0.6073, Generator Loss: 2.1496
D(x): 0.8030, D(G(z)): 0.1943
Epoch: [7/20], Batch Num: [35/600]
Discriminator Loss: 0.7523, Generator Loss: 2.3806
D(x): 0.8006, D(G(z)): 0.2580
Epoch: [7/20], Batch Num: [36/600]
Discriminator Loss: 0.9845, Generator Loss: 2.4138
D(x): 0.7257, D(G(z)): 0.2535
Epoch: [7/20], Batch Num: [37/600]
Discriminator Loss: 0.9558, Generator Loss: 1.9071
D(x): 0.7086, D(G(z)): 0.2615
Epoch: [7/20], Batch Num: [38/600]
Discriminator Loss: 0.9216, Generator Loss: 2.1648
D(x): 0.7829, D(G(z)): 0.2950
Epoch: [7/20], Batch Num: [39/600]
Discriminator Loss: 1.0661, Generator Loss: 2.0501
D(x): 0.6750, D(G(z)): 0.2916
Epoch: [7/20], Batch Num: [40/600]
Discriminator Loss: 1.0639, Generator Loss: 1.8031
D(x): 0.6743, D(G(z)): 0.2649
Epoch: [7/20], Batch Num: [41/600]
Discriminator Loss: 1.2350, Generator Loss: 1.5020
D(x): 0.6563, D(G(z)): 0.2992
Epoch: [7/20], Batch Num: [42/600]
Discriminator Loss: 1.1865, Generator Loss: 1.3909
D(x): 0.6950, D(G(z)): 0.3555
Epoch: [7/20], Batch Num: [43/600]
Discriminator Loss: 1.2447, Generator Loss: 1.4775
D(x): 0.7091, D(G(z)): 0.3792
Epoch: [7/20], Batch Num: [44/600]
Discriminator Loss: 1.1679, Generator Loss: 1.8650
D(x): 0.7121, D(G(z)): 0.3900
Epoch: [7/20], Batch Num: [45/600]
Discriminator Loss: 0.9291, Generator Loss: 1.9848
D(x): 0.7168, D(G(z)): 0.2678
Epoch: [7/20], Batch Num: [46/600]
Discriminator Loss: 1.1906, Generator Loss: 1.7836
D(x): 0.5872, D(G(z)): 0.2331
Epoch: [7/20], Batch Num: [47/600]
Discriminator Loss: 1.0164, Generator Loss: 1.4174
D(x): 0.6607, D(G(z)): 0.2692
Epoch: [7/20], Batch Num: [48/600]
Discriminator Loss: 1.0726, Generator Loss: 1.1980
D(x): 0.6862, D(G(z)): 0.3445
Epoch: [7/20], Batch Num: [49/600]
Discriminator Loss: 0.9725, Generator Loss: 1.6575
D(x): 0.7949, D(G(z)): 0.4000
Epoch: [7/20], Batch Num: [50/600]
Discriminator Loss: 1.0412, Generator Loss: 1.9535
D(x): 0.7037, D(G(z)): 0.3333
Epoch: [7/20], Batch Num: [51/600]
Discriminator Loss: 0.9288, Generator Loss: 2.2560
D(x): 0.6998, D(G(z)): 0.2638
Epoch: [7/20], Batch Num: [52/600]
Discriminator Loss: 0.9569, Generator Loss: 2.2970
D(x): 0.6209, D(G(z)): 0.1943
Epoch: [7/20], Batch Num: [53/600]
Discriminator Loss: 0.8860, Generator Loss: 2.0782
D(x): 0.6804, D(G(z)): 0.2408
Epoch: [7/20], Batch Num: [54/600]
Discriminator Loss: 0.7906, Generator Loss: 1.5993
D(x): 0.7127, D(G(z)): 0.2274
Epoch: [7/20], Batch Num: [55/600]
Discriminator Loss: 0.8347, Generator Loss: 1.5365
D(x): 0.7604, D(G(z)): 0.2981
Epoch: [7/20], Batch Num: [56/600]
Discriminator Loss: 0.7581, Generator Loss: 1.7323
D(x): 0.7631, D(G(z)): 0.2950
Epoch: [7/20], Batch Num: [57/600]
Discriminator Loss: 0.7689, Generator Loss: 1.8366
D(x): 0.8160, D(G(z)): 0.3227
Epoch: [7/20], Batch Num: [58/600]
Discriminator Loss: 0.6802, Generator Loss: 2.1526
D(x): 0.7646, D(G(z)): 0.2339
Epoch: [7/20], Batch Num: [59/600]
Discriminator Loss: 0.6817, Generator Loss: 2.3260
D(x): 0.7202, D(G(z)): 0.1992
Epoch: [7/20], Batch Num: [60/600]
Discriminator Loss: 0.7709, Generator Loss: 2.2228
D(x): 0.7119, D(G(z)): 0.2132
Epoch: [7/20], Batch Num: [61/600]
Discriminator Loss: 0.7496, Generator Loss: 1.8254
D(x): 0.7069, D(G(z)): 0.1932
Epoch: [7/20], Batch Num: [62/600]
Discriminator Loss: 0.7327, Generator Loss: 1.6355
D(x): 0.7688, D(G(z)): 0.2769
Epoch: [7/20], Batch Num: [63/600]
Discriminator Loss: 0.6691, Generator Loss: 1.9478
D(x): 0.8319, D(G(z)): 0.3012
Epoch: [7/20], Batch Num: [64/600]
Discriminator Loss: 0.5594, Generator Loss: 2.2911
D(x): 0.8835, D(G(z)): 0.2670
Epoch: [7/20], Batch Num: [65/600]
Discriminator Loss: 0.5959, Generator Loss: 2.6242
D(x): 0.7907, D(G(z)): 0.2055
Epoch: [7/20], Batch Num: [66/600]
Discriminator Loss: 0.6809, Generator Loss: 2.7990
D(x): 0.7345, D(G(z)): 0.1527
Epoch: [7/20], Batch Num: [67/600]
Discriminator Loss: 0.6710, Generator Loss: 2.4976
D(x): 0.7054, D(G(z)): 0.1402
Epoch: [7/20], Batch Num: [68/600]
Discriminator Loss: 0.4921, Generator Loss: 2.2840
D(x): 0.8127, D(G(z)): 0.1537
Epoch: [7/20], Batch Num: [69/600]
Discriminator Loss: 0.6850, Generator Loss: 2.0059
D(x): 0.8442, D(G(z)): 0.2693
Epoch: [7/20], Batch Num: [70/600]
Discriminator Loss: 0.7401, Generator Loss: 2.1959
D(x): 0.7772, D(G(z)): 0.2483
Epoch: [7/20], Batch Num: [71/600]
Discriminator Loss: 0.7771, Generator Loss: 2.4501
D(x): 0.8062, D(G(z)): 0.2781
Epoch: [7/20], Batch Num: [72/600]
Discriminator Loss: 0.6298, Generator Loss: 2.7057
D(x): 0.8229, D(G(z)): 0.2085
Epoch: [7/20], Batch Num: [73/600]
Discriminator Loss: 0.6602, Generator Loss: 2.7132
D(x): 0.7475, D(G(z)): 0.1423
Epoch: [7/20], Batch Num: [74/600]
Discriminator Loss: 0.5925, Generator Loss: 2.3258
D(x): 0.7619, D(G(z)): 0.1502
Epoch: [7/20], Batch Num: [75/600]
Discriminator Loss: 0.8040, Generator Loss: 2.0559
D(x): 0.7585, D(G(z)): 0.2240
Epoch: [7/20], Batch Num: [76/600]
Discriminator Loss: 0.8546, Generator Loss: 1.9970
D(x): 0.7689, D(G(z)): 0.2885
Epoch: [7/20], Batch Num: [77/600]
Discriminator Loss: 0.7423, Generator Loss: 2.2056
D(x): 0.8183, D(G(z)): 0.2892
Epoch: [7/20], Batch Num: [78/600]
Discriminator Loss: 0.8390, Generator Loss: 2.6329
D(x): 0.7609, D(G(z)): 0.2360
Epoch: [7/20], Batch Num: [79/600]
Discriminator Loss: 0.9768, Generator Loss: 2.4894
D(x): 0.6574, D(G(z)): 0.2093
Epoch: [7/20], Batch Num: [80/600]
Discriminator Loss: 0.8446, Generator Loss: 2.1222
D(x): 0.7046, D(G(z)): 0.1944
Epoch: [7/20], Batch Num: [81/600]
Discriminator Loss: 0.9936, Generator Loss: 2.0049
D(x): 0.6942, D(G(z)): 0.2492
Epoch: [7/20], Batch Num: [82/600]
Discriminator Loss: 0.9321, Generator Loss: 1.8303
D(x): 0.7562, D(G(z)): 0.3033
Epoch: [7/20], Batch Num: [83/600]
Discriminator Loss: 0.9902, Generator Loss: 2.3926
D(x): 0.8125, D(G(z)): 0.3382
Epoch: [7/20], Batch Num: [84/600]
Discriminator Loss: 1.0023, Generator Loss: 2.6911
D(x): 0.6987, D(G(z)): 0.2481
Epoch: [7/20], Batch Num: [85/600]
Discriminator Loss: 1.0611, Generator Loss: 2.4773
D(x): 0.5978, D(G(z)): 0.1532
Epoch: [7/20], Batch Num: [86/600]
Discriminator Loss: 0.9812, Generator Loss: 1.8895
D(x): 0.6637, D(G(z)): 0.2009
Epoch: [7/20], Batch Num: [87/600]
Discriminator Loss: 1.1234, Generator Loss: 1.9238
D(x): 0.7268, D(G(z)): 0.3233
Epoch: [7/20], Batch Num: [88/600]
Discriminator Loss: 1.0346, Generator Loss: 2.1638
D(x): 0.7781, D(G(z)): 0.3270
Epoch: [7/20], Batch Num: [89/600]
Discriminator Loss: 1.1839, Generator Loss: 2.1525
D(x): 0.6114, D(G(z)): 0.2233
Epoch: [7/20], Batch Num: [90/600]
Discriminator Loss: 1.0811, Generator Loss: 1.8516
D(x): 0.7233, D(G(z)): 0.2898
Epoch: [7/20], Batch Num: [91/600]
Discriminator Loss: 1.0766, Generator Loss: 1.9884
D(x): 0.6977, D(G(z)): 0.2712
Epoch: [7/20], Batch Num: [92/600]
Discriminator Loss: 1.0610, Generator Loss: 1.9879
D(x): 0.6940, D(G(z)): 0.2979
Epoch: [7/20], Batch Num: [93/600]
Discriminator Loss: 0.9386, Generator Loss: 1.8383
D(x): 0.7053, D(G(z)): 0.2889
Epoch: [7/20], Batch Num: [94/600]
Discriminator Loss: 0.9743, Generator Loss: 1.8514
D(x): 0.6921, D(G(z)): 0.2864
Epoch: [7/20], Batch Num: [95/600]
Discriminator Loss: 0.9371, Generator Loss: 1.7474
D(x): 0.6944, D(G(z)): 0.2533
Epoch: [7/20], Batch Num: [96/600]
Discriminator Loss: 1.1755, Generator Loss: 1.6086
D(x): 0.6336, D(G(z)): 0.2789
Epoch: [7/20], Batch Num: [97/600]
Discriminator Loss: 0.9225, Generator Loss: 1.4686
D(x): 0.7248, D(G(z)): 0.3090
Epoch: [7/20], Batch Num: [98/600]
Discriminator Loss: 0.9669, Generator Loss: 1.7720
D(x): 0.7146, D(G(z)): 0.3056
Epoch: [7/20], Batch Num: [99/600]
Discriminator Loss: 0.8739, Generator Loss: 1.8526
D(x): 0.6994, D(G(z)): 0.2692
Epoch: 7, Batch Num: [100/600]

Epoch: [7/20], Batch Num: [100/600]
Discriminator Loss: 0.8948, Generator Loss: 1.8776
D(x): 0.6954, D(G(z)): 0.2619
Epoch: [7/20], Batch Num: [101/600]
Discriminator Loss: 0.8212, Generator Loss: 1.9626
D(x): 0.7250, D(G(z)): 0.2511
Epoch: [7/20], Batch Num: [102/600]
Discriminator Loss: 0.8056, Generator Loss: 1.8506
D(x): 0.7082, D(G(z)): 0.2376
Epoch: [7/20], Batch Num: [103/600]
Discriminator Loss: 0.8694, Generator Loss: 1.7871
D(x): 0.6953, D(G(z)): 0.2598
Epoch: [7/20], Batch Num: [104/600]
Discriminator Loss: 0.7546, Generator Loss: 1.9408
D(x): 0.7445, D(G(z)): 0.2518
Epoch: [7/20], Batch Num: [105/600]
Discriminator Loss: 0.7515, Generator Loss: 1.8091
D(x): 0.7132, D(G(z)): 0.2243
Epoch: [7/20], Batch Num: [106/600]
Discriminator Loss: 0.6085, Generator Loss: 1.9111
D(x): 0.7769, D(G(z)): 0.2156
Epoch: [7/20], Batch Num: [107/600]
Discriminator Loss: 0.7066, Generator Loss: 2.0273
D(x): 0.7749, D(G(z)): 0.2438
Epoch: [7/20], Batch Num: [108/600]
Discriminator Loss: 0.6239, Generator Loss: 2.0705
D(x): 0.7744, D(G(z)): 0.2098
Epoch: [7/20], Batch Num: [109/600]
Discriminator Loss: 0.6138, Generator Loss: 2.3545
D(x): 0.8009, D(G(z)): 0.2265
Epoch: [7/20], Batch Num: [110/600]
Discriminator Loss: 0.7380, Generator Loss: 2.3298
D(x): 0.7618, D(G(z)): 0.2366
Epoch: [7/20], Batch Num: [111/600]
Discriminator Loss: 0.6788, Generator Loss: 2.4566
D(x): 0.7377, D(G(z)): 0.1694
Epoch: [7/20], Batch Num: [112/600]
Discriminator Loss: 0.5517, Generator Loss: 2.1840
D(x): 0.7842, D(G(z)): 0.1472
Epoch: [7/20], Batch Num: [113/600]
Discriminator Loss: 0.5932, Generator Loss: 2.3808
D(x): 0.8202, D(G(z)): 0.2106
Epoch: [7/20], Batch Num: [114/600]
Discriminator Loss: 0.7701, Generator Loss: 2.2084
D(x): 0.7858, D(G(z)): 0.2464
Epoch: [7/20], Batch Num: [115/600]
Discriminator Loss: 0.6176, Generator Loss: 2.4396
D(x): 0.8253, D(G(z)): 0.2338
Epoch: [7/20], Batch Num: [116/600]
Discriminator Loss: 0.6383, Generator Loss: 2.7896
D(x): 0.8189, D(G(z)): 0.2192
Epoch: [7/20], Batch Num: [117/600]
Discriminator Loss: 0.5011, Generator Loss: 3.0880
D(x): 0.7937, D(G(z)): 0.1300
Epoch: [7/20], Batch Num: [118/600]
Discriminator Loss: 0.4903, Generator Loss: 2.3842
D(x): 0.7865, D(G(z)): 0.1089
Epoch: [7/20], Batch Num: [119/600]
Discriminator Loss: 0.5622, Generator Loss: 2.1550
D(x): 0.7886, D(G(z)): 0.1682
Epoch: [7/20], Batch Num: [120/600]
Discriminator Loss: 0.5579, Generator Loss: 2.0026
D(x): 0.8316, D(G(z)): 0.2109
Epoch: [7/20], Batch Num: [121/600]
Discriminator Loss: 0.6676, Generator Loss: 2.3908
D(x): 0.8847, D(G(z)): 0.2968
Epoch: [7/20], Batch Num: [122/600]
Discriminator Loss: 0.7095, Generator Loss: 2.6427
D(x): 0.8112, D(G(z)): 0.2343
Epoch: [7/20], Batch Num: [123/600]
Discriminator Loss: 0.6747, Generator Loss: 2.5027
D(x): 0.7606, D(G(z)): 0.1532
Epoch: [7/20], Batch Num: [124/600]
Discriminator Loss: 0.8184, Generator Loss: 2.3934
D(x): 0.7144, D(G(z)): 0.1715
Epoch: [7/20], Batch Num: [125/600]
Discriminator Loss: 0.8272, Generator Loss: 1.9281
D(x): 0.7456, D(G(z)): 0.2196
Epoch: [7/20], Batch Num: [126/600]
Discriminator Loss: 0.7464, Generator Loss: 2.2039
D(x): 0.7822, D(G(z)): 0.2216
Epoch: [7/20], Batch Num: [127/600]
Discriminator Loss: 0.8197, Generator Loss: 2.0117
D(x): 0.8096, D(G(z)): 0.2802
Epoch: [7/20], Batch Num: [128/600]
Discriminator Loss: 1.0218, Generator Loss: 1.9882
D(x): 0.7307, D(G(z)): 0.3004
Epoch: [7/20], Batch Num: [129/600]
Discriminator Loss: 0.8682, Generator Loss: 1.9362
D(x): 0.7188, D(G(z)): 0.2397
Epoch: [7/20], Batch Num: [130/600]
Discriminator Loss: 0.7547, Generator Loss: 1.9889
D(x): 0.7887, D(G(z)): 0.2633
Epoch: [7/20], Batch Num: [131/600]
Discriminator Loss: 0.8647, Generator Loss: 1.8917
D(x): 0.7487, D(G(z)): 0.2704
Epoch: [7/20], Batch Num: [132/600]
Discriminator Loss: 0.8969, Generator Loss: 1.9086
D(x): 0.7214, D(G(z)): 0.2400
Epoch: [7/20], Batch Num: [133/600]
Discriminator Loss: 0.9964, Generator Loss: 1.9543
D(x): 0.6982, D(G(z)): 0.2743
Epoch: [7/20], Batch Num: [134/600]
Discriminator Loss: 0.8803, Generator Loss: 1.7647
D(x): 0.7386, D(G(z)): 0.2892
Epoch: [7/20], Batch Num: [135/600]
Discriminator Loss: 1.1239, Generator Loss: 1.6949
D(x): 0.6733, D(G(z)): 0.2923
Epoch: [7/20], Batch Num: [136/600]
Discriminator Loss: 0.9138, Generator Loss: 1.9333
D(x): 0.7406, D(G(z)): 0.3290
Epoch: [7/20], Batch Num: [137/600]
Discriminator Loss: 0.8943, Generator Loss: 1.7086
D(x): 0.7167, D(G(z)): 0.2809
Epoch: [7/20], Batch Num: [138/600]
Discriminator Loss: 0.8059, Generator Loss: 1.7155
D(x): 0.7597, D(G(z)): 0.2762
Epoch: [7/20], Batch Num: [139/600]
Discriminator Loss: 0.8084, Generator Loss: 1.7691
D(x): 0.7306, D(G(z)): 0.2837
Epoch: [7/20], Batch Num: [140/600]
Discriminator Loss: 0.9497, Generator Loss: 1.7200
D(x): 0.6530, D(G(z)): 0.2553
Epoch: [7/20], Batch Num: [141/600]
Discriminator Loss: 0.9182, Generator Loss: 1.5441
D(x): 0.7433, D(G(z)): 0.3353
Epoch: [7/20], Batch Num: [142/600]
Discriminator Loss: 0.8591, Generator Loss: 1.4146
D(x): 0.7509, D(G(z)): 0.3036
Epoch: [7/20], Batch Num: [143/600]
Discriminator Loss: 0.8548, Generator Loss: 1.6831
D(x): 0.7900, D(G(z)): 0.3531
Epoch: [7/20], Batch Num: [144/600]
Discriminator Loss: 0.7349, Generator Loss: 1.9881
D(x): 0.7425, D(G(z)): 0.2600
Epoch: [7/20], Batch Num: [145/600]
Discriminator Loss: 0.7616, Generator Loss: 1.9415
D(x): 0.7292, D(G(z)): 0.2531
Epoch: [7/20], Batch Num: [146/600]
Discriminator Loss: 0.7423, Generator Loss: 2.0576
D(x): 0.7218, D(G(z)): 0.2275
Epoch: [7/20], Batch Num: [147/600]
Discriminator Loss: 0.8267, Generator Loss: 1.8641
D(x): 0.7031, D(G(z)): 0.2464
Epoch: [7/20], Batch Num: [148/600]
Discriminator Loss: 0.8444, Generator Loss: 1.5324
D(x): 0.7135, D(G(z)): 0.2732
Epoch: [7/20], Batch Num: [149/600]
Discriminator Loss: 0.6795, Generator Loss: 1.6187
D(x): 0.8431, D(G(z)): 0.3108
Epoch: [7/20], Batch Num: [150/600]
Discriminator Loss: 0.5466, Generator Loss: 2.0161
D(x): 0.8660, D(G(z)): 0.2680
Epoch: [7/20], Batch Num: [151/600]
Discriminator Loss: 0.6416, Generator Loss: 2.3026
D(x): 0.8178, D(G(z)): 0.2525
Epoch: [7/20], Batch Num: [152/600]
Discriminator Loss: 0.7226, Generator Loss: 2.3652
D(x): 0.7070, D(G(z)): 0.1869
Epoch: [7/20], Batch Num: [153/600]
Discriminator Loss: 0.6598, Generator Loss: 2.1951
D(x): 0.7658, D(G(z)): 0.2233
Epoch: [7/20], Batch Num: [154/600]
Discriminator Loss: 0.6622, Generator Loss: 2.0022
D(x): 0.7681, D(G(z)): 0.2067
Epoch: [7/20], Batch Num: [155/600]
Discriminator Loss: 0.6669, Generator Loss: 1.7626
D(x): 0.8087, D(G(z)): 0.2464
Epoch: [7/20], Batch Num: [156/600]
Discriminator Loss: 0.5529, Generator Loss: 2.0272
D(x): 0.8535, D(G(z)): 0.2362
Epoch: [7/20], Batch Num: [157/600]
Discriminator Loss: 0.6731, Generator Loss: 2.6500
D(x): 0.8507, D(G(z)): 0.2953
Epoch: [7/20], Batch Num: [158/600]
Discriminator Loss: 0.5578, Generator Loss: 2.6291
D(x): 0.8035, D(G(z)): 0.1766
Epoch: [7/20], Batch Num: [159/600]
Discriminator Loss: 0.6712, Generator Loss: 2.5767
D(x): 0.7226, D(G(z)): 0.1289
Epoch: [7/20], Batch Num: [160/600]
Discriminator Loss: 0.5411, Generator Loss: 2.1347
D(x): 0.7778, D(G(z)): 0.1668
Epoch: [7/20], Batch Num: [161/600]
Discriminator Loss: 0.6663, Generator Loss: 1.7901
D(x): 0.7738, D(G(z)): 0.1963
Epoch: [7/20], Batch Num: [162/600]
Discriminator Loss: 0.7095, Generator Loss: 2.0151
D(x): 0.9008, D(G(z)): 0.3524
Epoch: [7/20], Batch Num: [163/600]
Discriminator Loss: 0.7560, Generator Loss: 2.6614
D(x): 0.8533, D(G(z)): 0.3037
Epoch: [7/20], Batch Num: [164/600]
Discriminator Loss: 0.7402, Generator Loss: 2.7068
D(x): 0.7832, D(G(z)): 0.2171
Epoch: [7/20], Batch Num: [165/600]
Discriminator Loss: 0.7716, Generator Loss: 3.0791
D(x): 0.6978, D(G(z)): 0.1519
Epoch: [7/20], Batch Num: [166/600]
Discriminator Loss: 0.8525, Generator Loss: 2.4911
D(x): 0.6707, D(G(z)): 0.1491
Epoch: [7/20], Batch Num: [167/600]
Discriminator Loss: 0.6992, Generator Loss: 1.8176
D(x): 0.8241, D(G(z)): 0.2568
Epoch: [7/20], Batch Num: [168/600]
Discriminator Loss: 0.9076, Generator Loss: 1.6954
D(x): 0.7581, D(G(z)): 0.2781
Epoch: [7/20], Batch Num: [169/600]
Discriminator Loss: 0.9166, Generator Loss: 2.1504
D(x): 0.8501, D(G(z)): 0.3972
Epoch: [7/20], Batch Num: [170/600]
Discriminator Loss: 0.8047, Generator Loss: 2.3762
D(x): 0.8072, D(G(z)): 0.2874
Epoch: [7/20], Batch Num: [171/600]
Discriminator Loss: 0.7783, Generator Loss: 2.5576
D(x): 0.7052, D(G(z)): 0.1725
Epoch: [7/20], Batch Num: [172/600]
Discriminator Loss: 0.9686, Generator Loss: 2.2888
D(x): 0.6864, D(G(z)): 0.2240
Epoch: [7/20], Batch Num: [173/600]
Discriminator Loss: 0.8871, Generator Loss: 1.7088
D(x): 0.6824, D(G(z)): 0.2128
Epoch: [7/20], Batch Num: [174/600]
Discriminator Loss: 0.7010, Generator Loss: 1.6296
D(x): 0.7904, D(G(z)): 0.2649
Epoch: [7/20], Batch Num: [175/600]
Discriminator Loss: 0.8050, Generator Loss: 1.7831
D(x): 0.8545, D(G(z)): 0.3512
Epoch: [7/20], Batch Num: [176/600]
Discriminator Loss: 0.7956, Generator Loss: 1.9727
D(x): 0.8013, D(G(z)): 0.3136
Epoch: [7/20], Batch Num: [177/600]
Discriminator Loss: 0.7031, Generator Loss: 2.3850
D(x): 0.7685, D(G(z)): 0.2333
Epoch: [7/20], Batch Num: [178/600]
Discriminator Loss: 0.7510, Generator Loss: 2.3320
D(x): 0.7391, D(G(z)): 0.1842
Epoch: [7/20], Batch Num: [179/600]
Discriminator Loss: 0.6965, Generator Loss: 2.2375
D(x): 0.7207, D(G(z)): 0.1785
Epoch: [7/20], Batch Num: [180/600]
Discriminator Loss: 0.7852, Generator Loss: 1.7333
D(x): 0.7258, D(G(z)): 0.2178
Epoch: [7/20], Batch Num: [181/600]
Discriminator Loss: 0.7422, Generator Loss: 1.7117
D(x): 0.7825, D(G(z)): 0.2546
Epoch: [7/20], Batch Num: [182/600]
Discriminator Loss: 0.8113, Generator Loss: 1.8660
D(x): 0.8333, D(G(z)): 0.3414
Epoch: [7/20], Batch Num: [183/600]
Discriminator Loss: 0.6460, Generator Loss: 2.0589
D(x): 0.8425, D(G(z)): 0.2684
Epoch: [7/20], Batch Num: [184/600]
Discriminator Loss: 0.7710, Generator Loss: 2.1617
D(x): 0.7372, D(G(z)): 0.2271
Epoch: [7/20], Batch Num: [185/600]
Discriminator Loss: 0.5888, Generator Loss: 2.4318
D(x): 0.8073, D(G(z)): 0.2025
Epoch: [7/20], Batch Num: [186/600]
Discriminator Loss: 0.7695, Generator Loss: 2.2043
D(x): 0.7236, D(G(z)): 0.2065
Epoch: [7/20], Batch Num: [187/600]
Discriminator Loss: 0.5802, Generator Loss: 2.3629
D(x): 0.8038, D(G(z)): 0.1996
Epoch: [7/20], Batch Num: [188/600]
Discriminator Loss: 0.5051, Generator Loss: 2.3321
D(x): 0.8721, D(G(z)): 0.2435
Epoch: [7/20], Batch Num: [189/600]
Discriminator Loss: 0.5748, Generator Loss: 2.6811
D(x): 0.8095, D(G(z)): 0.1898
Epoch: [7/20], Batch Num: [190/600]
Discriminator Loss: 0.5472, Generator Loss: 2.5067
D(x): 0.8231, D(G(z)): 0.1785
Epoch: [7/20], Batch Num: [191/600]
Discriminator Loss: 0.6636, Generator Loss: 2.6499
D(x): 0.7700, D(G(z)): 0.1857
Epoch: [7/20], Batch Num: [192/600]
Discriminator Loss: 0.5415, Generator Loss: 2.3544
D(x): 0.8255, D(G(z)): 0.2023
Epoch: [7/20], Batch Num: [193/600]
Discriminator Loss: 0.5134, Generator Loss: 2.3317
D(x): 0.8213, D(G(z)): 0.1749
Epoch: [7/20], Batch Num: [194/600]
Discriminator Loss: 0.5490, Generator Loss: 2.2043
D(x): 0.8298, D(G(z)): 0.1893
Epoch: [7/20], Batch Num: [195/600]
Discriminator Loss: 0.5845, Generator Loss: 2.2264
D(x): 0.8577, D(G(z)): 0.2478
Epoch: [7/20], Batch Num: [196/600]
Discriminator Loss: 0.6054, Generator Loss: 2.2043
D(x): 0.8468, D(G(z)): 0.2547
Epoch: [7/20], Batch Num: [197/600]
Discriminator Loss: 0.7298, Generator Loss: 2.4845
D(x): 0.7639, D(G(z)): 0.2207
Epoch: [7/20], Batch Num: [198/600]
Discriminator Loss: 0.7688, Generator Loss: 2.6649
D(x): 0.7391, D(G(z)): 0.1987
Epoch: [7/20], Batch Num: [199/600]
Discriminator Loss: 0.5823, Generator Loss: 2.4379
D(x): 0.8019, D(G(z)): 0.1580
Epoch: 7, Batch Num: [200/600]

Epoch: [7/20], Batch Num: [200/600]
Discriminator Loss: 0.9648, Generator Loss: 2.5637
D(x): 0.6844, D(G(z)): 0.2033
Epoch: [7/20], Batch Num: [201/600]
Discriminator Loss: 0.6821, Generator Loss: 2.2620
D(x): 0.8052, D(G(z)): 0.2291
Epoch: [7/20], Batch Num: [202/600]
Discriminator Loss: 0.6862, Generator Loss: 2.2710
D(x): 0.8037, D(G(z)): 0.2521
Epoch: [7/20], Batch Num: [203/600]
Discriminator Loss: 0.6636, Generator Loss: 2.1031
D(x): 0.7869, D(G(z)): 0.2202
Epoch: [7/20], Batch Num: [204/600]
Discriminator Loss: 0.7231, Generator Loss: 2.2872
D(x): 0.7959, D(G(z)): 0.2639
Epoch: [7/20], Batch Num: [205/600]
Discriminator Loss: 0.7352, Generator Loss: 2.1744
D(x): 0.7720, D(G(z)): 0.2219
Epoch: [7/20], Batch Num: [206/600]
Discriminator Loss: 0.7274, Generator Loss: 2.2171
D(x): 0.7449, D(G(z)): 0.1720
Epoch: [7/20], Batch Num: [207/600]
Discriminator Loss: 0.7010, Generator Loss: 2.0255
D(x): 0.7890, D(G(z)): 0.2209
Epoch: [7/20], Batch Num: [208/600]
Discriminator Loss: 1.0072, Generator Loss: 2.1314
D(x): 0.7270, D(G(z)): 0.3040
Epoch: [7/20], Batch Num: [209/600]
Discriminator Loss: 0.9054, Generator Loss: 2.0762
D(x): 0.7381, D(G(z)): 0.2755
Epoch: [7/20], Batch Num: [210/600]
Discriminator Loss: 0.7643, Generator Loss: 2.0378
D(x): 0.7734, D(G(z)): 0.2130
Epoch: [7/20], Batch Num: [211/600]
Discriminator Loss: 0.9459, Generator Loss: 2.0040
D(x): 0.7067, D(G(z)): 0.2747
Epoch: [7/20], Batch Num: [212/600]
Discriminator Loss: 0.7244, Generator Loss: 1.7714
D(x): 0.7495, D(G(z)): 0.2291
Epoch: [7/20], Batch Num: [213/600]
Discriminator Loss: 0.9300, Generator Loss: 2.0022
D(x): 0.7755, D(G(z)): 0.3265
Epoch: [7/20], Batch Num: [214/600]
Discriminator Loss: 0.7814, Generator Loss: 2.1969
D(x): 0.7701, D(G(z)): 0.2516
Epoch: [7/20], Batch Num: [215/600]
Discriminator Loss: 0.7110, Generator Loss: 2.1096
D(x): 0.7830, D(G(z)): 0.2336
Epoch: [7/20], Batch Num: [216/600]
Discriminator Loss: 0.6265, Generator Loss: 1.9507
D(x): 0.7872, D(G(z)): 0.1877
Epoch: [7/20], Batch Num: [217/600]
Discriminator Loss: 0.6004, Generator Loss: 1.9637
D(x): 0.8004, D(G(z)): 0.2198
Epoch: [7/20], Batch Num: [218/600]
Discriminator Loss: 0.7194, Generator Loss: 1.6276
D(x): 0.7418, D(G(z)): 0.2112
Epoch: [7/20], Batch Num: [219/600]
Discriminator Loss: 0.7153, Generator Loss: 1.6790
D(x): 0.8179, D(G(z)): 0.2786
Epoch: [7/20], Batch Num: [220/600]
Discriminator Loss: 0.6473, Generator Loss: 1.8826
D(x): 0.8050, D(G(z)): 0.2568
Epoch: [7/20], Batch Num: [221/600]
Discriminator Loss: 0.5803, Generator Loss: 2.0701
D(x): 0.8239, D(G(z)): 0.2366
Epoch: [7/20], Batch Num: [222/600]
Discriminator Loss: 0.6274, Generator Loss: 1.8851
D(x): 0.7806, D(G(z)): 0.2105
Epoch: [7/20], Batch Num: [223/600]
Discriminator Loss: 0.5492, Generator Loss: 2.0543
D(x): 0.8378, D(G(z)): 0.2273
Epoch: [7/20], Batch Num: [224/600]
Discriminator Loss: 0.6240, Generator Loss: 2.3409
D(x): 0.8237, D(G(z)): 0.2359
Epoch: [7/20], Batch Num: [225/600]
Discriminator Loss: 0.4771, Generator Loss: 1.9321
D(x): 0.8276, D(G(z)): 0.1785
Epoch: [7/20], Batch Num: [226/600]
Discriminator Loss: 0.5570, Generator Loss: 2.2167
D(x): 0.8274, D(G(z)): 0.1884
Epoch: [7/20], Batch Num: [227/600]
Discriminator Loss: 0.5482, Generator Loss: 2.2204
D(x): 0.8509, D(G(z)): 0.2285
Epoch: [7/20], Batch Num: [228/600]
Discriminator Loss: 0.5845, Generator Loss: 1.9597
D(x): 0.8137, D(G(z)): 0.2026
Epoch: [7/20], Batch Num: [229/600]
Discriminator Loss: 0.5805, Generator Loss: 2.0321
D(x): 0.8014, D(G(z)): 0.2138
Epoch: [7/20], Batch Num: [230/600]
Discriminator Loss: 0.4791, Generator Loss: 2.2440
D(x): 0.8746, D(G(z)): 0.2264
Epoch: [7/20], Batch Num: [231/600]
Discriminator Loss: 0.5491, Generator Loss: 2.2914
D(x): 0.8595, D(G(z)): 0.2384
Epoch: [7/20], Batch Num: [232/600]
Discriminator Loss: 0.5044, Generator Loss: 2.3344
D(x): 0.8129, D(G(z)): 0.1588
Epoch: [7/20], Batch Num: [233/600]
Discriminator Loss: 0.5285, Generator Loss: 2.5497
D(x): 0.8357, D(G(z)): 0.2054
Epoch: [7/20], Batch Num: [234/600]
Discriminator Loss: 0.3965, Generator Loss: 2.4682
D(x): 0.8518, D(G(z)): 0.1503
Epoch: [7/20], Batch Num: [235/600]
Discriminator Loss: 0.4726, Generator Loss: 2.1567
D(x): 0.8369, D(G(z)): 0.1629
Epoch: [7/20], Batch Num: [236/600]
Discriminator Loss: 0.5523, Generator Loss: 1.9930
D(x): 0.8669, D(G(z)): 0.2429
Epoch: [7/20], Batch Num: [237/600]
Discriminator Loss: 0.4970, Generator Loss: 2.2842
D(x): 0.8782, D(G(z)): 0.2273
Epoch: [7/20], Batch Num: [238/600]
Discriminator Loss: 0.5414, Generator Loss: 2.5158
D(x): 0.8464, D(G(z)): 0.1921
Epoch: [7/20], Batch Num: [239/600]
Discriminator Loss: 0.5949, Generator Loss: 2.6975
D(x): 0.8099, D(G(z)): 0.1771
Epoch: [7/20], Batch Num: [240/600]
Discriminator Loss: 0.6572, Generator Loss: 2.3368
D(x): 0.7624, D(G(z)): 0.1370
Epoch: [7/20], Batch Num: [241/600]
Discriminator Loss: 0.5657, Generator Loss: 2.1182
D(x): 0.7743, D(G(z)): 0.1527
Epoch: [7/20], Batch Num: [242/600]
Discriminator Loss: 0.6623, Generator Loss: 1.7892
D(x): 0.8353, D(G(z)): 0.2779
Epoch: [7/20], Batch Num: [243/600]
Discriminator Loss: 0.5859, Generator Loss: 2.2042
D(x): 0.8935, D(G(z)): 0.2977
Epoch: [7/20], Batch Num: [244/600]
Discriminator Loss: 0.5227, Generator Loss: 2.5108
D(x): 0.8329, D(G(z)): 0.1820
Epoch: [7/20], Batch Num: [245/600]
Discriminator Loss: 0.4451, Generator Loss: 2.8354
D(x): 0.8436, D(G(z)): 0.1547
Epoch: [7/20], Batch Num: [246/600]
Discriminator Loss: 0.7628, Generator Loss: 2.5557
D(x): 0.7080, D(G(z)): 0.1263
Epoch: [7/20], Batch Num: [247/600]
Discriminator Loss: 0.6775, Generator Loss: 2.3553
D(x): 0.8030, D(G(z)): 0.1948
Epoch: [7/20], Batch Num: [248/600]
Discriminator Loss: 0.7184, Generator Loss: 1.8916
D(x): 0.7810, D(G(z)): 0.2400
Epoch: [7/20], Batch Num: [249/600]
Discriminator Loss: 0.6640, Generator Loss: 1.9990
D(x): 0.8434, D(G(z)): 0.2795
Epoch: [7/20], Batch Num: [250/600]
Discriminator Loss: 0.7800, Generator Loss: 2.4533
D(x): 0.7891, D(G(z)): 0.2812
Epoch: [7/20], Batch Num: [251/600]
Discriminator Loss: 0.6701, Generator Loss: 2.4669
D(x): 0.8054, D(G(z)): 0.1956
Epoch: [7/20], Batch Num: [252/600]
Discriminator Loss: 0.6151, Generator Loss: 2.5543
D(x): 0.7943, D(G(z)): 0.1846
Epoch: [7/20], Batch Num: [253/600]
Discriminator Loss: 0.7653, Generator Loss: 2.2197
D(x): 0.7332, D(G(z)): 0.1615
Epoch: [7/20], Batch Num: [254/600]
Discriminator Loss: 0.6891, Generator Loss: 1.8759
D(x): 0.8151, D(G(z)): 0.2468
Epoch: [7/20], Batch Num: [255/600]
Discriminator Loss: 0.7141, Generator Loss: 2.0226
D(x): 0.8481, D(G(z)): 0.2669
Epoch: [7/20], Batch Num: [256/600]
Discriminator Loss: 0.6838, Generator Loss: 2.2445
D(x): 0.8121, D(G(z)): 0.2220
Epoch: [7/20], Batch Num: [257/600]
Discriminator Loss: 0.5014, Generator Loss: 2.3129
D(x): 0.8340, D(G(z)): 0.1749
Epoch: [7/20], Batch Num: [258/600]
Discriminator Loss: 0.7001, Generator Loss: 2.3493
D(x): 0.7690, D(G(z)): 0.1770
Epoch: [7/20], Batch Num: [259/600]
Discriminator Loss: 0.6126, Generator Loss: 2.2859
D(x): 0.7895, D(G(z)): 0.1931
Epoch: [7/20], Batch Num: [260/600]
Discriminator Loss: 0.6798, Generator Loss: 2.2915
D(x): 0.7957, D(G(z)): 0.2399
Epoch: [7/20], Batch Num: [261/600]
Discriminator Loss: 0.7549, Generator Loss: 2.2566
D(x): 0.7686, D(G(z)): 0.2246
Epoch: [7/20], Batch Num: [262/600]
Discriminator Loss: 0.6641, Generator Loss: 2.1787
D(x): 0.8035, D(G(z)): 0.2111
Epoch: [7/20], Batch Num: [263/600]
Discriminator Loss: 0.6250, Generator Loss: 2.3684
D(x): 0.8150, D(G(z)): 0.1936
Epoch: [7/20], Batch Num: [264/600]
Discriminator Loss: 0.4859, Generator Loss: 2.5206
D(x): 0.8220, D(G(z)): 0.1407
Epoch: [7/20], Batch Num: [265/600]
Discriminator Loss: 0.6445, Generator Loss: 2.4096
D(x): 0.7950, D(G(z)): 0.2019
Epoch: [7/20], Batch Num: [266/600]
Discriminator Loss: 0.6269, Generator Loss: 2.5499
D(x): 0.8283, D(G(z)): 0.2197
Epoch: [7/20], Batch Num: [267/600]
Discriminator Loss: 0.6315, Generator Loss: 2.4024
D(x): 0.7628, D(G(z)): 0.1514
Epoch: [7/20], Batch Num: [268/600]
Discriminator Loss: 0.7387, Generator Loss: 2.4867
D(x): 0.7984, D(G(z)): 0.2179
Epoch: [7/20], Batch Num: [269/600]
Discriminator Loss: 0.7373, Generator Loss: 2.7052
D(x): 0.8322, D(G(z)): 0.2476
Epoch: [7/20], Batch Num: [270/600]
Discriminator Loss: 0.4610, Generator Loss: 2.7501
D(x): 0.8401, D(G(z)): 0.1525
Epoch: [7/20], Batch Num: [271/600]
Discriminator Loss: 0.7046, Generator Loss: 2.5503
D(x): 0.7268, D(G(z)): 0.1307
Epoch: [7/20], Batch Num: [272/600]
Discriminator Loss: 0.6632, Generator Loss: 2.3337
D(x): 0.8055, D(G(z)): 0.1933
Epoch: [7/20], Batch Num: [273/600]
Discriminator Loss: 0.7375, Generator Loss: 2.1254
D(x): 0.7838, D(G(z)): 0.1993
Epoch: [7/20], Batch Num: [274/600]
Discriminator Loss: 0.7336, Generator Loss: 2.0100
D(x): 0.8222, D(G(z)): 0.2534
Epoch: [7/20], Batch Num: [275/600]
Discriminator Loss: 0.6971, Generator Loss: 2.5422
D(x): 0.8076, D(G(z)): 0.2474
Epoch: [7/20], Batch Num: [276/600]
Discriminator Loss: 0.5723, Generator Loss: 2.7185
D(x): 0.8117, D(G(z)): 0.1880
Epoch: [7/20], Batch Num: [277/600]
Discriminator Loss: 0.7756, Generator Loss: 2.8078
D(x): 0.7506, D(G(z)): 0.1546
Epoch: [7/20], Batch Num: [278/600]
Discriminator Loss: 0.7995, Generator Loss: 2.6544
D(x): 0.7321, D(G(z)): 0.1448
Epoch: [7/20], Batch Num: [279/600]
Discriminator Loss: 0.6800, Generator Loss: 2.1237
D(x): 0.7889, D(G(z)): 0.1823
Epoch: [7/20], Batch Num: [280/600]
Discriminator Loss: 0.6836, Generator Loss: 2.2266
D(x): 0.8083, D(G(z)): 0.2316
Epoch: [7/20], Batch Num: [281/600]
Discriminator Loss: 0.8850, Generator Loss: 2.0715
D(x): 0.7537, D(G(z)): 0.2346
Epoch: [7/20], Batch Num: [282/600]
Discriminator Loss: 0.7560, Generator Loss: 2.3786
D(x): 0.7867, D(G(z)): 0.2323
Epoch: [7/20], Batch Num: [283/600]
Discriminator Loss: 0.7475, Generator Loss: 2.2803
D(x): 0.7612, D(G(z)): 0.1973
Epoch: [7/20], Batch Num: [284/600]
Discriminator Loss: 0.6944, Generator Loss: 2.1941
D(x): 0.7817, D(G(z)): 0.2027
Epoch: [7/20], Batch Num: [285/600]
Discriminator Loss: 0.6112, Generator Loss: 2.2253
D(x): 0.8280, D(G(z)): 0.1710
Epoch: [7/20], Batch Num: [286/600]
Discriminator Loss: 0.6048, Generator Loss: 2.2812
D(x): 0.8215, D(G(z)): 0.1881
Epoch: [7/20], Batch Num: [287/600]
Discriminator Loss: 0.5756, Generator Loss: 2.3411
D(x): 0.8401, D(G(z)): 0.1876
Epoch: [7/20], Batch Num: [288/600]
Discriminator Loss: 0.5595, Generator Loss: 2.5659
D(x): 0.8230, D(G(z)): 0.1753
Epoch: [7/20], Batch Num: [289/600]
Discriminator Loss: 0.4421, Generator Loss: 2.6335
D(x): 0.8808, D(G(z)): 0.1937
Epoch: [7/20], Batch Num: [290/600]
Discriminator Loss: 0.4342, Generator Loss: 2.4926
D(x): 0.8750, D(G(z)): 0.1694
Epoch: [7/20], Batch Num: [291/600]
Discriminator Loss: 0.4795, Generator Loss: 2.7568
D(x): 0.8424, D(G(z)): 0.1569
Epoch: [7/20], Batch Num: [292/600]
Discriminator Loss: 0.7111, Generator Loss: 2.6027
D(x): 0.7690, D(G(z)): 0.1880
Epoch: [7/20], Batch Num: [293/600]
Discriminator Loss: 0.4942, Generator Loss: 2.4448
D(x): 0.8495, D(G(z)): 0.1818
Epoch: [7/20], Batch Num: [294/600]
Discriminator Loss: 0.4401, Generator Loss: 2.6980
D(x): 0.8899, D(G(z)): 0.2049
Epoch: [7/20], Batch Num: [295/600]
Discriminator Loss: 0.4387, Generator Loss: 2.6226
D(x): 0.8460, D(G(z)): 0.1480
Epoch: [7/20], Batch Num: [296/600]
Discriminator Loss: 0.4367, Generator Loss: 2.5396
D(x): 0.8380, D(G(z)): 0.1452
Epoch: [7/20], Batch Num: [297/600]
Discriminator Loss: 0.4396, Generator Loss: 2.1336
D(x): 0.8492, D(G(z)): 0.1535
Epoch: [7/20], Batch Num: [298/600]
Discriminator Loss: 0.5541, Generator Loss: 2.3844
D(x): 0.8838, D(G(z)): 0.2302
Epoch: [7/20], Batch Num: [299/600]
Discriminator Loss: 0.4515, Generator Loss: 2.5554
D(x): 0.8821, D(G(z)): 0.1997
Epoch: 7, Batch Num: [300/600]

Epoch: [7/20], Batch Num: [300/600]
Discriminator Loss: 0.5816, Generator Loss: 2.7158
D(x): 0.8025, D(G(z)): 0.1614
Epoch: [7/20], Batch Num: [301/600]
Discriminator Loss: 0.5123, Generator Loss: 2.6850
D(x): 0.8304, D(G(z)): 0.1462
Epoch: [7/20], Batch Num: [302/600]
Discriminator Loss: 0.5319, Generator Loss: 2.5198
D(x): 0.8144, D(G(z)): 0.1663
Epoch: [7/20], Batch Num: [303/600]
Discriminator Loss: 0.4805, Generator Loss: 2.4427
D(x): 0.8477, D(G(z)): 0.1792
Epoch: [7/20], Batch Num: [304/600]
Discriminator Loss: 0.5766, Generator Loss: 2.6160
D(x): 0.8738, D(G(z)): 0.2235
Epoch: [7/20], Batch Num: [305/600]
Discriminator Loss: 0.6461, Generator Loss: 2.4194
D(x): 0.8429, D(G(z)): 0.2152
Epoch: [7/20], Batch Num: [306/600]
Discriminator Loss: 0.6348, Generator Loss: 2.5866
D(x): 0.8085, D(G(z)): 0.1999
Epoch: [7/20], Batch Num: [307/600]
Discriminator Loss: 0.5100, Generator Loss: 2.5739
D(x): 0.8360, D(G(z)): 0.1614
Epoch: [7/20], Batch Num: [308/600]
Discriminator Loss: 0.7467, Generator Loss: 2.3390
D(x): 0.7351, D(G(z)): 0.1642
Epoch: [7/20], Batch Num: [309/600]
Discriminator Loss: 0.9086, Generator Loss: 1.9417
D(x): 0.7496, D(G(z)): 0.2529
Epoch: [7/20], Batch Num: [310/600]
Discriminator Loss: 0.9071, Generator Loss: 1.8090
D(x): 0.8057, D(G(z)): 0.3389
Epoch: [7/20], Batch Num: [311/600]
Discriminator Loss: 1.0170, Generator Loss: 2.2635
D(x): 0.7948, D(G(z)): 0.3813
Epoch: [7/20], Batch Num: [312/600]
Discriminator Loss: 0.8805, Generator Loss: 2.3752
D(x): 0.7427, D(G(z)): 0.2736
Epoch: [7/20], Batch Num: [313/600]
Discriminator Loss: 0.8270, Generator Loss: 2.4329
D(x): 0.6992, D(G(z)): 0.1724
Epoch: [7/20], Batch Num: [314/600]
Discriminator Loss: 0.8535, Generator Loss: 2.3322
D(x): 0.6798, D(G(z)): 0.1700
Epoch: [7/20], Batch Num: [315/600]
Discriminator Loss: 0.7336, Generator Loss: 2.2354
D(x): 0.7785, D(G(z)): 0.2327
Epoch: [7/20], Batch Num: [316/600]
Discriminator Loss: 0.7975, Generator Loss: 2.1087
D(x): 0.7742, D(G(z)): 0.2833
Epoch: [7/20], Batch Num: [317/600]
Discriminator Loss: 1.0021, Generator Loss: 1.8586
D(x): 0.7244, D(G(z)): 0.2897
Epoch: [7/20], Batch Num: [318/600]
Discriminator Loss: 0.8396, Generator Loss: 1.8230
D(x): 0.8322, D(G(z)): 0.3278
Epoch: [7/20], Batch Num: [319/600]
Discriminator Loss: 0.7530, Generator Loss: 2.0041
D(x): 0.7530, D(G(z)): 0.2314
Epoch: [7/20], Batch Num: [320/600]
Discriminator Loss: 0.8451, Generator Loss: 2.0310
D(x): 0.7344, D(G(z)): 0.2719
Epoch: [7/20], Batch Num: [321/600]
Discriminator Loss: 0.8562, Generator Loss: 1.9305
D(x): 0.7278, D(G(z)): 0.2515
Epoch: [7/20], Batch Num: [322/600]
Discriminator Loss: 1.0599, Generator Loss: 1.6225
D(x): 0.6804, D(G(z)): 0.2894
Epoch: [7/20], Batch Num: [323/600]
Discriminator Loss: 0.8468, Generator Loss: 1.7280
D(x): 0.7864, D(G(z)): 0.3285
Epoch: [7/20], Batch Num: [324/600]
Discriminator Loss: 0.8076, Generator Loss: 1.6077
D(x): 0.7558, D(G(z)): 0.2876
Epoch: [7/20], Batch Num: [325/600]
Discriminator Loss: 0.7959, Generator Loss: 1.7338
D(x): 0.7753, D(G(z)): 0.3039
Epoch: [7/20], Batch Num: [326/600]
Discriminator Loss: 0.8494, Generator Loss: 1.7852
D(x): 0.7680, D(G(z)): 0.3184
Epoch: [7/20], Batch Num: [327/600]
Discriminator Loss: 0.8443, Generator Loss: 1.8561
D(x): 0.6569, D(G(z)): 0.2151
Epoch: [7/20], Batch Num: [328/600]
Discriminator Loss: 0.8810, Generator Loss: 1.7746
D(x): 0.7181, D(G(z)): 0.2994
Epoch: [7/20], Batch Num: [329/600]
Discriminator Loss: 0.8247, Generator Loss: 1.6489
D(x): 0.7327, D(G(z)): 0.2669
Epoch: [7/20], Batch Num: [330/600]
Discriminator Loss: 0.6719, Generator Loss: 1.7173
D(x): 0.7777, D(G(z)): 0.2643
Epoch: [7/20], Batch Num: [331/600]
Discriminator Loss: 0.6364, Generator Loss: 1.6805
D(x): 0.7981, D(G(z)): 0.2635
Epoch: [7/20], Batch Num: [332/600]
Discriminator Loss: 0.7233, Generator Loss: 1.7255
D(x): 0.7850, D(G(z)): 0.2898
Epoch: [7/20], Batch Num: [333/600]
Discriminator Loss: 0.9262, Generator Loss: 1.6367
D(x): 0.7240, D(G(z)): 0.2971
Epoch: [7/20], Batch Num: [334/600]
Discriminator Loss: 0.5918, Generator Loss: 1.6756
D(x): 0.7997, D(G(z)): 0.2444
Epoch: [7/20], Batch Num: [335/600]
Discriminator Loss: 0.6950, Generator Loss: 1.7765
D(x): 0.7869, D(G(z)): 0.2650
Epoch: [7/20], Batch Num: [336/600]
Discriminator Loss: 0.6690, Generator Loss: 1.8262
D(x): 0.8057, D(G(z)): 0.2732
Epoch: [7/20], Batch Num: [337/600]
Discriminator Loss: 0.5800, Generator Loss: 1.9439
D(x): 0.8328, D(G(z)): 0.2366
Epoch: [7/20], Batch Num: [338/600]
Discriminator Loss: 0.5664, Generator Loss: 2.0690
D(x): 0.8197, D(G(z)): 0.2322
Epoch: [7/20], Batch Num: [339/600]
Discriminator Loss: 0.5141, Generator Loss: 2.2550
D(x): 0.8106, D(G(z)): 0.1905
Epoch: [7/20], Batch Num: [340/600]
Discriminator Loss: 0.5810, Generator Loss: 2.0572
D(x): 0.7895, D(G(z)): 0.1831
Epoch: [7/20], Batch Num: [341/600]
Discriminator Loss: 0.5606, Generator Loss: 2.0127
D(x): 0.8125, D(G(z)): 0.2115
Epoch: [7/20], Batch Num: [342/600]
Discriminator Loss: 0.4665, Generator Loss: 2.1081
D(x): 0.8533, D(G(z)): 0.2036
Epoch: [7/20], Batch Num: [343/600]
Discriminator Loss: 0.5373, Generator Loss: 2.0330
D(x): 0.8380, D(G(z)): 0.2157
Epoch: [7/20], Batch Num: [344/600]
Discriminator Loss: 0.4850, Generator Loss: 2.0567
D(x): 0.8758, D(G(z)): 0.2238
Epoch: [7/20], Batch Num: [345/600]
Discriminator Loss: 0.5560, Generator Loss: 2.2747
D(x): 0.8442, D(G(z)): 0.2402
Epoch: [7/20], Batch Num: [346/600]
Discriminator Loss: 0.4600, Generator Loss: 2.2987
D(x): 0.8469, D(G(z)): 0.1833
Epoch: [7/20], Batch Num: [347/600]
Discriminator Loss: 0.5017, Generator Loss: 2.2397
D(x): 0.8109, D(G(z)): 0.1684
Epoch: [7/20], Batch Num: [348/600]
Discriminator Loss: 0.4650, Generator Loss: 2.4681
D(x): 0.8677, D(G(z)): 0.1984
Epoch: [7/20], Batch Num: [349/600]
Discriminator Loss: 0.4049, Generator Loss: 2.2627
D(x): 0.8595, D(G(z)): 0.1575
Epoch: [7/20], Batch Num: [350/600]
Discriminator Loss: 0.3943, Generator Loss: 2.4483
D(x): 0.9026, D(G(z)): 0.1823
Epoch: [7/20], Batch Num: [351/600]
Discriminator Loss: 0.6035, Generator Loss: 2.3595
D(x): 0.7870, D(G(z)): 0.1661
Epoch: [7/20], Batch Num: [352/600]
Discriminator Loss: 0.6126, Generator Loss: 2.4030
D(x): 0.8140, D(G(z)): 0.1908
Epoch: [7/20], Batch Num: [353/600]
Discriminator Loss: 0.5410, Generator Loss: 1.9757
D(x): 0.8612, D(G(z)): 0.1927
Epoch: [7/20], Batch Num: [354/600]
Discriminator Loss: 0.7160, Generator Loss: 2.2904
D(x): 0.8302, D(G(z)): 0.2747
Epoch: [7/20], Batch Num: [355/600]
Discriminator Loss: 0.5843, Generator Loss: 2.5192
D(x): 0.8767, D(G(z)): 0.2288
Epoch: [7/20], Batch Num: [356/600]
Discriminator Loss: 0.6014, Generator Loss: 2.6038
D(x): 0.7953, D(G(z)): 0.1701
Epoch: [7/20], Batch Num: [357/600]
Discriminator Loss: 0.5041, Generator Loss: 2.4959
D(x): 0.8267, D(G(z)): 0.1602
Epoch: [7/20], Batch Num: [358/600]
Discriminator Loss: 0.6225, Generator Loss: 2.2316
D(x): 0.7755, D(G(z)): 0.1485
Epoch: [7/20], Batch Num: [359/600]
Discriminator Loss: 0.7805, Generator Loss: 1.9998
D(x): 0.8074, D(G(z)): 0.2551
Epoch: [7/20], Batch Num: [360/600]
Discriminator Loss: 0.6485, Generator Loss: 2.1030
D(x): 0.8377, D(G(z)): 0.2437
Epoch: [7/20], Batch Num: [361/600]
Discriminator Loss: 0.4888, Generator Loss: 2.0664
D(x): 0.8761, D(G(z)): 0.2071
Epoch: [7/20], Batch Num: [362/600]
Discriminator Loss: 0.6454, Generator Loss: 2.3514
D(x): 0.8086, D(G(z)): 0.2207
Epoch: [7/20], Batch Num: [363/600]
Discriminator Loss: 0.6535, Generator Loss: 2.1166
D(x): 0.7698, D(G(z)): 0.1713
Epoch: [7/20], Batch Num: [364/600]
Discriminator Loss: 0.6289, Generator Loss: 2.3110
D(x): 0.8302, D(G(z)): 0.2164
Epoch: [7/20], Batch Num: [365/600]
Discriminator Loss: 0.6221, Generator Loss: 2.5984
D(x): 0.8043, D(G(z)): 0.2155
Epoch: [7/20], Batch Num: [366/600]
Discriminator Loss: 0.7173, Generator Loss: 2.7574
D(x): 0.8161, D(G(z)): 0.2043
Epoch: [7/20], Batch Num: [367/600]
Discriminator Loss: 0.6313, Generator Loss: 2.1880
D(x): 0.7759, D(G(z)): 0.1786
Epoch: [7/20], Batch Num: [368/600]
Discriminator Loss: 0.6052, Generator Loss: 2.4935
D(x): 0.8294, D(G(z)): 0.2175
Epoch: [7/20], Batch Num: [369/600]
Discriminator Loss: 0.8664, Generator Loss: 2.2564
D(x): 0.7274, D(G(z)): 0.1845
Epoch: [7/20], Batch Num: [370/600]
Discriminator Loss: 0.6904, Generator Loss: 2.3360
D(x): 0.8175, D(G(z)): 0.2295
Epoch: [7/20], Batch Num: [371/600]
Discriminator Loss: 0.7155, Generator Loss: 2.5652
D(x): 0.8272, D(G(z)): 0.2834
Epoch: [7/20], Batch Num: [372/600]
Discriminator Loss: 0.6362, Generator Loss: 3.0497
D(x): 0.7963, D(G(z)): 0.1769
Epoch: [7/20], Batch Num: [373/600]
Discriminator Loss: 0.7932, Generator Loss: 2.3330
D(x): 0.7036, D(G(z)): 0.1414
Epoch: [7/20], Batch Num: [374/600]
Discriminator Loss: 0.6971, Generator Loss: 2.3489
D(x): 0.7808, D(G(z)): 0.1972
Epoch: [7/20], Batch Num: [375/600]
Discriminator Loss: 0.6644, Generator Loss: 2.2692
D(x): 0.8145, D(G(z)): 0.2226
Epoch: [7/20], Batch Num: [376/600]
Discriminator Loss: 0.5931, Generator Loss: 2.1111
D(x): 0.8350, D(G(z)): 0.2108
Epoch: [7/20], Batch Num: [377/600]
Discriminator Loss: 0.5237, Generator Loss: 2.3243
D(x): 0.8274, D(G(z)): 0.1936
Epoch: [7/20], Batch Num: [378/600]
Discriminator Loss: 0.5642, Generator Loss: 2.2448
D(x): 0.8500, D(G(z)): 0.2225
Epoch: [7/20], Batch Num: [379/600]
Discriminator Loss: 0.6488, Generator Loss: 2.2449
D(x): 0.7779, D(G(z)): 0.1857
Epoch: [7/20], Batch Num: [380/600]
Discriminator Loss: 0.5746, Generator Loss: 2.2610
D(x): 0.8196, D(G(z)): 0.1984
Epoch: [7/20], Batch Num: [381/600]
Discriminator Loss: 0.6077, Generator Loss: 2.5671
D(x): 0.8277, D(G(z)): 0.1928
Epoch: [7/20], Batch Num: [382/600]
Discriminator Loss: 0.5447, Generator Loss: 2.3314
D(x): 0.8206, D(G(z)): 0.1722
Epoch: [7/20], Batch Num: [383/600]
Discriminator Loss: 0.6462, Generator Loss: 2.2709
D(x): 0.7976, D(G(z)): 0.1966
Epoch: [7/20], Batch Num: [384/600]
Discriminator Loss: 0.5731, Generator Loss: 2.1860
D(x): 0.8259, D(G(z)): 0.2020
Epoch: [7/20], Batch Num: [385/600]
Discriminator Loss: 0.5849, Generator Loss: 2.0190
D(x): 0.8310, D(G(z)): 0.2190
Epoch: [7/20], Batch Num: [386/600]
Discriminator Loss: 0.5292, Generator Loss: 2.2153
D(x): 0.8521, D(G(z)): 0.2169
Epoch: [7/20], Batch Num: [387/600]
Discriminator Loss: 0.6283, Generator Loss: 2.1404
D(x): 0.8083, D(G(z)): 0.1937
Epoch: [7/20], Batch Num: [388/600]
Discriminator Loss: 0.6108, Generator Loss: 2.1456
D(x): 0.7772, D(G(z)): 0.1801
Epoch: [7/20], Batch Num: [389/600]
Discriminator Loss: 0.6040, Generator Loss: 2.0118
D(x): 0.7984, D(G(z)): 0.1896
Epoch: [7/20], Batch Num: [390/600]
Discriminator Loss: 0.6577, Generator Loss: 2.0831
D(x): 0.8332, D(G(z)): 0.2530
Epoch: [7/20], Batch Num: [391/600]
Discriminator Loss: 0.5857, Generator Loss: 2.5263
D(x): 0.8443, D(G(z)): 0.2264
Epoch: [7/20], Batch Num: [392/600]
Discriminator Loss: 0.7649, Generator Loss: 2.3399
D(x): 0.7540, D(G(z)): 0.1831
Epoch: [7/20], Batch Num: [393/600]
Discriminator Loss: 0.6796, Generator Loss: 2.2620
D(x): 0.7841, D(G(z)): 0.1772
Epoch: [7/20], Batch Num: [394/600]
Discriminator Loss: 0.7292, Generator Loss: 2.0520
D(x): 0.8137, D(G(z)): 0.2393
Epoch: [7/20], Batch Num: [395/600]
Discriminator Loss: 0.7916, Generator Loss: 2.1385
D(x): 0.7661, D(G(z)): 0.2067
Epoch: [7/20], Batch Num: [396/600]
Discriminator Loss: 0.8395, Generator Loss: 2.3106
D(x): 0.7949, D(G(z)): 0.2752
Epoch: [7/20], Batch Num: [397/600]
Discriminator Loss: 0.7527, Generator Loss: 2.0785
D(x): 0.7457, D(G(z)): 0.1849
Epoch: [7/20], Batch Num: [398/600]
Discriminator Loss: 0.7563, Generator Loss: 2.0461
D(x): 0.8021, D(G(z)): 0.2494
Epoch: [7/20], Batch Num: [399/600]
Discriminator Loss: 0.6896, Generator Loss: 1.8945
D(x): 0.7920, D(G(z)): 0.2185
Epoch: 7, Batch Num: [400/600]

Epoch: [7/20], Batch Num: [400/600]
Discriminator Loss: 0.7886, Generator Loss: 1.9931
D(x): 0.7748, D(G(z)): 0.2416
Epoch: [7/20], Batch Num: [401/600]
Discriminator Loss: 0.7043, Generator Loss: 2.1719
D(x): 0.8019, D(G(z)): 0.2235
Epoch: [7/20], Batch Num: [402/600]
Discriminator Loss: 0.6218, Generator Loss: 2.2296
D(x): 0.8054, D(G(z)): 0.1807
Epoch: [7/20], Batch Num: [403/600]
Discriminator Loss: 0.7272, Generator Loss: 1.8603
D(x): 0.7432, D(G(z)): 0.1873
Epoch: [7/20], Batch Num: [404/600]
Discriminator Loss: 0.6930, Generator Loss: 1.5755
D(x): 0.8017, D(G(z)): 0.2486
Epoch: [7/20], Batch Num: [405/600]
Discriminator Loss: 0.6765, Generator Loss: 1.8750
D(x): 0.8502, D(G(z)): 0.2683
Epoch: [7/20], Batch Num: [406/600]
Discriminator Loss: 0.7139, Generator Loss: 2.4891
D(x): 0.8555, D(G(z)): 0.2835
Epoch: [7/20], Batch Num: [407/600]
Discriminator Loss: 0.6929, Generator Loss: 2.3805
D(x): 0.7580, D(G(z)): 0.1411
Epoch: [7/20], Batch Num: [408/600]
Discriminator Loss: 0.5603, Generator Loss: 2.4716
D(x): 0.8001, D(G(z)): 0.1501
Epoch: [7/20], Batch Num: [409/600]
Discriminator Loss: 0.7175, Generator Loss: 2.1858
D(x): 0.7621, D(G(z)): 0.1844
Epoch: [7/20], Batch Num: [410/600]
Discriminator Loss: 0.7805, Generator Loss: 1.6510
D(x): 0.7480, D(G(z)): 0.1974
Epoch: [7/20], Batch Num: [411/600]
Discriminator Loss: 0.6286, Generator Loss: 1.5644
D(x): 0.9094, D(G(z)): 0.2880
Epoch: [7/20], Batch Num: [412/600]
Discriminator Loss: 0.6425, Generator Loss: 2.0264
D(x): 0.8726, D(G(z)): 0.2858
Epoch: [7/20], Batch Num: [413/600]
Discriminator Loss: 0.5451, Generator Loss: 2.2203
D(x): 0.8324, D(G(z)): 0.1874
Epoch: [7/20], Batch Num: [414/600]
Discriminator Loss: 0.4886, Generator Loss: 2.3377
D(x): 0.8380, D(G(z)): 0.1735
Epoch: [7/20], Batch Num: [415/600]
Discriminator Loss: 0.4354, Generator Loss: 2.2428
D(x): 0.8432, D(G(z)): 0.1407
Epoch: [7/20], Batch Num: [416/600]
Discriminator Loss: 0.3546, Generator Loss: 2.3276
D(x): 0.8788, D(G(z)): 0.1338
Epoch: [7/20], Batch Num: [417/600]
Discriminator Loss: 0.4046, Generator Loss: 2.1440
D(x): 0.8475, D(G(z)): 0.1403
Epoch: [7/20], Batch Num: [418/600]
Discriminator Loss: 0.3655, Generator Loss: 2.1351
D(x): 0.9083, D(G(z)): 0.1828
Epoch: [7/20], Batch Num: [419/600]
Discriminator Loss: 0.3653, Generator Loss: 2.2314
D(x): 0.9123, D(G(z)): 0.1807
Epoch: [7/20], Batch Num: [420/600]
Discriminator Loss: 0.3016, Generator Loss: 2.6641
D(x): 0.9472, D(G(z)): 0.1791
Epoch: [7/20], Batch Num: [421/600]
Discriminator Loss: 0.2996, Generator Loss: 2.7777
D(x): 0.8990, D(G(z)): 0.1234
Epoch: [7/20], Batch Num: [422/600]
Discriminator Loss: 0.3191, Generator Loss: 2.9256
D(x): 0.8609, D(G(z)): 0.0894
Epoch: [7/20], Batch Num: [423/600]
Discriminator Loss: 0.4683, Generator Loss: 2.6722
D(x): 0.8252, D(G(z)): 0.1144
Epoch: [7/20], Batch Num: [424/600]
Discriminator Loss: 0.3148, Generator Loss: 2.2752
D(x): 0.8949, D(G(z)): 0.1304
Epoch: [7/20], Batch Num: [425/600]
Discriminator Loss: 0.3595, Generator Loss: 2.2257
D(x): 0.9200, D(G(z)): 0.1892
Epoch: [7/20], Batch Num: [426/600]
Discriminator Loss: 0.4198, Generator Loss: 2.2209
D(x): 0.8729, D(G(z)): 0.1718
Epoch: [7/20], Batch Num: [427/600]
Discriminator Loss: 0.3853, Generator Loss: 2.5193
D(x): 0.9360, D(G(z)): 0.2009
Epoch: [7/20], Batch Num: [428/600]
Discriminator Loss: 0.4291, Generator Loss: 2.9758
D(x): 0.8748, D(G(z)): 0.1524
Epoch: [7/20], Batch Num: [429/600]
Discriminator Loss: 0.4177, Generator Loss: 2.9028
D(x): 0.8601, D(G(z)): 0.1295
Epoch: [7/20], Batch Num: [430/600]
Discriminator Loss: 0.3840, Generator Loss: 2.5146
D(x): 0.8637, D(G(z)): 0.0983
Epoch: [7/20], Batch Num: [431/600]
Discriminator Loss: 0.4132, Generator Loss: 2.5315
D(x): 0.8856, D(G(z)): 0.1445
Epoch: [7/20], Batch Num: [432/600]
Discriminator Loss: 0.3729, Generator Loss: 2.3520
D(x): 0.8855, D(G(z)): 0.1558
Epoch: [7/20], Batch Num: [433/600]
Discriminator Loss: 0.5024, Generator Loss: 2.1852
D(x): 0.8779, D(G(z)): 0.1792
Epoch: [7/20], Batch Num: [434/600]
Discriminator Loss: 0.5367, Generator Loss: 2.3703
D(x): 0.8653, D(G(z)): 0.2099
Epoch: [7/20], Batch Num: [435/600]
Discriminator Loss: 0.5793, Generator Loss: 2.6946
D(x): 0.8595, D(G(z)): 0.1974
Epoch: [7/20], Batch Num: [436/600]
Discriminator Loss: 0.5999, Generator Loss: 2.6660
D(x): 0.8431, D(G(z)): 0.1808
Epoch: [7/20], Batch Num: [437/600]
Discriminator Loss: 0.8093, Generator Loss: 2.4597
D(x): 0.7585, D(G(z)): 0.1583
Epoch: [7/20], Batch Num: [438/600]
Discriminator Loss: 0.8381, Generator Loss: 2.0250
D(x): 0.7627, D(G(z)): 0.1958
Epoch: [7/20], Batch Num: [439/600]
Discriminator Loss: 0.6779, Generator Loss: 1.8887
D(x): 0.8274, D(G(z)): 0.2419
Epoch: [7/20], Batch Num: [440/600]
Discriminator Loss: 0.8532, Generator Loss: 1.9193
D(x): 0.8032, D(G(z)): 0.2755
Epoch: [7/20], Batch Num: [441/600]
Discriminator Loss: 0.6254, Generator Loss: 1.8590
D(x): 0.8221, D(G(z)): 0.2053
Epoch: [7/20], Batch Num: [442/600]
Discriminator Loss: 0.6896, Generator Loss: 2.3379
D(x): 0.8179, D(G(z)): 0.2542
Epoch: [7/20], Batch Num: [443/600]
Discriminator Loss: 0.7877, Generator Loss: 1.9741
D(x): 0.7141, D(G(z)): 0.1550
Epoch: [7/20], Batch Num: [444/600]
Discriminator Loss: 0.8807, Generator Loss: 1.9729
D(x): 0.7761, D(G(z)): 0.2504
Epoch: [7/20], Batch Num: [445/600]
Discriminator Loss: 0.8456, Generator Loss: 2.0755
D(x): 0.7642, D(G(z)): 0.2617
Epoch: [7/20], Batch Num: [446/600]
Discriminator Loss: 0.6110, Generator Loss: 2.0373
D(x): 0.8359, D(G(z)): 0.2365
Epoch: [7/20], Batch Num: [447/600]
Discriminator Loss: 0.8096, Generator Loss: 1.9385
D(x): 0.7437, D(G(z)): 0.2239
Epoch: [7/20], Batch Num: [448/600]
Discriminator Loss: 0.9944, Generator Loss: 1.8799
D(x): 0.7028, D(G(z)): 0.1985
Epoch: [7/20], Batch Num: [449/600]
Discriminator Loss: 0.7575, Generator Loss: 1.6704
D(x): 0.7621, D(G(z)): 0.2120
Epoch: [7/20], Batch Num: [450/600]
Discriminator Loss: 0.7157, Generator Loss: 1.6526
D(x): 0.8167, D(G(z)): 0.2787
Epoch: [7/20], Batch Num: [451/600]
Discriminator Loss: 0.7520, Generator Loss: 1.7141
D(x): 0.7773, D(G(z)): 0.2539
Epoch: [7/20], Batch Num: [452/600]
Discriminator Loss: 0.7751, Generator Loss: 1.8202
D(x): 0.7817, D(G(z)): 0.2560
Epoch: [7/20], Batch Num: [453/600]
Discriminator Loss: 0.7004, Generator Loss: 2.0794
D(x): 0.7982, D(G(z)): 0.2405
Epoch: [7/20], Batch Num: [454/600]
Discriminator Loss: 0.5929, Generator Loss: 2.1787
D(x): 0.7840, D(G(z)): 0.1912
Epoch: [7/20], Batch Num: [455/600]
Discriminator Loss: 0.6347, Generator Loss: 2.1166
D(x): 0.7717, D(G(z)): 0.1722
Epoch: [7/20], Batch Num: [456/600]
Discriminator Loss: 0.5756, Generator Loss: 1.9484
D(x): 0.7936, D(G(z)): 0.1832
Epoch: [7/20], Batch Num: [457/600]
Discriminator Loss: 0.6441, Generator Loss: 1.9970
D(x): 0.7873, D(G(z)): 0.1955
Epoch: [7/20], Batch Num: [458/600]
Discriminator Loss: 0.5116, Generator Loss: 1.7681
D(x): 0.8690, D(G(z)): 0.2266
Epoch: [7/20], Batch Num: [459/600]
Discriminator Loss: 0.5994, Generator Loss: 2.0631
D(x): 0.8491, D(G(z)): 0.2423
Epoch: [7/20], Batch Num: [460/600]
Discriminator Loss: 0.4651, Generator Loss: 2.4515
D(x): 0.8547, D(G(z)): 0.1780
Epoch: [7/20], Batch Num: [461/600]
Discriminator Loss: 0.4861, Generator Loss: 2.5712
D(x): 0.8205, D(G(z)): 0.1363
Epoch: [7/20], Batch Num: [462/600]
Discriminator Loss: 0.5161, Generator Loss: 2.7362
D(x): 0.8301, D(G(z)): 0.1648
Epoch: [7/20], Batch Num: [463/600]
Discriminator Loss: 0.4762, Generator Loss: 2.5524
D(x): 0.8271, D(G(z)): 0.1350
Epoch: [7/20], Batch Num: [464/600]
Discriminator Loss: 0.4562, Generator Loss: 2.2881
D(x): 0.8449, D(G(z)): 0.1384
Epoch: [7/20], Batch Num: [465/600]
Discriminator Loss: 0.4132, Generator Loss: 2.4702
D(x): 0.8797, D(G(z)): 0.1619
Epoch: [7/20], Batch Num: [466/600]
Discriminator Loss: 0.5036, Generator Loss: 2.4681
D(x): 0.8455, D(G(z)): 0.1745
Epoch: [7/20], Batch Num: [467/600]
Discriminator Loss: 0.2912, Generator Loss: 2.4777
D(x): 0.9141, D(G(z)): 0.1496
Epoch: [7/20], Batch Num: [468/600]
Discriminator Loss: 0.4332, Generator Loss: 2.6801
D(x): 0.8894, D(G(z)): 0.1700
Epoch: [7/20], Batch Num: [469/600]
Discriminator Loss: 0.4771, Generator Loss: 2.8236
D(x): 0.8388, D(G(z)): 0.1100
Epoch: [7/20], Batch Num: [470/600]
Discriminator Loss: 0.5741, Generator Loss: 2.6378
D(x): 0.8043, D(G(z)): 0.1233
Epoch: [7/20], Batch Num: [471/600]
Discriminator Loss: 0.3799, Generator Loss: 2.4216
D(x): 0.8749, D(G(z)): 0.1379
Epoch: [7/20], Batch Num: [472/600]
Discriminator Loss: 0.5064, Generator Loss: 2.5436
D(x): 0.8859, D(G(z)): 0.1911
Epoch: [7/20], Batch Num: [473/600]
Discriminator Loss: 0.5884, Generator Loss: 2.9143
D(x): 0.8514, D(G(z)): 0.2086
Epoch: [7/20], Batch Num: [474/600]
Discriminator Loss: 0.4670, Generator Loss: 3.0555
D(x): 0.8569, D(G(z)): 0.1309
Epoch: [7/20], Batch Num: [475/600]
Discriminator Loss: 0.4443, Generator Loss: 3.0161
D(x): 0.8355, D(G(z)): 0.0967
Epoch: [7/20], Batch Num: [476/600]
Discriminator Loss: 0.7531, Generator Loss: 2.6180
D(x): 0.7572, D(G(z)): 0.1590
Epoch: [7/20], Batch Num: [477/600]
Discriminator Loss: 0.6956, Generator Loss: 2.4342
D(x): 0.7894, D(G(z)): 0.1858
Epoch: [7/20], Batch Num: [478/600]
Discriminator Loss: 0.5907, Generator Loss: 2.3659
D(x): 0.8588, D(G(z)): 0.2370
Epoch: [7/20], Batch Num: [479/600]
Discriminator Loss: 0.6383, Generator Loss: 2.7212
D(x): 0.8622, D(G(z)): 0.2249
Epoch: [7/20], Batch Num: [480/600]
Discriminator Loss: 0.6055, Generator Loss: 2.7042
D(x): 0.7969, D(G(z)): 0.1451
Epoch: [7/20], Batch Num: [481/600]
Discriminator Loss: 0.7611, Generator Loss: 2.6542
D(x): 0.7497, D(G(z)): 0.1623
Epoch: [7/20], Batch Num: [482/600]
Discriminator Loss: 0.4547, Generator Loss: 2.7635
D(x): 0.8722, D(G(z)): 0.1584
Epoch: [7/20], Batch Num: [483/600]
Discriminator Loss: 0.5803, Generator Loss: 2.2766
D(x): 0.7803, D(G(z)): 0.1371
Epoch: [7/20], Batch Num: [484/600]
Discriminator Loss: 0.6340, Generator Loss: 2.1344
D(x): 0.8422, D(G(z)): 0.2219
Epoch: [7/20], Batch Num: [485/600]
Discriminator Loss: 0.5733, Generator Loss: 2.3075
D(x): 0.8669, D(G(z)): 0.2368
Epoch: [7/20], Batch Num: [486/600]
Discriminator Loss: 0.6440, Generator Loss: 2.3441
D(x): 0.7974, D(G(z)): 0.1677
Epoch: [7/20], Batch Num: [487/600]
Discriminator Loss: 0.6352, Generator Loss: 2.4574
D(x): 0.7972, D(G(z)): 0.1984
Epoch: [7/20], Batch Num: [488/600]
Discriminator Loss: 0.6472, Generator Loss: 2.4181
D(x): 0.8056, D(G(z)): 0.1937
Epoch: [7/20], Batch Num: [489/600]
Discriminator Loss: 0.5617, Generator Loss: 2.5790
D(x): 0.8258, D(G(z)): 0.2042
Epoch: [7/20], Batch Num: [490/600]
Discriminator Loss: 0.8320, Generator Loss: 2.3365
D(x): 0.7458, D(G(z)): 0.2178
Epoch: [7/20], Batch Num: [491/600]
Discriminator Loss: 0.7858, Generator Loss: 2.2193
D(x): 0.7746, D(G(z)): 0.2086
Epoch: [7/20], Batch Num: [492/600]
Discriminator Loss: 0.8519, Generator Loss: 2.1179
D(x): 0.7739, D(G(z)): 0.2178
Epoch: [7/20], Batch Num: [493/600]
Discriminator Loss: 0.5689, Generator Loss: 2.1396
D(x): 0.8171, D(G(z)): 0.1852
Epoch: [7/20], Batch Num: [494/600]
Discriminator Loss: 0.5461, Generator Loss: 2.3555
D(x): 0.8446, D(G(z)): 0.1910
Epoch: [7/20], Batch Num: [495/600]
Discriminator Loss: 0.5606, Generator Loss: 2.0496
D(x): 0.8055, D(G(z)): 0.1557
Epoch: [7/20], Batch Num: [496/600]
Discriminator Loss: 0.7338, Generator Loss: 2.2166
D(x): 0.8119, D(G(z)): 0.2269
Epoch: [7/20], Batch Num: [497/600]
Discriminator Loss: 0.4496, Generator Loss: 2.1902
D(x): 0.8778, D(G(z)): 0.2036
Epoch: [7/20], Batch Num: [498/600]
Discriminator Loss: 0.7152, Generator Loss: 2.4814
D(x): 0.8084, D(G(z)): 0.2302
Epoch: [7/20], Batch Num: [499/600]
Discriminator Loss: 0.6237, Generator Loss: 2.6394
D(x): 0.8006, D(G(z)): 0.1801
Epoch: 7, Batch Num: [500/600]

Epoch: [7/20], Batch Num: [500/600]
Discriminator Loss: 0.5476, Generator Loss: 2.7486
D(x): 0.8230, D(G(z)): 0.1610
Epoch: [7/20], Batch Num: [501/600]
Discriminator Loss: 0.4432, Generator Loss: 2.5046
D(x): 0.8339, D(G(z)): 0.1410
Epoch: [7/20], Batch Num: [502/600]
Discriminator Loss: 0.4870, Generator Loss: 2.4072
D(x): 0.8170, D(G(z)): 0.1114
Epoch: [7/20], Batch Num: [503/600]
Discriminator Loss: 0.5071, Generator Loss: 2.1933
D(x): 0.8458, D(G(z)): 0.1755
Epoch: [7/20], Batch Num: [504/600]
Discriminator Loss: 0.4265, Generator Loss: 2.3726
D(x): 0.9037, D(G(z)): 0.1862
Epoch: [7/20], Batch Num: [505/600]
Discriminator Loss: 0.5349, Generator Loss: 2.6410
D(x): 0.8463, D(G(z)): 0.1999
Epoch: [7/20], Batch Num: [506/600]
Discriminator Loss: 0.4580, Generator Loss: 2.6615
D(x): 0.8572, D(G(z)): 0.1454
Epoch: [7/20], Batch Num: [507/600]
Discriminator Loss: 0.3922, Generator Loss: 3.1388
D(x): 0.8995, D(G(z)): 0.1639
Epoch: [7/20], Batch Num: [508/600]
Discriminator Loss: 0.2804, Generator Loss: 3.2507
D(x): 0.9472, D(G(z)): 0.1218
Epoch: [7/20], Batch Num: [509/600]
Discriminator Loss: 0.3611, Generator Loss: 3.3413
D(x): 0.8592, D(G(z)): 0.0876
Epoch: [7/20], Batch Num: [510/600]
Discriminator Loss: 0.4443, Generator Loss: 3.4087
D(x): 0.8232, D(G(z)): 0.0691
Epoch: [7/20], Batch Num: [511/600]
Discriminator Loss: 0.3065, Generator Loss: 2.9668
D(x): 0.8847, D(G(z)): 0.0902
Epoch: [7/20], Batch Num: [512/600]
Discriminator Loss: 0.3254, Generator Loss: 2.8213
D(x): 0.9129, D(G(z)): 0.1300
Epoch: [7/20], Batch Num: [513/600]
Discriminator Loss: 0.5228, Generator Loss: 2.6915
D(x): 0.8566, D(G(z)): 0.1855
Epoch: [7/20], Batch Num: [514/600]
Discriminator Loss: 0.2969, Generator Loss: 2.8122
D(x): 0.9420, D(G(z)): 0.1778
Epoch: [7/20], Batch Num: [515/600]
Discriminator Loss: 0.4457, Generator Loss: 3.3950
D(x): 0.8629, D(G(z)): 0.1396
Epoch: [7/20], Batch Num: [516/600]
Discriminator Loss: 0.3967, Generator Loss: 3.3630
D(x): 0.8598, D(G(z)): 0.1067
Epoch: [7/20], Batch Num: [517/600]
Discriminator Loss: 0.3554, Generator Loss: 3.0969
D(x): 0.8699, D(G(z)): 0.0836
Epoch: [7/20], Batch Num: [518/600]
Discriminator Loss: 0.3838, Generator Loss: 3.0088
D(x): 0.8474, D(G(z)): 0.0991
Epoch: [7/20], Batch Num: [519/600]
Discriminator Loss: 0.2944, Generator Loss: 3.0156
D(x): 0.9308, D(G(z)): 0.1319
Epoch: [7/20], Batch Num: [520/600]
Discriminator Loss: 0.3745, Generator Loss: 3.0132
D(x): 0.9233, D(G(z)): 0.1620
Epoch: [7/20], Batch Num: [521/600]
Discriminator Loss: 0.3679, Generator Loss: 3.2131
D(x): 0.8912, D(G(z)): 0.1355
Epoch: [7/20], Batch Num: [522/600]
Discriminator Loss: 0.4966, Generator Loss: 3.1301
D(x): 0.8484, D(G(z)): 0.1383
Epoch: [7/20], Batch Num: [523/600]
Discriminator Loss: 0.3826, Generator Loss: 3.1863
D(x): 0.8897, D(G(z)): 0.1151
Epoch: [7/20], Batch Num: [524/600]
Discriminator Loss: 0.3660, Generator Loss: 3.1861
D(x): 0.8785, D(G(z)): 0.1134
Epoch: [7/20], Batch Num: [525/600]
Discriminator Loss: 0.5720, Generator Loss: 2.9439
D(x): 0.8320, D(G(z)): 0.1523
Epoch: [7/20], Batch Num: [526/600]
Discriminator Loss: 0.4382, Generator Loss: 2.5577
D(x): 0.8717, D(G(z)): 0.1435
Epoch: [7/20], Batch Num: [527/600]
Discriminator Loss: 0.6309, Generator Loss: 2.5628
D(x): 0.8472, D(G(z)): 0.1919
Epoch: [7/20], Batch Num: [528/600]
Discriminator Loss: 0.5529, Generator Loss: 2.5721
D(x): 0.8334, D(G(z)): 0.1459
Epoch: [7/20], Batch Num: [529/600]
Discriminator Loss: 0.5317, Generator Loss: 2.8420
D(x): 0.8513, D(G(z)): 0.1834
Epoch: [7/20], Batch Num: [530/600]
Discriminator Loss: 0.6304, Generator Loss: 2.5157
D(x): 0.8141, D(G(z)): 0.1493
Epoch: [7/20], Batch Num: [531/600]
Discriminator Loss: 0.5821, Generator Loss: 2.3124
D(x): 0.8465, D(G(z)): 0.1651
Epoch: [7/20], Batch Num: [532/600]
Discriminator Loss: 0.6640, Generator Loss: 2.0543
D(x): 0.8258, D(G(z)): 0.2039
Epoch: [7/20], Batch Num: [533/600]
Discriminator Loss: 0.6549, Generator Loss: 2.2423
D(x): 0.8044, D(G(z)): 0.1970
Epoch: [7/20], Batch Num: [534/600]
Discriminator Loss: 0.6337, Generator Loss: 2.2161
D(x): 0.8217, D(G(z)): 0.2195
Epoch: [7/20], Batch Num: [535/600]
Discriminator Loss: 0.6863, Generator Loss: 2.1478
D(x): 0.7934, D(G(z)): 0.2130
Epoch: [7/20], Batch Num: [536/600]
Discriminator Loss: 0.5745, Generator Loss: 2.2010
D(x): 0.8495, D(G(z)): 0.2112
Epoch: [7/20], Batch Num: [537/600]
Discriminator Loss: 0.7689, Generator Loss: 2.0139
D(x): 0.7969, D(G(z)): 0.2278
Epoch: [7/20], Batch Num: [538/600]
Discriminator Loss: 0.8715, Generator Loss: 1.9594
D(x): 0.7529, D(G(z)): 0.2114
Epoch: [7/20], Batch Num: [539/600]
Discriminator Loss: 0.7511, Generator Loss: 2.0639
D(x): 0.8428, D(G(z)): 0.2883
Epoch: [7/20], Batch Num: [540/600]
Discriminator Loss: 0.6046, Generator Loss: 2.2626
D(x): 0.8127, D(G(z)): 0.1826
Epoch: [7/20], Batch Num: [541/600]
Discriminator Loss: 0.8315, Generator Loss: 1.8581
D(x): 0.7253, D(G(z)): 0.2007
Epoch: [7/20], Batch Num: [542/600]
Discriminator Loss: 0.8915, Generator Loss: 1.8152
D(x): 0.7625, D(G(z)): 0.2529
Epoch: [7/20], Batch Num: [543/600]
Discriminator Loss: 0.9273, Generator Loss: 1.7360
D(x): 0.7132, D(G(z)): 0.2733
Epoch: [7/20], Batch Num: [544/600]
Discriminator Loss: 0.6762, Generator Loss: 2.0334
D(x): 0.8367, D(G(z)): 0.2583
Epoch: [7/20], Batch Num: [545/600]
Discriminator Loss: 0.7254, Generator Loss: 2.0084
D(x): 0.7682, D(G(z)): 0.2053
Epoch: [7/20], Batch Num: [546/600]
Discriminator Loss: 0.7037, Generator Loss: 2.0380
D(x): 0.8135, D(G(z)): 0.2214
Epoch: [7/20], Batch Num: [547/600]
Discriminator Loss: 0.5634, Generator Loss: 2.1683
D(x): 0.8241, D(G(z)): 0.1859
Epoch: [7/20], Batch Num: [548/600]
Discriminator Loss: 0.5445, Generator Loss: 1.9816
D(x): 0.8359, D(G(z)): 0.1933
Epoch: [7/20], Batch Num: [549/600]
Discriminator Loss: 0.6563, Generator Loss: 2.1510
D(x): 0.7828, D(G(z)): 0.1873
Epoch: [7/20], Batch Num: [550/600]
Discriminator Loss: 0.5334, Generator Loss: 2.0415
D(x): 0.8387, D(G(z)): 0.2021
Epoch: [7/20], Batch Num: [551/600]
Discriminator Loss: 0.4232, Generator Loss: 2.0190
D(x): 0.8727, D(G(z)): 0.1682
Epoch: [7/20], Batch Num: [552/600]
Discriminator Loss: 0.3903, Generator Loss: 2.3735
D(x): 0.9234, D(G(z)): 0.2110
Epoch: [7/20], Batch Num: [553/600]
Discriminator Loss: 0.3910, Generator Loss: 2.6281
D(x): 0.8740, D(G(z)): 0.1603
Epoch: [7/20], Batch Num: [554/600]
Discriminator Loss: 0.3718, Generator Loss: 2.9273
D(x): 0.8776, D(G(z)): 0.1478
Epoch: [7/20], Batch Num: [555/600]
Discriminator Loss: 0.4025, Generator Loss: 2.9820
D(x): 0.8346, D(G(z)): 0.1105
Epoch: [7/20], Batch Num: [556/600]
Discriminator Loss: 0.3293, Generator Loss: 3.0065
D(x): 0.8707, D(G(z)): 0.1048
Epoch: [7/20], Batch Num: [557/600]
Discriminator Loss: 0.4091, Generator Loss: 2.6951
D(x): 0.8560, D(G(z)): 0.1065
Epoch: [7/20], Batch Num: [558/600]
Discriminator Loss: 0.3228, Generator Loss: 2.5287
D(x): 0.8981, D(G(z)): 0.1344
Epoch: [7/20], Batch Num: [559/600]
Discriminator Loss: 0.3467, Generator Loss: 2.5878
D(x): 0.9341, D(G(z)): 0.1873
Epoch: [7/20], Batch Num: [560/600]
Discriminator Loss: 0.4067, Generator Loss: 3.0124
D(x): 0.8965, D(G(z)): 0.1672
Epoch: [7/20], Batch Num: [561/600]
Discriminator Loss: 0.3583, Generator Loss: 3.1687
D(x): 0.8806, D(G(z)): 0.1076
Epoch: [7/20], Batch Num: [562/600]
Discriminator Loss: 0.4258, Generator Loss: 3.2354
D(x): 0.8656, D(G(z)): 0.1219
Epoch: [7/20], Batch Num: [563/600]
Discriminator Loss: 0.3126, Generator Loss: 3.3962
D(x): 0.8969, D(G(z)): 0.1056
Epoch: [7/20], Batch Num: [564/600]
Discriminator Loss: 0.3523, Generator Loss: 3.1445
D(x): 0.8854, D(G(z)): 0.1023
Epoch: [7/20], Batch Num: [565/600]
Discriminator Loss: 0.4070, Generator Loss: 2.7812
D(x): 0.8712, D(G(z)): 0.1109
Epoch: [7/20], Batch Num: [566/600]
Discriminator Loss: 0.4754, Generator Loss: 2.4555
D(x): 0.8477, D(G(z)): 0.1432
Epoch: [7/20], Batch Num: [567/600]
Discriminator Loss: 0.3674, Generator Loss: 2.4627
D(x): 0.9050, D(G(z)): 0.1580
Epoch: [7/20], Batch Num: [568/600]
Discriminator Loss: 0.5290, Generator Loss: 2.5282
D(x): 0.9051, D(G(z)): 0.2080
Epoch: [7/20], Batch Num: [569/600]
Discriminator Loss: 0.4311, Generator Loss: 3.0045
D(x): 0.8742, D(G(z)): 0.1609
Epoch: [7/20], Batch Num: [570/600]
Discriminator Loss: 0.6172, Generator Loss: 3.1011
D(x): 0.8011, D(G(z)): 0.1325
Epoch: [7/20], Batch Num: [571/600]
Discriminator Loss: 0.5631, Generator Loss: 2.8265
D(x): 0.8341, D(G(z)): 0.1520
Epoch: [7/20], Batch Num: [572/600]
Discriminator Loss: 0.9635, Generator Loss: 2.7848
D(x): 0.7271, D(G(z)): 0.1673
Epoch: [7/20], Batch Num: [573/600]
Discriminator Loss: 0.7057, Generator Loss: 2.5025
D(x): 0.7844, D(G(z)): 0.1610
Epoch: [7/20], Batch Num: [574/600]
Discriminator Loss: 0.6747, Generator Loss: 2.0882
D(x): 0.8147, D(G(z)): 0.2234
Epoch: [7/20], Batch Num: [575/600]
Discriminator Loss: 0.9015, Generator Loss: 2.6877
D(x): 0.8124, D(G(z)): 0.2679
Epoch: [7/20], Batch Num: [576/600]
Discriminator Loss: 0.9052, Generator Loss: 2.7148
D(x): 0.7469, D(G(z)): 0.2222
Epoch: [7/20], Batch Num: [577/600]
Discriminator Loss: 0.9119, Generator Loss: 2.7597
D(x): 0.7145, D(G(z)): 0.1621
Epoch: [7/20], Batch Num: [578/600]
Discriminator Loss: 0.5862, Generator Loss: 2.4198
D(x): 0.8069, D(G(z)): 0.1819
Epoch: [7/20], Batch Num: [579/600]
Discriminator Loss: 0.7022, Generator Loss: 1.8482
D(x): 0.7517, D(G(z)): 0.1624
Epoch: [7/20], Batch Num: [580/600]
Discriminator Loss: 0.9099, Generator Loss: 2.0284
D(x): 0.7968, D(G(z)): 0.2902
Epoch: [7/20], Batch Num: [581/600]
Discriminator Loss: 0.9841, Generator Loss: 2.5299
D(x): 0.8079, D(G(z)): 0.3314
Epoch: [7/20], Batch Num: [582/600]
Discriminator Loss: 0.7045, Generator Loss: 2.9062
D(x): 0.7644, D(G(z)): 0.1578
Epoch: [7/20], Batch Num: [583/600]
Discriminator Loss: 0.8294, Generator Loss: 2.4442
D(x): 0.7078, D(G(z)): 0.1379
Epoch: [7/20], Batch Num: [584/600]
Discriminator Loss: 0.7455, Generator Loss: 2.3521
D(x): 0.7780, D(G(z)): 0.1882
Epoch: [7/20], Batch Num: [585/600]
Discriminator Loss: 0.6808, Generator Loss: 1.7552
D(x): 0.8061, D(G(z)): 0.2005
Epoch: [7/20], Batch Num: [586/600]
Discriminator Loss: 0.7299, Generator Loss: 1.6544
D(x): 0.8337, D(G(z)): 0.2581
Epoch: [7/20], Batch Num: [587/600]
Discriminator Loss: 0.8146, Generator Loss: 2.0673
D(x): 0.8476, D(G(z)): 0.3130
Epoch: [7/20], Batch Num: [588/600]
Discriminator Loss: 0.6561, Generator Loss: 2.4099
D(x): 0.8070, D(G(z)): 0.2296
Epoch: [7/20], Batch Num: [589/600]
Discriminator Loss: 0.7574, Generator Loss: 2.3939
D(x): 0.7416, D(G(z)): 0.1773
Epoch: [7/20], Batch Num: [590/600]
Discriminator Loss: 0.7879, Generator Loss: 2.2359
D(x): 0.7285, D(G(z)): 0.1590
Epoch: [7/20], Batch Num: [591/600]
Discriminator Loss: 0.6574, Generator Loss: 1.8022
D(x): 0.7618, D(G(z)): 0.1625
Epoch: [7/20], Batch Num: [592/600]
Discriminator Loss: 0.5294, Generator Loss: 1.7006
D(x): 0.8726, D(G(z)): 0.2378
Epoch: [7/20], Batch Num: [593/600]
Discriminator Loss: 0.5476, Generator Loss: 2.0525
D(x): 0.8592, D(G(z)): 0.2238
Epoch: [7/20], Batch Num: [594/600]
Discriminator Loss: 0.5758, Generator Loss: 2.4535
D(x): 0.9067, D(G(z)): 0.2773
Epoch: [7/20], Batch Num: [595/600]
Discriminator Loss: 0.4350, Generator Loss: 2.9036
D(x): 0.8853, D(G(z)): 0.1707
Epoch: [7/20], Batch Num: [596/600]
Discriminator Loss: 0.6245, Generator Loss: 3.0219
D(x): 0.7622, D(G(z)): 0.0961
Epoch: [7/20], Batch Num: [597/600]
Discriminator Loss: 0.8755, Generator Loss: 2.5573
D(x): 0.6380, D(G(z)): 0.1016
Epoch: [7/20], Batch Num: [598/600]
Discriminator Loss: 0.4994, Generator Loss: 2.0606
D(x): 0.8286, D(G(z)): 0.1407
Epoch: [7/20], Batch Num: [599/600]
Discriminator Loss: 0.5687, Generator Loss: 2.1362
D(x): 0.8763, D(G(z)): 0.2526
Epoch: 8, Batch Num: [0/600]

Epoch: [8/20], Batch Num: [0/600]
Discriminator Loss: 0.4696, Generator Loss: 2.2924
D(x): 0.9241, D(G(z)): 0.2434
Epoch: [8/20], Batch Num: [1/600]
Discriminator Loss: 0.3580, Generator Loss: 2.9697
D(x): 0.9406, D(G(z)): 0.2028
Epoch: [8/20], Batch Num: [2/600]
Discriminator Loss: 0.2631, Generator Loss: 3.4941
D(x): 0.9183, D(G(z)): 0.1112
Epoch: [8/20], Batch Num: [3/600]
Discriminator Loss: 0.4611, Generator Loss: 3.4713
D(x): 0.7983, D(G(z)): 0.0743
Epoch: [8/20], Batch Num: [4/600]
Discriminator Loss: 0.4387, Generator Loss: 3.2454
D(x): 0.8262, D(G(z)): 0.0695
Epoch: [8/20], Batch Num: [5/600]
Discriminator Loss: 0.3220, Generator Loss: 2.9944
D(x): 0.8708, D(G(z)): 0.0777
Epoch: [8/20], Batch Num: [6/600]
Discriminator Loss: 0.3654, Generator Loss: 2.6114
D(x): 0.8709, D(G(z)): 0.1197
Epoch: [8/20], Batch Num: [7/600]
Discriminator Loss: 0.2791, Generator Loss: 2.4613
D(x): 0.9461, D(G(z)): 0.1658
Epoch: [8/20], Batch Num: [8/600]
Discriminator Loss: 0.4520, Generator Loss: 2.4616
D(x): 0.9152, D(G(z)): 0.1971
Epoch: [8/20], Batch Num: [9/600]
Discriminator Loss: 0.3321, Generator Loss: 2.6660
D(x): 0.9207, D(G(z)): 0.1420
Epoch: [8/20], Batch Num: [10/600]
Discriminator Loss: 0.3970, Generator Loss: 2.7952
D(x): 0.8704, D(G(z)): 0.1456
Epoch: [8/20], Batch Num: [11/600]
Discriminator Loss: 0.4519, Generator Loss: 3.2355
D(x): 0.9155, D(G(z)): 0.1636
Epoch: [8/20], Batch Num: [12/600]
Discriminator Loss: 0.4913, Generator Loss: 3.0845
D(x): 0.8337, D(G(z)): 0.1414
Epoch: [8/20], Batch Num: [13/600]
Discriminator Loss: 0.4620, Generator Loss: 2.8440
D(x): 0.8496, D(G(z)): 0.1205
Epoch: [8/20], Batch Num: [14/600]
Discriminator Loss: 0.5328, Generator Loss: 2.7044
D(x): 0.8269, D(G(z)): 0.1345
Epoch: [8/20], Batch Num: [15/600]
Discriminator Loss: 0.5466, Generator Loss: 2.5507
D(x): 0.8510, D(G(z)): 0.1838
Epoch: [8/20], Batch Num: [16/600]
Discriminator Loss: 0.7171, Generator Loss: 2.4036
D(x): 0.8367, D(G(z)): 0.2329
Epoch: [8/20], Batch Num: [17/600]
Discriminator Loss: 0.5357, Generator Loss: 2.5073
D(x): 0.8564, D(G(z)): 0.1871
Epoch: [8/20], Batch Num: [18/600]
Discriminator Loss: 0.6522, Generator Loss: 2.7827
D(x): 0.8242, D(G(z)): 0.1782
Epoch: [8/20], Batch Num: [19/600]
Discriminator Loss: 0.6717, Generator Loss: 2.8866
D(x): 0.7928, D(G(z)): 0.1674
Epoch: [8/20], Batch Num: [20/600]
Discriminator Loss: 0.7435, Generator Loss: 2.7948
D(x): 0.8057, D(G(z)): 0.1690
Epoch: [8/20], Batch Num: [21/600]
Discriminator Loss: 0.6681, Generator Loss: 2.3867
D(x): 0.7596, D(G(z)): 0.1596
Epoch: [8/20], Batch Num: [22/600]
Discriminator Loss: 0.7431, Generator Loss: 2.1706
D(x): 0.8486, D(G(z)): 0.2159
Epoch: [8/20], Batch Num: [23/600]
Discriminator Loss: 0.9230, Generator Loss: 2.1780
D(x): 0.8035, D(G(z)): 0.2682
Epoch: [8/20], Batch Num: [24/600]
Discriminator Loss: 0.7704, Generator Loss: 2.5280
D(x): 0.8113, D(G(z)): 0.2333
Epoch: [8/20], Batch Num: [25/600]
Discriminator Loss: 0.5330, Generator Loss: 2.3539
D(x): 0.8195, D(G(z)): 0.1655
Epoch: [8/20], Batch Num: [26/600]
Discriminator Loss: 0.8660, Generator Loss: 2.5555
D(x): 0.7702, D(G(z)): 0.2651
Epoch: [8/20], Batch Num: [27/600]
Discriminator Loss: 0.9489, Generator Loss: 2.2158
D(x): 0.6678, D(G(z)): 0.1727
Epoch: [8/20], Batch Num: [28/600]
Discriminator Loss: 0.7673, Generator Loss: 2.0533
D(x): 0.7615, D(G(z)): 0.2212
Epoch: [8/20], Batch Num: [29/600]
Discriminator Loss: 0.7306, Generator Loss: 1.6990
D(x): 0.7619, D(G(z)): 0.2084
Epoch: [8/20], Batch Num: [30/600]
Discriminator Loss: 0.8160, Generator Loss: 1.5836
D(x): 0.8219, D(G(z)): 0.3338
Epoch: [8/20], Batch Num: [31/600]
Discriminator Loss: 0.8762, Generator Loss: 2.0803
D(x): 0.8583, D(G(z)): 0.3397
Epoch: [8/20], Batch Num: [32/600]
Discriminator Loss: 0.8197, Generator Loss: 2.2292
D(x): 0.8044, D(G(z)): 0.2830
Epoch: [8/20], Batch Num: [33/600]
Discriminator Loss: 0.7978, Generator Loss: 2.4779
D(x): 0.7017, D(G(z)): 0.1876
Epoch: [8/20], Batch Num: [34/600]
Discriminator Loss: 1.0598, Generator Loss: 2.1143
D(x): 0.5779, D(G(z)): 0.1855
Epoch: [8/20], Batch Num: [35/600]
Discriminator Loss: 0.8519, Generator Loss: 1.8610
D(x): 0.7278, D(G(z)): 0.2352
Epoch: [8/20], Batch Num: [36/600]
Discriminator Loss: 1.1601, Generator Loss: 1.5124
D(x): 0.7209, D(G(z)): 0.3566
Epoch: [8/20], Batch Num: [37/600]
Discriminator Loss: 1.1707, Generator Loss: 1.5636
D(x): 0.6911, D(G(z)): 0.3451
Epoch: [8/20], Batch Num: [38/600]
Discriminator Loss: 0.8741, Generator Loss: 1.7360
D(x): 0.7981, D(G(z)): 0.3505
Epoch: [8/20], Batch Num: [39/600]
Discriminator Loss: 1.1177, Generator Loss: 2.1766
D(x): 0.6839, D(G(z)): 0.3281
Epoch: [8/20], Batch Num: [40/600]
Discriminator Loss: 0.8846, Generator Loss: 2.4844
D(x): 0.6698, D(G(z)): 0.2045
Epoch: [8/20], Batch Num: [41/600]
Discriminator Loss: 0.8410, Generator Loss: 2.2446
D(x): 0.6668, D(G(z)): 0.1782
Epoch: [8/20], Batch Num: [42/600]
Discriminator Loss: 0.9540, Generator Loss: 2.0766
D(x): 0.6257, D(G(z)): 0.1674
Epoch: [8/20], Batch Num: [43/600]
Discriminator Loss: 0.6719, Generator Loss: 1.5961
D(x): 0.7898, D(G(z)): 0.2205
Epoch: [8/20], Batch Num: [44/600]
Discriminator Loss: 0.8626, Generator Loss: 1.7774
D(x): 0.7844, D(G(z)): 0.3075
Epoch: [8/20], Batch Num: [45/600]
Discriminator Loss: 0.7651, Generator Loss: 1.9034
D(x): 0.8043, D(G(z)): 0.2850
Epoch: [8/20], Batch Num: [46/600]
Discriminator Loss: 0.6900, Generator Loss: 2.0434
D(x): 0.7966, D(G(z)): 0.2494
Epoch: [8/20], Batch Num: [47/600]
Discriminator Loss: 0.6575, Generator Loss: 2.3585
D(x): 0.8257, D(G(z)): 0.2469
Epoch: [8/20], Batch Num: [48/600]
Discriminator Loss: 0.5606, Generator Loss: 2.4917
D(x): 0.8026, D(G(z)): 0.1891
Epoch: [8/20], Batch Num: [49/600]
Discriminator Loss: 0.6466, Generator Loss: 2.6129
D(x): 0.7377, D(G(z)): 0.1701
Epoch: [8/20], Batch Num: [50/600]
Discriminator Loss: 0.5003, Generator Loss: 2.4765
D(x): 0.7958, D(G(z)): 0.1426
Epoch: [8/20], Batch Num: [51/600]
Discriminator Loss: 0.5462, Generator Loss: 2.4036
D(x): 0.8078, D(G(z)): 0.1742
Epoch: [8/20], Batch Num: [52/600]
Discriminator Loss: 0.4347, Generator Loss: 2.4531
D(x): 0.8463, D(G(z)): 0.1705
Epoch: [8/20], Batch Num: [53/600]
Discriminator Loss: 0.3438, Generator Loss: 2.1986
D(x): 0.9312, D(G(z)): 0.1967
Epoch: [8/20], Batch Num: [54/600]
Discriminator Loss: 0.4507, Generator Loss: 2.7138
D(x): 0.8738, D(G(z)): 0.1805
Epoch: [8/20], Batch Num: [55/600]
Discriminator Loss: 0.4297, Generator Loss: 2.4765
D(x): 0.8500, D(G(z)): 0.1543
Epoch: [8/20], Batch Num: [56/600]
Discriminator Loss: 0.3684, Generator Loss: 2.8826
D(x): 0.8771, D(G(z)): 0.1489
Epoch: [8/20], Batch Num: [57/600]
Discriminator Loss: 0.4263, Generator Loss: 2.5560
D(x): 0.8388, D(G(z)): 0.1442
Epoch: [8/20], Batch Num: [58/600]
Discriminator Loss: 0.4548, Generator Loss: 2.5449
D(x): 0.8557, D(G(z)): 0.1585
Epoch: [8/20], Batch Num: [59/600]
Discriminator Loss: 0.4902, Generator Loss: 2.5228
D(x): 0.8028, D(G(z)): 0.1611
Epoch: [8/20], Batch Num: [60/600]
Discriminator Loss: 0.4498, Generator Loss: 2.5070
D(x): 0.8761, D(G(z)): 0.1838
Epoch: [8/20], Batch Num: [61/600]
Discriminator Loss: 0.5308, Generator Loss: 2.1533
D(x): 0.8260, D(G(z)): 0.1816
Epoch: [8/20], Batch Num: [62/600]
Discriminator Loss: 0.5193, Generator Loss: 2.5315
D(x): 0.9069, D(G(z)): 0.2400
Epoch: [8/20], Batch Num: [63/600]
Discriminator Loss: 0.6855, Generator Loss: 2.7175
D(x): 0.8063, D(G(z)): 0.2178
Epoch: [8/20], Batch Num: [64/600]
Discriminator Loss: 0.5531, Generator Loss: 3.0910
D(x): 0.7893, D(G(z)): 0.1185
Epoch: [8/20], Batch Num: [65/600]
Discriminator Loss: 0.6133, Generator Loss: 2.8378
D(x): 0.7944, D(G(z)): 0.1291
Epoch: [8/20], Batch Num: [66/600]
Discriminator Loss: 0.5857, Generator Loss: 2.3956
D(x): 0.8086, D(G(z)): 0.1528
Epoch: [8/20], Batch Num: [67/600]
Discriminator Loss: 0.5274, Generator Loss: 2.2041
D(x): 0.8438, D(G(z)): 0.1773
Epoch: [8/20], Batch Num: [68/600]
Discriminator Loss: 0.6181, Generator Loss: 2.2631
D(x): 0.8436, D(G(z)): 0.2213
Epoch: [8/20], Batch Num: [69/600]
Discriminator Loss: 0.7307, Generator Loss: 2.3430
D(x): 0.8325, D(G(z)): 0.2395
Epoch: [8/20], Batch Num: [70/600]
Discriminator Loss: 0.7162, Generator Loss: 2.5977
D(x): 0.7722, D(G(z)): 0.1873
Epoch: [8/20], Batch Num: [71/600]
Discriminator Loss: 0.7405, Generator Loss: 2.4319
D(x): 0.7439, D(G(z)): 0.1572
Epoch: [8/20], Batch Num: [72/600]
Discriminator Loss: 0.7295, Generator Loss: 2.6223
D(x): 0.8284, D(G(z)): 0.2429
Epoch: [8/20], Batch Num: [73/600]
Discriminator Loss: 0.8923, Generator Loss: 2.3670
D(x): 0.7418, D(G(z)): 0.1897
Epoch: [8/20], Batch Num: [74/600]
Discriminator Loss: 0.8120, Generator Loss: 2.1006
D(x): 0.7315, D(G(z)): 0.2142
Epoch: [8/20], Batch Num: [75/600]
Discriminator Loss: 1.0118, Generator Loss: 2.1083
D(x): 0.7217, D(G(z)): 0.2294
Epoch: [8/20], Batch Num: [76/600]
Discriminator Loss: 1.1224, Generator Loss: 2.3118
D(x): 0.7061, D(G(z)): 0.3175
Epoch: [8/20], Batch Num: [77/600]
Discriminator Loss: 0.9594, Generator Loss: 2.3549
D(x): 0.7593, D(G(z)): 0.2526
Epoch: [8/20], Batch Num: [78/600]
Discriminator Loss: 1.0853, Generator Loss: 2.4051
D(x): 0.6546, D(G(z)): 0.2078
Epoch: [8/20], Batch Num: [79/600]
Discriminator Loss: 0.8431, Generator Loss: 1.9749
D(x): 0.7232, D(G(z)): 0.2106
Epoch: [8/20], Batch Num: [80/600]
Discriminator Loss: 1.0401, Generator Loss: 1.8453
D(x): 0.6955, D(G(z)): 0.2308
Epoch: [8/20], Batch Num: [81/600]
Discriminator Loss: 0.8458, Generator Loss: 1.8928
D(x): 0.7704, D(G(z)): 0.2898
Epoch: [8/20], Batch Num: [82/600]
Discriminator Loss: 0.8221, Generator Loss: 1.8687
D(x): 0.7571, D(G(z)): 0.2660
Epoch: [8/20], Batch Num: [83/600]
Discriminator Loss: 0.9820, Generator Loss: 1.9146
D(x): 0.7338, D(G(z)): 0.2766
Epoch: [8/20], Batch Num: [84/600]
Discriminator Loss: 0.9418, Generator Loss: 1.9161
D(x): 0.7162, D(G(z)): 0.2508
Epoch: [8/20], Batch Num: [85/600]
Discriminator Loss: 0.7949, Generator Loss: 2.0010
D(x): 0.7680, D(G(z)): 0.2425
Epoch: [8/20], Batch Num: [86/600]
Discriminator Loss: 0.6035, Generator Loss: 1.9750
D(x): 0.7958, D(G(z)): 0.2187
Epoch: [8/20], Batch Num: [87/600]
Discriminator Loss: 0.7095, Generator Loss: 1.9610
D(x): 0.7512, D(G(z)): 0.2254
Epoch: [8/20], Batch Num: [88/600]
Discriminator Loss: 0.7919, Generator Loss: 1.7049
D(x): 0.7374, D(G(z)): 0.2100
Epoch: [8/20], Batch Num: [89/600]
Discriminator Loss: 0.7893, Generator Loss: 1.6495
D(x): 0.7700, D(G(z)): 0.2419
Epoch: [8/20], Batch Num: [90/600]
Discriminator Loss: 0.6724, Generator Loss: 1.6804
D(x): 0.8022, D(G(z)): 0.2691
Epoch: [8/20], Batch Num: [91/600]
Discriminator Loss: 0.6868, Generator Loss: 1.6422
D(x): 0.8637, D(G(z)): 0.3019
Epoch: [8/20], Batch Num: [92/600]
Discriminator Loss: 0.6154, Generator Loss: 2.1962
D(x): 0.8429, D(G(z)): 0.2650
Epoch: [8/20], Batch Num: [93/600]
Discriminator Loss: 0.6651, Generator Loss: 2.2435
D(x): 0.7732, D(G(z)): 0.2002
Epoch: [8/20], Batch Num: [94/600]
Discriminator Loss: 0.6783, Generator Loss: 2.3080
D(x): 0.7491, D(G(z)): 0.1652
Epoch: [8/20], Batch Num: [95/600]
Discriminator Loss: 0.7919, Generator Loss: 2.3613
D(x): 0.6882, D(G(z)): 0.1639
Epoch: [8/20], Batch Num: [96/600]
Discriminator Loss: 0.5913, Generator Loss: 1.9297
D(x): 0.7713, D(G(z)): 0.1670
Epoch: [8/20], Batch Num: [97/600]
Discriminator Loss: 0.6157, Generator Loss: 1.6879
D(x): 0.7836, D(G(z)): 0.2096
Epoch: [8/20], Batch Num: [98/600]
Discriminator Loss: 0.7451, Generator Loss: 1.6788
D(x): 0.8441, D(G(z)): 0.3130
Epoch: [8/20], Batch Num: [99/600]
Discriminator Loss: 0.4951, Generator Loss: 1.8140
D(x): 0.9092, D(G(z)): 0.2761
Epoch: 8, Batch Num: [100/600]

Epoch: [8/20], Batch Num: [100/600]
Discriminator Loss: 0.6004, Generator Loss: 2.2460
D(x): 0.8390, D(G(z)): 0.2503
Epoch: [8/20], Batch Num: [101/600]
Discriminator Loss: 0.4739, Generator Loss: 2.5551
D(x): 0.8300, D(G(z)): 0.1577
Epoch: [8/20], Batch Num: [102/600]
Discriminator Loss: 0.6228, Generator Loss: 2.6552
D(x): 0.7423, D(G(z)): 0.1547
Epoch: [8/20], Batch Num: [103/600]
Discriminator Loss: 0.6244, Generator Loss: 2.4837
D(x): 0.7120, D(G(z)): 0.1147
Epoch: [8/20], Batch Num: [104/600]
Discriminator Loss: 0.5478, Generator Loss: 1.9803
D(x): 0.8067, D(G(z)): 0.1665
Epoch: [8/20], Batch Num: [105/600]
Discriminator Loss: 0.6103, Generator Loss: 1.8618
D(x): 0.8448, D(G(z)): 0.2672
Epoch: [8/20], Batch Num: [106/600]
Discriminator Loss: 0.4638, Generator Loss: 2.0006
D(x): 0.8603, D(G(z)): 0.2116
Epoch: [8/20], Batch Num: [107/600]
Discriminator Loss: 0.6364, Generator Loss: 2.1534
D(x): 0.8516, D(G(z)): 0.2768
Epoch: [8/20], Batch Num: [108/600]
Discriminator Loss: 0.6191, Generator Loss: 2.4911
D(x): 0.8303, D(G(z)): 0.2332
Epoch: [8/20], Batch Num: [109/600]
Discriminator Loss: 0.6412, Generator Loss: 2.4600
D(x): 0.8070, D(G(z)): 0.2208
Epoch: [8/20], Batch Num: [110/600]
Discriminator Loss: 0.5063, Generator Loss: 2.5309
D(x): 0.7937, D(G(z)): 0.1339
Epoch: [8/20], Batch Num: [111/600]
Discriminator Loss: 0.5855, Generator Loss: 2.6625
D(x): 0.7630, D(G(z)): 0.1535
Epoch: [8/20], Batch Num: [112/600]
Discriminator Loss: 0.5676, Generator Loss: 2.3664
D(x): 0.7839, D(G(z)): 0.1735
Epoch: [8/20], Batch Num: [113/600]
Discriminator Loss: 0.6374, Generator Loss: 2.3637
D(x): 0.7948, D(G(z)): 0.2028
Epoch: [8/20], Batch Num: [114/600]
Discriminator Loss: 0.6079, Generator Loss: 2.0051
D(x): 0.8479, D(G(z)): 0.2369
Epoch: [8/20], Batch Num: [115/600]
Discriminator Loss: 0.6886, Generator Loss: 2.4017
D(x): 0.8535, D(G(z)): 0.2848
Epoch: [8/20], Batch Num: [116/600]
Discriminator Loss: 0.5860, Generator Loss: 2.6479
D(x): 0.8585, D(G(z)): 0.2112
Epoch: [8/20], Batch Num: [117/600]
Discriminator Loss: 0.6558, Generator Loss: 2.7088
D(x): 0.7918, D(G(z)): 0.1861
Epoch: [8/20], Batch Num: [118/600]
Discriminator Loss: 0.8438, Generator Loss: 2.7625
D(x): 0.7426, D(G(z)): 0.1930
Epoch: [8/20], Batch Num: [119/600]
Discriminator Loss: 0.8821, Generator Loss: 2.5981
D(x): 0.6996, D(G(z)): 0.1794
Epoch: [8/20], Batch Num: [120/600]
Discriminator Loss: 1.0075, Generator Loss: 2.3242
D(x): 0.7018, D(G(z)): 0.2264
Epoch: [8/20], Batch Num: [121/600]
Discriminator Loss: 1.0405, Generator Loss: 1.8974
D(x): 0.6898, D(G(z)): 0.2316
Epoch: [8/20], Batch Num: [122/600]
Discriminator Loss: 0.9599, Generator Loss: 1.9112
D(x): 0.7345, D(G(z)): 0.2796
Epoch: [8/20], Batch Num: [123/600]
Discriminator Loss: 1.0262, Generator Loss: 1.8120
D(x): 0.7478, D(G(z)): 0.3121
Epoch: [8/20], Batch Num: [124/600]
Discriminator Loss: 1.1738, Generator Loss: 1.9659
D(x): 0.6764, D(G(z)): 0.3347
Epoch: [8/20], Batch Num: [125/600]
Discriminator Loss: 1.0292, Generator Loss: 2.0262
D(x): 0.7120, D(G(z)): 0.2680
Epoch: [8/20], Batch Num: [126/600]
Discriminator Loss: 1.1966, Generator Loss: 2.0845
D(x): 0.6512, D(G(z)): 0.2780
Epoch: [8/20], Batch Num: [127/600]
Discriminator Loss: 1.0131, Generator Loss: 2.1100
D(x): 0.6509, D(G(z)): 0.2105
Epoch: [8/20], Batch Num: [128/600]
Discriminator Loss: 1.0961, Generator Loss: 1.9019
D(x): 0.6511, D(G(z)): 0.2784
Epoch: [8/20], Batch Num: [129/600]
Discriminator Loss: 1.2802, Generator Loss: 1.5791
D(x): 0.5888, D(G(z)): 0.2558
Epoch: [8/20], Batch Num: [130/600]
Discriminator Loss: 1.1570, Generator Loss: 1.5531
D(x): 0.6729, D(G(z)): 0.3054
Epoch: [8/20], Batch Num: [131/600]
Discriminator Loss: 1.0298, Generator Loss: 1.6000
D(x): 0.7199, D(G(z)): 0.3337
Epoch: [8/20], Batch Num: [132/600]
Discriminator Loss: 0.8912, Generator Loss: 1.7861
D(x): 0.7603, D(G(z)): 0.3196
Epoch: [8/20], Batch Num: [133/600]
Discriminator Loss: 0.8069, Generator Loss: 1.9827
D(x): 0.7557, D(G(z)): 0.2772
Epoch: [8/20], Batch Num: [134/600]
Discriminator Loss: 0.6206, Generator Loss: 2.0630
D(x): 0.8085, D(G(z)): 0.2484
Epoch: [8/20], Batch Num: [135/600]
Discriminator Loss: 0.8101, Generator Loss: 2.4656
D(x): 0.7157, D(G(z)): 0.2230
Epoch: [8/20], Batch Num: [136/600]
Discriminator Loss: 0.6726, Generator Loss: 2.4929
D(x): 0.7575, D(G(z)): 0.1854
Epoch: [8/20], Batch Num: [137/600]
Discriminator Loss: 0.5001, Generator Loss: 2.4281
D(x): 0.8126, D(G(z)): 0.1650
Epoch: [8/20], Batch Num: [138/600]
Discriminator Loss: 0.5572, Generator Loss: 2.6860
D(x): 0.8044, D(G(z)): 0.1884
Epoch: [8/20], Batch Num: [139/600]
Discriminator Loss: 0.5457, Generator Loss: 2.6778
D(x): 0.7946, D(G(z)): 0.1696
Epoch: [8/20], Batch Num: [140/600]
Discriminator Loss: 0.5106, Generator Loss: 2.4592
D(x): 0.7788, D(G(z)): 0.1336
Epoch: [8/20], Batch Num: [141/600]
Discriminator Loss: 0.3662, Generator Loss: 2.6958
D(x): 0.8798, D(G(z)): 0.1646
Epoch: [8/20], Batch Num: [142/600]
Discriminator Loss: 0.4317, Generator Loss: 3.0483
D(x): 0.9316, D(G(z)): 0.2126
Epoch: [8/20], Batch Num: [143/600]
Discriminator Loss: 0.3855, Generator Loss: 3.0982
D(x): 0.9121, D(G(z)): 0.1612
Epoch: [8/20], Batch Num: [144/600]
Discriminator Loss: 0.4430, Generator Loss: 3.5666
D(x): 0.8586, D(G(z)): 0.1701
Epoch: [8/20], Batch Num: [145/600]
Discriminator Loss: 0.4053, Generator Loss: 3.4939
D(x): 0.8357, D(G(z)): 0.1192
Epoch: [8/20], Batch Num: [146/600]
Discriminator Loss: 0.4711, Generator Loss: 3.2979
D(x): 0.8204, D(G(z)): 0.0982
Epoch: [8/20], Batch Num: [147/600]
Discriminator Loss: 0.6116, Generator Loss: 2.8044
D(x): 0.7468, D(G(z)): 0.0969
Epoch: [8/20], Batch Num: [148/600]
Discriminator Loss: 0.5862, Generator Loss: 2.4060
D(x): 0.8479, D(G(z)): 0.1999
Epoch: [8/20], Batch Num: [149/600]
Discriminator Loss: 0.5059, Generator Loss: 2.7872
D(x): 0.9072, D(G(z)): 0.2382
Epoch: [8/20], Batch Num: [150/600]
Discriminator Loss: 0.4751, Generator Loss: 3.1488
D(x): 0.8651, D(G(z)): 0.1835
Epoch: [8/20], Batch Num: [151/600]
Discriminator Loss: 0.5319, Generator Loss: 2.8169
D(x): 0.7696, D(G(z)): 0.1261
Epoch: [8/20], Batch Num: [152/600]
Discriminator Loss: 0.6780, Generator Loss: 2.6074
D(x): 0.7823, D(G(z)): 0.1828
Epoch: [8/20], Batch Num: [153/600]
Discriminator Loss: 0.7709, Generator Loss: 2.2693
D(x): 0.7975, D(G(z)): 0.2601
Epoch: [8/20], Batch Num: [154/600]
Discriminator Loss: 0.9092, Generator Loss: 2.0727
D(x): 0.7458, D(G(z)): 0.2315
Epoch: [8/20], Batch Num: [155/600]
Discriminator Loss: 1.0764, Generator Loss: 2.2650
D(x): 0.7571, D(G(z)): 0.3453
Epoch: [8/20], Batch Num: [156/600]
Discriminator Loss: 1.0096, Generator Loss: 2.5564
D(x): 0.7560, D(G(z)): 0.3064
Epoch: [8/20], Batch Num: [157/600]
Discriminator Loss: 1.0166, Generator Loss: 2.5968
D(x): 0.6842, D(G(z)): 0.2292
Epoch: [8/20], Batch Num: [158/600]
Discriminator Loss: 1.2594, Generator Loss: 2.0687
D(x): 0.6124, D(G(z)): 0.2298
Epoch: [8/20], Batch Num: [159/600]
Discriminator Loss: 1.0745, Generator Loss: 1.6390
D(x): 0.7055, D(G(z)): 0.2903
Epoch: [8/20], Batch Num: [160/600]
Discriminator Loss: 0.9263, Generator Loss: 1.6491
D(x): 0.7762, D(G(z)): 0.2973
Epoch: [8/20], Batch Num: [161/600]
Discriminator Loss: 1.0464, Generator Loss: 1.6807
D(x): 0.8157, D(G(z)): 0.3733
Epoch: [8/20], Batch Num: [162/600]
Discriminator Loss: 1.0243, Generator Loss: 2.0909
D(x): 0.7417, D(G(z)): 0.2991
Epoch: [8/20], Batch Num: [163/600]
Discriminator Loss: 0.9795, Generator Loss: 2.2119
D(x): 0.7147, D(G(z)): 0.2583
Epoch: [8/20], Batch Num: [164/600]
Discriminator Loss: 1.2009, Generator Loss: 2.2247
D(x): 0.6313, D(G(z)): 0.2441
Epoch: [8/20], Batch Num: [165/600]
Discriminator Loss: 1.0082, Generator Loss: 1.8827
D(x): 0.6511, D(G(z)): 0.1958
Epoch: [8/20], Batch Num: [166/600]
Discriminator Loss: 1.0127, Generator Loss: 1.5906
D(x): 0.7373, D(G(z)): 0.2893
Epoch: [8/20], Batch Num: [167/600]
Discriminator Loss: 0.7874, Generator Loss: 1.5836
D(x): 0.8492, D(G(z)): 0.3261
Epoch: [8/20], Batch Num: [168/600]
Discriminator Loss: 0.6272, Generator Loss: 1.9733
D(x): 0.8805, D(G(z)): 0.2880
Epoch: [8/20], Batch Num: [169/600]
Discriminator Loss: 0.6777, Generator Loss: 2.0262
D(x): 0.8325, D(G(z)): 0.2660
Epoch: [8/20], Batch Num: [170/600]
Discriminator Loss: 0.5497, Generator Loss: 2.3246
D(x): 0.8509, D(G(z)): 0.2060
Epoch: [8/20], Batch Num: [171/600]
Discriminator Loss: 0.6845, Generator Loss: 2.1256
D(x): 0.7703, D(G(z)): 0.1939
Epoch: [8/20], Batch Num: [172/600]
Discriminator Loss: 0.5506, Generator Loss: 2.2496
D(x): 0.8139, D(G(z)): 0.1706
Epoch: [8/20], Batch Num: [173/600]
Discriminator Loss: 0.6348, Generator Loss: 2.0601
D(x): 0.8084, D(G(z)): 0.2284
Epoch: [8/20], Batch Num: [174/600]
Discriminator Loss: 0.4790, Generator Loss: 1.9058
D(x): 0.8483, D(G(z)): 0.1881
Epoch: [8/20], Batch Num: [175/600]
Discriminator Loss: 0.5994, Generator Loss: 1.9216
D(x): 0.8599, D(G(z)): 0.2474
Epoch: [8/20], Batch Num: [176/600]
Discriminator Loss: 0.6489, Generator Loss: 1.8828
D(x): 0.8707, D(G(z)): 0.2591
Epoch: [8/20], Batch Num: [177/600]
Discriminator Loss: 0.5444, Generator Loss: 1.9311
D(x): 0.8294, D(G(z)): 0.1917
Epoch: [8/20], Batch Num: [178/600]
Discriminator Loss: 0.5258, Generator Loss: 2.2806
D(x): 0.9170, D(G(z)): 0.2487
Epoch: [8/20], Batch Num: [179/600]
Discriminator Loss: 0.3601, Generator Loss: 2.5054
D(x): 0.8991, D(G(z)): 0.1707
Epoch: [8/20], Batch Num: [180/600]
Discriminator Loss: 0.5436, Generator Loss: 2.3553
D(x): 0.8618, D(G(z)): 0.1952
Epoch: [8/20], Batch Num: [181/600]
Discriminator Loss: 0.4769, Generator Loss: 2.6634
D(x): 0.8498, D(G(z)): 0.1639
Epoch: [8/20], Batch Num: [182/600]
Discriminator Loss: 0.5007, Generator Loss: 2.4121
D(x): 0.8211, D(G(z)): 0.1464
Epoch: [8/20], Batch Num: [183/600]
Discriminator Loss: 0.4824, Generator Loss: 2.4052
D(x): 0.8146, D(G(z)): 0.1307
Epoch: [8/20], Batch Num: [184/600]
Discriminator Loss: 0.6407, Generator Loss: 2.3502
D(x): 0.8134, D(G(z)): 0.1742
Epoch: [8/20], Batch Num: [185/600]
Discriminator Loss: 0.5241, Generator Loss: 2.2308
D(x): 0.8579, D(G(z)): 0.1899
Epoch: [8/20], Batch Num: [186/600]
Discriminator Loss: 0.6079, Generator Loss: 2.0585
D(x): 0.8857, D(G(z)): 0.2400
Epoch: [8/20], Batch Num: [187/600]
Discriminator Loss: 0.5648, Generator Loss: 1.9349
D(x): 0.8443, D(G(z)): 0.2000
Epoch: [8/20], Batch Num: [188/600]
Discriminator Loss: 0.5346, Generator Loss: 2.2624
D(x): 0.8938, D(G(z)): 0.2418
Epoch: [8/20], Batch Num: [189/600]
Discriminator Loss: 0.5073, Generator Loss: 2.2407
D(x): 0.8421, D(G(z)): 0.1639
Epoch: [8/20], Batch Num: [190/600]
Discriminator Loss: 0.4391, Generator Loss: 2.4245
D(x): 0.8566, D(G(z)): 0.1564
Epoch: [8/20], Batch Num: [191/600]
Discriminator Loss: 0.5005, Generator Loss: 2.4718
D(x): 0.9000, D(G(z)): 0.2209
Epoch: [8/20], Batch Num: [192/600]
Discriminator Loss: 0.5639, Generator Loss: 2.5096
D(x): 0.8215, D(G(z)): 0.1713
Epoch: [8/20], Batch Num: [193/600]
Discriminator Loss: 0.6152, Generator Loss: 2.6122
D(x): 0.7942, D(G(z)): 0.1659
Epoch: [8/20], Batch Num: [194/600]
Discriminator Loss: 0.4792, Generator Loss: 2.3945
D(x): 0.8561, D(G(z)): 0.1705
Epoch: [8/20], Batch Num: [195/600]
Discriminator Loss: 0.5615, Generator Loss: 2.1820
D(x): 0.8136, D(G(z)): 0.1799
Epoch: [8/20], Batch Num: [196/600]
Discriminator Loss: 0.6256, Generator Loss: 2.3205
D(x): 0.8847, D(G(z)): 0.2518
Epoch: [8/20], Batch Num: [197/600]
Discriminator Loss: 0.5377, Generator Loss: 2.3117
D(x): 0.8385, D(G(z)): 0.1736
Epoch: [8/20], Batch Num: [198/600]
Discriminator Loss: 0.5585, Generator Loss: 2.0086
D(x): 0.8490, D(G(z)): 0.1714
Epoch: [8/20], Batch Num: [199/600]
Discriminator Loss: 0.6053, Generator Loss: 2.3640
D(x): 0.8134, D(G(z)): 0.1783
Epoch: 8, Batch Num: [200/600]

Epoch: [8/20], Batch Num: [200/600]
Discriminator Loss: 0.6476, Generator Loss: 2.3366
D(x): 0.8307, D(G(z)): 0.2293
Epoch: [8/20], Batch Num: [201/600]
Discriminator Loss: 0.6103, Generator Loss: 2.3870
D(x): 0.8578, D(G(z)): 0.2146
Epoch: [8/20], Batch Num: [202/600]
Discriminator Loss: 0.7153, Generator Loss: 2.3310
D(x): 0.7563, D(G(z)): 0.1787
Epoch: [8/20], Batch Num: [203/600]
Discriminator Loss: 0.4653, Generator Loss: 2.2862
D(x): 0.8281, D(G(z)): 0.1415
Epoch: [8/20], Batch Num: [204/600]
Discriminator Loss: 0.6198, Generator Loss: 2.4709
D(x): 0.8726, D(G(z)): 0.2470
Epoch: [8/20], Batch Num: [205/600]
Discriminator Loss: 0.6675, Generator Loss: 2.2741
D(x): 0.8215, D(G(z)): 0.2212
Epoch: [8/20], Batch Num: [206/600]
Discriminator Loss: 0.6045, Generator Loss: 2.2115
D(x): 0.8406, D(G(z)): 0.2093
Epoch: [8/20], Batch Num: [207/600]
Discriminator Loss: 0.6763, Generator Loss: 1.9652
D(x): 0.8140, D(G(z)): 0.2303
Epoch: [8/20], Batch Num: [208/600]
Discriminator Loss: 0.7782, Generator Loss: 1.9929
D(x): 0.7941, D(G(z)): 0.2442
Epoch: [8/20], Batch Num: [209/600]
Discriminator Loss: 0.7115, Generator Loss: 2.1125
D(x): 0.7838, D(G(z)): 0.2261
Epoch: [8/20], Batch Num: [210/600]
Discriminator Loss: 0.6322, Generator Loss: 2.0246
D(x): 0.8014, D(G(z)): 0.2064
Epoch: [8/20], Batch Num: [211/600]
Discriminator Loss: 0.6760, Generator Loss: 1.9026
D(x): 0.8435, D(G(z)): 0.2686
Epoch: [8/20], Batch Num: [212/600]
Discriminator Loss: 0.6137, Generator Loss: 1.8212
D(x): 0.8367, D(G(z)): 0.2514
Epoch: [8/20], Batch Num: [213/600]
Discriminator Loss: 0.6152, Generator Loss: 1.9397
D(x): 0.8310, D(G(z)): 0.2417
Epoch: [8/20], Batch Num: [214/600]
Discriminator Loss: 0.6694, Generator Loss: 2.1707
D(x): 0.8281, D(G(z)): 0.2570
Epoch: [8/20], Batch Num: [215/600]
Discriminator Loss: 0.6389, Generator Loss: 2.1563
D(x): 0.8113, D(G(z)): 0.2058
Epoch: [8/20], Batch Num: [216/600]
Discriminator Loss: 0.6661, Generator Loss: 2.1604
D(x): 0.7864, D(G(z)): 0.2089
Epoch: [8/20], Batch Num: [217/600]
Discriminator Loss: 0.7883, Generator Loss: 1.8270
D(x): 0.7585, D(G(z)): 0.2538
Epoch: [8/20], Batch Num: [218/600]
Discriminator Loss: 0.6136, Generator Loss: 1.8500
D(x): 0.7965, D(G(z)): 0.2010
Epoch: [8/20], Batch Num: [219/600]
Discriminator Loss: 0.6417, Generator Loss: 1.9682
D(x): 0.8422, D(G(z)): 0.2523
Epoch: [8/20], Batch Num: [220/600]
Discriminator Loss: 0.6843, Generator Loss: 1.9992
D(x): 0.8578, D(G(z)): 0.2938
Epoch: [8/20], Batch Num: [221/600]
Discriminator Loss: 0.5167, Generator Loss: 2.0515
D(x): 0.8610, D(G(z)): 0.2004
Epoch: [8/20], Batch Num: [222/600]
Discriminator Loss: 0.6314, Generator Loss: 1.9444
D(x): 0.7493, D(G(z)): 0.1747
Epoch: [8/20], Batch Num: [223/600]
Discriminator Loss: 0.7288, Generator Loss: 1.8751
D(x): 0.7398, D(G(z)): 0.1939
Epoch: [8/20], Batch Num: [224/600]
Discriminator Loss: 0.7226, Generator Loss: 1.8473
D(x): 0.7501, D(G(z)): 0.2185
Epoch: [8/20], Batch Num: [225/600]
Discriminator Loss: 0.7042, Generator Loss: 1.7656
D(x): 0.8233, D(G(z)): 0.2885
Epoch: [8/20], Batch Num: [226/600]
Discriminator Loss: 0.5742, Generator Loss: 1.8998
D(x): 0.8364, D(G(z)): 0.2562
Epoch: [8/20], Batch Num: [227/600]
Discriminator Loss: 0.7685, Generator Loss: 1.9048
D(x): 0.7714, D(G(z)): 0.2406
Epoch: [8/20], Batch Num: [228/600]
Discriminator Loss: 0.6581, Generator Loss: 1.9704
D(x): 0.8127, D(G(z)): 0.2486
Epoch: [8/20], Batch Num: [229/600]
Discriminator Loss: 0.5847, Generator Loss: 2.1802
D(x): 0.8080, D(G(z)): 0.2232
Epoch: [8/20], Batch Num: [230/600]
Discriminator Loss: 0.5681, Generator Loss: 2.0459
D(x): 0.7776, D(G(z)): 0.1774
Epoch: [8/20], Batch Num: [231/600]
Discriminator Loss: 0.6600, Generator Loss: 1.9323
D(x): 0.7716, D(G(z)): 0.1988
Epoch: [8/20], Batch Num: [232/600]
Discriminator Loss: 0.6003, Generator Loss: 1.6740
D(x): 0.7929, D(G(z)): 0.2006
Epoch: [8/20], Batch Num: [233/600]
Discriminator Loss: 0.6092, Generator Loss: 1.5944
D(x): 0.8465, D(G(z)): 0.2882
Epoch: [8/20], Batch Num: [234/600]
Discriminator Loss: 0.6480, Generator Loss: 1.8626
D(x): 0.8680, D(G(z)): 0.2950
Epoch: [8/20], Batch Num: [235/600]
Discriminator Loss: 0.5917, Generator Loss: 1.8986
D(x): 0.8137, D(G(z)): 0.2189
Epoch: [8/20], Batch Num: [236/600]
Discriminator Loss: 0.6613, Generator Loss: 2.2844
D(x): 0.8141, D(G(z)): 0.2308
Epoch: [8/20], Batch Num: [237/600]
Discriminator Loss: 0.5272, Generator Loss: 2.3023
D(x): 0.8194, D(G(z)): 0.1862
Epoch: [8/20], Batch Num: [238/600]
Discriminator Loss: 0.6749, Generator Loss: 2.3793
D(x): 0.7387, D(G(z)): 0.1699
Epoch: [8/20], Batch Num: [239/600]
Discriminator Loss: 0.7405, Generator Loss: 2.0715
D(x): 0.7291, D(G(z)): 0.1789
Epoch: [8/20], Batch Num: [240/600]
Discriminator Loss: 0.6382, Generator Loss: 1.7935
D(x): 0.8073, D(G(z)): 0.2148
Epoch: [8/20], Batch Num: [241/600]
Discriminator Loss: 0.4963, Generator Loss: 1.7774
D(x): 0.8767, D(G(z)): 0.2255
Epoch: [8/20], Batch Num: [242/600]
Discriminator Loss: 0.7404, Generator Loss: 1.8945
D(x): 0.7933, D(G(z)): 0.2526
Epoch: [8/20], Batch Num: [243/600]
Discriminator Loss: 0.6441, Generator Loss: 2.2274
D(x): 0.8410, D(G(z)): 0.2688
Epoch: [8/20], Batch Num: [244/600]
Discriminator Loss: 0.5720, Generator Loss: 2.3152
D(x): 0.7975, D(G(z)): 0.1687
Epoch: [8/20], Batch Num: [245/600]
Discriminator Loss: 0.5126, Generator Loss: 2.4624
D(x): 0.8496, D(G(z)): 0.1846
Epoch: [8/20], Batch Num: [246/600]
Discriminator Loss: 0.5558, Generator Loss: 2.4027
D(x): 0.7714, D(G(z)): 0.1415
Epoch: [8/20], Batch Num: [247/600]
Discriminator Loss: 0.5836, Generator Loss: 2.0898
D(x): 0.7697, D(G(z)): 0.1462
Epoch: [8/20], Batch Num: [248/600]
Discriminator Loss: 0.5020, Generator Loss: 2.1176
D(x): 0.8396, D(G(z)): 0.1961
Epoch: [8/20], Batch Num: [249/600]
Discriminator Loss: 0.6314, Generator Loss: 1.9084
D(x): 0.8515, D(G(z)): 0.2522
Epoch: [8/20], Batch Num: [250/600]
Discriminator Loss: 0.5117, Generator Loss: 2.3716
D(x): 0.8841, D(G(z)): 0.2291
Epoch: [8/20], Batch Num: [251/600]
Discriminator Loss: 0.4874, Generator Loss: 2.2617
D(x): 0.8289, D(G(z)): 0.1779
Epoch: [8/20], Batch Num: [252/600]
Discriminator Loss: 0.4401, Generator Loss: 2.5500
D(x): 0.8294, D(G(z)): 0.1388
Epoch: [8/20], Batch Num: [253/600]
Discriminator Loss: 0.6334, Generator Loss: 2.4190
D(x): 0.7503, D(G(z)): 0.1334
Epoch: [8/20], Batch Num: [254/600]
Discriminator Loss: 0.4182, Generator Loss: 2.0662
D(x): 0.8484, D(G(z)): 0.1273
Epoch: [8/20], Batch Num: [255/600]
Discriminator Loss: 0.5149, Generator Loss: 2.1316
D(x): 0.8798, D(G(z)): 0.2439
Epoch: [8/20], Batch Num: [256/600]
Discriminator Loss: 0.5364, Generator Loss: 2.3842
D(x): 0.8309, D(G(z)): 0.2101
Epoch: [8/20], Batch Num: [257/600]
Discriminator Loss: 0.5721, Generator Loss: 2.4250
D(x): 0.8130, D(G(z)): 0.1933
Epoch: [8/20], Batch Num: [258/600]
Discriminator Loss: 0.5493, Generator Loss: 2.5042
D(x): 0.8208, D(G(z)): 0.1736
Epoch: [8/20], Batch Num: [259/600]
Discriminator Loss: 0.4975, Generator Loss: 2.7419
D(x): 0.8462, D(G(z)): 0.1509
Epoch: [8/20], Batch Num: [260/600]
Discriminator Loss: 0.5259, Generator Loss: 2.7647
D(x): 0.8059, D(G(z)): 0.1420
Epoch: [8/20], Batch Num: [261/600]
Discriminator Loss: 0.5642, Generator Loss: 2.5057
D(x): 0.7862, D(G(z)): 0.1451
Epoch: [8/20], Batch Num: [262/600]
Discriminator Loss: 0.6084, Generator Loss: 2.2796
D(x): 0.8326, D(G(z)): 0.1805
Epoch: [8/20], Batch Num: [263/600]
Discriminator Loss: 0.4390, Generator Loss: 2.7068
D(x): 0.9344, D(G(z)): 0.2189
Epoch: [8/20], Batch Num: [264/600]
Discriminator Loss: 0.4846, Generator Loss: 2.5394
D(x): 0.8231, D(G(z)): 0.1415
Epoch: [8/20], Batch Num: [265/600]
Discriminator Loss: 0.4656, Generator Loss: 2.8030
D(x): 0.8256, D(G(z)): 0.1338
Epoch: [8/20], Batch Num: [266/600]
Discriminator Loss: 0.6390, Generator Loss: 2.5610
D(x): 0.7961, D(G(z)): 0.1473
Epoch: [8/20], Batch Num: [267/600]
Discriminator Loss: 0.4734, Generator Loss: 2.5840
D(x): 0.8436, D(G(z)): 0.1716
Epoch: [8/20], Batch Num: [268/600]
Discriminator Loss: 0.5017, Generator Loss: 2.0468
D(x): 0.8575, D(G(z)): 0.1680
Epoch: [8/20], Batch Num: [269/600]
Discriminator Loss: 0.6016, Generator Loss: 2.3224
D(x): 0.8762, D(G(z)): 0.2291
Epoch: [8/20], Batch Num: [270/600]
Discriminator Loss: 0.5294, Generator Loss: 2.7102
D(x): 0.8456, D(G(z)): 0.1791
Epoch: [8/20], Batch Num: [271/600]
Discriminator Loss: 0.5753, Generator Loss: 2.9064
D(x): 0.8223, D(G(z)): 0.1642
Epoch: [8/20], Batch Num: [272/600]
Discriminator Loss: 0.4985, Generator Loss: 2.6990
D(x): 0.7996, D(G(z)): 0.1205
Epoch: [8/20], Batch Num: [273/600]
Discriminator Loss: 0.4959, Generator Loss: 2.5563
D(x): 0.8498, D(G(z)): 0.1592
Epoch: [8/20], Batch Num: [274/600]
Discriminator Loss: 0.6380, Generator Loss: 2.2146
D(x): 0.7759, D(G(z)): 0.1505
Epoch: [8/20], Batch Num: [275/600]
Discriminator Loss: 0.5516, Generator Loss: 2.2730
D(x): 0.8475, D(G(z)): 0.2121
Epoch: [8/20], Batch Num: [276/600]
Discriminator Loss: 0.6604, Generator Loss: 2.4308
D(x): 0.8195, D(G(z)): 0.2324
Epoch: [8/20], Batch Num: [277/600]
Discriminator Loss: 0.5057, Generator Loss: 2.4783
D(x): 0.8728, D(G(z)): 0.2088
Epoch: [8/20], Batch Num: [278/600]
Discriminator Loss: 0.4776, Generator Loss: 2.9060
D(x): 0.8157, D(G(z)): 0.1177
Epoch: [8/20], Batch Num: [279/600]
Discriminator Loss: 0.5666, Generator Loss: 2.7476
D(x): 0.8216, D(G(z)): 0.1420
Epoch: [8/20], Batch Num: [280/600]
Discriminator Loss: 0.4272, Generator Loss: 2.6145
D(x): 0.8564, D(G(z)): 0.1392
Epoch: [8/20], Batch Num: [281/600]
Discriminator Loss: 0.6357, Generator Loss: 2.6833
D(x): 0.7898, D(G(z)): 0.1612
Epoch: [8/20], Batch Num: [282/600]
Discriminator Loss: 0.5660, Generator Loss: 1.9324
D(x): 0.8220, D(G(z)): 0.1817
Epoch: [8/20], Batch Num: [283/600]
Discriminator Loss: 0.5946, Generator Loss: 2.3241
D(x): 0.8645, D(G(z)): 0.2312
Epoch: [8/20], Batch Num: [284/600]
Discriminator Loss: 0.6081, Generator Loss: 2.9470
D(x): 0.8285, D(G(z)): 0.2172
Epoch: [8/20], Batch Num: [285/600]
Discriminator Loss: 0.5628, Generator Loss: 2.9764
D(x): 0.8021, D(G(z)): 0.1472
Epoch: [8/20], Batch Num: [286/600]
Discriminator Loss: 0.4691, Generator Loss: 2.6565
D(x): 0.8275, D(G(z)): 0.1155
Epoch: [8/20], Batch Num: [287/600]
Discriminator Loss: 0.5573, Generator Loss: 2.3843
D(x): 0.7921, D(G(z)): 0.1351
Epoch: [8/20], Batch Num: [288/600]
Discriminator Loss: 0.4438, Generator Loss: 1.8582
D(x): 0.8661, D(G(z)): 0.1590
Epoch: [8/20], Batch Num: [289/600]
Discriminator Loss: 0.5224, Generator Loss: 2.3251
D(x): 0.9330, D(G(z)): 0.2818
Epoch: [8/20], Batch Num: [290/600]
Discriminator Loss: 0.4249, Generator Loss: 2.9599
D(x): 0.8978, D(G(z)): 0.1965
Epoch: [8/20], Batch Num: [291/600]
Discriminator Loss: 0.3522, Generator Loss: 3.2854
D(x): 0.8435, D(G(z)): 0.0843
Epoch: [8/20], Batch Num: [292/600]
Discriminator Loss: 0.4399, Generator Loss: 3.0880
D(x): 0.8206, D(G(z)): 0.0755
Epoch: [8/20], Batch Num: [293/600]
Discriminator Loss: 0.4757, Generator Loss: 2.6479
D(x): 0.8096, D(G(z)): 0.0744
Epoch: [8/20], Batch Num: [294/600]
Discriminator Loss: 0.3609, Generator Loss: 2.2034
D(x): 0.8796, D(G(z)): 0.1300
Epoch: [8/20], Batch Num: [295/600]
Discriminator Loss: 0.4991, Generator Loss: 1.9154
D(x): 0.9098, D(G(z)): 0.2340
Epoch: [8/20], Batch Num: [296/600]
Discriminator Loss: 0.4235, Generator Loss: 2.5439
D(x): 0.9220, D(G(z)): 0.2101
Epoch: [8/20], Batch Num: [297/600]
Discriminator Loss: 0.5459, Generator Loss: 2.8732
D(x): 0.8552, D(G(z)): 0.1783
Epoch: [8/20], Batch Num: [298/600]
Discriminator Loss: 0.2430, Generator Loss: 2.9276
D(x): 0.9280, D(G(z)): 0.1198
Epoch: [8/20], Batch Num: [299/600]
Discriminator Loss: 0.4034, Generator Loss: 3.1191
D(x): 0.8570, D(G(z)): 0.1051
Epoch: 8, Batch Num: [300/600]

Epoch: [8/20], Batch Num: [300/600]
Discriminator Loss: 0.3968, Generator Loss: 3.2484
D(x): 0.8827, D(G(z)): 0.1254
Epoch: [8/20], Batch Num: [301/600]
Discriminator Loss: 0.3464, Generator Loss: 2.9154
D(x): 0.8814, D(G(z)): 0.0987
Epoch: [8/20], Batch Num: [302/600]
Discriminator Loss: 0.4272, Generator Loss: 2.5844
D(x): 0.8600, D(G(z)): 0.1184
Epoch: [8/20], Batch Num: [303/600]
Discriminator Loss: 0.3325, Generator Loss: 2.5573
D(x): 0.9361, D(G(z)): 0.1659
Epoch: [8/20], Batch Num: [304/600]
Discriminator Loss: 0.3448, Generator Loss: 3.0346
D(x): 0.9587, D(G(z)): 0.1773
Epoch: [8/20], Batch Num: [305/600]
Discriminator Loss: 0.5435, Generator Loss: 3.2643
D(x): 0.8500, D(G(z)): 0.1777
Epoch: [8/20], Batch Num: [306/600]
Discriminator Loss: 0.4212, Generator Loss: 3.5674
D(x): 0.8589, D(G(z)): 0.0962
Epoch: [8/20], Batch Num: [307/600]
Discriminator Loss: 0.6371, Generator Loss: 3.3388
D(x): 0.7568, D(G(z)): 0.0965
Epoch: [8/20], Batch Num: [308/600]
Discriminator Loss: 0.4135, Generator Loss: 2.6346
D(x): 0.8533, D(G(z)): 0.0833
Epoch: [8/20], Batch Num: [309/600]
Discriminator Loss: 0.5054, Generator Loss: 2.3997
D(x): 0.8704, D(G(z)): 0.1882
Epoch: [8/20], Batch Num: [310/600]
Discriminator Loss: 0.5355, Generator Loss: 2.7387
D(x): 0.9128, D(G(z)): 0.2314
Epoch: [8/20], Batch Num: [311/600]
Discriminator Loss: 0.4963, Generator Loss: 2.9750
D(x): 0.8907, D(G(z)): 0.1962
Epoch: [8/20], Batch Num: [312/600]
Discriminator Loss: 0.4679, Generator Loss: 3.4995
D(x): 0.8299, D(G(z)): 0.1227
Epoch: [8/20], Batch Num: [313/600]
Discriminator Loss: 0.5257, Generator Loss: 3.3298
D(x): 0.7814, D(G(z)): 0.0856
Epoch: [8/20], Batch Num: [314/600]
Discriminator Loss: 0.4350, Generator Loss: 2.7634
D(x): 0.8179, D(G(z)): 0.1003
Epoch: [8/20], Batch Num: [315/600]
Discriminator Loss: 0.6549, Generator Loss: 2.3079
D(x): 0.8131, D(G(z)): 0.1790
Epoch: [8/20], Batch Num: [316/600]
Discriminator Loss: 0.4743, Generator Loss: 2.0857
D(x): 0.9054, D(G(z)): 0.2293
Epoch: [8/20], Batch Num: [317/600]
Discriminator Loss: 0.7754, Generator Loss: 2.7966
D(x): 0.8719, D(G(z)): 0.2867
Epoch: [8/20], Batch Num: [318/600]
Discriminator Loss: 0.5688, Generator Loss: 3.0656
D(x): 0.8780, D(G(z)): 0.2111
Epoch: [8/20], Batch Num: [319/600]
Discriminator Loss: 0.5153, Generator Loss: 3.2336
D(x): 0.8164, D(G(z)): 0.1231
Epoch: [8/20], Batch Num: [320/600]
Discriminator Loss: 0.6619, Generator Loss: 3.1928
D(x): 0.7648, D(G(z)): 0.1223
Epoch: [8/20], Batch Num: [321/600]
Discriminator Loss: 0.4939, Generator Loss: 2.7467
D(x): 0.8052, D(G(z)): 0.1077
Epoch: [8/20], Batch Num: [322/600]
Discriminator Loss: 0.6449, Generator Loss: 2.2291
D(x): 0.8005, D(G(z)): 0.1620
Epoch: [8/20], Batch Num: [323/600]
Discriminator Loss: 0.5839, Generator Loss: 2.1249
D(x): 0.8621, D(G(z)): 0.2099
Epoch: [8/20], Batch Num: [324/600]
Discriminator Loss: 0.7659, Generator Loss: 2.1027
D(x): 0.8097, D(G(z)): 0.2285
Epoch: [8/20], Batch Num: [325/600]
Discriminator Loss: 0.7221, Generator Loss: 2.2780
D(x): 0.8200, D(G(z)): 0.2287
Epoch: [8/20], Batch Num: [326/600]
Discriminator Loss: 0.7065, Generator Loss: 2.6107
D(x): 0.8274, D(G(z)): 0.2347
Epoch: [8/20], Batch Num: [327/600]
Discriminator Loss: 0.5290, Generator Loss: 3.0093
D(x): 0.8201, D(G(z)): 0.1567
Epoch: [8/20], Batch Num: [328/600]
Discriminator Loss: 0.7205, Generator Loss: 2.5758
D(x): 0.7071, D(G(z)): 0.1060
Epoch: [8/20], Batch Num: [329/600]
Discriminator Loss: 0.7406, Generator Loss: 2.5260
D(x): 0.7450, D(G(z)): 0.1506
Epoch: [8/20], Batch Num: [330/600]
Discriminator Loss: 0.7864, Generator Loss: 2.1260
D(x): 0.7540, D(G(z)): 0.2304
Epoch: [8/20], Batch Num: [331/600]
Discriminator Loss: 0.9006, Generator Loss: 1.9771
D(x): 0.8174, D(G(z)): 0.2841
Epoch: [8/20], Batch Num: [332/600]
Discriminator Loss: 0.8460, Generator Loss: 2.0150
D(x): 0.7722, D(G(z)): 0.2767
Epoch: [8/20], Batch Num: [333/600]
Discriminator Loss: 0.7327, Generator Loss: 2.5242
D(x): 0.7958, D(G(z)): 0.2192
Epoch: [8/20], Batch Num: [334/600]
Discriminator Loss: 0.7589, Generator Loss: 2.6223
D(x): 0.7219, D(G(z)): 0.1529
Epoch: [8/20], Batch Num: [335/600]
Discriminator Loss: 0.5854, Generator Loss: 2.5783
D(x): 0.7869, D(G(z)): 0.1457
Epoch: [8/20], Batch Num: [336/600]
Discriminator Loss: 0.7293, Generator Loss: 2.0174
D(x): 0.7402, D(G(z)): 0.1594
Epoch: [8/20], Batch Num: [337/600]
Discriminator Loss: 0.8309, Generator Loss: 1.9295
D(x): 0.7737, D(G(z)): 0.2628
Epoch: [8/20], Batch Num: [338/600]
Discriminator Loss: 0.9014, Generator Loss: 2.1463
D(x): 0.7832, D(G(z)): 0.3141
Epoch: [8/20], Batch Num: [339/600]
Discriminator Loss: 0.7357, Generator Loss: 2.6763
D(x): 0.8149, D(G(z)): 0.2146
Epoch: [8/20], Batch Num: [340/600]
Discriminator Loss: 0.5475, Generator Loss: 2.6756
D(x): 0.8292, D(G(z)): 0.1718
Epoch: [8/20], Batch Num: [341/600]
Discriminator Loss: 0.8117, Generator Loss: 2.7385
D(x): 0.7013, D(G(z)): 0.1331
Epoch: [8/20], Batch Num: [342/600]
Discriminator Loss: 0.6697, Generator Loss: 2.3433
D(x): 0.7476, D(G(z)): 0.1580
Epoch: [8/20], Batch Num: [343/600]
Discriminator Loss: 0.5878, Generator Loss: 2.1290
D(x): 0.8113, D(G(z)): 0.1952
Epoch: [8/20], Batch Num: [344/600]
Discriminator Loss: 0.6317, Generator Loss: 2.0640
D(x): 0.7970, D(G(z)): 0.2006
Epoch: [8/20], Batch Num: [345/600]
Discriminator Loss: 0.4541, Generator Loss: 2.2284
D(x): 0.9051, D(G(z)): 0.2304
Epoch: [8/20], Batch Num: [346/600]
Discriminator Loss: 0.7057, Generator Loss: 2.3326
D(x): 0.8079, D(G(z)): 0.2408
Epoch: [8/20], Batch Num: [347/600]
Discriminator Loss: 0.5903, Generator Loss: 2.4721
D(x): 0.7932, D(G(z)): 0.1578
Epoch: [8/20], Batch Num: [348/600]
Discriminator Loss: 0.6466, Generator Loss: 2.4857
D(x): 0.7651, D(G(z)): 0.1467
Epoch: [8/20], Batch Num: [349/600]
Discriminator Loss: 0.5286, Generator Loss: 2.5147
D(x): 0.8181, D(G(z)): 0.1419
Epoch: [8/20], Batch Num: [350/600]
Discriminator Loss: 0.5704, Generator Loss: 2.2361
D(x): 0.7943, D(G(z)): 0.1338
Epoch: [8/20], Batch Num: [351/600]
Discriminator Loss: 0.5275, Generator Loss: 2.1036
D(x): 0.8587, D(G(z)): 0.2137
Epoch: [8/20], Batch Num: [352/600]
Discriminator Loss: 0.4502, Generator Loss: 2.1880
D(x): 0.8773, D(G(z)): 0.1698
Epoch: [8/20], Batch Num: [353/600]
Discriminator Loss: 0.5842, Generator Loss: 2.2455
D(x): 0.8619, D(G(z)): 0.2179
Epoch: [8/20], Batch Num: [354/600]
Discriminator Loss: 0.5449, Generator Loss: 2.7755
D(x): 0.8548, D(G(z)): 0.1865
Epoch: [8/20], Batch Num: [355/600]
Discriminator Loss: 0.6397, Generator Loss: 2.7128
D(x): 0.7844, D(G(z)): 0.1325
Epoch: [8/20], Batch Num: [356/600]
Discriminator Loss: 0.5150, Generator Loss: 2.3239
D(x): 0.7846, D(G(z)): 0.1022
Epoch: [8/20], Batch Num: [357/600]
Discriminator Loss: 0.4710, Generator Loss: 2.3800
D(x): 0.8809, D(G(z)): 0.2028
Epoch: [8/20], Batch Num: [358/600]
Discriminator Loss: 0.4524, Generator Loss: 2.2851
D(x): 0.8815, D(G(z)): 0.1838
Epoch: [8/20], Batch Num: [359/600]
Discriminator Loss: 0.4799, Generator Loss: 2.5408
D(x): 0.8835, D(G(z)): 0.2083
Epoch: [8/20], Batch Num: [360/600]
Discriminator Loss: 0.4660, Generator Loss: 2.8311
D(x): 0.8630, D(G(z)): 0.1808
Epoch: [8/20], Batch Num: [361/600]
Discriminator Loss: 0.4615, Generator Loss: 2.7564
D(x): 0.8534, D(G(z)): 0.1486
Epoch: [8/20], Batch Num: [362/600]
Discriminator Loss: 0.4432, Generator Loss: 2.8341
D(x): 0.8500, D(G(z)): 0.1438
Epoch: [8/20], Batch Num: [363/600]
Discriminator Loss: 0.7413, Generator Loss: 2.3004
D(x): 0.7476, D(G(z)): 0.1271
Epoch: [8/20], Batch Num: [364/600]
Discriminator Loss: 0.5920, Generator Loss: 2.0906
D(x): 0.8200, D(G(z)): 0.1858
Epoch: [8/20], Batch Num: [365/600]
Discriminator Loss: 0.5998, Generator Loss: 1.8989
D(x): 0.8694, D(G(z)): 0.2526
Epoch: [8/20], Batch Num: [366/600]
Discriminator Loss: 0.6900, Generator Loss: 2.2797
D(x): 0.8931, D(G(z)): 0.2899
Epoch: [8/20], Batch Num: [367/600]
Discriminator Loss: 0.6866, Generator Loss: 2.5865
D(x): 0.8180, D(G(z)): 0.2482
Epoch: [8/20], Batch Num: [368/600]
Discriminator Loss: 0.5861, Generator Loss: 2.4119
D(x): 0.7839, D(G(z)): 0.1441
Epoch: [8/20], Batch Num: [369/600]
Discriminator Loss: 0.5341, Generator Loss: 2.1715
D(x): 0.8343, D(G(z)): 0.1711
Epoch: [8/20], Batch Num: [370/600]
Discriminator Loss: 0.6509, Generator Loss: 2.1099
D(x): 0.7865, D(G(z)): 0.1647
Epoch: [8/20], Batch Num: [371/600]
Discriminator Loss: 0.5596, Generator Loss: 2.0311
D(x): 0.8352, D(G(z)): 0.1901
Epoch: [8/20], Batch Num: [372/600]
Discriminator Loss: 0.5893, Generator Loss: 1.7701
D(x): 0.8423, D(G(z)): 0.2168
Epoch: [8/20], Batch Num: [373/600]
Discriminator Loss: 0.6962, Generator Loss: 2.0151
D(x): 0.8834, D(G(z)): 0.2975
Epoch: [8/20], Batch Num: [374/600]
Discriminator Loss: 0.6226, Generator Loss: 2.4516
D(x): 0.8185, D(G(z)): 0.2028
Epoch: [8/20], Batch Num: [375/600]
Discriminator Loss: 0.5420, Generator Loss: 2.5877
D(x): 0.7929, D(G(z)): 0.1396
Epoch: [8/20], Batch Num: [376/600]
Discriminator Loss: 0.4548, Generator Loss: 2.5326
D(x): 0.8389, D(G(z)): 0.1546
Epoch: [8/20], Batch Num: [377/600]
Discriminator Loss: 0.4964, Generator Loss: 2.4943
D(x): 0.8350, D(G(z)): 0.1513
Epoch: [8/20], Batch Num: [378/600]
Discriminator Loss: 0.4811, Generator Loss: 2.1130
D(x): 0.8561, D(G(z)): 0.1495
Epoch: [8/20], Batch Num: [379/600]
Discriminator Loss: 0.5327, Generator Loss: 2.2930
D(x): 0.8551, D(G(z)): 0.1924
Epoch: [8/20], Batch Num: [380/600]
Discriminator Loss: 0.5708, Generator Loss: 2.1112
D(x): 0.8722, D(G(z)): 0.2394
Epoch: [8/20], Batch Num: [381/600]
Discriminator Loss: 0.4595, Generator Loss: 2.1983
D(x): 0.8817, D(G(z)): 0.2003
Epoch: [8/20], Batch Num: [382/600]
Discriminator Loss: 0.5066, Generator Loss: 2.2111
D(x): 0.8926, D(G(z)): 0.2089
Epoch: [8/20], Batch Num: [383/600]
Discriminator Loss: 0.5566, Generator Loss: 2.4348
D(x): 0.8277, D(G(z)): 0.1621
Epoch: [8/20], Batch Num: [384/600]
Discriminator Loss: 0.4179, Generator Loss: 2.5590
D(x): 0.8703, D(G(z)): 0.1626
Epoch: [8/20], Batch Num: [385/600]
Discriminator Loss: 0.5795, Generator Loss: 2.3855
D(x): 0.8484, D(G(z)): 0.1801
Epoch: [8/20], Batch Num: [386/600]
Discriminator Loss: 0.4666, Generator Loss: 2.5346
D(x): 0.8722, D(G(z)): 0.1820
Epoch: [8/20], Batch Num: [387/600]
Discriminator Loss: 0.4510, Generator Loss: 2.3510
D(x): 0.8551, D(G(z)): 0.1628
Epoch: [8/20], Batch Num: [388/600]
Discriminator Loss: 0.4364, Generator Loss: 2.3520
D(x): 0.8639, D(G(z)): 0.1685
Epoch: [8/20], Batch Num: [389/600]
Discriminator Loss: 0.3867, Generator Loss: 2.8670
D(x): 0.9266, D(G(z)): 0.1971
Epoch: [8/20], Batch Num: [390/600]
Discriminator Loss: 0.4201, Generator Loss: 2.8763
D(x): 0.8889, D(G(z)): 0.1847
Epoch: [8/20], Batch Num: [391/600]
Discriminator Loss: 0.4385, Generator Loss: 2.9557
D(x): 0.8455, D(G(z)): 0.1247
Epoch: [8/20], Batch Num: [392/600]
Discriminator Loss: 0.4163, Generator Loss: 2.7986
D(x): 0.8409, D(G(z)): 0.1068
Epoch: [8/20], Batch Num: [393/600]
Discriminator Loss: 0.5154, Generator Loss: 2.3824
D(x): 0.8053, D(G(z)): 0.1015
Epoch: [8/20], Batch Num: [394/600]
Discriminator Loss: 0.4051, Generator Loss: 2.4963
D(x): 0.9103, D(G(z)): 0.1916
Epoch: [8/20], Batch Num: [395/600]
Discriminator Loss: 0.4184, Generator Loss: 2.4565
D(x): 0.9064, D(G(z)): 0.1951
Epoch: [8/20], Batch Num: [396/600]
Discriminator Loss: 0.3861, Generator Loss: 2.4199
D(x): 0.9000, D(G(z)): 0.1658
Epoch: [8/20], Batch Num: [397/600]
Discriminator Loss: 0.4139, Generator Loss: 2.5924
D(x): 0.9009, D(G(z)): 0.1845
Epoch: [8/20], Batch Num: [398/600]
Discriminator Loss: 0.3446, Generator Loss: 2.9063
D(x): 0.8781, D(G(z)): 0.1228
Epoch: [8/20], Batch Num: [399/600]
Discriminator Loss: 0.4937, Generator Loss: 2.3786
D(x): 0.8289, D(G(z)): 0.1279
Epoch: 8, Batch Num: [400/600]

Epoch: [8/20], Batch Num: [400/600]
Discriminator Loss: 0.5716, Generator Loss: 2.4519
D(x): 0.8183, D(G(z)): 0.1465
Epoch: [8/20], Batch Num: [401/600]
Discriminator Loss: 0.4507, Generator Loss: 2.1543
D(x): 0.8998, D(G(z)): 0.1801
Epoch: [8/20], Batch Num: [402/600]
Discriminator Loss: 0.4920, Generator Loss: 2.4077
D(x): 0.8700, D(G(z)): 0.1827
Epoch: [8/20], Batch Num: [403/600]
Discriminator Loss: 0.4590, Generator Loss: 2.2292
D(x): 0.8690, D(G(z)): 0.1881
Epoch: [8/20], Batch Num: [404/600]
Discriminator Loss: 0.5706, Generator Loss: 2.5750
D(x): 0.8872, D(G(z)): 0.2227
Epoch: [8/20], Batch Num: [405/600]
Discriminator Loss: 0.5144, Generator Loss: 2.6282
D(x): 0.8088, D(G(z)): 0.1281
Epoch: [8/20], Batch Num: [406/600]
Discriminator Loss: 0.6311, Generator Loss: 2.4248
D(x): 0.7780, D(G(z)): 0.1530
Epoch: [8/20], Batch Num: [407/600]
Discriminator Loss: 0.6526, Generator Loss: 2.4633
D(x): 0.8014, D(G(z)): 0.1797
Epoch: [8/20], Batch Num: [408/600]
Discriminator Loss: 0.7823, Generator Loss: 1.8937
D(x): 0.7815, D(G(z)): 0.2103
Epoch: [8/20], Batch Num: [409/600]
Discriminator Loss: 0.7900, Generator Loss: 1.7393
D(x): 0.8199, D(G(z)): 0.2841
Epoch: [8/20], Batch Num: [410/600]
Discriminator Loss: 0.6507, Generator Loss: 2.2448
D(x): 0.8887, D(G(z)): 0.2757
Epoch: [8/20], Batch Num: [411/600]
Discriminator Loss: 0.5838, Generator Loss: 2.6591
D(x): 0.8469, D(G(z)): 0.2113
Epoch: [8/20], Batch Num: [412/600]
Discriminator Loss: 0.6427, Generator Loss: 2.8382
D(x): 0.7855, D(G(z)): 0.1806
Epoch: [8/20], Batch Num: [413/600]
Discriminator Loss: 0.6957, Generator Loss: 2.6267
D(x): 0.7495, D(G(z)): 0.1183
Epoch: [8/20], Batch Num: [414/600]
Discriminator Loss: 0.8060, Generator Loss: 2.3606
D(x): 0.7065, D(G(z)): 0.1390
Epoch: [8/20], Batch Num: [415/600]
Discriminator Loss: 0.6374, Generator Loss: 2.2367
D(x): 0.8252, D(G(z)): 0.2115
Epoch: [8/20], Batch Num: [416/600]
Discriminator Loss: 0.7323, Generator Loss: 2.1539
D(x): 0.8251, D(G(z)): 0.2444
Epoch: [8/20], Batch Num: [417/600]
Discriminator Loss: 0.6747, Generator Loss: 2.1561
D(x): 0.8762, D(G(z)): 0.2740
Epoch: [8/20], Batch Num: [418/600]
Discriminator Loss: 0.7125, Generator Loss: 2.3449
D(x): 0.8044, D(G(z)): 0.2387
Epoch: [8/20], Batch Num: [419/600]
Discriminator Loss: 0.7758, Generator Loss: 2.6162
D(x): 0.7544, D(G(z)): 0.2018
Epoch: [8/20], Batch Num: [420/600]
Discriminator Loss: 0.8416, Generator Loss: 2.5356
D(x): 0.7208, D(G(z)): 0.1886
Epoch: [8/20], Batch Num: [421/600]
Discriminator Loss: 0.8656, Generator Loss: 2.2576
D(x): 0.7174, D(G(z)): 0.1616
Epoch: [8/20], Batch Num: [422/600]
Discriminator Loss: 0.7320, Generator Loss: 2.0854
D(x): 0.7471, D(G(z)): 0.1672
Epoch: [8/20], Batch Num: [423/600]
Discriminator Loss: 0.7839, Generator Loss: 1.7040
D(x): 0.8144, D(G(z)): 0.2814
Epoch: [8/20], Batch Num: [424/600]
Discriminator Loss: 0.7962, Generator Loss: 1.8791
D(x): 0.8591, D(G(z)): 0.3192
Epoch: [8/20], Batch Num: [425/600]
Discriminator Loss: 0.9057, Generator Loss: 2.6731
D(x): 0.7924, D(G(z)): 0.2844
Epoch: [8/20], Batch Num: [426/600]
Discriminator Loss: 0.7040, Generator Loss: 2.4929
D(x): 0.7401, D(G(z)): 0.1514
Epoch: [8/20], Batch Num: [427/600]
Discriminator Loss: 0.6520, Generator Loss: 2.5578
D(x): 0.7505, D(G(z)): 0.1348
Epoch: [8/20], Batch Num: [428/600]
Discriminator Loss: 0.7473, Generator Loss: 2.2343
D(x): 0.7304, D(G(z)): 0.1678
Epoch: [8/20], Batch Num: [429/600]
Discriminator Loss: 0.6370, Generator Loss: 2.0009
D(x): 0.8111, D(G(z)): 0.2149
Epoch: [8/20], Batch Num: [430/600]
Discriminator Loss: 0.6253, Generator Loss: 1.7297
D(x): 0.8305, D(G(z)): 0.2343
Epoch: [8/20], Batch Num: [431/600]
Discriminator Loss: 0.6881, Generator Loss: 2.1954
D(x): 0.8465, D(G(z)): 0.2613
Epoch: [8/20], Batch Num: [432/600]
Discriminator Loss: 0.6710, Generator Loss: 2.4232
D(x): 0.8106, D(G(z)): 0.2393
Epoch: [8/20], Batch Num: [433/600]
Discriminator Loss: 0.6632, Generator Loss: 2.9677
D(x): 0.7772, D(G(z)): 0.1522
Epoch: [8/20], Batch Num: [434/600]
Discriminator Loss: 0.6364, Generator Loss: 2.5253
D(x): 0.7348, D(G(z)): 0.1342
Epoch: [8/20], Batch Num: [435/600]
Discriminator Loss: 0.7949, Generator Loss: 2.1366
D(x): 0.7353, D(G(z)): 0.1925
Epoch: [8/20], Batch Num: [436/600]
Discriminator Loss: 0.6722, Generator Loss: 1.9335
D(x): 0.7944, D(G(z)): 0.2153
Epoch: [8/20], Batch Num: [437/600]
Discriminator Loss: 0.7563, Generator Loss: 2.0672
D(x): 0.8417, D(G(z)): 0.2783
Epoch: [8/20], Batch Num: [438/600]
Discriminator Loss: 0.7256, Generator Loss: 2.2767
D(x): 0.8700, D(G(z)): 0.2829
Epoch: [8/20], Batch Num: [439/600]
Discriminator Loss: 0.7057, Generator Loss: 2.7211
D(x): 0.7786, D(G(z)): 0.1937
Epoch: [8/20], Batch Num: [440/600]
Discriminator Loss: 0.6956, Generator Loss: 2.6186
D(x): 0.7390, D(G(z)): 0.1534
Epoch: [8/20], Batch Num: [441/600]
Discriminator Loss: 0.8815, Generator Loss: 2.3935
D(x): 0.7012, D(G(z)): 0.2038
Epoch: [8/20], Batch Num: [442/600]
Discriminator Loss: 0.8714, Generator Loss: 1.8372
D(x): 0.7254, D(G(z)): 0.2078
Epoch: [8/20], Batch Num: [443/600]
Discriminator Loss: 0.9346, Generator Loss: 1.8675
D(x): 0.7581, D(G(z)): 0.2818
Epoch: [8/20], Batch Num: [444/600]
Discriminator Loss: 0.8247, Generator Loss: 2.2988
D(x): 0.7960, D(G(z)): 0.2838
Epoch: [8/20], Batch Num: [445/600]
Discriminator Loss: 0.8191, Generator Loss: 2.0271
D(x): 0.7582, D(G(z)): 0.2801
Epoch: [8/20], Batch Num: [446/600]
Discriminator Loss: 0.8508, Generator Loss: 2.4361
D(x): 0.7750, D(G(z)): 0.2701
Epoch: [8/20], Batch Num: [447/600]
Discriminator Loss: 0.7663, Generator Loss: 2.5292
D(x): 0.7084, D(G(z)): 0.1693
Epoch: [8/20], Batch Num: [448/600]
Discriminator Loss: 0.7154, Generator Loss: 2.2722
D(x): 0.7113, D(G(z)): 0.1577
Epoch: [8/20], Batch Num: [449/600]
Discriminator Loss: 0.8330, Generator Loss: 1.7286
D(x): 0.7087, D(G(z)): 0.2158
Epoch: [8/20], Batch Num: [450/600]
Discriminator Loss: 1.0198, Generator Loss: 1.6171
D(x): 0.7127, D(G(z)): 0.3000
Epoch: [8/20], Batch Num: [451/600]
Discriminator Loss: 0.8905, Generator Loss: 1.8662
D(x): 0.8193, D(G(z)): 0.3464
Epoch: [8/20], Batch Num: [452/600]
Discriminator Loss: 0.8709, Generator Loss: 1.8571
D(x): 0.7109, D(G(z)): 0.2409
Epoch: [8/20], Batch Num: [453/600]
Discriminator Loss: 0.9144, Generator Loss: 1.8745
D(x): 0.6588, D(G(z)): 0.2194
Epoch: [8/20], Batch Num: [454/600]
Discriminator Loss: 0.8251, Generator Loss: 1.8963
D(x): 0.7160, D(G(z)): 0.2325
Epoch: [8/20], Batch Num: [455/600]
Discriminator Loss: 0.7483, Generator Loss: 1.6327
D(x): 0.7335, D(G(z)): 0.2305
Epoch: [8/20], Batch Num: [456/600]
Discriminator Loss: 0.7992, Generator Loss: 1.8391
D(x): 0.7729, D(G(z)): 0.2822
Epoch: [8/20], Batch Num: [457/600]
Discriminator Loss: 0.6775, Generator Loss: 1.8636
D(x): 0.7746, D(G(z)): 0.2336
Epoch: [8/20], Batch Num: [458/600]
Discriminator Loss: 0.8022, Generator Loss: 2.0842
D(x): 0.7312, D(G(z)): 0.2415
Epoch: [8/20], Batch Num: [459/600]
Discriminator Loss: 0.8667, Generator Loss: 1.7111
D(x): 0.6849, D(G(z)): 0.2109
Epoch: [8/20], Batch Num: [460/600]
Discriminator Loss: 0.6911, Generator Loss: 1.7753
D(x): 0.7626, D(G(z)): 0.2256
Epoch: [8/20], Batch Num: [461/600]
Discriminator Loss: 0.7731, Generator Loss: 2.0190
D(x): 0.8215, D(G(z)): 0.2972
Epoch: [8/20], Batch Num: [462/600]
Discriminator Loss: 0.6071, Generator Loss: 2.2338
D(x): 0.8256, D(G(z)): 0.2496
Epoch: [8/20], Batch Num: [463/600]
Discriminator Loss: 0.6236, Generator Loss: 2.1835
D(x): 0.7462, D(G(z)): 0.1730
Epoch: [8/20], Batch Num: [464/600]
Discriminator Loss: 0.6034, Generator Loss: 2.0359
D(x): 0.7885, D(G(z)): 0.2031
Epoch: [8/20], Batch Num: [465/600]
Discriminator Loss: 0.6463, Generator Loss: 2.2587
D(x): 0.8038, D(G(z)): 0.2289
Epoch: [8/20], Batch Num: [466/600]
Discriminator Loss: 0.5917, Generator Loss: 2.1186
D(x): 0.7869, D(G(z)): 0.1966
Epoch: [8/20], Batch Num: [467/600]
Discriminator Loss: 0.6837, Generator Loss: 2.1673
D(x): 0.8071, D(G(z)): 0.2287
Epoch: [8/20], Batch Num: [468/600]
Discriminator Loss: 0.5521, Generator Loss: 2.2199
D(x): 0.7926, D(G(z)): 0.1831
Epoch: [8/20], Batch Num: [469/600]
Discriminator Loss: 0.7060, Generator Loss: 2.0095
D(x): 0.7367, D(G(z)): 0.1987
Epoch: [8/20], Batch Num: [470/600]
Discriminator Loss: 0.6202, Generator Loss: 2.0325
D(x): 0.8545, D(G(z)): 0.2363
Epoch: [8/20], Batch Num: [471/600]
Discriminator Loss: 0.8013, Generator Loss: 2.1291
D(x): 0.7274, D(G(z)): 0.2439
Epoch: [8/20], Batch Num: [472/600]
Discriminator Loss: 0.5985, Generator Loss: 2.4692
D(x): 0.8194, D(G(z)): 0.1998
Epoch: [8/20], Batch Num: [473/600]
Discriminator Loss: 0.7422, Generator Loss: 2.0704
D(x): 0.7249, D(G(z)): 0.1923
Epoch: [8/20], Batch Num: [474/600]
Discriminator Loss: 0.8090, Generator Loss: 1.8278
D(x): 0.7193, D(G(z)): 0.2135
Epoch: [8/20], Batch Num: [475/600]
Discriminator Loss: 0.9294, Generator Loss: 1.8216
D(x): 0.7410, D(G(z)): 0.2885
Epoch: [8/20], Batch Num: [476/600]
Discriminator Loss: 0.8883, Generator Loss: 2.0004
D(x): 0.8113, D(G(z)): 0.3305
Epoch: [8/20], Batch Num: [477/600]
Discriminator Loss: 0.7113, Generator Loss: 2.4680
D(x): 0.8011, D(G(z)): 0.2547
Epoch: [8/20], Batch Num: [478/600]
Discriminator Loss: 0.8252, Generator Loss: 2.3109
D(x): 0.6824, D(G(z)): 0.1686
Epoch: [8/20], Batch Num: [479/600]
Discriminator Loss: 0.9510, Generator Loss: 2.2913
D(x): 0.6550, D(G(z)): 0.1938
Epoch: [8/20], Batch Num: [480/600]
Discriminator Loss: 0.9704, Generator Loss: 1.8425
D(x): 0.6538, D(G(z)): 0.2011
Epoch: [8/20], Batch Num: [481/600]
Discriminator Loss: 1.1412, Generator Loss: 1.6291
D(x): 0.7331, D(G(z)): 0.3838
Epoch: [8/20], Batch Num: [482/600]
Discriminator Loss: 0.9613, Generator Loss: 1.7258
D(x): 0.7651, D(G(z)): 0.3199
Epoch: [8/20], Batch Num: [483/600]
Discriminator Loss: 1.1859, Generator Loss: 2.0569
D(x): 0.6524, D(G(z)): 0.3257
Epoch: [8/20], Batch Num: [484/600]
Discriminator Loss: 1.2142, Generator Loss: 1.6992
D(x): 0.5899, D(G(z)): 0.2596
Epoch: [8/20], Batch Num: [485/600]
Discriminator Loss: 1.0414, Generator Loss: 1.8521
D(x): 0.6711, D(G(z)): 0.2802
Epoch: [8/20], Batch Num: [486/600]
Discriminator Loss: 0.9580, Generator Loss: 1.5359
D(x): 0.6503, D(G(z)): 0.2421
Epoch: [8/20], Batch Num: [487/600]
Discriminator Loss: 1.0231, Generator Loss: 1.8305
D(x): 0.7230, D(G(z)): 0.3652
Epoch: [8/20], Batch Num: [488/600]
Discriminator Loss: 0.8841, Generator Loss: 1.7697
D(x): 0.6907, D(G(z)): 0.2622
Epoch: [8/20], Batch Num: [489/600]
Discriminator Loss: 0.8658, Generator Loss: 1.5969
D(x): 0.7037, D(G(z)): 0.2686
Epoch: [8/20], Batch Num: [490/600]
Discriminator Loss: 0.9839, Generator Loss: 1.6591
D(x): 0.6360, D(G(z)): 0.2798
Epoch: [8/20], Batch Num: [491/600]
Discriminator Loss: 0.9331, Generator Loss: 1.6256
D(x): 0.6897, D(G(z)): 0.2860
Epoch: [8/20], Batch Num: [492/600]
Discriminator Loss: 0.8483, Generator Loss: 1.8036
D(x): 0.7375, D(G(z)): 0.3037
Epoch: [8/20], Batch Num: [493/600]
Discriminator Loss: 0.7588, Generator Loss: 1.8879
D(x): 0.7000, D(G(z)): 0.2098
Epoch: [8/20], Batch Num: [494/600]
Discriminator Loss: 0.7252, Generator Loss: 1.8722
D(x): 0.7068, D(G(z)): 0.2107
Epoch: [8/20], Batch Num: [495/600]
Discriminator Loss: 0.8058, Generator Loss: 1.7209
D(x): 0.7171, D(G(z)): 0.2345
Epoch: [8/20], Batch Num: [496/600]
Discriminator Loss: 0.7247, Generator Loss: 1.8014
D(x): 0.7644, D(G(z)): 0.2314
Epoch: [8/20], Batch Num: [497/600]
Discriminator Loss: 0.6999, Generator Loss: 2.0815
D(x): 0.7963, D(G(z)): 0.2698
Epoch: [8/20], Batch Num: [498/600]
Discriminator Loss: 0.6091, Generator Loss: 2.1877
D(x): 0.7865, D(G(z)): 0.2206
Epoch: [8/20], Batch Num: [499/600]
Discriminator Loss: 0.5445, Generator Loss: 2.1677
D(x): 0.7816, D(G(z)): 0.1785
Epoch: 8, Batch Num: [500/600]

Epoch: [8/20], Batch Num: [500/600]
Discriminator Loss: 0.5277, Generator Loss: 2.1441
D(x): 0.8357, D(G(z)): 0.2115
Epoch: [8/20], Batch Num: [501/600]
Discriminator Loss: 0.5324, Generator Loss: 2.1596
D(x): 0.7985, D(G(z)): 0.1782
Epoch: [8/20], Batch Num: [502/600]
Discriminator Loss: 0.6919, Generator Loss: 2.1842
D(x): 0.7922, D(G(z)): 0.2450
Epoch: [8/20], Batch Num: [503/600]
Discriminator Loss: 0.7000, Generator Loss: 2.3486
D(x): 0.8067, D(G(z)): 0.2510
Epoch: [8/20], Batch Num: [504/600]
Discriminator Loss: 0.5641, Generator Loss: 2.4328
D(x): 0.8101, D(G(z)): 0.2107
Epoch: [8/20], Batch Num: [505/600]
Discriminator Loss: 0.6254, Generator Loss: 2.3637
D(x): 0.7567, D(G(z)): 0.1805
Epoch: [8/20], Batch Num: [506/600]
Discriminator Loss: 0.6058, Generator Loss: 2.2919
D(x): 0.8311, D(G(z)): 0.2150
Epoch: [8/20], Batch Num: [507/600]
Discriminator Loss: 0.7447, Generator Loss: 2.3025
D(x): 0.8154, D(G(z)): 0.2603
Epoch: [8/20], Batch Num: [508/600]
Discriminator Loss: 0.6151, Generator Loss: 2.5043
D(x): 0.8227, D(G(z)): 0.2033
Epoch: [8/20], Batch Num: [509/600]
Discriminator Loss: 0.6331, Generator Loss: 2.2864
D(x): 0.7628, D(G(z)): 0.1656
Epoch: [8/20], Batch Num: [510/600]
Discriminator Loss: 0.7964, Generator Loss: 2.1184
D(x): 0.7823, D(G(z)): 0.2471
Epoch: [8/20], Batch Num: [511/600]
Discriminator Loss: 0.7463, Generator Loss: 2.1312
D(x): 0.7660, D(G(z)): 0.2360
Epoch: [8/20], Batch Num: [512/600]
Discriminator Loss: 0.8683, Generator Loss: 2.1003
D(x): 0.7648, D(G(z)): 0.2779
Epoch: [8/20], Batch Num: [513/600]
Discriminator Loss: 0.9457, Generator Loss: 2.2351
D(x): 0.7249, D(G(z)): 0.2674
Epoch: [8/20], Batch Num: [514/600]
Discriminator Loss: 0.8665, Generator Loss: 1.7397
D(x): 0.7003, D(G(z)): 0.2138
Epoch: [8/20], Batch Num: [515/600]
Discriminator Loss: 1.1302, Generator Loss: 1.6203
D(x): 0.7369, D(G(z)): 0.3558
Epoch: [8/20], Batch Num: [516/600]
Discriminator Loss: 0.9817, Generator Loss: 1.8715
D(x): 0.7526, D(G(z)): 0.3555
Epoch: [8/20], Batch Num: [517/600]
Discriminator Loss: 0.9136, Generator Loss: 1.6552
D(x): 0.7329, D(G(z)): 0.2924
Epoch: [8/20], Batch Num: [518/600]
Discriminator Loss: 0.9317, Generator Loss: 1.9363
D(x): 0.7425, D(G(z)): 0.3074
Epoch: [8/20], Batch Num: [519/600]
Discriminator Loss: 0.8285, Generator Loss: 1.7271
D(x): 0.7409, D(G(z)): 0.2785
Epoch: [8/20], Batch Num: [520/600]
Discriminator Loss: 0.9392, Generator Loss: 1.7336
D(x): 0.7051, D(G(z)): 0.2764
Epoch: [8/20], Batch Num: [521/600]
Discriminator Loss: 0.7555, Generator Loss: 1.7717
D(x): 0.7788, D(G(z)): 0.2750
Epoch: [8/20], Batch Num: [522/600]
Discriminator Loss: 1.0430, Generator Loss: 1.8900
D(x): 0.7190, D(G(z)): 0.3409
Epoch: [8/20], Batch Num: [523/600]
Discriminator Loss: 0.8151, Generator Loss: 2.0148
D(x): 0.7374, D(G(z)): 0.2553
Epoch: [8/20], Batch Num: [524/600]
Discriminator Loss: 0.9187, Generator Loss: 1.7904
D(x): 0.6687, D(G(z)): 0.2118
Epoch: [8/20], Batch Num: [525/600]
Discriminator Loss: 0.5425, Generator Loss: 1.7145
D(x): 0.8171, D(G(z)): 0.2231
Epoch: [8/20], Batch Num: [526/600]
Discriminator Loss: 0.7566, Generator Loss: 1.9769
D(x): 0.7731, D(G(z)): 0.2725
Epoch: [8/20], Batch Num: [527/600]
Discriminator Loss: 0.6362, Generator Loss: 2.0626
D(x): 0.8066, D(G(z)): 0.2597
Epoch: [8/20], Batch Num: [528/600]
Discriminator Loss: 0.6423, Generator Loss: 1.9986
D(x): 0.7816, D(G(z)): 0.2194
Epoch: [8/20], Batch Num: [529/600]
Discriminator Loss: 0.5310, Generator Loss: 2.2283
D(x): 0.8399, D(G(z)): 0.2370
Epoch: [8/20], Batch Num: [530/600]
Discriminator Loss: 0.4994, Generator Loss: 2.3920
D(x): 0.8156, D(G(z)): 0.1888
Epoch: [8/20], Batch Num: [531/600]
Discriminator Loss: 0.5707, Generator Loss: 2.5425
D(x): 0.7946, D(G(z)): 0.1938
Epoch: [8/20], Batch Num: [532/600]
Discriminator Loss: 0.5085, Generator Loss: 2.5360
D(x): 0.8595, D(G(z)): 0.2166
Epoch: [8/20], Batch Num: [533/600]
Discriminator Loss: 0.4679, Generator Loss: 2.6801
D(x): 0.8109, D(G(z)): 0.1514
Epoch: [8/20], Batch Num: [534/600]
Discriminator Loss: 0.3843, Generator Loss: 2.5204
D(x): 0.8581, D(G(z)): 0.1613
Epoch: [8/20], Batch Num: [535/600]
Discriminator Loss: 0.5058, Generator Loss: 2.6454
D(x): 0.8168, D(G(z)): 0.1653
Epoch: [8/20], Batch Num: [536/600]
Discriminator Loss: 0.4386, Generator Loss: 2.3479
D(x): 0.8682, D(G(z)): 0.1831
Epoch: [8/20], Batch Num: [537/600]
Discriminator Loss: 0.4231, Generator Loss: 2.6403
D(x): 0.8771, D(G(z)): 0.1857
Epoch: [8/20], Batch Num: [538/600]
Discriminator Loss: 0.3745, Generator Loss: 2.5985
D(x): 0.9071, D(G(z)): 0.1875
Epoch: [8/20], Batch Num: [539/600]
Discriminator Loss: 0.4432, Generator Loss: 2.8350
D(x): 0.8760, D(G(z)): 0.1872
Epoch: [8/20], Batch Num: [540/600]
Discriminator Loss: 0.5368, Generator Loss: 2.6694
D(x): 0.8022, D(G(z)): 0.1404
Epoch: [8/20], Batch Num: [541/600]
Discriminator Loss: 0.3829, Generator Loss: 2.5809
D(x): 0.8525, D(G(z)): 0.1310
Epoch: [8/20], Batch Num: [542/600]
Discriminator Loss: 0.5328, Generator Loss: 2.0566
D(x): 0.8088, D(G(z)): 0.1441
Epoch: [8/20], Batch Num: [543/600]
Discriminator Loss: 0.4805, Generator Loss: 2.0298
D(x): 0.9018, D(G(z)): 0.2173
Epoch: [8/20], Batch Num: [544/600]
Discriminator Loss: 0.6330, Generator Loss: 2.4176
D(x): 0.8850, D(G(z)): 0.2816
Epoch: [8/20], Batch Num: [545/600]
Discriminator Loss: 0.6049, Generator Loss: 2.9139
D(x): 0.8590, D(G(z)): 0.2378
Epoch: [8/20], Batch Num: [546/600]
Discriminator Loss: 0.6721, Generator Loss: 2.9362
D(x): 0.7687, D(G(z)): 0.1568
Epoch: [8/20], Batch Num: [547/600]
Discriminator Loss: 0.6761, Generator Loss: 2.8676
D(x): 0.7498, D(G(z)): 0.1619
Epoch: [8/20], Batch Num: [548/600]
Discriminator Loss: 0.7478, Generator Loss: 2.2170
D(x): 0.7294, D(G(z)): 0.1994
Epoch: [8/20], Batch Num: [549/600]
Discriminator Loss: 0.8117, Generator Loss: 1.9676
D(x): 0.8110, D(G(z)): 0.2671
Epoch: [8/20], Batch Num: [550/600]
Discriminator Loss: 1.0321, Generator Loss: 1.8592
D(x): 0.7204, D(G(z)): 0.3082
Epoch: [8/20], Batch Num: [551/600]
Discriminator Loss: 1.0176, Generator Loss: 2.1234
D(x): 0.7996, D(G(z)): 0.3436
Epoch: [8/20], Batch Num: [552/600]
Discriminator Loss: 0.7870, Generator Loss: 2.3576
D(x): 0.7503, D(G(z)): 0.2624
Epoch: [8/20], Batch Num: [553/600]
Discriminator Loss: 0.9980, Generator Loss: 2.1886
D(x): 0.6749, D(G(z)): 0.2437
Epoch: [8/20], Batch Num: [554/600]
Discriminator Loss: 0.8546, Generator Loss: 1.9934
D(x): 0.7005, D(G(z)): 0.2187
Epoch: [8/20], Batch Num: [555/600]
Discriminator Loss: 1.2650, Generator Loss: 1.2854
D(x): 0.5805, D(G(z)): 0.2644
Epoch: [8/20], Batch Num: [556/600]
Discriminator Loss: 1.0581, Generator Loss: 1.4660
D(x): 0.7697, D(G(z)): 0.3975
Epoch: [8/20], Batch Num: [557/600]
Discriminator Loss: 1.0207, Generator Loss: 1.8513
D(x): 0.7601, D(G(z)): 0.3923
Epoch: [8/20], Batch Num: [558/600]
Discriminator Loss: 0.9594, Generator Loss: 2.1751
D(x): 0.6984, D(G(z)): 0.2470
Epoch: [8/20], Batch Num: [559/600]
Discriminator Loss: 0.8574, Generator Loss: 2.4178
D(x): 0.7222, D(G(z)): 0.2421
Epoch: [8/20], Batch Num: [560/600]
Discriminator Loss: 0.9038, Generator Loss: 2.0926
D(x): 0.6190, D(G(z)): 0.1770
Epoch: [8/20], Batch Num: [561/600]
Discriminator Loss: 0.8115, Generator Loss: 1.7937
D(x): 0.6763, D(G(z)): 0.1992
Epoch: [8/20], Batch Num: [562/600]
Discriminator Loss: 0.7986, Generator Loss: 1.6017
D(x): 0.7919, D(G(z)): 0.3017
Epoch: [8/20], Batch Num: [563/600]
Discriminator Loss: 0.8932, Generator Loss: 1.8183
D(x): 0.8172, D(G(z)): 0.3661
Epoch: [8/20], Batch Num: [564/600]
Discriminator Loss: 0.7630, Generator Loss: 2.1743
D(x): 0.7747, D(G(z)): 0.2860
Epoch: [8/20], Batch Num: [565/600]
Discriminator Loss: 0.8360, Generator Loss: 2.3237
D(x): 0.6410, D(G(z)): 0.1923
Epoch: [8/20], Batch Num: [566/600]
Discriminator Loss: 0.7568, Generator Loss: 2.1789
D(x): 0.7491, D(G(z)): 0.2381
Epoch: [8/20], Batch Num: [567/600]
Discriminator Loss: 0.7820, Generator Loss: 1.9013
D(x): 0.6621, D(G(z)): 0.1792
Epoch: [8/20], Batch Num: [568/600]
Discriminator Loss: 0.6955, Generator Loss: 1.5999
D(x): 0.7759, D(G(z)): 0.2455
Epoch: [8/20], Batch Num: [569/600]
Discriminator Loss: 0.7888, Generator Loss: 1.6348
D(x): 0.7896, D(G(z)): 0.3007
Epoch: [8/20], Batch Num: [570/600]
Discriminator Loss: 0.6821, Generator Loss: 1.9280
D(x): 0.8568, D(G(z)): 0.3179
Epoch: [8/20], Batch Num: [571/600]
Discriminator Loss: 0.7079, Generator Loss: 2.2064
D(x): 0.7600, D(G(z)): 0.2368
Epoch: [8/20], Batch Num: [572/600]
Discriminator Loss: 0.7550, Generator Loss: 2.3231
D(x): 0.7260, D(G(z)): 0.2092
Epoch: [8/20], Batch Num: [573/600]
Discriminator Loss: 0.7748, Generator Loss: 2.1874
D(x): 0.6914, D(G(z)): 0.1780
Epoch: [8/20], Batch Num: [574/600]
Discriminator Loss: 0.9153, Generator Loss: 1.7128
D(x): 0.6784, D(G(z)): 0.2331
Epoch: [8/20], Batch Num: [575/600]
Discriminator Loss: 0.8922, Generator Loss: 1.6391
D(x): 0.7694, D(G(z)): 0.3254
Epoch: [8/20], Batch Num: [576/600]
Discriminator Loss: 0.8735, Generator Loss: 1.7540
D(x): 0.7797, D(G(z)): 0.3428
Epoch: [8/20], Batch Num: [577/600]
Discriminator Loss: 1.0627, Generator Loss: 2.2900
D(x): 0.7204, D(G(z)): 0.3172
Epoch: [8/20], Batch Num: [578/600]
Discriminator Loss: 0.8924, Generator Loss: 2.3587
D(x): 0.6947, D(G(z)): 0.2323
Epoch: [8/20], Batch Num: [579/600]
Discriminator Loss: 1.0666, Generator Loss: 2.1750
D(x): 0.6435, D(G(z)): 0.2253
Epoch: [8/20], Batch Num: [580/600]
Discriminator Loss: 0.8860, Generator Loss: 1.6876
D(x): 0.6831, D(G(z)): 0.2255
Epoch: [8/20], Batch Num: [581/600]
Discriminator Loss: 0.8439, Generator Loss: 1.3055
D(x): 0.6990, D(G(z)): 0.2544
Epoch: [8/20], Batch Num: [582/600]
Discriminator Loss: 1.1033, Generator Loss: 1.4866
D(x): 0.7780, D(G(z)): 0.4251
Epoch: [8/20], Batch Num: [583/600]
Discriminator Loss: 0.8818, Generator Loss: 1.5844
D(x): 0.7913, D(G(z)): 0.3730
Epoch: [8/20], Batch Num: [584/600]
Discriminator Loss: 0.9569, Generator Loss: 2.1935
D(x): 0.7162, D(G(z)): 0.2964
Epoch: [8/20], Batch Num: [585/600]
Discriminator Loss: 1.1274, Generator Loss: 2.0128
D(x): 0.5666, D(G(z)): 0.1979
Epoch: [8/20], Batch Num: [586/600]
Discriminator Loss: 0.9189, Generator Loss: 1.9312
D(x): 0.6745, D(G(z)): 0.2271
Epoch: [8/20], Batch Num: [587/600]
Discriminator Loss: 0.9352, Generator Loss: 1.8324
D(x): 0.6940, D(G(z)): 0.2594
Epoch: [8/20], Batch Num: [588/600]
Discriminator Loss: 1.0089, Generator Loss: 1.5314
D(x): 0.6758, D(G(z)): 0.2789
Epoch: [8/20], Batch Num: [589/600]
Discriminator Loss: 0.8288, Generator Loss: 1.7315
D(x): 0.7924, D(G(z)): 0.3309
Epoch: [8/20], Batch Num: [590/600]
Discriminator Loss: 0.8024, Generator Loss: 1.7685
D(x): 0.7896, D(G(z)): 0.3023
Epoch: [8/20], Batch Num: [591/600]
Discriminator Loss: 0.8279, Generator Loss: 2.2975
D(x): 0.7420, D(G(z)): 0.2717
Epoch: [8/20], Batch Num: [592/600]
Discriminator Loss: 0.7863, Generator Loss: 2.3785
D(x): 0.7141, D(G(z)): 0.2091
Epoch: [8/20], Batch Num: [593/600]
Discriminator Loss: 0.7177, Generator Loss: 2.2473
D(x): 0.7198, D(G(z)): 0.1857
Epoch: [8/20], Batch Num: [594/600]
Discriminator Loss: 0.7298, Generator Loss: 1.9873
D(x): 0.6953, D(G(z)): 0.1745
Epoch: [8/20], Batch Num: [595/600]
Discriminator Loss: 0.7028, Generator Loss: 1.7477
D(x): 0.7905, D(G(z)): 0.2448
Epoch: [8/20], Batch Num: [596/600]
Discriminator Loss: 0.7074, Generator Loss: 1.7869
D(x): 0.8105, D(G(z)): 0.2844
Epoch: [8/20], Batch Num: [597/600]
Discriminator Loss: 0.7852, Generator Loss: 2.1949
D(x): 0.8222, D(G(z)): 0.3157
Epoch: [8/20], Batch Num: [598/600]
Discriminator Loss: 0.6173, Generator Loss: 2.5464
D(x): 0.8255, D(G(z)): 0.2490
Epoch: [8/20], Batch Num: [599/600]
Discriminator Loss: 0.6360, Generator Loss: 2.7326
D(x): 0.7742, D(G(z)): 0.1978
Epoch: 9, Batch Num: [0/600]

Epoch: [9/20], Batch Num: [0/600]
Discriminator Loss: 0.7457, Generator Loss: 2.4994
D(x): 0.6869, D(G(z)): 0.1316
Epoch: [9/20], Batch Num: [1/600]
Discriminator Loss: 0.5122, Generator Loss: 2.3394
D(x): 0.7949, D(G(z)): 0.1520
Epoch: [9/20], Batch Num: [2/600]
Discriminator Loss: 0.6908, Generator Loss: 1.7992
D(x): 0.7301, D(G(z)): 0.1704
Epoch: [9/20], Batch Num: [3/600]
Discriminator Loss: 0.5421, Generator Loss: 1.8640
D(x): 0.8660, D(G(z)): 0.2468
Epoch: [9/20], Batch Num: [4/600]
Discriminator Loss: 0.6093, Generator Loss: 1.7480
D(x): 0.8662, D(G(z)): 0.2714
Epoch: [9/20], Batch Num: [5/600]
Discriminator Loss: 0.7208, Generator Loss: 2.0557
D(x): 0.8312, D(G(z)): 0.3020
Epoch: [9/20], Batch Num: [6/600]
Discriminator Loss: 0.7709, Generator Loss: 2.4356
D(x): 0.7948, D(G(z)): 0.2377
Epoch: [9/20], Batch Num: [7/600]
Discriminator Loss: 0.8196, Generator Loss: 2.4463
D(x): 0.7391, D(G(z)): 0.1767
Epoch: [9/20], Batch Num: [8/600]
Discriminator Loss: 0.5497, Generator Loss: 2.1606
D(x): 0.7897, D(G(z)): 0.1520
Epoch: [9/20], Batch Num: [9/600]
Discriminator Loss: 0.8035, Generator Loss: 1.9992
D(x): 0.7344, D(G(z)): 0.1955
Epoch: [9/20], Batch Num: [10/600]
Discriminator Loss: 0.8253, Generator Loss: 1.9878
D(x): 0.8189, D(G(z)): 0.3044
Epoch: [9/20], Batch Num: [11/600]
Discriminator Loss: 0.6701, Generator Loss: 1.9512
D(x): 0.7892, D(G(z)): 0.2367
Epoch: [9/20], Batch Num: [12/600]
Discriminator Loss: 0.6952, Generator Loss: 1.8653
D(x): 0.8220, D(G(z)): 0.2645
Epoch: [9/20], Batch Num: [13/600]
Discriminator Loss: 0.7639, Generator Loss: 2.0862
D(x): 0.7789, D(G(z)): 0.2579
Epoch: [9/20], Batch Num: [14/600]
Discriminator Loss: 0.6517, Generator Loss: 2.1180
D(x): 0.7981, D(G(z)): 0.2109
Epoch: [9/20], Batch Num: [15/600]
Discriminator Loss: 0.6544, Generator Loss: 2.1341
D(x): 0.7490, D(G(z)): 0.1738
Epoch: [9/20], Batch Num: [16/600]
Discriminator Loss: 0.7216, Generator Loss: 2.1353
D(x): 0.7981, D(G(z)): 0.2326
Epoch: [9/20], Batch Num: [17/600]
Discriminator Loss: 0.8631, Generator Loss: 2.1886
D(x): 0.7580, D(G(z)): 0.2688
Epoch: [9/20], Batch Num: [18/600]
Discriminator Loss: 0.6993, Generator Loss: 2.1009
D(x): 0.8023, D(G(z)): 0.2541
Epoch: [9/20], Batch Num: [19/600]
Discriminator Loss: 0.7741, Generator Loss: 2.5353
D(x): 0.7695, D(G(z)): 0.2676
Epoch: [9/20], Batch Num: [20/600]
Discriminator Loss: 0.5485, Generator Loss: 2.3444
D(x): 0.8021, D(G(z)): 0.1739
Epoch: [9/20], Batch Num: [21/600]
Discriminator Loss: 0.6552, Generator Loss: 2.2848
D(x): 0.7759, D(G(z)): 0.1916
Epoch: [9/20], Batch Num: [22/600]
Discriminator Loss: 0.6092, Generator Loss: 2.5298
D(x): 0.7924, D(G(z)): 0.2074
Epoch: [9/20], Batch Num: [23/600]
Discriminator Loss: 0.5314, Generator Loss: 2.2149
D(x): 0.8095, D(G(z)): 0.1663
Epoch: [9/20], Batch Num: [24/600]
Discriminator Loss: 0.5899, Generator Loss: 2.1224
D(x): 0.8067, D(G(z)): 0.2007
Epoch: [9/20], Batch Num: [25/600]
Discriminator Loss: 0.3971, Generator Loss: 2.2020
D(x): 0.8893, D(G(z)): 0.1861
Epoch: [9/20], Batch Num: [26/600]
Discriminator Loss: 0.5879, Generator Loss: 2.1753
D(x): 0.8326, D(G(z)): 0.2231
Epoch: [9/20], Batch Num: [27/600]
Discriminator Loss: 0.5499, Generator Loss: 2.4683
D(x): 0.8512, D(G(z)): 0.2303
Epoch: [9/20], Batch Num: [28/600]
Discriminator Loss: 0.5273, Generator Loss: 2.7333
D(x): 0.8241, D(G(z)): 0.1701
Epoch: [9/20], Batch Num: [29/600]
Discriminator Loss: 0.6024, Generator Loss: 2.8764
D(x): 0.7673, D(G(z)): 0.1423
Epoch: [9/20], Batch Num: [30/600]
Discriminator Loss: 0.5721, Generator Loss: 2.5477
D(x): 0.7611, D(G(z)): 0.1343
Epoch: [9/20], Batch Num: [31/600]
Discriminator Loss: 0.4940, Generator Loss: 2.2061
D(x): 0.8300, D(G(z)): 0.1832
Epoch: [9/20], Batch Num: [32/600]
Discriminator Loss: 0.5662, Generator Loss: 2.5954
D(x): 0.8584, D(G(z)): 0.2401
Epoch: [9/20], Batch Num: [33/600]
Discriminator Loss: 0.6324, Generator Loss: 2.1773
D(x): 0.8261, D(G(z)): 0.2195
Epoch: [9/20], Batch Num: [34/600]
Discriminator Loss: 0.6205, Generator Loss: 2.6676
D(x): 0.8664, D(G(z)): 0.2526
Epoch: [9/20], Batch Num: [35/600]
Discriminator Loss: 0.6209, Generator Loss: 2.7653
D(x): 0.7923, D(G(z)): 0.1854
Epoch: [9/20], Batch Num: [36/600]
Discriminator Loss: 0.5736, Generator Loss: 2.9202
D(x): 0.8220, D(G(z)): 0.1846
Epoch: [9/20], Batch Num: [37/600]
Discriminator Loss: 0.5637, Generator Loss: 2.6221
D(x): 0.7680, D(G(z)): 0.1246
Epoch: [9/20], Batch Num: [38/600]
Discriminator Loss: 0.8227, Generator Loss: 2.4035
D(x): 0.7217, D(G(z)): 0.1904
Epoch: [9/20], Batch Num: [39/600]
Discriminator Loss: 0.6169, Generator Loss: 1.7312
D(x): 0.7995, D(G(z)): 0.1946
Epoch: [9/20], Batch Num: [40/600]
Discriminator Loss: 0.8906, Generator Loss: 2.0089
D(x): 0.8570, D(G(z)): 0.3691
Epoch: [9/20], Batch Num: [41/600]
Discriminator Loss: 0.7032, Generator Loss: 2.3846
D(x): 0.8016, D(G(z)): 0.2583
Epoch: [9/20], Batch Num: [42/600]
Discriminator Loss: 0.7952, Generator Loss: 2.6728
D(x): 0.7346, D(G(z)): 0.2123
Epoch: [9/20], Batch Num: [43/600]
Discriminator Loss: 0.8890, Generator Loss: 2.4380
D(x): 0.6846, D(G(z)): 0.1930
Epoch: [9/20], Batch Num: [44/600]
Discriminator Loss: 0.9079, Generator Loss: 1.8317
D(x): 0.6839, D(G(z)): 0.2095
Epoch: [9/20], Batch Num: [45/600]
Discriminator Loss: 0.9184, Generator Loss: 1.8509
D(x): 0.8177, D(G(z)): 0.3737
Epoch: [9/20], Batch Num: [46/600]
Discriminator Loss: 0.8455, Generator Loss: 2.1395
D(x): 0.7795, D(G(z)): 0.3229
Epoch: [9/20], Batch Num: [47/600]
Discriminator Loss: 0.7290, Generator Loss: 2.4158
D(x): 0.7610, D(G(z)): 0.2368
Epoch: [9/20], Batch Num: [48/600]
Discriminator Loss: 0.8524, Generator Loss: 2.6279
D(x): 0.6732, D(G(z)): 0.1738
Epoch: [9/20], Batch Num: [49/600]
Discriminator Loss: 0.8337, Generator Loss: 2.1688
D(x): 0.6783, D(G(z)): 0.1717
Epoch: [9/20], Batch Num: [50/600]
Discriminator Loss: 0.7524, Generator Loss: 1.9321
D(x): 0.7635, D(G(z)): 0.2347
Epoch: [9/20], Batch Num: [51/600]
Discriminator Loss: 0.6430, Generator Loss: 1.7399
D(x): 0.7987, D(G(z)): 0.2438
Epoch: [9/20], Batch Num: [52/600]
Discriminator Loss: 0.5480, Generator Loss: 2.1274
D(x): 0.8521, D(G(z)): 0.2410
Epoch: [9/20], Batch Num: [53/600]
Discriminator Loss: 0.6115, Generator Loss: 2.4938
D(x): 0.8732, D(G(z)): 0.2837
Epoch: [9/20], Batch Num: [54/600]
Discriminator Loss: 0.5922, Generator Loss: 2.9040
D(x): 0.7938, D(G(z)): 0.1960
Epoch: [9/20], Batch Num: [55/600]
Discriminator Loss: 0.6176, Generator Loss: 3.2046
D(x): 0.7291, D(G(z)): 0.1301
Epoch: [9/20], Batch Num: [56/600]
Discriminator Loss: 0.5472, Generator Loss: 2.5530
D(x): 0.7573, D(G(z)): 0.1078
Epoch: [9/20], Batch Num: [57/600]
Discriminator Loss: 0.4097, Generator Loss: 2.3074
D(x): 0.8589, D(G(z)): 0.1613
Epoch: [9/20], Batch Num: [58/600]
Discriminator Loss: 0.5330, Generator Loss: 2.1846
D(x): 0.8591, D(G(z)): 0.2209
Epoch: [9/20], Batch Num: [59/600]
Discriminator Loss: 0.4367, Generator Loss: 2.8132
D(x): 0.8941, D(G(z)): 0.2112
Epoch: [9/20], Batch Num: [60/600]
Discriminator Loss: 0.4823, Generator Loss: 2.9550
D(x): 0.8517, D(G(z)): 0.1751
Epoch: [9/20], Batch Num: [61/600]
Discriminator Loss: 0.4773, Generator Loss: 3.0581
D(x): 0.8482, D(G(z)): 0.1635
Epoch: [9/20], Batch Num: [62/600]
Discriminator Loss: 0.5233, Generator Loss: 2.9435
D(x): 0.8268, D(G(z)): 0.1502
Epoch: [9/20], Batch Num: [63/600]
Discriminator Loss: 0.4772, Generator Loss: 3.0800
D(x): 0.8503, D(G(z)): 0.1437
Epoch: [9/20], Batch Num: [64/600]
Discriminator Loss: 0.4536, Generator Loss: 2.8747
D(x): 0.8341, D(G(z)): 0.1119
Epoch: [9/20], Batch Num: [65/600]
Discriminator Loss: 0.4918, Generator Loss: 2.8910
D(x): 0.8652, D(G(z)): 0.1915
Epoch: [9/20], Batch Num: [66/600]
Discriminator Loss: 0.5590, Generator Loss: 2.8592
D(x): 0.9034, D(G(z)): 0.2309
Epoch: [9/20], Batch Num: [67/600]
Discriminator Loss: 0.7148, Generator Loss: 2.7716
D(x): 0.7717, D(G(z)): 0.1804
Epoch: [9/20], Batch Num: [68/600]
Discriminator Loss: 0.5703, Generator Loss: 3.2045
D(x): 0.8287, D(G(z)): 0.1605
Epoch: [9/20], Batch Num: [69/600]
Discriminator Loss: 0.6395, Generator Loss: 3.0324
D(x): 0.8287, D(G(z)): 0.2035
Epoch: [9/20], Batch Num: [70/600]
Discriminator Loss: 0.5890, Generator Loss: 2.7690
D(x): 0.7974, D(G(z)): 0.1582
Epoch: [9/20], Batch Num: [71/600]
Discriminator Loss: 0.6015, Generator Loss: 2.6154
D(x): 0.8435, D(G(z)): 0.2334
Epoch: [9/20], Batch Num: [72/600]
Discriminator Loss: 0.6442, Generator Loss: 2.8303
D(x): 0.8250, D(G(z)): 0.1924
Epoch: [9/20], Batch Num: [73/600]
Discriminator Loss: 0.5516, Generator Loss: 2.5250
D(x): 0.8369, D(G(z)): 0.1874
Epoch: [9/20], Batch Num: [74/600]
Discriminator Loss: 0.8892, Generator Loss: 2.7304
D(x): 0.7669, D(G(z)): 0.2332
Epoch: [9/20], Batch Num: [75/600]
Discriminator Loss: 0.7745, Generator Loss: 2.3800
D(x): 0.7932, D(G(z)): 0.2188
Epoch: [9/20], Batch Num: [76/600]
Discriminator Loss: 0.7192, Generator Loss: 2.1393
D(x): 0.7740, D(G(z)): 0.1878
Epoch: [9/20], Batch Num: [77/600]
Discriminator Loss: 0.8640, Generator Loss: 2.2517
D(x): 0.7950, D(G(z)): 0.2875
Epoch: [9/20], Batch Num: [78/600]
Discriminator Loss: 0.7493, Generator Loss: 2.3137
D(x): 0.8200, D(G(z)): 0.2583
Epoch: [9/20], Batch Num: [79/600]
Discriminator Loss: 0.9754, Generator Loss: 2.5815
D(x): 0.7383, D(G(z)): 0.2591
Epoch: [9/20], Batch Num: [80/600]
Discriminator Loss: 1.0209, Generator Loss: 2.5507
D(x): 0.7074, D(G(z)): 0.2133
Epoch: [9/20], Batch Num: [81/600]
Discriminator Loss: 0.9090, Generator Loss: 2.3090
D(x): 0.7149, D(G(z)): 0.2161
Epoch: [9/20], Batch Num: [82/600]
Discriminator Loss: 0.8644, Generator Loss: 2.0171
D(x): 0.7844, D(G(z)): 0.2517
Epoch: [9/20], Batch Num: [83/600]
Discriminator Loss: 0.8541, Generator Loss: 2.1911
D(x): 0.8418, D(G(z)): 0.3271
Epoch: [9/20], Batch Num: [84/600]
Discriminator Loss: 0.6357, Generator Loss: 2.6898
D(x): 0.8787, D(G(z)): 0.2478
Epoch: [9/20], Batch Num: [85/600]
Discriminator Loss: 0.9854, Generator Loss: 2.6741
D(x): 0.6799, D(G(z)): 0.1354
Epoch: [9/20], Batch Num: [86/600]
Discriminator Loss: 1.0680, Generator Loss: 2.4188
D(x): 0.6654, D(G(z)): 0.1872
Epoch: [9/20], Batch Num: [87/600]
Discriminator Loss: 0.9953, Generator Loss: 1.9581
D(x): 0.7564, D(G(z)): 0.2727
Epoch: [9/20], Batch Num: [88/600]
Discriminator Loss: 0.8466, Generator Loss: 1.8766
D(x): 0.7526, D(G(z)): 0.2590
Epoch: [9/20], Batch Num: [89/600]
Discriminator Loss: 0.7601, Generator Loss: 1.7817
D(x): 0.8408, D(G(z)): 0.2964
Epoch: [9/20], Batch Num: [90/600]
Discriminator Loss: 0.7053, Generator Loss: 2.0896
D(x): 0.8344, D(G(z)): 0.3077
Epoch: [9/20], Batch Num: [91/600]
Discriminator Loss: 0.7517, Generator Loss: 2.3294
D(x): 0.7854, D(G(z)): 0.2455
Epoch: [9/20], Batch Num: [92/600]
Discriminator Loss: 0.6354, Generator Loss: 2.6272
D(x): 0.7942, D(G(z)): 0.2044
Epoch: [9/20], Batch Num: [93/600]
Discriminator Loss: 0.7375, Generator Loss: 2.4584
D(x): 0.6959, D(G(z)): 0.1417
Epoch: [9/20], Batch Num: [94/600]
Discriminator Loss: 0.8764, Generator Loss: 2.2082
D(x): 0.7057, D(G(z)): 0.2054
Epoch: [9/20], Batch Num: [95/600]
Discriminator Loss: 0.6886, Generator Loss: 1.8175
D(x): 0.7780, D(G(z)): 0.2120
Epoch: [9/20], Batch Num: [96/600]
Discriminator Loss: 0.6846, Generator Loss: 1.6379
D(x): 0.8627, D(G(z)): 0.3154
Epoch: [9/20], Batch Num: [97/600]
Discriminator Loss: 0.8525, Generator Loss: 1.7966
D(x): 0.8249, D(G(z)): 0.3665
Epoch: [9/20], Batch Num: [98/600]
Discriminator Loss: 0.8326, Generator Loss: 2.3248
D(x): 0.7828, D(G(z)): 0.2963
Epoch: [9/20], Batch Num: [99/600]
Discriminator Loss: 0.5867, Generator Loss: 2.5446
D(x): 0.8409, D(G(z)): 0.2223
Epoch: 9, Batch Num: [100/600]

Epoch: [9/20], Batch Num: [100/600]
Discriminator Loss: 0.8567, Generator Loss: 2.6325
D(x): 0.6632, D(G(z)): 0.1547
Epoch: [9/20], Batch Num: [101/600]
Discriminator Loss: 0.8411, Generator Loss: 2.2258
D(x): 0.6580, D(G(z)): 0.1531
Epoch: [9/20], Batch Num: [102/600]
Discriminator Loss: 0.7059, Generator Loss: 1.8072
D(x): 0.7622, D(G(z)): 0.2188
Epoch: [9/20], Batch Num: [103/600]
Discriminator Loss: 0.7571, Generator Loss: 1.7987
D(x): 0.8242, D(G(z)): 0.2901
Epoch: [9/20], Batch Num: [104/600]
Discriminator Loss: 0.7385, Generator Loss: 1.8645
D(x): 0.8498, D(G(z)): 0.3241
Epoch: [9/20], Batch Num: [105/600]
Discriminator Loss: 1.0068, Generator Loss: 1.8582
D(x): 0.7573, D(G(z)): 0.3467
Epoch: [9/20], Batch Num: [106/600]
Discriminator Loss: 0.9218, Generator Loss: 1.9684
D(x): 0.7790, D(G(z)): 0.3189
Epoch: [9/20], Batch Num: [107/600]
Discriminator Loss: 0.8632, Generator Loss: 1.9405
D(x): 0.7136, D(G(z)): 0.2517
Epoch: [9/20], Batch Num: [108/600]
Discriminator Loss: 0.9374, Generator Loss: 1.9546
D(x): 0.6828, D(G(z)): 0.2623
Epoch: [9/20], Batch Num: [109/600]
Discriminator Loss: 0.7858, Generator Loss: 1.7967
D(x): 0.7165, D(G(z)): 0.2281
Epoch: [9/20], Batch Num: [110/600]
Discriminator Loss: 0.9642, Generator Loss: 1.4904
D(x): 0.6870, D(G(z)): 0.2385
Epoch: [9/20], Batch Num: [111/600]
Discriminator Loss: 0.9261, Generator Loss: 1.4334
D(x): 0.7595, D(G(z)): 0.3383
Epoch: [9/20], Batch Num: [112/600]
Discriminator Loss: 0.8600, Generator Loss: 1.4958
D(x): 0.7840, D(G(z)): 0.3290
Epoch: [9/20], Batch Num: [113/600]
Discriminator Loss: 0.8470, Generator Loss: 1.7493
D(x): 0.8145, D(G(z)): 0.3710
Epoch: [9/20], Batch Num: [114/600]
Discriminator Loss: 0.6750, Generator Loss: 2.1209
D(x): 0.8239, D(G(z)): 0.2850
Epoch: [9/20], Batch Num: [115/600]
Discriminator Loss: 0.8658, Generator Loss: 2.0592
D(x): 0.7030, D(G(z)): 0.2567
Epoch: [9/20], Batch Num: [116/600]
Discriminator Loss: 0.7964, Generator Loss: 2.1594
D(x): 0.6932, D(G(z)): 0.2131
Epoch: [9/20], Batch Num: [117/600]
Discriminator Loss: 0.7805, Generator Loss: 2.0402
D(x): 0.7166, D(G(z)): 0.2257
Epoch: [9/20], Batch Num: [118/600]
Discriminator Loss: 0.6438, Generator Loss: 1.8797
D(x): 0.7659, D(G(z)): 0.2030
Epoch: [9/20], Batch Num: [119/600]
Discriminator Loss: 0.8130, Generator Loss: 1.8707
D(x): 0.7688, D(G(z)): 0.2920
Epoch: [9/20], Batch Num: [120/600]
Discriminator Loss: 0.6396, Generator Loss: 1.7703
D(x): 0.8101, D(G(z)): 0.2677
Epoch: [9/20], Batch Num: [121/600]
Discriminator Loss: 0.6219, Generator Loss: 2.2290
D(x): 0.8511, D(G(z)): 0.3002
Epoch: [9/20], Batch Num: [122/600]
Discriminator Loss: 0.8022, Generator Loss: 2.2522
D(x): 0.7894, D(G(z)): 0.3061
Epoch: [9/20], Batch Num: [123/600]
Discriminator Loss: 0.7831, Generator Loss: 2.4517
D(x): 0.7435, D(G(z)): 0.2435
Epoch: [9/20], Batch Num: [124/600]
Discriminator Loss: 0.7073, Generator Loss: 2.1728
D(x): 0.7344, D(G(z)): 0.1937
Epoch: [9/20], Batch Num: [125/600]
Discriminator Loss: 0.8610, Generator Loss: 1.9998
D(x): 0.7058, D(G(z)): 0.2575
Epoch: [9/20], Batch Num: [126/600]
Discriminator Loss: 0.7791, Generator Loss: 2.0866
D(x): 0.7387, D(G(z)): 0.2441
Epoch: [9/20], Batch Num: [127/600]
Discriminator Loss: 0.7058, Generator Loss: 2.0972
D(x): 0.7839, D(G(z)): 0.2626
Epoch: [9/20], Batch Num: [128/600]
Discriminator Loss: 0.7135, Generator Loss: 1.8086
D(x): 0.7679, D(G(z)): 0.2483
Epoch: [9/20], Batch Num: [129/600]
Discriminator Loss: 0.6392, Generator Loss: 2.0776
D(x): 0.8078, D(G(z)): 0.2573
Epoch: [9/20], Batch Num: [130/600]
Discriminator Loss: 0.7370, Generator Loss: 1.9383
D(x): 0.7599, D(G(z)): 0.2335
Epoch: [9/20], Batch Num: [131/600]
Discriminator Loss: 0.7916, Generator Loss: 1.8135
D(x): 0.7378, D(G(z)): 0.2713
Epoch: [9/20], Batch Num: [132/600]
Discriminator Loss: 0.7399, Generator Loss: 1.6978
D(x): 0.7231, D(G(z)): 0.2324
Epoch: [9/20], Batch Num: [133/600]
Discriminator Loss: 0.8054, Generator Loss: 1.8538
D(x): 0.8056, D(G(z)): 0.3114
Epoch: [9/20], Batch Num: [134/600]
Discriminator Loss: 0.9536, Generator Loss: 2.0100
D(x): 0.7252, D(G(z)): 0.3257
Epoch: [9/20], Batch Num: [135/600]
Discriminator Loss: 0.8167, Generator Loss: 2.0577
D(x): 0.7073, D(G(z)): 0.2420
Epoch: [9/20], Batch Num: [136/600]
Discriminator Loss: 0.8217, Generator Loss: 1.6660
D(x): 0.6880, D(G(z)): 0.2310
Epoch: [9/20], Batch Num: [137/600]
Discriminator Loss: 0.8395, Generator Loss: 1.6558
D(x): 0.7690, D(G(z)): 0.3076
Epoch: [9/20], Batch Num: [138/600]
Discriminator Loss: 1.1171, Generator Loss: 1.4901
D(x): 0.6748, D(G(z)): 0.3425
Epoch: [9/20], Batch Num: [139/600]
Discriminator Loss: 1.1244, Generator Loss: 1.6294
D(x): 0.7566, D(G(z)): 0.4205
Epoch: [9/20], Batch Num: [140/600]
Discriminator Loss: 1.2500, Generator Loss: 1.9287
D(x): 0.6362, D(G(z)): 0.3340
Epoch: [9/20], Batch Num: [141/600]
Discriminator Loss: 1.1950, Generator Loss: 1.7974
D(x): 0.6202, D(G(z)): 0.3212
Epoch: [9/20], Batch Num: [142/600]
Discriminator Loss: 1.1598, Generator Loss: 1.5944
D(x): 0.5863, D(G(z)): 0.2315
Epoch: [9/20], Batch Num: [143/600]
Discriminator Loss: 1.2466, Generator Loss: 1.2936
D(x): 0.5719, D(G(z)): 0.3084
Epoch: [9/20], Batch Num: [144/600]
Discriminator Loss: 1.3165, Generator Loss: 1.1838
D(x): 0.6608, D(G(z)): 0.4479
Epoch: [9/20], Batch Num: [145/600]
Discriminator Loss: 1.1252, Generator Loss: 1.2032
D(x): 0.7085, D(G(z)): 0.4071
Epoch: [9/20], Batch Num: [146/600]
Discriminator Loss: 1.2366, Generator Loss: 1.4301
D(x): 0.6833, D(G(z)): 0.4421
Epoch: [9/20], Batch Num: [147/600]
Discriminator Loss: 1.1540, Generator Loss: 1.4803
D(x): 0.6299, D(G(z)): 0.3512
Epoch: [9/20], Batch Num: [148/600]
Discriminator Loss: 1.2295, Generator Loss: 1.5356
D(x): 0.5738, D(G(z)): 0.3198
Epoch: [9/20], Batch Num: [149/600]
Discriminator Loss: 1.2111, Generator Loss: 1.2627
D(x): 0.5950, D(G(z)): 0.3466
Epoch: [9/20], Batch Num: [150/600]
Discriminator Loss: 0.9755, Generator Loss: 1.3569
D(x): 0.6494, D(G(z)): 0.3139
Epoch: [9/20], Batch Num: [151/600]
Discriminator Loss: 1.1127, Generator Loss: 1.1882
D(x): 0.6255, D(G(z)): 0.3608
Epoch: [9/20], Batch Num: [152/600]
Discriminator Loss: 0.9582, Generator Loss: 1.2491
D(x): 0.7355, D(G(z)): 0.4044
Epoch: [9/20], Batch Num: [153/600]
Discriminator Loss: 1.0191, Generator Loss: 1.2378
D(x): 0.7237, D(G(z)): 0.4183
Epoch: [9/20], Batch Num: [154/600]
Discriminator Loss: 0.8835, Generator Loss: 1.4141
D(x): 0.7490, D(G(z)): 0.3721
Epoch: [9/20], Batch Num: [155/600]
Discriminator Loss: 0.8947, Generator Loss: 1.4737
D(x): 0.7043, D(G(z)): 0.3315
Epoch: [9/20], Batch Num: [156/600]
Discriminator Loss: 0.8133, Generator Loss: 1.4433
D(x): 0.6948, D(G(z)): 0.2844
Epoch: [9/20], Batch Num: [157/600]
Discriminator Loss: 0.8815, Generator Loss: 1.5807
D(x): 0.6477, D(G(z)): 0.2709
Epoch: [9/20], Batch Num: [158/600]
Discriminator Loss: 0.8191, Generator Loss: 1.5458
D(x): 0.6794, D(G(z)): 0.2638
Epoch: [9/20], Batch Num: [159/600]
Discriminator Loss: 0.7486, Generator Loss: 1.4858
D(x): 0.7273, D(G(z)): 0.2764
Epoch: [9/20], Batch Num: [160/600]
Discriminator Loss: 0.7288, Generator Loss: 1.4538
D(x): 0.7751, D(G(z)): 0.3176
Epoch: [9/20], Batch Num: [161/600]
Discriminator Loss: 0.6701, Generator Loss: 1.5753
D(x): 0.7935, D(G(z)): 0.2930
Epoch: [9/20], Batch Num: [162/600]
Discriminator Loss: 0.6372, Generator Loss: 1.6296
D(x): 0.8403, D(G(z)): 0.3130
Epoch: [9/20], Batch Num: [163/600]
Discriminator Loss: 0.6265, Generator Loss: 1.8294
D(x): 0.8243, D(G(z)): 0.2917
Epoch: [9/20], Batch Num: [164/600]
Discriminator Loss: 0.5573, Generator Loss: 2.0629
D(x): 0.8105, D(G(z)): 0.2431
Epoch: [9/20], Batch Num: [165/600]
Discriminator Loss: 0.5298, Generator Loss: 2.0627
D(x): 0.7858, D(G(z)): 0.1748
Epoch: [9/20], Batch Num: [166/600]
Discriminator Loss: 0.4880, Generator Loss: 2.3039
D(x): 0.8223, D(G(z)): 0.1825
Epoch: [9/20], Batch Num: [167/600]
Discriminator Loss: 0.4357, Generator Loss: 2.3271
D(x): 0.8325, D(G(z)): 0.1660
Epoch: [9/20], Batch Num: [168/600]
Discriminator Loss: 0.4253, Generator Loss: 2.4393
D(x): 0.8285, D(G(z)): 0.1469
Epoch: [9/20], Batch Num: [169/600]
Discriminator Loss: 0.3905, Generator Loss: 2.6139
D(x): 0.8767, D(G(z)): 0.1884
Epoch: [9/20], Batch Num: [170/600]
Discriminator Loss: 0.4294, Generator Loss: 2.4544
D(x): 0.8625, D(G(z)): 0.1865
Epoch: [9/20], Batch Num: [171/600]
Discriminator Loss: 0.3740, Generator Loss: 2.3869
D(x): 0.9022, D(G(z)): 0.1726
Epoch: [9/20], Batch Num: [172/600]
Discriminator Loss: 0.3909, Generator Loss: 2.6900
D(x): 0.8585, D(G(z)): 0.1437
Epoch: [9/20], Batch Num: [173/600]
Discriminator Loss: 0.3758, Generator Loss: 3.2095
D(x): 0.8993, D(G(z)): 0.1807
Epoch: [9/20], Batch Num: [174/600]
Discriminator Loss: 0.3688, Generator Loss: 3.3291
D(x): 0.8467, D(G(z)): 0.1178
Epoch: [9/20], Batch Num: [175/600]
Discriminator Loss: 0.4167, Generator Loss: 3.0231
D(x): 0.8528, D(G(z)): 0.1473
Epoch: [9/20], Batch Num: [176/600]
Discriminator Loss: 0.4538, Generator Loss: 2.9301
D(x): 0.8091, D(G(z)): 0.0921
Epoch: [9/20], Batch Num: [177/600]
Discriminator Loss: 0.4019, Generator Loss: 2.5035
D(x): 0.8485, D(G(z)): 0.1322
Epoch: [9/20], Batch Num: [178/600]
Discriminator Loss: 0.5252, Generator Loss: 1.9622
D(x): 0.8353, D(G(z)): 0.1832
Epoch: [9/20], Batch Num: [179/600]
Discriminator Loss: 0.6768, Generator Loss: 2.4153
D(x): 0.8627, D(G(z)): 0.2557
Epoch: [9/20], Batch Num: [180/600]
Discriminator Loss: 0.6015, Generator Loss: 3.0525
D(x): 0.8379, D(G(z)): 0.2119
Epoch: [9/20], Batch Num: [181/600]
Discriminator Loss: 0.5422, Generator Loss: 3.1573
D(x): 0.7998, D(G(z)): 0.1201
Epoch: [9/20], Batch Num: [182/600]
Discriminator Loss: 0.7803, Generator Loss: 2.8563
D(x): 0.7023, D(G(z)): 0.1239
Epoch: [9/20], Batch Num: [183/600]
Discriminator Loss: 0.7646, Generator Loss: 2.3838
D(x): 0.7832, D(G(z)): 0.2119
Epoch: [9/20], Batch Num: [184/600]
Discriminator Loss: 0.5556, Generator Loss: 2.3290
D(x): 0.8656, D(G(z)): 0.2037
Epoch: [9/20], Batch Num: [185/600]
Discriminator Loss: 0.7511, Generator Loss: 2.3922
D(x): 0.8018, D(G(z)): 0.2354
Epoch: [9/20], Batch Num: [186/600]
Discriminator Loss: 0.8869, Generator Loss: 2.5271
D(x): 0.7437, D(G(z)): 0.2533
Epoch: [9/20], Batch Num: [187/600]
Discriminator Loss: 0.8918, Generator Loss: 2.2268
D(x): 0.7271, D(G(z)): 0.2038
Epoch: [9/20], Batch Num: [188/600]
Discriminator Loss: 0.9135, Generator Loss: 1.9362
D(x): 0.7200, D(G(z)): 0.2071
Epoch: [9/20], Batch Num: [189/600]
Discriminator Loss: 1.0204, Generator Loss: 1.8477
D(x): 0.7295, D(G(z)): 0.2653
Epoch: [9/20], Batch Num: [190/600]
Discriminator Loss: 0.9349, Generator Loss: 1.7229
D(x): 0.7741, D(G(z)): 0.2775
Epoch: [9/20], Batch Num: [191/600]
Discriminator Loss: 1.1360, Generator Loss: 2.0929
D(x): 0.7405, D(G(z)): 0.3194
Epoch: [9/20], Batch Num: [192/600]
Discriminator Loss: 1.2762, Generator Loss: 2.4189
D(x): 0.6686, D(G(z)): 0.2521
Epoch: [9/20], Batch Num: [193/600]
Discriminator Loss: 1.0817, Generator Loss: 2.1596
D(x): 0.6428, D(G(z)): 0.2015
Epoch: [9/20], Batch Num: [194/600]
Discriminator Loss: 0.8857, Generator Loss: 1.7589
D(x): 0.7371, D(G(z)): 0.2229
Epoch: [9/20], Batch Num: [195/600]
Discriminator Loss: 1.2221, Generator Loss: 1.6409
D(x): 0.6559, D(G(z)): 0.2833
Epoch: [9/20], Batch Num: [196/600]
Discriminator Loss: 1.2198, Generator Loss: 1.5982
D(x): 0.6823, D(G(z)): 0.3215
Epoch: [9/20], Batch Num: [197/600]
Discriminator Loss: 1.1193, Generator Loss: 1.7500
D(x): 0.7614, D(G(z)): 0.3628
Epoch: [9/20], Batch Num: [198/600]
Discriminator Loss: 1.1441, Generator Loss: 1.9270
D(x): 0.6623, D(G(z)): 0.2717
Epoch: [9/20], Batch Num: [199/600]
Discriminator Loss: 1.0371, Generator Loss: 1.8036
D(x): 0.6625, D(G(z)): 0.2705
Epoch: 9, Batch Num: [200/600]

Epoch: [9/20], Batch Num: [200/600]
Discriminator Loss: 1.1654, Generator Loss: 1.6942
D(x): 0.6112, D(G(z)): 0.2225
Epoch: [9/20], Batch Num: [201/600]
Discriminator Loss: 0.9916, Generator Loss: 1.6587
D(x): 0.6794, D(G(z)): 0.2308
Epoch: [9/20], Batch Num: [202/600]
Discriminator Loss: 1.0239, Generator Loss: 1.4560
D(x): 0.7144, D(G(z)): 0.3158
Epoch: [9/20], Batch Num: [203/600]
Discriminator Loss: 0.9605, Generator Loss: 1.2713
D(x): 0.7253, D(G(z)): 0.2984
Epoch: [9/20], Batch Num: [204/600]
Discriminator Loss: 0.8710, Generator Loss: 1.4606
D(x): 0.7538, D(G(z)): 0.3116
Epoch: [9/20], Batch Num: [205/600]
Discriminator Loss: 0.8713, Generator Loss: 1.5672
D(x): 0.7775, D(G(z)): 0.3154
Epoch: [9/20], Batch Num: [206/600]
Discriminator Loss: 0.7363, Generator Loss: 1.7656
D(x): 0.7478, D(G(z)): 0.2513
Epoch: [9/20], Batch Num: [207/600]
Discriminator Loss: 0.7118, Generator Loss: 1.8382
D(x): 0.7768, D(G(z)): 0.2695
Epoch: [9/20], Batch Num: [208/600]
Discriminator Loss: 0.7655, Generator Loss: 1.8208
D(x): 0.6839, D(G(z)): 0.1868
Epoch: [9/20], Batch Num: [209/600]
Discriminator Loss: 0.6075, Generator Loss: 1.7717
D(x): 0.7449, D(G(z)): 0.2045
Epoch: [9/20], Batch Num: [210/600]
Discriminator Loss: 0.6877, Generator Loss: 1.7303
D(x): 0.7360, D(G(z)): 0.2071
Epoch: [9/20], Batch Num: [211/600]
Discriminator Loss: 0.7950, Generator Loss: 1.5685
D(x): 0.7610, D(G(z)): 0.2601
Epoch: [9/20], Batch Num: [212/600]
Discriminator Loss: 0.6324, Generator Loss: 1.4790
D(x): 0.8139, D(G(z)): 0.2597
Epoch: [9/20], Batch Num: [213/600]
Discriminator Loss: 0.6161, Generator Loss: 1.6860
D(x): 0.8367, D(G(z)): 0.2748
Epoch: [9/20], Batch Num: [214/600]
Discriminator Loss: 0.5718, Generator Loss: 1.8788
D(x): 0.8163, D(G(z)): 0.2310
Epoch: [9/20], Batch Num: [215/600]
Discriminator Loss: 0.5702, Generator Loss: 1.9815
D(x): 0.8273, D(G(z)): 0.2190
Epoch: [9/20], Batch Num: [216/600]
Discriminator Loss: 0.5018, Generator Loss: 2.1067
D(x): 0.8049, D(G(z)): 0.1781
Epoch: [9/20], Batch Num: [217/600]
Discriminator Loss: 0.4540, Generator Loss: 2.2079
D(x): 0.8394, D(G(z)): 0.1577
Epoch: [9/20], Batch Num: [218/600]
Discriminator Loss: 0.6090, Generator Loss: 2.2056
D(x): 0.7730, D(G(z)): 0.1695
Epoch: [9/20], Batch Num: [219/600]
Discriminator Loss: 0.6357, Generator Loss: 2.0061
D(x): 0.7336, D(G(z)): 0.1616
Epoch: [9/20], Batch Num: [220/600]
Discriminator Loss: 0.5470, Generator Loss: 1.9821
D(x): 0.8225, D(G(z)): 0.1676
Epoch: [9/20], Batch Num: [221/600]
Discriminator Loss: 0.5759, Generator Loss: 1.8807
D(x): 0.8353, D(G(z)): 0.2089
Epoch: [9/20], Batch Num: [222/600]
Discriminator Loss: 0.5700, Generator Loss: 1.8653
D(x): 0.8537, D(G(z)): 0.2312
Epoch: [9/20], Batch Num: [223/600]
Discriminator Loss: 0.5180, Generator Loss: 1.8906
D(x): 0.8972, D(G(z)): 0.2328
Epoch: [9/20], Batch Num: [224/600]
Discriminator Loss: 0.5486, Generator Loss: 2.1146
D(x): 0.8559, D(G(z)): 0.2340
Epoch: [9/20], Batch Num: [225/600]
Discriminator Loss: 0.6316, Generator Loss: 2.3590
D(x): 0.8305, D(G(z)): 0.2184
Epoch: [9/20], Batch Num: [226/600]
Discriminator Loss: 0.3965, Generator Loss: 2.5149
D(x): 0.8570, D(G(z)): 0.1596
Epoch: [9/20], Batch Num: [227/600]
Discriminator Loss: 0.6127, Generator Loss: 2.8062
D(x): 0.7806, D(G(z)): 0.1605
Epoch: [9/20], Batch Num: [228/600]
Discriminator Loss: 0.6015, Generator Loss: 2.4211
D(x): 0.7850, D(G(z)): 0.1603
Epoch: [9/20], Batch Num: [229/600]
Discriminator Loss: 0.7153, Generator Loss: 2.1359
D(x): 0.7445, D(G(z)): 0.1616
Epoch: [9/20], Batch Num: [230/600]
Discriminator Loss: 0.5376, Generator Loss: 1.9575
D(x): 0.8400, D(G(z)): 0.2041
Epoch: [9/20], Batch Num: [231/600]
Discriminator Loss: 0.5890, Generator Loss: 1.7446
D(x): 0.8673, D(G(z)): 0.2386
Epoch: [9/20], Batch Num: [232/600]
Discriminator Loss: 0.5849, Generator Loss: 1.9911
D(x): 0.8801, D(G(z)): 0.2487
Epoch: [9/20], Batch Num: [233/600]
Discriminator Loss: 0.6585, Generator Loss: 2.2044
D(x): 0.8591, D(G(z)): 0.2991
Epoch: [9/20], Batch Num: [234/600]
Discriminator Loss: 0.8545, Generator Loss: 2.3011
D(x): 0.7204, D(G(z)): 0.2197
Epoch: [9/20], Batch Num: [235/600]
Discriminator Loss: 0.7159, Generator Loss: 2.2589
D(x): 0.7551, D(G(z)): 0.1799
Epoch: [9/20], Batch Num: [236/600]
Discriminator Loss: 0.5825, Generator Loss: 2.0178
D(x): 0.7916, D(G(z)): 0.1864
Epoch: [9/20], Batch Num: [237/600]
Discriminator Loss: 0.7069, Generator Loss: 1.9453
D(x): 0.7713, D(G(z)): 0.2134
Epoch: [9/20], Batch Num: [238/600]
Discriminator Loss: 0.5730, Generator Loss: 1.6839
D(x): 0.8401, D(G(z)): 0.2288
Epoch: [9/20], Batch Num: [239/600]
Discriminator Loss: 0.7476, Generator Loss: 1.5889
D(x): 0.8063, D(G(z)): 0.2826
Epoch: [9/20], Batch Num: [240/600]
Discriminator Loss: 0.8654, Generator Loss: 1.8289
D(x): 0.7851, D(G(z)): 0.3185
Epoch: [9/20], Batch Num: [241/600]
Discriminator Loss: 0.8648, Generator Loss: 2.0992
D(x): 0.7449, D(G(z)): 0.2708
Epoch: [9/20], Batch Num: [242/600]
Discriminator Loss: 0.7877, Generator Loss: 2.1929
D(x): 0.7267, D(G(z)): 0.2337
Epoch: [9/20], Batch Num: [243/600]
Discriminator Loss: 0.8780, Generator Loss: 1.9010
D(x): 0.7284, D(G(z)): 0.2402
Epoch: [9/20], Batch Num: [244/600]
Discriminator Loss: 0.7848, Generator Loss: 1.5492
D(x): 0.7781, D(G(z)): 0.2701
Epoch: [9/20], Batch Num: [245/600]
Discriminator Loss: 0.7906, Generator Loss: 1.8867
D(x): 0.8214, D(G(z)): 0.2990
Epoch: [9/20], Batch Num: [246/600]
Discriminator Loss: 0.7107, Generator Loss: 1.8678
D(x): 0.8244, D(G(z)): 0.3010
Epoch: [9/20], Batch Num: [247/600]
Discriminator Loss: 0.6980, Generator Loss: 1.9443
D(x): 0.8095, D(G(z)): 0.2564
Epoch: [9/20], Batch Num: [248/600]
Discriminator Loss: 0.6733, Generator Loss: 2.0443
D(x): 0.7931, D(G(z)): 0.2331
Epoch: [9/20], Batch Num: [249/600]
Discriminator Loss: 0.6513, Generator Loss: 1.8858
D(x): 0.7627, D(G(z)): 0.2117
Epoch: [9/20], Batch Num: [250/600]
Discriminator Loss: 0.6786, Generator Loss: 1.7859
D(x): 0.7771, D(G(z)): 0.2174
Epoch: [9/20], Batch Num: [251/600]
Discriminator Loss: 0.6573, Generator Loss: 1.8067
D(x): 0.8384, D(G(z)): 0.2628
Epoch: [9/20], Batch Num: [252/600]
Discriminator Loss: 0.6940, Generator Loss: 1.9502
D(x): 0.8598, D(G(z)): 0.2957
Epoch: [9/20], Batch Num: [253/600]
Discriminator Loss: 0.6704, Generator Loss: 1.9926
D(x): 0.8264, D(G(z)): 0.2694
Epoch: [9/20], Batch Num: [254/600]
Discriminator Loss: 0.5666, Generator Loss: 2.2061
D(x): 0.8093, D(G(z)): 0.2107
Epoch: [9/20], Batch Num: [255/600]
Discriminator Loss: 0.6345, Generator Loss: 2.1461
D(x): 0.7627, D(G(z)): 0.1973
Epoch: [9/20], Batch Num: [256/600]
Discriminator Loss: 0.5736, Generator Loss: 2.1563
D(x): 0.8142, D(G(z)): 0.2093
Epoch: [9/20], Batch Num: [257/600]
Discriminator Loss: 0.6310, Generator Loss: 1.9834
D(x): 0.8139, D(G(z)): 0.2370
Epoch: [9/20], Batch Num: [258/600]
Discriminator Loss: 0.4748, Generator Loss: 1.7085
D(x): 0.8955, D(G(z)): 0.2427
Epoch: [9/20], Batch Num: [259/600]
Discriminator Loss: 0.5560, Generator Loss: 1.9889
D(x): 0.8562, D(G(z)): 0.2385
Epoch: [9/20], Batch Num: [260/600]
Discriminator Loss: 0.5079, Generator Loss: 1.9679
D(x): 0.8662, D(G(z)): 0.2096
Epoch: [9/20], Batch Num: [261/600]
Discriminator Loss: 0.5537, Generator Loss: 2.1546
D(x): 0.8406, D(G(z)): 0.2220
Epoch: [9/20], Batch Num: [262/600]
Discriminator Loss: 0.4825, Generator Loss: 2.2947
D(x): 0.8510, D(G(z)): 0.2000
Epoch: [9/20], Batch Num: [263/600]
Discriminator Loss: 0.4949, Generator Loss: 2.1803
D(x): 0.8107, D(G(z)): 0.1729
Epoch: [9/20], Batch Num: [264/600]
Discriminator Loss: 0.4429, Generator Loss: 1.8201
D(x): 0.8341, D(G(z)): 0.1739
Epoch: [9/20], Batch Num: [265/600]
Discriminator Loss: 0.5035, Generator Loss: 2.0325
D(x): 0.8769, D(G(z)): 0.2480
Epoch: [9/20], Batch Num: [266/600]
Discriminator Loss: 0.7099, Generator Loss: 2.2428
D(x): 0.8398, D(G(z)): 0.2771
Epoch: [9/20], Batch Num: [267/600]
Discriminator Loss: 0.5344, Generator Loss: 2.4442
D(x): 0.8649, D(G(z)): 0.2393
Epoch: [9/20], Batch Num: [268/600]
Discriminator Loss: 0.5603, Generator Loss: 2.1643
D(x): 0.8089, D(G(z)): 0.1801
Epoch: [9/20], Batch Num: [269/600]
Discriminator Loss: 0.5706, Generator Loss: 2.0748
D(x): 0.7978, D(G(z)): 0.1616
Epoch: [9/20], Batch Num: [270/600]
Discriminator Loss: 0.6130, Generator Loss: 2.1034
D(x): 0.8108, D(G(z)): 0.2080
Epoch: [9/20], Batch Num: [271/600]
Discriminator Loss: 0.6572, Generator Loss: 1.9294
D(x): 0.8761, D(G(z)): 0.2995
Epoch: [9/20], Batch Num: [272/600]
Discriminator Loss: 0.5912, Generator Loss: 2.5066
D(x): 0.8555, D(G(z)): 0.2341
Epoch: [9/20], Batch Num: [273/600]
Discriminator Loss: 0.5818, Generator Loss: 2.4973
D(x): 0.7854, D(G(z)): 0.1789
Epoch: [9/20], Batch Num: [274/600]
Discriminator Loss: 0.5672, Generator Loss: 2.2574
D(x): 0.8000, D(G(z)): 0.1595
Epoch: [9/20], Batch Num: [275/600]
Discriminator Loss: 0.5490, Generator Loss: 2.0689
D(x): 0.8257, D(G(z)): 0.1932
Epoch: [9/20], Batch Num: [276/600]
Discriminator Loss: 0.4951, Generator Loss: 2.0780
D(x): 0.8218, D(G(z)): 0.1585
Epoch: [9/20], Batch Num: [277/600]
Discriminator Loss: 0.4900, Generator Loss: 1.9315
D(x): 0.8687, D(G(z)): 0.2310
Epoch: [9/20], Batch Num: [278/600]
Discriminator Loss: 0.5251, Generator Loss: 2.2277
D(x): 0.9267, D(G(z)): 0.2768
Epoch: [9/20], Batch Num: [279/600]
Discriminator Loss: 0.5760, Generator Loss: 2.5289
D(x): 0.8037, D(G(z)): 0.1992
Epoch: [9/20], Batch Num: [280/600]
Discriminator Loss: 0.5252, Generator Loss: 2.7114
D(x): 0.8204, D(G(z)): 0.1681
Epoch: [9/20], Batch Num: [281/600]
Discriminator Loss: 0.6920, Generator Loss: 2.5919
D(x): 0.7470, D(G(z)): 0.1544
Epoch: [9/20], Batch Num: [282/600]
Discriminator Loss: 0.5042, Generator Loss: 2.3063
D(x): 0.8616, D(G(z)): 0.1723
Epoch: [9/20], Batch Num: [283/600]
Discriminator Loss: 0.6120, Generator Loss: 2.1239
D(x): 0.7948, D(G(z)): 0.1942
Epoch: [9/20], Batch Num: [284/600]
Discriminator Loss: 0.7157, Generator Loss: 1.8768
D(x): 0.8528, D(G(z)): 0.2916
Epoch: [9/20], Batch Num: [285/600]
Discriminator Loss: 0.7588, Generator Loss: 2.3912
D(x): 0.8559, D(G(z)): 0.2936
Epoch: [9/20], Batch Num: [286/600]
Discriminator Loss: 0.6047, Generator Loss: 2.5653
D(x): 0.8325, D(G(z)): 0.1810
Epoch: [9/20], Batch Num: [287/600]
Discriminator Loss: 0.7832, Generator Loss: 2.9311
D(x): 0.7315, D(G(z)): 0.1621
Epoch: [9/20], Batch Num: [288/600]
Discriminator Loss: 0.6464, Generator Loss: 2.3762
D(x): 0.7622, D(G(z)): 0.1401
Epoch: [9/20], Batch Num: [289/600]
Discriminator Loss: 0.6407, Generator Loss: 1.8516
D(x): 0.7885, D(G(z)): 0.1955
Epoch: [9/20], Batch Num: [290/600]
Discriminator Loss: 0.8619, Generator Loss: 1.7898
D(x): 0.7926, D(G(z)): 0.2697
Epoch: [9/20], Batch Num: [291/600]
Discriminator Loss: 0.6938, Generator Loss: 2.2018
D(x): 0.8412, D(G(z)): 0.2978
Epoch: [9/20], Batch Num: [292/600]
Discriminator Loss: 0.8320, Generator Loss: 2.2898
D(x): 0.7473, D(G(z)): 0.2334
Epoch: [9/20], Batch Num: [293/600]
Discriminator Loss: 0.9779, Generator Loss: 2.0008
D(x): 0.6708, D(G(z)): 0.2039
Epoch: [9/20], Batch Num: [294/600]
Discriminator Loss: 0.8093, Generator Loss: 2.0371
D(x): 0.7381, D(G(z)): 0.2168
Epoch: [9/20], Batch Num: [295/600]
Discriminator Loss: 0.9445, Generator Loss: 1.8438
D(x): 0.7327, D(G(z)): 0.2731
Epoch: [9/20], Batch Num: [296/600]
Discriminator Loss: 0.8635, Generator Loss: 1.8220
D(x): 0.7859, D(G(z)): 0.2777
Epoch: [9/20], Batch Num: [297/600]
Discriminator Loss: 0.9504, Generator Loss: 2.1694
D(x): 0.7570, D(G(z)): 0.2906
Epoch: [9/20], Batch Num: [298/600]
Discriminator Loss: 1.0185, Generator Loss: 2.4762
D(x): 0.6749, D(G(z)): 0.2269
Epoch: [9/20], Batch Num: [299/600]
Discriminator Loss: 0.9051, Generator Loss: 2.2855
D(x): 0.6991, D(G(z)): 0.2077
Epoch: 9, Batch Num: [300/600]

Epoch: [9/20], Batch Num: [300/600]
Discriminator Loss: 0.9829, Generator Loss: 2.0918
D(x): 0.6945, D(G(z)): 0.2124
Epoch: [9/20], Batch Num: [301/600]
Discriminator Loss: 0.7103, Generator Loss: 1.7242
D(x): 0.7842, D(G(z)): 0.2328
Epoch: [9/20], Batch Num: [302/600]
Discriminator Loss: 0.8695, Generator Loss: 1.8751
D(x): 0.8014, D(G(z)): 0.3059
Epoch: [9/20], Batch Num: [303/600]
Discriminator Loss: 0.7735, Generator Loss: 1.8575
D(x): 0.7804, D(G(z)): 0.2620
Epoch: [9/20], Batch Num: [304/600]
Discriminator Loss: 0.7027, Generator Loss: 2.1146
D(x): 0.7892, D(G(z)): 0.2231
Epoch: [9/20], Batch Num: [305/600]
Discriminator Loss: 0.8414, Generator Loss: 2.2532
D(x): 0.7301, D(G(z)): 0.2254
Epoch: [9/20], Batch Num: [306/600]
Discriminator Loss: 0.7697, Generator Loss: 2.0757
D(x): 0.7343, D(G(z)): 0.2087
Epoch: [9/20], Batch Num: [307/600]
Discriminator Loss: 0.7911, Generator Loss: 1.8964
D(x): 0.7435, D(G(z)): 0.1911
Epoch: [9/20], Batch Num: [308/600]
Discriminator Loss: 0.6439, Generator Loss: 1.9378
D(x): 0.8395, D(G(z)): 0.2834
Epoch: [9/20], Batch Num: [309/600]
Discriminator Loss: 0.6489, Generator Loss: 2.0178
D(x): 0.8269, D(G(z)): 0.2597
Epoch: [9/20], Batch Num: [310/600]
Discriminator Loss: 0.6289, Generator Loss: 2.1264
D(x): 0.7816, D(G(z)): 0.1999
Epoch: [9/20], Batch Num: [311/600]
Discriminator Loss: 0.7032, Generator Loss: 2.1245
D(x): 0.7676, D(G(z)): 0.2014
Epoch: [9/20], Batch Num: [312/600]
Discriminator Loss: 0.6707, Generator Loss: 1.9980
D(x): 0.7654, D(G(z)): 0.2102
Epoch: [9/20], Batch Num: [313/600]
Discriminator Loss: 0.5737, Generator Loss: 1.9253
D(x): 0.8118, D(G(z)): 0.1890
Epoch: [9/20], Batch Num: [314/600]
Discriminator Loss: 0.7243, Generator Loss: 1.9254
D(x): 0.8172, D(G(z)): 0.2731
Epoch: [9/20], Batch Num: [315/600]
Discriminator Loss: 0.6317, Generator Loss: 2.2200
D(x): 0.8051, D(G(z)): 0.2250
Epoch: [9/20], Batch Num: [316/600]
Discriminator Loss: 0.5459, Generator Loss: 2.4686
D(x): 0.8438, D(G(z)): 0.2052
Epoch: [9/20], Batch Num: [317/600]
Discriminator Loss: 0.4700, Generator Loss: 2.3798
D(x): 0.8486, D(G(z)): 0.1779
Epoch: [9/20], Batch Num: [318/600]
Discriminator Loss: 0.5385, Generator Loss: 2.6489
D(x): 0.8231, D(G(z)): 0.1592
Epoch: [9/20], Batch Num: [319/600]
Discriminator Loss: 0.5554, Generator Loss: 2.5301
D(x): 0.7773, D(G(z)): 0.1387
Epoch: [9/20], Batch Num: [320/600]
Discriminator Loss: 0.5399, Generator Loss: 2.4105
D(x): 0.8095, D(G(z)): 0.1705
Epoch: [9/20], Batch Num: [321/600]
Discriminator Loss: 0.5670, Generator Loss: 1.9903
D(x): 0.8180, D(G(z)): 0.1685
Epoch: [9/20], Batch Num: [322/600]
Discriminator Loss: 0.5878, Generator Loss: 2.1692
D(x): 0.8663, D(G(z)): 0.2430
Epoch: [9/20], Batch Num: [323/600]
Discriminator Loss: 0.6283, Generator Loss: 2.5410
D(x): 0.8641, D(G(z)): 0.2383
Epoch: [9/20], Batch Num: [324/600]
Discriminator Loss: 0.6917, Generator Loss: 2.3978
D(x): 0.7490, D(G(z)): 0.1611
Epoch: [9/20], Batch Num: [325/600]
Discriminator Loss: 0.5894, Generator Loss: 2.3094
D(x): 0.8393, D(G(z)): 0.2124
Epoch: [9/20], Batch Num: [326/600]
Discriminator Loss: 0.5427, Generator Loss: 2.3217
D(x): 0.8397, D(G(z)): 0.1866
Epoch: [9/20], Batch Num: [327/600]
Discriminator Loss: 0.6947, Generator Loss: 2.4831
D(x): 0.7899, D(G(z)): 0.1896
Epoch: [9/20], Batch Num: [328/600]
Discriminator Loss: 0.6797, Generator Loss: 2.2963
D(x): 0.7672, D(G(z)): 0.1656
Epoch: [9/20], Batch Num: [329/600]
Discriminator Loss: 0.6520, Generator Loss: 2.3388
D(x): 0.8447, D(G(z)): 0.2365
Epoch: [9/20], Batch Num: [330/600]
Discriminator Loss: 0.6917, Generator Loss: 1.9408
D(x): 0.7736, D(G(z)): 0.1996
Epoch: [9/20], Batch Num: [331/600]
Discriminator Loss: 0.5248, Generator Loss: 2.1286
D(x): 0.8514, D(G(z)): 0.2187
Epoch: [9/20], Batch Num: [332/600]
Discriminator Loss: 0.5473, Generator Loss: 2.0456
D(x): 0.8561, D(G(z)): 0.2253
Epoch: [9/20], Batch Num: [333/600]
Discriminator Loss: 0.5229, Generator Loss: 2.2019
D(x): 0.8721, D(G(z)): 0.1974
Epoch: [9/20], Batch Num: [334/600]
Discriminator Loss: 0.6571, Generator Loss: 2.4277
D(x): 0.8228, D(G(z)): 0.2364
Epoch: [9/20], Batch Num: [335/600]
Discriminator Loss: 0.7665, Generator Loss: 2.6333
D(x): 0.7514, D(G(z)): 0.1923
Epoch: [9/20], Batch Num: [336/600]
Discriminator Loss: 0.4761, Generator Loss: 2.3012
D(x): 0.8317, D(G(z)): 0.1644
Epoch: [9/20], Batch Num: [337/600]
Discriminator Loss: 0.6867, Generator Loss: 2.0987
D(x): 0.7547, D(G(z)): 0.1896
Epoch: [9/20], Batch Num: [338/600]
Discriminator Loss: 0.6487, Generator Loss: 1.7461
D(x): 0.7960, D(G(z)): 0.2269
Epoch: [9/20], Batch Num: [339/600]
Discriminator Loss: 0.6646, Generator Loss: 1.9969
D(x): 0.8371, D(G(z)): 0.2702
Epoch: [9/20], Batch Num: [340/600]
Discriminator Loss: 0.8141, Generator Loss: 2.1127
D(x): 0.7838, D(G(z)): 0.2633
Epoch: [9/20], Batch Num: [341/600]
Discriminator Loss: 0.8304, Generator Loss: 2.1258
D(x): 0.7239, D(G(z)): 0.2406
Epoch: [9/20], Batch Num: [342/600]
Discriminator Loss: 0.9031, Generator Loss: 2.1141
D(x): 0.7366, D(G(z)): 0.2315
Epoch: [9/20], Batch Num: [343/600]
Discriminator Loss: 0.7699, Generator Loss: 1.9567
D(x): 0.7398, D(G(z)): 0.1885
Epoch: [9/20], Batch Num: [344/600]
Discriminator Loss: 0.8239, Generator Loss: 1.7409
D(x): 0.7421, D(G(z)): 0.2324
Epoch: [9/20], Batch Num: [345/600]
Discriminator Loss: 0.8516, Generator Loss: 1.6132
D(x): 0.7683, D(G(z)): 0.2939
Epoch: [9/20], Batch Num: [346/600]
Discriminator Loss: 0.9012, Generator Loss: 1.6720
D(x): 0.7969, D(G(z)): 0.3345
Epoch: [9/20], Batch Num: [347/600]
Discriminator Loss: 1.0393, Generator Loss: 1.9161
D(x): 0.6908, D(G(z)): 0.2940
Epoch: [9/20], Batch Num: [348/600]
Discriminator Loss: 0.9172, Generator Loss: 1.9377
D(x): 0.7259, D(G(z)): 0.2741
Epoch: [9/20], Batch Num: [349/600]
Discriminator Loss: 0.8736, Generator Loss: 1.9260
D(x): 0.7150, D(G(z)): 0.2463
Epoch: [9/20], Batch Num: [350/600]
Discriminator Loss: 0.7771, Generator Loss: 2.0149
D(x): 0.7406, D(G(z)): 0.2265
Epoch: [9/20], Batch Num: [351/600]
Discriminator Loss: 0.7588, Generator Loss: 1.5481
D(x): 0.7588, D(G(z)): 0.2476
Epoch: [9/20], Batch Num: [352/600]
Discriminator Loss: 0.8058, Generator Loss: 1.7519
D(x): 0.8119, D(G(z)): 0.3342
Epoch: [9/20], Batch Num: [353/600]
Discriminator Loss: 0.7928, Generator Loss: 1.7073
D(x): 0.7531, D(G(z)): 0.2644
Epoch: [9/20], Batch Num: [354/600]
Discriminator Loss: 0.7772, Generator Loss: 1.8423
D(x): 0.7876, D(G(z)): 0.2809
Epoch: [9/20], Batch Num: [355/600]
Discriminator Loss: 0.7106, Generator Loss: 1.7242
D(x): 0.7526, D(G(z)): 0.2332
Epoch: [9/20], Batch Num: [356/600]
Discriminator Loss: 0.7482, Generator Loss: 1.8506
D(x): 0.7723, D(G(z)): 0.2839
Epoch: [9/20], Batch Num: [357/600]
Discriminator Loss: 0.8263, Generator Loss: 1.9631
D(x): 0.7551, D(G(z)): 0.2495
Epoch: [9/20], Batch Num: [358/600]
Discriminator Loss: 0.7248, Generator Loss: 1.7502
D(x): 0.7509, D(G(z)): 0.2205
Epoch: [9/20], Batch Num: [359/600]
Discriminator Loss: 0.8136, Generator Loss: 1.8643
D(x): 0.7524, D(G(z)): 0.2655
Epoch: [9/20], Batch Num: [360/600]
Discriminator Loss: 0.8330, Generator Loss: 1.7808
D(x): 0.7708, D(G(z)): 0.2908
Epoch: [9/20], Batch Num: [361/600]
Discriminator Loss: 0.5997, Generator Loss: 1.9246
D(x): 0.8657, D(G(z)): 0.2776
Epoch: [9/20], Batch Num: [362/600]
Discriminator Loss: 0.5805, Generator Loss: 2.1733
D(x): 0.8015, D(G(z)): 0.2086
Epoch: [9/20], Batch Num: [363/600]
Discriminator Loss: 0.6283, Generator Loss: 2.3105
D(x): 0.7655, D(G(z)): 0.2034
Epoch: [9/20], Batch Num: [364/600]
Discriminator Loss: 0.6009, Generator Loss: 2.0853
D(x): 0.7894, D(G(z)): 0.1961
Epoch: [9/20], Batch Num: [365/600]
Discriminator Loss: 0.6068, Generator Loss: 1.9292
D(x): 0.7979, D(G(z)): 0.2071
Epoch: [9/20], Batch Num: [366/600]
Discriminator Loss: 0.4880, Generator Loss: 1.8707
D(x): 0.8348, D(G(z)): 0.2041
Epoch: [9/20], Batch Num: [367/600]
Discriminator Loss: 0.6422, Generator Loss: 1.7085
D(x): 0.8285, D(G(z)): 0.2582
Epoch: [9/20], Batch Num: [368/600]
Discriminator Loss: 0.5829, Generator Loss: 1.7655
D(x): 0.8394, D(G(z)): 0.2473
Epoch: [9/20], Batch Num: [369/600]
Discriminator Loss: 0.5471, Generator Loss: 2.0100
D(x): 0.8992, D(G(z)): 0.2754
Epoch: [9/20], Batch Num: [370/600]
Discriminator Loss: 0.4282, Generator Loss: 2.2398
D(x): 0.8573, D(G(z)): 0.1779
Epoch: [9/20], Batch Num: [371/600]
Discriminator Loss: 0.4711, Generator Loss: 2.3410
D(x): 0.8192, D(G(z)): 0.1633
Epoch: [9/20], Batch Num: [372/600]
Discriminator Loss: 0.5649, Generator Loss: 2.0045
D(x): 0.7757, D(G(z)): 0.1536
Epoch: [9/20], Batch Num: [373/600]
Discriminator Loss: 0.4839, Generator Loss: 2.1143
D(x): 0.8651, D(G(z)): 0.2034
Epoch: [9/20], Batch Num: [374/600]
Discriminator Loss: 0.4668, Generator Loss: 2.1563
D(x): 0.8304, D(G(z)): 0.1695
Epoch: [9/20], Batch Num: [375/600]
Discriminator Loss: 0.5960, Generator Loss: 2.0321
D(x): 0.8515, D(G(z)): 0.2394
Epoch: [9/20], Batch Num: [376/600]
Discriminator Loss: 0.5543, Generator Loss: 2.2005
D(x): 0.8711, D(G(z)): 0.2466
Epoch: [9/20], Batch Num: [377/600]
Discriminator Loss: 0.5951, Generator Loss: 2.1763
D(x): 0.8098, D(G(z)): 0.1965
Epoch: [9/20], Batch Num: [378/600]
Discriminator Loss: 0.4750, Generator Loss: 2.4068
D(x): 0.8610, D(G(z)): 0.1822
Epoch: [9/20], Batch Num: [379/600]
Discriminator Loss: 0.5123, Generator Loss: 2.2337
D(x): 0.8004, D(G(z)): 0.1456
Epoch: [9/20], Batch Num: [380/600]
Discriminator Loss: 0.4786, Generator Loss: 2.2462
D(x): 0.8319, D(G(z)): 0.1605
Epoch: [9/20], Batch Num: [381/600]
Discriminator Loss: 0.6694, Generator Loss: 2.0522
D(x): 0.7893, D(G(z)): 0.2018
Epoch: [9/20], Batch Num: [382/600]
Discriminator Loss: 0.7029, Generator Loss: 2.0976
D(x): 0.8252, D(G(z)): 0.2692
Epoch: [9/20], Batch Num: [383/600]
Discriminator Loss: 0.5820, Generator Loss: 2.0234
D(x): 0.8665, D(G(z)): 0.2497
Epoch: [9/20], Batch Num: [384/600]
Discriminator Loss: 0.7337, Generator Loss: 2.4661
D(x): 0.8031, D(G(z)): 0.2317
Epoch: [9/20], Batch Num: [385/600]
Discriminator Loss: 0.7511, Generator Loss: 2.2464
D(x): 0.7678, D(G(z)): 0.1877
Epoch: [9/20], Batch Num: [386/600]
Discriminator Loss: 0.6308, Generator Loss: 2.3477
D(x): 0.7703, D(G(z)): 0.1612
Epoch: [9/20], Batch Num: [387/600]
Discriminator Loss: 0.6772, Generator Loss: 1.8480
D(x): 0.7654, D(G(z)): 0.1690
Epoch: [9/20], Batch Num: [388/600]
Discriminator Loss: 0.6814, Generator Loss: 1.5970
D(x): 0.8026, D(G(z)): 0.2408
Epoch: [9/20], Batch Num: [389/600]
Discriminator Loss: 1.0306, Generator Loss: 1.8592
D(x): 0.8110, D(G(z)): 0.3687
Epoch: [9/20], Batch Num: [390/600]
Discriminator Loss: 0.8990, Generator Loss: 2.3477
D(x): 0.7777, D(G(z)): 0.2898
Epoch: [9/20], Batch Num: [391/600]
Discriminator Loss: 0.9166, Generator Loss: 2.5198
D(x): 0.6699, D(G(z)): 0.2036
Epoch: [9/20], Batch Num: [392/600]
Discriminator Loss: 0.9673, Generator Loss: 2.1560
D(x): 0.6310, D(G(z)): 0.1748
Epoch: [9/20], Batch Num: [393/600]
Discriminator Loss: 0.8309, Generator Loss: 1.7638
D(x): 0.6982, D(G(z)): 0.1977
Epoch: [9/20], Batch Num: [394/600]
Discriminator Loss: 0.9412, Generator Loss: 1.5660
D(x): 0.7823, D(G(z)): 0.3295
Epoch: [9/20], Batch Num: [395/600]
Discriminator Loss: 0.9859, Generator Loss: 1.7146
D(x): 0.7713, D(G(z)): 0.3569
Epoch: [9/20], Batch Num: [396/600]
Discriminator Loss: 1.0079, Generator Loss: 1.7103
D(x): 0.7011, D(G(z)): 0.2861
Epoch: [9/20], Batch Num: [397/600]
Discriminator Loss: 0.6539, Generator Loss: 2.0022
D(x): 0.7711, D(G(z)): 0.2173
Epoch: [9/20], Batch Num: [398/600]
Discriminator Loss: 0.9407, Generator Loss: 2.3429
D(x): 0.7216, D(G(z)): 0.2695
Epoch: [9/20], Batch Num: [399/600]
Discriminator Loss: 1.0416, Generator Loss: 2.1407
D(x): 0.6370, D(G(z)): 0.1874
Epoch: 9, Batch Num: [400/600]

Epoch: [9/20], Batch Num: [400/600]
Discriminator Loss: 0.9254, Generator Loss: 2.1685
D(x): 0.6710, D(G(z)): 0.1941
Epoch: [9/20], Batch Num: [401/600]
Discriminator Loss: 0.8983, Generator Loss: 1.9301
D(x): 0.7581, D(G(z)): 0.3080
Epoch: [9/20], Batch Num: [402/600]
Discriminator Loss: 0.8776, Generator Loss: 1.7252
D(x): 0.7897, D(G(z)): 0.3094
Epoch: [9/20], Batch Num: [403/600]
Discriminator Loss: 0.8986, Generator Loss: 1.8216
D(x): 0.7207, D(G(z)): 0.2903
Epoch: [9/20], Batch Num: [404/600]
Discriminator Loss: 0.6998, Generator Loss: 1.9304
D(x): 0.7718, D(G(z)): 0.2284
Epoch: [9/20], Batch Num: [405/600]
Discriminator Loss: 0.7954, Generator Loss: 2.1712
D(x): 0.7316, D(G(z)): 0.2463
Epoch: [9/20], Batch Num: [406/600]
Discriminator Loss: 0.8461, Generator Loss: 2.2207
D(x): 0.7315, D(G(z)): 0.2357
Epoch: [9/20], Batch Num: [407/600]
Discriminator Loss: 0.7413, Generator Loss: 2.1253
D(x): 0.7409, D(G(z)): 0.2145
Epoch: [9/20], Batch Num: [408/600]
Discriminator Loss: 0.6761, Generator Loss: 2.0632
D(x): 0.8118, D(G(z)): 0.2628
Epoch: [9/20], Batch Num: [409/600]
Discriminator Loss: 0.5708, Generator Loss: 2.3066
D(x): 0.8329, D(G(z)): 0.2266
Epoch: [9/20], Batch Num: [410/600]
Discriminator Loss: 0.5715, Generator Loss: 2.3058
D(x): 0.8093, D(G(z)): 0.2069
Epoch: [9/20], Batch Num: [411/600]
Discriminator Loss: 0.7341, Generator Loss: 2.3309
D(x): 0.7660, D(G(z)): 0.2225
Epoch: [9/20], Batch Num: [412/600]
Discriminator Loss: 0.5871, Generator Loss: 2.7726
D(x): 0.8051, D(G(z)): 0.1851
Epoch: [9/20], Batch Num: [413/600]
Discriminator Loss: 0.5085, Generator Loss: 2.3204
D(x): 0.8084, D(G(z)): 0.1512
Epoch: [9/20], Batch Num: [414/600]
Discriminator Loss: 0.4989, Generator Loss: 2.2407
D(x): 0.7978, D(G(z)): 0.1487
Epoch: [9/20], Batch Num: [415/600]
Discriminator Loss: 0.4692, Generator Loss: 2.1820
D(x): 0.8641, D(G(z)): 0.1909
Epoch: [9/20], Batch Num: [416/600]
Discriminator Loss: 0.5990, Generator Loss: 2.5031
D(x): 0.8663, D(G(z)): 0.2399
Epoch: [9/20], Batch Num: [417/600]
Discriminator Loss: 0.5300, Generator Loss: 2.4948
D(x): 0.8163, D(G(z)): 0.1713
Epoch: [9/20], Batch Num: [418/600]
Discriminator Loss: 0.6286, Generator Loss: 2.5958
D(x): 0.8184, D(G(z)): 0.2082
Epoch: [9/20], Batch Num: [419/600]
Discriminator Loss: 0.5557, Generator Loss: 2.7514
D(x): 0.8257, D(G(z)): 0.1911
Epoch: [9/20], Batch Num: [420/600]
Discriminator Loss: 0.6844, Generator Loss: 2.5358
D(x): 0.7538, D(G(z)): 0.1747
Epoch: [9/20], Batch Num: [421/600]
Discriminator Loss: 0.5311, Generator Loss: 2.3231
D(x): 0.7720, D(G(z)): 0.1306
Epoch: [9/20], Batch Num: [422/600]
Discriminator Loss: 0.6470, Generator Loss: 2.0932
D(x): 0.8021, D(G(z)): 0.2016
Epoch: [9/20], Batch Num: [423/600]
Discriminator Loss: 0.6051, Generator Loss: 2.0181
D(x): 0.8482, D(G(z)): 0.2210
Epoch: [9/20], Batch Num: [424/600]
Discriminator Loss: 0.5895, Generator Loss: 2.2687
D(x): 0.8682, D(G(z)): 0.2452
Epoch: [9/20], Batch Num: [425/600]
Discriminator Loss: 0.6044, Generator Loss: 2.3131
D(x): 0.8466, D(G(z)): 0.2234
Epoch: [9/20], Batch Num: [426/600]
Discriminator Loss: 0.6493, Generator Loss: 2.5976
D(x): 0.8299, D(G(z)): 0.2341
Epoch: [9/20], Batch Num: [427/600]
Discriminator Loss: 0.6875, Generator Loss: 2.7252
D(x): 0.7474, D(G(z)): 0.1388
Epoch: [9/20], Batch Num: [428/600]
Discriminator Loss: 0.6667, Generator Loss: 2.3784
D(x): 0.7740, D(G(z)): 0.1489
Epoch: [9/20], Batch Num: [429/600]
Discriminator Loss: 0.5526, Generator Loss: 2.2410
D(x): 0.8165, D(G(z)): 0.1812
Epoch: [9/20], Batch Num: [430/600]
Discriminator Loss: 0.4875, Generator Loss: 1.9839
D(x): 0.8624, D(G(z)): 0.1846
Epoch: [9/20], Batch Num: [431/600]
Discriminator Loss: 0.6509, Generator Loss: 1.7624
D(x): 0.7911, D(G(z)): 0.2023
Epoch: [9/20], Batch Num: [432/600]
Discriminator Loss: 0.6082, Generator Loss: 2.1002
D(x): 0.8467, D(G(z)): 0.2505
Epoch: [9/20], Batch Num: [433/600]
Discriminator Loss: 0.4507, Generator Loss: 2.4444
D(x): 0.8958, D(G(z)): 0.2251
Epoch: [9/20], Batch Num: [434/600]
Discriminator Loss: 0.6047, Generator Loss: 2.6510
D(x): 0.8105, D(G(z)): 0.1998
Epoch: [9/20], Batch Num: [435/600]
Discriminator Loss: 0.6069, Generator Loss: 2.5360
D(x): 0.7671, D(G(z)): 0.1559
Epoch: [9/20], Batch Num: [436/600]
Discriminator Loss: 0.5837, Generator Loss: 2.3328
D(x): 0.7852, D(G(z)): 0.1606
Epoch: [9/20], Batch Num: [437/600]
Discriminator Loss: 0.5164, Generator Loss: 2.0578
D(x): 0.8089, D(G(z)): 0.1655
Epoch: [9/20], Batch Num: [438/600]
Discriminator Loss: 0.6119, Generator Loss: 1.9993
D(x): 0.8206, D(G(z)): 0.2234
Epoch: [9/20], Batch Num: [439/600]
Discriminator Loss: 0.4490, Generator Loss: 1.9938
D(x): 0.8947, D(G(z)): 0.2277
Epoch: [9/20], Batch Num: [440/600]
Discriminator Loss: 0.6856, Generator Loss: 2.2599
D(x): 0.8063, D(G(z)): 0.2521
Epoch: [9/20], Batch Num: [441/600]
Discriminator Loss: 0.4690, Generator Loss: 2.4089
D(x): 0.8753, D(G(z)): 0.1992
Epoch: [9/20], Batch Num: [442/600]
Discriminator Loss: 0.6548, Generator Loss: 2.4090
D(x): 0.7982, D(G(z)): 0.1787
Epoch: [9/20], Batch Num: [443/600]
Discriminator Loss: 0.5667, Generator Loss: 2.6699
D(x): 0.7783, D(G(z)): 0.1227
Epoch: [9/20], Batch Num: [444/600]
Discriminator Loss: 0.6490, Generator Loss: 2.3938
D(x): 0.7769, D(G(z)): 0.1640
Epoch: [9/20], Batch Num: [445/600]
Discriminator Loss: 0.6304, Generator Loss: 2.1471
D(x): 0.8056, D(G(z)): 0.1991
Epoch: [9/20], Batch Num: [446/600]
Discriminator Loss: 0.7439, Generator Loss: 1.9448
D(x): 0.7632, D(G(z)): 0.2038
Epoch: [9/20], Batch Num: [447/600]
Discriminator Loss: 0.6215, Generator Loss: 1.7367
D(x): 0.8352, D(G(z)): 0.2283
Epoch: [9/20], Batch Num: [448/600]
Discriminator Loss: 0.6905, Generator Loss: 2.2162
D(x): 0.8651, D(G(z)): 0.2829
Epoch: [9/20], Batch Num: [449/600]
Discriminator Loss: 0.5422, Generator Loss: 2.4023
D(x): 0.8366, D(G(z)): 0.1826
Epoch: [9/20], Batch Num: [450/600]
Discriminator Loss: 0.8483, Generator Loss: 2.5034
D(x): 0.7191, D(G(z)): 0.1800
Epoch: [9/20], Batch Num: [451/600]
Discriminator Loss: 0.8733, Generator Loss: 2.1359
D(x): 0.6989, D(G(z)): 0.1939
Epoch: [9/20], Batch Num: [452/600]
Discriminator Loss: 0.7280, Generator Loss: 2.0470
D(x): 0.7849, D(G(z)): 0.2107
Epoch: [9/20], Batch Num: [453/600]
Discriminator Loss: 0.7769, Generator Loss: 1.6946
D(x): 0.7655, D(G(z)): 0.2179
Epoch: [9/20], Batch Num: [454/600]
Discriminator Loss: 0.9083, Generator Loss: 1.8096
D(x): 0.8243, D(G(z)): 0.3390
Epoch: [9/20], Batch Num: [455/600]
Discriminator Loss: 0.6608, Generator Loss: 2.0818
D(x): 0.8403, D(G(z)): 0.2539
Epoch: [9/20], Batch Num: [456/600]
Discriminator Loss: 0.7176, Generator Loss: 2.4194
D(x): 0.7554, D(G(z)): 0.2024
Epoch: [9/20], Batch Num: [457/600]
Discriminator Loss: 0.9314, Generator Loss: 2.4761
D(x): 0.6862, D(G(z)): 0.1974
Epoch: [9/20], Batch Num: [458/600]
Discriminator Loss: 0.7120, Generator Loss: 1.9968
D(x): 0.7384, D(G(z)): 0.1752
Epoch: [9/20], Batch Num: [459/600]
Discriminator Loss: 0.7216, Generator Loss: 1.8474
D(x): 0.7509, D(G(z)): 0.2087
Epoch: [9/20], Batch Num: [460/600]
Discriminator Loss: 0.6446, Generator Loss: 1.7998
D(x): 0.8108, D(G(z)): 0.2151
Epoch: [9/20], Batch Num: [461/600]
Discriminator Loss: 0.8337, Generator Loss: 1.9093
D(x): 0.8379, D(G(z)): 0.3103
Epoch: [9/20], Batch Num: [462/600]
Discriminator Loss: 0.7131, Generator Loss: 1.9068
D(x): 0.7950, D(G(z)): 0.2528
Epoch: [9/20], Batch Num: [463/600]
Discriminator Loss: 0.7393, Generator Loss: 2.1111
D(x): 0.7385, D(G(z)): 0.1917
Epoch: [9/20], Batch Num: [464/600]
Discriminator Loss: 0.6376, Generator Loss: 2.0475
D(x): 0.7806, D(G(z)): 0.2064
Epoch: [9/20], Batch Num: [465/600]
Discriminator Loss: 0.6787, Generator Loss: 2.0106
D(x): 0.7598, D(G(z)): 0.1940
Epoch: [9/20], Batch Num: [466/600]
Discriminator Loss: 0.7318, Generator Loss: 1.8436
D(x): 0.7739, D(G(z)): 0.2247
Epoch: [9/20], Batch Num: [467/600]
Discriminator Loss: 0.6467, Generator Loss: 1.8071
D(x): 0.7912, D(G(z)): 0.2049
Epoch: [9/20], Batch Num: [468/600]
Discriminator Loss: 0.8041, Generator Loss: 1.7974
D(x): 0.7566, D(G(z)): 0.2294
Epoch: [9/20], Batch Num: [469/600]
Discriminator Loss: 0.6405, Generator Loss: 1.7731
D(x): 0.8481, D(G(z)): 0.2735
Epoch: [9/20], Batch Num: [470/600]
Discriminator Loss: 0.8234, Generator Loss: 1.9582
D(x): 0.7713, D(G(z)): 0.2726
Epoch: [9/20], Batch Num: [471/600]
Discriminator Loss: 0.7226, Generator Loss: 2.0116
D(x): 0.7712, D(G(z)): 0.2372
Epoch: [9/20], Batch Num: [472/600]
Discriminator Loss: 0.5597, Generator Loss: 2.0913
D(x): 0.7917, D(G(z)): 0.1840
Epoch: [9/20], Batch Num: [473/600]
Discriminator Loss: 0.5526, Generator Loss: 1.9625
D(x): 0.8130, D(G(z)): 0.1970
Epoch: [9/20], Batch Num: [474/600]
Discriminator Loss: 0.8533, Generator Loss: 1.9646
D(x): 0.7997, D(G(z)): 0.2627
Epoch: [9/20], Batch Num: [475/600]
Discriminator Loss: 0.7193, Generator Loss: 2.2511
D(x): 0.7598, D(G(z)): 0.2217
Epoch: [9/20], Batch Num: [476/600]
Discriminator Loss: 0.7004, Generator Loss: 1.8144
D(x): 0.7382, D(G(z)): 0.1652
Epoch: [9/20], Batch Num: [477/600]
Discriminator Loss: 0.6818, Generator Loss: 1.7699
D(x): 0.7939, D(G(z)): 0.2287
Epoch: [9/20], Batch Num: [478/600]
Discriminator Loss: 0.6312, Generator Loss: 1.8142
D(x): 0.8433, D(G(z)): 0.2457
Epoch: [9/20], Batch Num: [479/600]
Discriminator Loss: 0.6740, Generator Loss: 1.9972
D(x): 0.8374, D(G(z)): 0.2713
Epoch: [9/20], Batch Num: [480/600]
Discriminator Loss: 0.7037, Generator Loss: 1.9063
D(x): 0.7344, D(G(z)): 0.1871
Epoch: [9/20], Batch Num: [481/600]
Discriminator Loss: 0.6839, Generator Loss: 2.1332
D(x): 0.8063, D(G(z)): 0.2214
Epoch: [9/20], Batch Num: [482/600]
Discriminator Loss: 0.7987, Generator Loss: 2.1474
D(x): 0.7710, D(G(z)): 0.2561
Epoch: [9/20], Batch Num: [483/600]
Discriminator Loss: 0.6827, Generator Loss: 1.9060
D(x): 0.7781, D(G(z)): 0.2211
Epoch: [9/20], Batch Num: [484/600]
Discriminator Loss: 0.7668, Generator Loss: 2.0566
D(x): 0.7573, D(G(z)): 0.2295
Epoch: [9/20], Batch Num: [485/600]
Discriminator Loss: 0.7177, Generator Loss: 1.8183
D(x): 0.7564, D(G(z)): 0.2053
Epoch: [9/20], Batch Num: [486/600]
Discriminator Loss: 0.8688, Generator Loss: 1.7199
D(x): 0.7190, D(G(z)): 0.2312
Epoch: [9/20], Batch Num: [487/600]
Discriminator Loss: 0.6904, Generator Loss: 1.5059
D(x): 0.7797, D(G(z)): 0.2345
Epoch: [9/20], Batch Num: [488/600]
Discriminator Loss: 0.8291, Generator Loss: 1.7337
D(x): 0.8180, D(G(z)): 0.3312
Epoch: [9/20], Batch Num: [489/600]
Discriminator Loss: 0.9049, Generator Loss: 1.8272
D(x): 0.7859, D(G(z)): 0.3235
Epoch: [9/20], Batch Num: [490/600]
Discriminator Loss: 0.8797, Generator Loss: 2.0817
D(x): 0.7195, D(G(z)): 0.2560
Epoch: [9/20], Batch Num: [491/600]
Discriminator Loss: 0.7935, Generator Loss: 2.0279
D(x): 0.6941, D(G(z)): 0.1858
Epoch: [9/20], Batch Num: [492/600]
Discriminator Loss: 0.7930, Generator Loss: 1.8572
D(x): 0.6880, D(G(z)): 0.1776
Epoch: [9/20], Batch Num: [493/600]
Discriminator Loss: 0.8048, Generator Loss: 1.4050
D(x): 0.6998, D(G(z)): 0.2212
Epoch: [9/20], Batch Num: [494/600]
Discriminator Loss: 0.8045, Generator Loss: 1.4949
D(x): 0.8016, D(G(z)): 0.3055
Epoch: [9/20], Batch Num: [495/600]
Discriminator Loss: 0.7006, Generator Loss: 1.5289
D(x): 0.8279, D(G(z)): 0.2798
Epoch: [9/20], Batch Num: [496/600]
Discriminator Loss: 0.7285, Generator Loss: 1.9407
D(x): 0.8513, D(G(z)): 0.3383
Epoch: [9/20], Batch Num: [497/600]
Discriminator Loss: 0.6457, Generator Loss: 2.0719
D(x): 0.8252, D(G(z)): 0.2461
Epoch: [9/20], Batch Num: [498/600]
Discriminator Loss: 0.6897, Generator Loss: 2.4682
D(x): 0.7603, D(G(z)): 0.2208
Epoch: [9/20], Batch Num: [499/600]
Discriminator Loss: 0.7061, Generator Loss: 2.4810
D(x): 0.7160, D(G(z)): 0.1608
Epoch: 9, Batch Num: [500/600]

Epoch: [9/20], Batch Num: [500/600]
Discriminator Loss: 0.7266, Generator Loss: 2.2281
D(x): 0.6988, D(G(z)): 0.1612
Epoch: [9/20], Batch Num: [501/600]
Discriminator Loss: 0.7302, Generator Loss: 1.8157
D(x): 0.7039, D(G(z)): 0.1945
Epoch: [9/20], Batch Num: [502/600]
Discriminator Loss: 0.5799, Generator Loss: 1.7299
D(x): 0.8449, D(G(z)): 0.2393
Epoch: [9/20], Batch Num: [503/600]
Discriminator Loss: 0.5350, Generator Loss: 1.6512
D(x): 0.8833, D(G(z)): 0.2619
Epoch: [9/20], Batch Num: [504/600]
Discriminator Loss: 0.5988, Generator Loss: 1.9017
D(x): 0.8673, D(G(z)): 0.2919
Epoch: [9/20], Batch Num: [505/600]
Discriminator Loss: 0.6199, Generator Loss: 2.2964
D(x): 0.8427, D(G(z)): 0.2588
Epoch: [9/20], Batch Num: [506/600]
Discriminator Loss: 0.6831, Generator Loss: 2.5040
D(x): 0.7313, D(G(z)): 0.1719
Epoch: [9/20], Batch Num: [507/600]
Discriminator Loss: 0.5963, Generator Loss: 2.4026
D(x): 0.7267, D(G(z)): 0.1228
Epoch: [9/20], Batch Num: [508/600]
Discriminator Loss: 0.6710, Generator Loss: 2.5044
D(x): 0.7687, D(G(z)): 0.1852
Epoch: [9/20], Batch Num: [509/600]
Discriminator Loss: 0.6401, Generator Loss: 1.9100
D(x): 0.7824, D(G(z)): 0.1706
Epoch: [9/20], Batch Num: [510/600]
Discriminator Loss: 0.7092, Generator Loss: 1.6115
D(x): 0.8132, D(G(z)): 0.2632
Epoch: [9/20], Batch Num: [511/600]
Discriminator Loss: 0.6370, Generator Loss: 1.6247
D(x): 0.8590, D(G(z)): 0.2898
Epoch: [9/20], Batch Num: [512/600]
Discriminator Loss: 0.6979, Generator Loss: 2.0543
D(x): 0.8671, D(G(z)): 0.3191
Epoch: [9/20], Batch Num: [513/600]
Discriminator Loss: 0.6295, Generator Loss: 2.5210
D(x): 0.8223, D(G(z)): 0.2361
Epoch: [9/20], Batch Num: [514/600]
Discriminator Loss: 0.7382, Generator Loss: 2.6460
D(x): 0.6813, D(G(z)): 0.1364
Epoch: [9/20], Batch Num: [515/600]
Discriminator Loss: 0.6698, Generator Loss: 2.2445
D(x): 0.7230, D(G(z)): 0.1502
Epoch: [9/20], Batch Num: [516/600]
Discriminator Loss: 0.6748, Generator Loss: 2.1097
D(x): 0.7338, D(G(z)): 0.1839
Epoch: [9/20], Batch Num: [517/600]
Discriminator Loss: 0.6331, Generator Loss: 1.5664
D(x): 0.8407, D(G(z)): 0.2677
Epoch: [9/20], Batch Num: [518/600]
Discriminator Loss: 0.8666, Generator Loss: 1.8596
D(x): 0.8165, D(G(z)): 0.3420
Epoch: [9/20], Batch Num: [519/600]
Discriminator Loss: 0.6728, Generator Loss: 1.9848
D(x): 0.8301, D(G(z)): 0.2577
Epoch: [9/20], Batch Num: [520/600]
Discriminator Loss: 0.7294, Generator Loss: 2.3626
D(x): 0.7555, D(G(z)): 0.2395
Epoch: [9/20], Batch Num: [521/600]
Discriminator Loss: 0.8313, Generator Loss: 2.1776
D(x): 0.6561, D(G(z)): 0.1516
Epoch: [9/20], Batch Num: [522/600]
Discriminator Loss: 0.7919, Generator Loss: 1.8714
D(x): 0.7320, D(G(z)): 0.2228
Epoch: [9/20], Batch Num: [523/600]
Discriminator Loss: 0.7784, Generator Loss: 1.8110
D(x): 0.7855, D(G(z)): 0.2931
Epoch: [9/20], Batch Num: [524/600]
Discriminator Loss: 0.7895, Generator Loss: 1.7048
D(x): 0.7651, D(G(z)): 0.2539
Epoch: [9/20], Batch Num: [525/600]
Discriminator Loss: 0.8109, Generator Loss: 1.7591
D(x): 0.7926, D(G(z)): 0.3274
Epoch: [9/20], Batch Num: [526/600]
Discriminator Loss: 0.8367, Generator Loss: 2.1750
D(x): 0.7692, D(G(z)): 0.2951
Epoch: [9/20], Batch Num: [527/600]
Discriminator Loss: 1.0820, Generator Loss: 2.0362
D(x): 0.6695, D(G(z)): 0.2768
Epoch: [9/20], Batch Num: [528/600]
Discriminator Loss: 0.7704, Generator Loss: 1.8791
D(x): 0.7300, D(G(z)): 0.1979
Epoch: [9/20], Batch Num: [529/600]
Discriminator Loss: 0.8269, Generator Loss: 1.6038
D(x): 0.7083, D(G(z)): 0.2489
Epoch: [9/20], Batch Num: [530/600]
Discriminator Loss: 0.8893, Generator Loss: 1.6563
D(x): 0.7695, D(G(z)): 0.3095
Epoch: [9/20], Batch Num: [531/600]
Discriminator Loss: 0.7426, Generator Loss: 1.7434
D(x): 0.8115, D(G(z)): 0.2824
Epoch: [9/20], Batch Num: [532/600]
Discriminator Loss: 0.7881, Generator Loss: 1.7891
D(x): 0.7762, D(G(z)): 0.2834
Epoch: [9/20], Batch Num: [533/600]
Discriminator Loss: 0.8805, Generator Loss: 2.0533
D(x): 0.7326, D(G(z)): 0.2740
Epoch: [9/20], Batch Num: [534/600]
Discriminator Loss: 0.9248, Generator Loss: 1.8611
D(x): 0.6949, D(G(z)): 0.2284
Epoch: [9/20], Batch Num: [535/600]
Discriminator Loss: 1.0073, Generator Loss: 2.0330
D(x): 0.6764, D(G(z)): 0.2604
Epoch: [9/20], Batch Num: [536/600]
Discriminator Loss: 0.7498, Generator Loss: 1.7680
D(x): 0.7624, D(G(z)): 0.2337
Epoch: [9/20], Batch Num: [537/600]
Discriminator Loss: 0.9115, Generator Loss: 1.8863
D(x): 0.7060, D(G(z)): 0.2715
Epoch: [9/20], Batch Num: [538/600]
Discriminator Loss: 0.8677, Generator Loss: 1.7833
D(x): 0.7720, D(G(z)): 0.3011
Epoch: [9/20], Batch Num: [539/600]
Discriminator Loss: 0.7406, Generator Loss: 1.7901
D(x): 0.7687, D(G(z)): 0.2533
Epoch: [9/20], Batch Num: [540/600]
Discriminator Loss: 0.7491, Generator Loss: 1.9390
D(x): 0.7770, D(G(z)): 0.2779
Epoch: [9/20], Batch Num: [541/600]
Discriminator Loss: 0.6867, Generator Loss: 2.0832
D(x): 0.7736, D(G(z)): 0.2395
Epoch: [9/20], Batch Num: [542/600]
Discriminator Loss: 0.8406, Generator Loss: 1.9666
D(x): 0.7053, D(G(z)): 0.2434
Epoch: [9/20], Batch Num: [543/600]
Discriminator Loss: 0.6811, Generator Loss: 1.6905
D(x): 0.7736, D(G(z)): 0.2263
Epoch: [9/20], Batch Num: [544/600]
Discriminator Loss: 0.7877, Generator Loss: 1.5997
D(x): 0.7555, D(G(z)): 0.2605
Epoch: [9/20], Batch Num: [545/600]
Discriminator Loss: 0.6219, Generator Loss: 1.5421
D(x): 0.8035, D(G(z)): 0.2579
Epoch: [9/20], Batch Num: [546/600]
Discriminator Loss: 0.7190, Generator Loss: 1.5488
D(x): 0.8380, D(G(z)): 0.3208
Epoch: [9/20], Batch Num: [547/600]
Discriminator Loss: 0.6754, Generator Loss: 1.8506
D(x): 0.8080, D(G(z)): 0.2672
Epoch: [9/20], Batch Num: [548/600]
Discriminator Loss: 0.6794, Generator Loss: 1.6924
D(x): 0.7649, D(G(z)): 0.2389
Epoch: [9/20], Batch Num: [549/600]
Discriminator Loss: 0.7609, Generator Loss: 1.7305
D(x): 0.7519, D(G(z)): 0.2533
Epoch: [9/20], Batch Num: [550/600]
Discriminator Loss: 0.6840, Generator Loss: 1.7579
D(x): 0.7650, D(G(z)): 0.2475
Epoch: [9/20], Batch Num: [551/600]
Discriminator Loss: 0.6972, Generator Loss: 1.7798
D(x): 0.7404, D(G(z)): 0.2123
Epoch: [9/20], Batch Num: [552/600]
Discriminator Loss: 0.7416, Generator Loss: 1.7568
D(x): 0.8151, D(G(z)): 0.3018
Epoch: [9/20], Batch Num: [553/600]
Discriminator Loss: 0.7040, Generator Loss: 1.7616
D(x): 0.8313, D(G(z)): 0.2553
Epoch: [9/20], Batch Num: [554/600]
Discriminator Loss: 0.6762, Generator Loss: 1.9685
D(x): 0.7920, D(G(z)): 0.2244
Epoch: [9/20], Batch Num: [555/600]
Discriminator Loss: 0.6474, Generator Loss: 2.0477
D(x): 0.7794, D(G(z)): 0.2191
Epoch: [9/20], Batch Num: [556/600]
Discriminator Loss: 0.7414, Generator Loss: 2.2614
D(x): 0.7608, D(G(z)): 0.2328
Epoch: [9/20], Batch Num: [557/600]
Discriminator Loss: 0.7222, Generator Loss: 1.8674
D(x): 0.7777, D(G(z)): 0.2281
Epoch: [9/20], Batch Num: [558/600]
Discriminator Loss: 0.7254, Generator Loss: 1.7904
D(x): 0.7586, D(G(z)): 0.2312
Epoch: [9/20], Batch Num: [559/600]
Discriminator Loss: 0.5348, Generator Loss: 1.6986
D(x): 0.8316, D(G(z)): 0.2313
Epoch: [9/20], Batch Num: [560/600]
Discriminator Loss: 0.6416, Generator Loss: 1.5822
D(x): 0.8269, D(G(z)): 0.2589
Epoch: [9/20], Batch Num: [561/600]
Discriminator Loss: 0.7141, Generator Loss: 1.6813
D(x): 0.8339, D(G(z)): 0.3190
Epoch: [9/20], Batch Num: [562/600]
Discriminator Loss: 0.6650, Generator Loss: 1.9418
D(x): 0.8015, D(G(z)): 0.2319
Epoch: [9/20], Batch Num: [563/600]
Discriminator Loss: 0.6659, Generator Loss: 1.9161
D(x): 0.7922, D(G(z)): 0.2133
Epoch: [9/20], Batch Num: [564/600]
Discriminator Loss: 0.4692, Generator Loss: 2.0934
D(x): 0.8644, D(G(z)): 0.1996
Epoch: [9/20], Batch Num: [565/600]
Discriminator Loss: 0.5823, Generator Loss: 2.0518
D(x): 0.7936, D(G(z)): 0.1824
Epoch: [9/20], Batch Num: [566/600]
Discriminator Loss: 0.5706, Generator Loss: 2.2126
D(x): 0.8136, D(G(z)): 0.1616
Epoch: [9/20], Batch Num: [567/600]
Discriminator Loss: 0.5863, Generator Loss: 2.0207
D(x): 0.8110, D(G(z)): 0.1984
Epoch: [9/20], Batch Num: [568/600]
Discriminator Loss: 0.5882, Generator Loss: 1.7584
D(x): 0.8201, D(G(z)): 0.2015
Epoch: [9/20], Batch Num: [569/600]
Discriminator Loss: 0.4856, Generator Loss: 2.1289
D(x): 0.8773, D(G(z)): 0.2140
Epoch: [9/20], Batch Num: [570/600]
Discriminator Loss: 0.4282, Generator Loss: 1.8827
D(x): 0.8977, D(G(z)): 0.2054
Epoch: [9/20], Batch Num: [571/600]
Discriminator Loss: 0.5297, Generator Loss: 1.9073
D(x): 0.8742, D(G(z)): 0.2396
Epoch: [9/20], Batch Num: [572/600]
Discriminator Loss: 0.5225, Generator Loss: 2.4286
D(x): 0.8461, D(G(z)): 0.1992
Epoch: [9/20], Batch Num: [573/600]
Discriminator Loss: 0.5633, Generator Loss: 2.5203
D(x): 0.8221, D(G(z)): 0.1962
Epoch: [9/20], Batch Num: [574/600]
Discriminator Loss: 0.5691, Generator Loss: 2.5015
D(x): 0.8084, D(G(z)): 0.1702
Epoch: [9/20], Batch Num: [575/600]
Discriminator Loss: 0.4683, Generator Loss: 2.7087
D(x): 0.8513, D(G(z)): 0.1584
Epoch: [9/20], Batch Num: [576/600]
Discriminator Loss: 0.6247, Generator Loss: 2.2212
D(x): 0.7945, D(G(z)): 0.1816
Epoch: [9/20], Batch Num: [577/600]
Discriminator Loss: 0.5438, Generator Loss: 1.9826
D(x): 0.8155, D(G(z)): 0.1739
Epoch: [9/20], Batch Num: [578/600]
Discriminator Loss: 0.5764, Generator Loss: 2.0095
D(x): 0.8573, D(G(z)): 0.2507
Epoch: [9/20], Batch Num: [579/600]
Discriminator Loss: 0.4955, Generator Loss: 2.3761
D(x): 0.8639, D(G(z)): 0.2135
Epoch: [9/20], Batch Num: [580/600]
Discriminator Loss: 0.5268, Generator Loss: 2.2537
D(x): 0.8434, D(G(z)): 0.2107
Epoch: [9/20], Batch Num: [581/600]
Discriminator Loss: 0.3668, Generator Loss: 2.4698
D(x): 0.9048, D(G(z)): 0.1740
Epoch: [9/20], Batch Num: [582/600]
Discriminator Loss: 0.5263, Generator Loss: 2.5292
D(x): 0.7912, D(G(z)): 0.1273
Epoch: [9/20], Batch Num: [583/600]
Discriminator Loss: 0.6259, Generator Loss: 2.3821
D(x): 0.7684, D(G(z)): 0.1525
Epoch: [9/20], Batch Num: [584/600]
Discriminator Loss: 0.5081, Generator Loss: 2.0103
D(x): 0.8609, D(G(z)): 0.1885
Epoch: [9/20], Batch Num: [585/600]
Discriminator Loss: 0.5484, Generator Loss: 1.9998
D(x): 0.8577, D(G(z)): 0.2089
Epoch: [9/20], Batch Num: [586/600]
Discriminator Loss: 0.5091, Generator Loss: 2.4755
D(x): 0.8583, D(G(z)): 0.1975
Epoch: [9/20], Batch Num: [587/600]
Discriminator Loss: 0.5816, Generator Loss: 2.5686
D(x): 0.8639, D(G(z)): 0.2151
Epoch: [9/20], Batch Num: [588/600]
Discriminator Loss: 0.6066, Generator Loss: 2.7365
D(x): 0.7999, D(G(z)): 0.1763
Epoch: [9/20], Batch Num: [589/600]
Discriminator Loss: 0.6627, Generator Loss: 2.3772
D(x): 0.7501, D(G(z)): 0.1295
Epoch: [9/20], Batch Num: [590/600]
Discriminator Loss: 0.5213, Generator Loss: 2.4607
D(x): 0.8515, D(G(z)): 0.1792
Epoch: [9/20], Batch Num: [591/600]
Discriminator Loss: 0.5080, Generator Loss: 2.3844
D(x): 0.8447, D(G(z)): 0.1808
Epoch: [9/20], Batch Num: [592/600]
Discriminator Loss: 0.4806, Generator Loss: 2.2722
D(x): 0.8872, D(G(z)): 0.1990
Epoch: [9/20], Batch Num: [593/600]
Discriminator Loss: 0.4421, Generator Loss: 2.4464
D(x): 0.8650, D(G(z)): 0.1691
Epoch: [9/20], Batch Num: [594/600]
Discriminator Loss: 0.5674, Generator Loss: 2.6185
D(x): 0.8411, D(G(z)): 0.2060
Epoch: [9/20], Batch Num: [595/600]
Discriminator Loss: 0.6690, Generator Loss: 2.2853
D(x): 0.7673, D(G(z)): 0.1699
Epoch: [9/20], Batch Num: [596/600]
Discriminator Loss: 0.8951, Generator Loss: 1.7816
D(x): 0.7436, D(G(z)): 0.2226
Epoch: [9/20], Batch Num: [597/600]
Discriminator Loss: 0.7763, Generator Loss: 1.7405
D(x): 0.8197, D(G(z)): 0.2644
Epoch: [9/20], Batch Num: [598/600]
Discriminator Loss: 0.8847, Generator Loss: 2.5218
D(x): 0.8370, D(G(z)): 0.3331
Epoch: [9/20], Batch Num: [599/600]
Discriminator Loss: 0.8159, Generator Loss: 2.4921
D(x): 0.7127, D(G(z)): 0.1954
Epoch: 10, Batch Num: [0/600]

Epoch: [10/20], Batch Num: [0/600]
Discriminator Loss: 0.9283, Generator Loss: 2.1273
D(x): 0.6943, D(G(z)): 0.1679
Epoch: [10/20], Batch Num: [1/600]
Discriminator Loss: 0.5770, Generator Loss: 1.6858
D(x): 0.8065, D(G(z)): 0.1970
Epoch: [10/20], Batch Num: [2/600]
Discriminator Loss: 0.8165, Generator Loss: 1.7393
D(x): 0.8297, D(G(z)): 0.3097
Epoch: [10/20], Batch Num: [3/600]
Discriminator Loss: 0.8083, Generator Loss: 1.9790
D(x): 0.8345, D(G(z)): 0.2946
Epoch: [10/20], Batch Num: [4/600]
Discriminator Loss: 0.7233, Generator Loss: 2.1770
D(x): 0.7623, D(G(z)): 0.1940
Epoch: [10/20], Batch Num: [5/600]
Discriminator Loss: 0.7211, Generator Loss: 2.3544
D(x): 0.7646, D(G(z)): 0.2140
Epoch: [10/20], Batch Num: [6/600]
Discriminator Loss: 0.8258, Generator Loss: 2.0821
D(x): 0.7078, D(G(z)): 0.1750
Epoch: [10/20], Batch Num: [7/600]
Discriminator Loss: 0.8890, Generator Loss: 1.6155
D(x): 0.7676, D(G(z)): 0.2652
Epoch: [10/20], Batch Num: [8/600]
Discriminator Loss: 0.8805, Generator Loss: 1.7107
D(x): 0.7634, D(G(z)): 0.2948
Epoch: [10/20], Batch Num: [9/600]
Discriminator Loss: 0.7485, Generator Loss: 1.8209
D(x): 0.8290, D(G(z)): 0.2858
Epoch: [10/20], Batch Num: [10/600]
Discriminator Loss: 0.8159, Generator Loss: 2.0178
D(x): 0.8169, D(G(z)): 0.2901
Epoch: [10/20], Batch Num: [11/600]
Discriminator Loss: 0.7943, Generator Loss: 2.0003
D(x): 0.7511, D(G(z)): 0.2250
Epoch: [10/20], Batch Num: [12/600]
Discriminator Loss: 0.6920, Generator Loss: 2.0912
D(x): 0.7604, D(G(z)): 0.1754
Epoch: [10/20], Batch Num: [13/600]
Discriminator Loss: 0.7350, Generator Loss: 2.0461
D(x): 0.7831, D(G(z)): 0.2341
Epoch: [10/20], Batch Num: [14/600]
Discriminator Loss: 0.7306, Generator Loss: 1.8737
D(x): 0.7977, D(G(z)): 0.2543
Epoch: [10/20], Batch Num: [15/600]
Discriminator Loss: 0.6040, Generator Loss: 1.9183
D(x): 0.8231, D(G(z)): 0.2261
Epoch: [10/20], Batch Num: [16/600]
Discriminator Loss: 0.5679, Generator Loss: 1.7480
D(x): 0.7939, D(G(z)): 0.1750
Epoch: [10/20], Batch Num: [17/600]
Discriminator Loss: 0.6226, Generator Loss: 1.9964
D(x): 0.8693, D(G(z)): 0.2641
Epoch: [10/20], Batch Num: [18/600]
Discriminator Loss: 0.6427, Generator Loss: 2.0510
D(x): 0.8055, D(G(z)): 0.2494
Epoch: [10/20], Batch Num: [19/600]
Discriminator Loss: 0.6002, Generator Loss: 2.2909
D(x): 0.7918, D(G(z)): 0.1789
Epoch: [10/20], Batch Num: [20/600]
Discriminator Loss: 0.5047, Generator Loss: 2.0194
D(x): 0.8384, D(G(z)): 0.1790
Epoch: [10/20], Batch Num: [21/600]
Discriminator Loss: 0.4516, Generator Loss: 2.0331
D(x): 0.8583, D(G(z)): 0.1638
Epoch: [10/20], Batch Num: [22/600]
Discriminator Loss: 0.4865, Generator Loss: 2.1486
D(x): 0.8894, D(G(z)): 0.2337
Epoch: [10/20], Batch Num: [23/600]
Discriminator Loss: 0.5701, Generator Loss: 2.1979
D(x): 0.8325, D(G(z)): 0.2035
Epoch: [10/20], Batch Num: [24/600]
Discriminator Loss: 0.6116, Generator Loss: 2.4316
D(x): 0.8163, D(G(z)): 0.2047
Epoch: [10/20], Batch Num: [25/600]
Discriminator Loss: 0.5008, Generator Loss: 2.5190
D(x): 0.8526, D(G(z)): 0.1701
Epoch: [10/20], Batch Num: [26/600]
Discriminator Loss: 0.5646, Generator Loss: 2.4881
D(x): 0.7811, D(G(z)): 0.1342
Epoch: [10/20], Batch Num: [27/600]
Discriminator Loss: 0.4527, Generator Loss: 2.2129
D(x): 0.8425, D(G(z)): 0.1493
Epoch: [10/20], Batch Num: [28/600]
Discriminator Loss: 0.5670, Generator Loss: 2.0976
D(x): 0.8196, D(G(z)): 0.1751
Epoch: [10/20], Batch Num: [29/600]
Discriminator Loss: 0.5384, Generator Loss: 1.8765
D(x): 0.8590, D(G(z)): 0.2137
Epoch: [10/20], Batch Num: [30/600]
Discriminator Loss: 0.4719, Generator Loss: 2.0532
D(x): 0.8870, D(G(z)): 0.2032
Epoch: [10/20], Batch Num: [31/600]
Discriminator Loss: 0.3664, Generator Loss: 2.3773
D(x): 0.9059, D(G(z)): 0.1800
Epoch: [10/20], Batch Num: [32/600]
Discriminator Loss: 0.4996, Generator Loss: 2.4566
D(x): 0.8381, D(G(z)): 0.1705
Epoch: [10/20], Batch Num: [33/600]
Discriminator Loss: 0.3541, Generator Loss: 2.7821
D(x): 0.8937, D(G(z)): 0.1440
Epoch: [10/20], Batch Num: [34/600]
Discriminator Loss: 0.3960, Generator Loss: 2.7545
D(x): 0.8313, D(G(z)): 0.1169
Epoch: [10/20], Batch Num: [35/600]
Discriminator Loss: 0.4408, Generator Loss: 2.4391
D(x): 0.8103, D(G(z)): 0.1010
Epoch: [10/20], Batch Num: [36/600]
Discriminator Loss: 0.6005, Generator Loss: 1.9390
D(x): 0.7959, D(G(z)): 0.1677
Epoch: [10/20], Batch Num: [37/600]
Discriminator Loss: 0.5705, Generator Loss: 1.8428
D(x): 0.8616, D(G(z)): 0.2286
Epoch: [10/20], Batch Num: [38/600]
Discriminator Loss: 0.5326, Generator Loss: 2.3163
D(x): 0.9278, D(G(z)): 0.2909
Epoch: [10/20], Batch Num: [39/600]
Discriminator Loss: 0.5419, Generator Loss: 2.5950
D(x): 0.8660, D(G(z)): 0.2171
Epoch: [10/20], Batch Num: [40/600]
Discriminator Loss: 0.3927, Generator Loss: 2.9718
D(x): 0.8295, D(G(z)): 0.1066
Epoch: [10/20], Batch Num: [41/600]
Discriminator Loss: 0.6667, Generator Loss: 2.6527
D(x): 0.7387, D(G(z)): 0.1031
Epoch: [10/20], Batch Num: [42/600]
Discriminator Loss: 0.5812, Generator Loss: 2.4574
D(x): 0.8243, D(G(z)): 0.1621
Epoch: [10/20], Batch Num: [43/600]
Discriminator Loss: 0.6486, Generator Loss: 2.3045
D(x): 0.8042, D(G(z)): 0.1487
Epoch: [10/20], Batch Num: [44/600]
Discriminator Loss: 0.5895, Generator Loss: 1.9322
D(x): 0.8469, D(G(z)): 0.1934
Epoch: [10/20], Batch Num: [45/600]
Discriminator Loss: 0.5623, Generator Loss: 1.8106
D(x): 0.8363, D(G(z)): 0.1878
Epoch: [10/20], Batch Num: [46/600]
Discriminator Loss: 0.7559, Generator Loss: 1.9038
D(x): 0.8025, D(G(z)): 0.2380
Epoch: [10/20], Batch Num: [47/600]
Discriminator Loss: 0.5813, Generator Loss: 2.1665
D(x): 0.8653, D(G(z)): 0.2254
Epoch: [10/20], Batch Num: [48/600]
Discriminator Loss: 0.5735, Generator Loss: 2.4254
D(x): 0.8329, D(G(z)): 0.1850
Epoch: [10/20], Batch Num: [49/600]
Discriminator Loss: 0.5576, Generator Loss: 2.6395
D(x): 0.7921, D(G(z)): 0.1402
Epoch: [10/20], Batch Num: [50/600]
Discriminator Loss: 0.6670, Generator Loss: 2.3939
D(x): 0.7639, D(G(z)): 0.1415
Epoch: [10/20], Batch Num: [51/600]
Discriminator Loss: 0.6310, Generator Loss: 2.1178
D(x): 0.7668, D(G(z)): 0.1663
Epoch: [10/20], Batch Num: [52/600]
Discriminator Loss: 0.6989, Generator Loss: 1.9881
D(x): 0.8553, D(G(z)): 0.2727
Epoch: [10/20], Batch Num: [53/600]
Discriminator Loss: 0.7165, Generator Loss: 2.3776
D(x): 0.8414, D(G(z)): 0.2505
Epoch: [10/20], Batch Num: [54/600]
Discriminator Loss: 0.5976, Generator Loss: 2.3826
D(x): 0.8292, D(G(z)): 0.2015
Epoch: [10/20], Batch Num: [55/600]
Discriminator Loss: 0.6857, Generator Loss: 2.4550
D(x): 0.7451, D(G(z)): 0.1559
Epoch: [10/20], Batch Num: [56/600]
Discriminator Loss: 0.5882, Generator Loss: 2.5163
D(x): 0.7917, D(G(z)): 0.1210
Epoch: [10/20], Batch Num: [57/600]
Discriminator Loss: 0.6756, Generator Loss: 2.2237
D(x): 0.7757, D(G(z)): 0.1801
Epoch: [10/20], Batch Num: [58/600]
Discriminator Loss: 0.5809, Generator Loss: 2.1098
D(x): 0.8267, D(G(z)): 0.1965
Epoch: [10/20], Batch Num: [59/600]
Discriminator Loss: 0.6861, Generator Loss: 2.2841
D(x): 0.8664, D(G(z)): 0.2894
Epoch: [10/20], Batch Num: [60/600]
Discriminator Loss: 0.6159, Generator Loss: 2.3196
D(x): 0.7704, D(G(z)): 0.1473
Epoch: [10/20], Batch Num: [61/600]
Discriminator Loss: 0.5152, Generator Loss: 2.5734
D(x): 0.8322, D(G(z)): 0.1621
Epoch: [10/20], Batch Num: [62/600]
Discriminator Loss: 0.6355, Generator Loss: 2.4639
D(x): 0.7771, D(G(z)): 0.1558
Epoch: [10/20], Batch Num: [63/600]
Discriminator Loss: 0.5812, Generator Loss: 2.2682
D(x): 0.8355, D(G(z)): 0.1579
Epoch: [10/20], Batch Num: [64/600]
Discriminator Loss: 0.4842, Generator Loss: 2.3380
D(x): 0.8610, D(G(z)): 0.1970
Epoch: [10/20], Batch Num: [65/600]
Discriminator Loss: 0.5216, Generator Loss: 2.3668
D(x): 0.8611, D(G(z)): 0.1964
Epoch: [10/20], Batch Num: [66/600]
Discriminator Loss: 0.6353, Generator Loss: 2.2240
D(x): 0.7945, D(G(z)): 0.1778
Epoch: [10/20], Batch Num: [67/600]
Discriminator Loss: 0.6068, Generator Loss: 2.4926
D(x): 0.8228, D(G(z)): 0.1635
Epoch: [10/20], Batch Num: [68/600]
Discriminator Loss: 0.5112, Generator Loss: 2.1598
D(x): 0.8353, D(G(z)): 0.1501
Epoch: [10/20], Batch Num: [69/600]
Discriminator Loss: 0.5977, Generator Loss: 2.4415
D(x): 0.8561, D(G(z)): 0.2159
Epoch: [10/20], Batch Num: [70/600]
Discriminator Loss: 0.6878, Generator Loss: 2.5372
D(x): 0.8017, D(G(z)): 0.1993
Epoch: [10/20], Batch Num: [71/600]
Discriminator Loss: 0.6059, Generator Loss: 2.4032
D(x): 0.7824, D(G(z)): 0.1540
Epoch: [10/20], Batch Num: [72/600]
Discriminator Loss: 0.5192, Generator Loss: 2.3791
D(x): 0.7934, D(G(z)): 0.1330
Epoch: [10/20], Batch Num: [73/600]
Discriminator Loss: 0.5640, Generator Loss: 2.1025
D(x): 0.8224, D(G(z)): 0.1854
Epoch: [10/20], Batch Num: [74/600]
Discriminator Loss: 0.6152, Generator Loss: 2.1018
D(x): 0.8207, D(G(z)): 0.2174
Epoch: [10/20], Batch Num: [75/600]
Discriminator Loss: 0.4954, Generator Loss: 2.7341
D(x): 0.8688, D(G(z)): 0.2112
Epoch: [10/20], Batch Num: [76/600]
Discriminator Loss: 0.5872, Generator Loss: 2.7213
D(x): 0.8307, D(G(z)): 0.1832
Epoch: [10/20], Batch Num: [77/600]
Discriminator Loss: 0.5659, Generator Loss: 2.6982
D(x): 0.8172, D(G(z)): 0.1487
Epoch: [10/20], Batch Num: [78/600]
Discriminator Loss: 0.8871, Generator Loss: 2.4601
D(x): 0.7038, D(G(z)): 0.1276
Epoch: [10/20], Batch Num: [79/600]
Discriminator Loss: 0.7342, Generator Loss: 1.8648
D(x): 0.7493, D(G(z)): 0.1692
Epoch: [10/20], Batch Num: [80/600]
Discriminator Loss: 0.7021, Generator Loss: 1.8610
D(x): 0.8419, D(G(z)): 0.2642
Epoch: [10/20], Batch Num: [81/600]
Discriminator Loss: 0.5940, Generator Loss: 2.1750
D(x): 0.8810, D(G(z)): 0.2640
Epoch: [10/20], Batch Num: [82/600]
Discriminator Loss: 0.7069, Generator Loss: 2.4071
D(x): 0.7925, D(G(z)): 0.2264
Epoch: [10/20], Batch Num: [83/600]
Discriminator Loss: 0.6247, Generator Loss: 2.5616
D(x): 0.7733, D(G(z)): 0.1207
Epoch: [10/20], Batch Num: [84/600]
Discriminator Loss: 0.8153, Generator Loss: 2.2807
D(x): 0.7232, D(G(z)): 0.1719
Epoch: [10/20], Batch Num: [85/600]
Discriminator Loss: 0.6478, Generator Loss: 1.9383
D(x): 0.7717, D(G(z)): 0.1593
Epoch: [10/20], Batch Num: [86/600]
Discriminator Loss: 0.8840, Generator Loss: 1.9813
D(x): 0.8260, D(G(z)): 0.3151
Epoch: [10/20], Batch Num: [87/600]
Discriminator Loss: 0.8304, Generator Loss: 2.1695
D(x): 0.7841, D(G(z)): 0.2817
Epoch: [10/20], Batch Num: [88/600]
Discriminator Loss: 0.8545, Generator Loss: 2.1634
D(x): 0.7157, D(G(z)): 0.2101
Epoch: [10/20], Batch Num: [89/600]
Discriminator Loss: 0.7285, Generator Loss: 2.1433
D(x): 0.7397, D(G(z)): 0.2011
Epoch: [10/20], Batch Num: [90/600]
Discriminator Loss: 0.8229, Generator Loss: 1.9564
D(x): 0.7506, D(G(z)): 0.2234
Epoch: [10/20], Batch Num: [91/600]
Discriminator Loss: 0.8302, Generator Loss: 1.9001
D(x): 0.7555, D(G(z)): 0.2490
Epoch: [10/20], Batch Num: [92/600]
Discriminator Loss: 1.0206, Generator Loss: 1.7377
D(x): 0.6991, D(G(z)): 0.2737
Epoch: [10/20], Batch Num: [93/600]
Discriminator Loss: 0.8447, Generator Loss: 1.9448
D(x): 0.7887, D(G(z)): 0.2867
Epoch: [10/20], Batch Num: [94/600]
Discriminator Loss: 0.9002, Generator Loss: 1.8316
D(x): 0.7202, D(G(z)): 0.2293
Epoch: [10/20], Batch Num: [95/600]
Discriminator Loss: 1.0103, Generator Loss: 1.7356
D(x): 0.6767, D(G(z)): 0.2646
Epoch: [10/20], Batch Num: [96/600]
Discriminator Loss: 0.8837, Generator Loss: 1.7648
D(x): 0.7454, D(G(z)): 0.2955
Epoch: [10/20], Batch Num: [97/600]
Discriminator Loss: 0.9109, Generator Loss: 1.6778
D(x): 0.7010, D(G(z)): 0.2651
Epoch: [10/20], Batch Num: [98/600]
Discriminator Loss: 0.9701, Generator Loss: 1.6756
D(x): 0.6823, D(G(z)): 0.2594
Epoch: [10/20], Batch Num: [99/600]
Discriminator Loss: 1.0063, Generator Loss: 1.5546
D(x): 0.6979, D(G(z)): 0.2839
Epoch: 10, Batch Num: [100/600]

Epoch: [10/20], Batch Num: [100/600]
Discriminator Loss: 0.7479, Generator Loss: 1.6347
D(x): 0.7829, D(G(z)): 0.2523
Epoch: [10/20], Batch Num: [101/600]
Discriminator Loss: 0.7637, Generator Loss: 1.7574
D(x): 0.7810, D(G(z)): 0.2678
Epoch: [10/20], Batch Num: [102/600]
Discriminator Loss: 0.7878, Generator Loss: 1.7106
D(x): 0.7697, D(G(z)): 0.2632
Epoch: [10/20], Batch Num: [103/600]
Discriminator Loss: 0.7756, Generator Loss: 1.8365
D(x): 0.7602, D(G(z)): 0.2398
Epoch: [10/20], Batch Num: [104/600]
Discriminator Loss: 0.7138, Generator Loss: 1.9370
D(x): 0.7599, D(G(z)): 0.2245
Epoch: [10/20], Batch Num: [105/600]
Discriminator Loss: 0.8269, Generator Loss: 1.8975
D(x): 0.7154, D(G(z)): 0.2059
Epoch: [10/20], Batch Num: [106/600]
Discriminator Loss: 0.6498, Generator Loss: 1.7698
D(x): 0.7641, D(G(z)): 0.2019
Epoch: [10/20], Batch Num: [107/600]
Discriminator Loss: 0.7146, Generator Loss: 1.5522
D(x): 0.7288, D(G(z)): 0.2208
Epoch: [10/20], Batch Num: [108/600]
Discriminator Loss: 0.5487, Generator Loss: 1.5229
D(x): 0.8391, D(G(z)): 0.2421
Epoch: [10/20], Batch Num: [109/600]
Discriminator Loss: 0.6795, Generator Loss: 1.6227
D(x): 0.8281, D(G(z)): 0.3001
Epoch: [10/20], Batch Num: [110/600]
Discriminator Loss: 0.6503, Generator Loss: 1.7737
D(x): 0.8213, D(G(z)): 0.2737
Epoch: [10/20], Batch Num: [111/600]
Discriminator Loss: 0.5933, Generator Loss: 2.0075
D(x): 0.8363, D(G(z)): 0.2478
Epoch: [10/20], Batch Num: [112/600]
Discriminator Loss: 0.5457, Generator Loss: 2.2328
D(x): 0.8227, D(G(z)): 0.2085
Epoch: [10/20], Batch Num: [113/600]
Discriminator Loss: 0.5746, Generator Loss: 2.3094
D(x): 0.7773, D(G(z)): 0.1671
Epoch: [10/20], Batch Num: [114/600]
Discriminator Loss: 0.6219, Generator Loss: 2.2048
D(x): 0.7406, D(G(z)): 0.1427
Epoch: [10/20], Batch Num: [115/600]
Discriminator Loss: 0.4861, Generator Loss: 1.9694
D(x): 0.8170, D(G(z)): 0.1689
Epoch: [10/20], Batch Num: [116/600]
Discriminator Loss: 0.5663, Generator Loss: 1.8712
D(x): 0.8141, D(G(z)): 0.1927
Epoch: [10/20], Batch Num: [117/600]
Discriminator Loss: 0.4920, Generator Loss: 1.8368
D(x): 0.8617, D(G(z)): 0.2152
Epoch: [10/20], Batch Num: [118/600]
Discriminator Loss: 0.4742, Generator Loss: 1.8077
D(x): 0.8624, D(G(z)): 0.2135
Epoch: [10/20], Batch Num: [119/600]
Discriminator Loss: 0.4827, Generator Loss: 2.0725
D(x): 0.8970, D(G(z)): 0.2503
Epoch: [10/20], Batch Num: [120/600]
Discriminator Loss: 0.4616, Generator Loss: 2.3351
D(x): 0.8994, D(G(z)): 0.1937
Epoch: [10/20], Batch Num: [121/600]
Discriminator Loss: 0.4945, Generator Loss: 2.4714
D(x): 0.8106, D(G(z)): 0.1360
Epoch: [10/20], Batch Num: [122/600]
Discriminator Loss: 0.4540, Generator Loss: 2.5975
D(x): 0.7813, D(G(z)): 0.1081
Epoch: [10/20], Batch Num: [123/600]
Discriminator Loss: 0.3648, Generator Loss: 2.3605
D(x): 0.8744, D(G(z)): 0.1348
Epoch: [10/20], Batch Num: [124/600]
Discriminator Loss: 0.4378, Generator Loss: 2.2930
D(x): 0.8559, D(G(z)): 0.1670
Epoch: [10/20], Batch Num: [125/600]
Discriminator Loss: 0.4770, Generator Loss: 2.5050
D(x): 0.8748, D(G(z)): 0.1723
Epoch: [10/20], Batch Num: [126/600]
Discriminator Loss: 0.4549, Generator Loss: 2.2648
D(x): 0.8729, D(G(z)): 0.1902
Epoch: [10/20], Batch Num: [127/600]
Discriminator Loss: 0.5032, Generator Loss: 2.3821
D(x): 0.8227, D(G(z)): 0.1511
Epoch: [10/20], Batch Num: [128/600]
Discriminator Loss: 0.4393, Generator Loss: 2.3829
D(x): 0.8730, D(G(z)): 0.1666
Epoch: [10/20], Batch Num: [129/600]
Discriminator Loss: 0.6268, Generator Loss: 2.2913
D(x): 0.8356, D(G(z)): 0.1941
Epoch: [10/20], Batch Num: [130/600]
Discriminator Loss: 0.5244, Generator Loss: 2.4290
D(x): 0.8293, D(G(z)): 0.1794
Epoch: [10/20], Batch Num: [131/600]
Discriminator Loss: 0.5162, Generator Loss: 2.5613
D(x): 0.8384, D(G(z)): 0.1762
Epoch: [10/20], Batch Num: [132/600]
Discriminator Loss: 0.5573, Generator Loss: 2.5638
D(x): 0.8115, D(G(z)): 0.1798
Epoch: [10/20], Batch Num: [133/600]
Discriminator Loss: 0.6623, Generator Loss: 2.2627
D(x): 0.7551, D(G(z)): 0.1858
Epoch: [10/20], Batch Num: [134/600]
Discriminator Loss: 0.5052, Generator Loss: 2.2393
D(x): 0.8760, D(G(z)): 0.2105
Epoch: [10/20], Batch Num: [135/600]
Discriminator Loss: 0.6073, Generator Loss: 2.6156
D(x): 0.8458, D(G(z)): 0.2153
Epoch: [10/20], Batch Num: [136/600]
Discriminator Loss: 0.6571, Generator Loss: 2.5407
D(x): 0.7914, D(G(z)): 0.1994
Epoch: [10/20], Batch Num: [137/600]
Discriminator Loss: 0.8878, Generator Loss: 2.1934
D(x): 0.6820, D(G(z)): 0.1734
Epoch: [10/20], Batch Num: [138/600]
Discriminator Loss: 0.7135, Generator Loss: 1.9901
D(x): 0.7861, D(G(z)): 0.2155
Epoch: [10/20], Batch Num: [139/600]
Discriminator Loss: 0.6944, Generator Loss: 1.7686
D(x): 0.7712, D(G(z)): 0.2106
Epoch: [10/20], Batch Num: [140/600]
Discriminator Loss: 0.9245, Generator Loss: 1.9827
D(x): 0.7922, D(G(z)): 0.3216
Epoch: [10/20], Batch Num: [141/600]
Discriminator Loss: 0.7363, Generator Loss: 2.1266
D(x): 0.7846, D(G(z)): 0.2295
Epoch: [10/20], Batch Num: [142/600]
Discriminator Loss: 0.7552, Generator Loss: 2.2539
D(x): 0.7475, D(G(z)): 0.2296
Epoch: [10/20], Batch Num: [143/600]
Discriminator Loss: 0.7517, Generator Loss: 2.1264
D(x): 0.7457, D(G(z)): 0.1987
Epoch: [10/20], Batch Num: [144/600]
Discriminator Loss: 0.7564, Generator Loss: 1.9721
D(x): 0.7723, D(G(z)): 0.2423
Epoch: [10/20], Batch Num: [145/600]
Discriminator Loss: 0.7482, Generator Loss: 1.8902
D(x): 0.7534, D(G(z)): 0.2056
Epoch: [10/20], Batch Num: [146/600]
Discriminator Loss: 0.9610, Generator Loss: 1.7420
D(x): 0.7102, D(G(z)): 0.2712
Epoch: [10/20], Batch Num: [147/600]
Discriminator Loss: 0.6108, Generator Loss: 1.9178
D(x): 0.8177, D(G(z)): 0.2296
Epoch: [10/20], Batch Num: [148/600]
Discriminator Loss: 0.9044, Generator Loss: 1.7981
D(x): 0.7558, D(G(z)): 0.2903
Epoch: [10/20], Batch Num: [149/600]
Discriminator Loss: 0.8873, Generator Loss: 2.2159
D(x): 0.7707, D(G(z)): 0.2936
Epoch: [10/20], Batch Num: [150/600]
Discriminator Loss: 0.6071, Generator Loss: 2.3417
D(x): 0.8304, D(G(z)): 0.2062
Epoch: [10/20], Batch Num: [151/600]
Discriminator Loss: 0.8862, Generator Loss: 2.1924
D(x): 0.6842, D(G(z)): 0.1808
Epoch: [10/20], Batch Num: [152/600]
Discriminator Loss: 0.7833, Generator Loss: 2.0105
D(x): 0.7157, D(G(z)): 0.1850
Epoch: [10/20], Batch Num: [153/600]
Discriminator Loss: 0.6553, Generator Loss: 1.8888
D(x): 0.8088, D(G(z)): 0.2453
Epoch: [10/20], Batch Num: [154/600]
Discriminator Loss: 0.5940, Generator Loss: 2.0988
D(x): 0.8998, D(G(z)): 0.2815
Epoch: [10/20], Batch Num: [155/600]
Discriminator Loss: 0.6443, Generator Loss: 2.6563
D(x): 0.7961, D(G(z)): 0.1985
Epoch: [10/20], Batch Num: [156/600]
Discriminator Loss: 0.4979, Generator Loss: 2.5582
D(x): 0.7827, D(G(z)): 0.1291
Epoch: [10/20], Batch Num: [157/600]
Discriminator Loss: 0.4763, Generator Loss: 2.2924
D(x): 0.8214, D(G(z)): 0.1450
Epoch: [10/20], Batch Num: [158/600]
Discriminator Loss: 0.3327, Generator Loss: 2.4088
D(x): 0.8899, D(G(z)): 0.1447
Epoch: [10/20], Batch Num: [159/600]
Discriminator Loss: 0.3416, Generator Loss: 2.5707
D(x): 0.9163, D(G(z)): 0.1674
Epoch: [10/20], Batch Num: [160/600]
Discriminator Loss: 0.3583, Generator Loss: 2.7895
D(x): 0.8665, D(G(z)): 0.1285
Epoch: [10/20], Batch Num: [161/600]
Discriminator Loss: 0.4263, Generator Loss: 2.8344
D(x): 0.8711, D(G(z)): 0.1646
Epoch: [10/20], Batch Num: [162/600]
Discriminator Loss: 0.3720, Generator Loss: 2.7204
D(x): 0.8689, D(G(z)): 0.1277
Epoch: [10/20], Batch Num: [163/600]
Discriminator Loss: 0.4607, Generator Loss: 2.5940
D(x): 0.8186, D(G(z)): 0.1417
Epoch: [10/20], Batch Num: [164/600]
Discriminator Loss: 0.3766, Generator Loss: 2.6150
D(x): 0.9269, D(G(z)): 0.1755
Epoch: [10/20], Batch Num: [165/600]
Discriminator Loss: 0.5354, Generator Loss: 2.9780
D(x): 0.8614, D(G(z)): 0.2114
Epoch: [10/20], Batch Num: [166/600]
Discriminator Loss: 0.2462, Generator Loss: 3.2974
D(x): 0.8906, D(G(z)): 0.0945
Epoch: [10/20], Batch Num: [167/600]
Discriminator Loss: 0.4045, Generator Loss: 3.1718
D(x): 0.8432, D(G(z)): 0.1068
Epoch: [10/20], Batch Num: [168/600]
Discriminator Loss: 0.3925, Generator Loss: 2.5851
D(x): 0.8326, D(G(z)): 0.1121
Epoch: [10/20], Batch Num: [169/600]
Discriminator Loss: 0.5271, Generator Loss: 2.2234
D(x): 0.8979, D(G(z)): 0.1930
Epoch: [10/20], Batch Num: [170/600]
Discriminator Loss: 0.4169, Generator Loss: 2.4442
D(x): 0.8996, D(G(z)): 0.2019
Epoch: [10/20], Batch Num: [171/600]
Discriminator Loss: 0.4779, Generator Loss: 2.9651
D(x): 0.9031, D(G(z)): 0.1947
Epoch: [10/20], Batch Num: [172/600]
Discriminator Loss: 0.5744, Generator Loss: 3.2681
D(x): 0.8242, D(G(z)): 0.1588
Epoch: [10/20], Batch Num: [173/600]
Discriminator Loss: 0.6179, Generator Loss: 3.3284
D(x): 0.7462, D(G(z)): 0.1194
Epoch: [10/20], Batch Num: [174/600]
Discriminator Loss: 0.8275, Generator Loss: 2.6991
D(x): 0.7651, D(G(z)): 0.2161
Epoch: [10/20], Batch Num: [175/600]
Discriminator Loss: 0.5999, Generator Loss: 2.1236
D(x): 0.7907, D(G(z)): 0.1550
Epoch: [10/20], Batch Num: [176/600]
Discriminator Loss: 0.7117, Generator Loss: 2.2777
D(x): 0.8416, D(G(z)): 0.2808
Epoch: [10/20], Batch Num: [177/600]
Discriminator Loss: 0.8323, Generator Loss: 2.2776
D(x): 0.7895, D(G(z)): 0.2614
Epoch: [10/20], Batch Num: [178/600]
Discriminator Loss: 0.9788, Generator Loss: 2.4708
D(x): 0.7410, D(G(z)): 0.2293
Epoch: [10/20], Batch Num: [179/600]
Discriminator Loss: 0.8739, Generator Loss: 2.4284
D(x): 0.7399, D(G(z)): 0.2177
Epoch: [10/20], Batch Num: [180/600]
Discriminator Loss: 1.0457, Generator Loss: 2.0796
D(x): 0.6446, D(G(z)): 0.1952
Epoch: [10/20], Batch Num: [181/600]
Discriminator Loss: 1.0704, Generator Loss: 1.8286
D(x): 0.7609, D(G(z)): 0.3094
Epoch: [10/20], Batch Num: [182/600]
Discriminator Loss: 0.9301, Generator Loss: 1.9098
D(x): 0.8093, D(G(z)): 0.3205
Epoch: [10/20], Batch Num: [183/600]
Discriminator Loss: 0.9354, Generator Loss: 2.3801
D(x): 0.7293, D(G(z)): 0.2591
Epoch: [10/20], Batch Num: [184/600]
Discriminator Loss: 1.1524, Generator Loss: 2.0068
D(x): 0.6224, D(G(z)): 0.2185
Epoch: [10/20], Batch Num: [185/600]
Discriminator Loss: 0.9671, Generator Loss: 1.8055
D(x): 0.7117, D(G(z)): 0.2618
Epoch: [10/20], Batch Num: [186/600]
Discriminator Loss: 0.9907, Generator Loss: 1.4089
D(x): 0.7150, D(G(z)): 0.3134
Epoch: [10/20], Batch Num: [187/600]
Discriminator Loss: 0.8434, Generator Loss: 1.6020
D(x): 0.7815, D(G(z)): 0.3038
Epoch: [10/20], Batch Num: [188/600]
Discriminator Loss: 1.0705, Generator Loss: 2.1045
D(x): 0.7290, D(G(z)): 0.3561
Epoch: [10/20], Batch Num: [189/600]
Discriminator Loss: 0.8008, Generator Loss: 2.3112
D(x): 0.7433, D(G(z)): 0.2321
Epoch: [10/20], Batch Num: [190/600]
Discriminator Loss: 0.6345, Generator Loss: 2.3744
D(x): 0.7595, D(G(z)): 0.1695
Epoch: [10/20], Batch Num: [191/600]
Discriminator Loss: 0.6759, Generator Loss: 2.0234
D(x): 0.7024, D(G(z)): 0.1496
Epoch: [10/20], Batch Num: [192/600]
Discriminator Loss: 0.5762, Generator Loss: 2.1230
D(x): 0.8187, D(G(z)): 0.2133
Epoch: [10/20], Batch Num: [193/600]
Discriminator Loss: 0.6209, Generator Loss: 2.0146
D(x): 0.8134, D(G(z)): 0.2334
Epoch: [10/20], Batch Num: [194/600]
Discriminator Loss: 0.6716, Generator Loss: 2.1318
D(x): 0.8405, D(G(z)): 0.2813
Epoch: [10/20], Batch Num: [195/600]
Discriminator Loss: 0.6556, Generator Loss: 2.1482
D(x): 0.7805, D(G(z)): 0.1886
Epoch: [10/20], Batch Num: [196/600]
Discriminator Loss: 0.5075, Generator Loss: 2.1717
D(x): 0.8455, D(G(z)): 0.2126
Epoch: [10/20], Batch Num: [197/600]
Discriminator Loss: 0.5025, Generator Loss: 2.3303
D(x): 0.8112, D(G(z)): 0.1663
Epoch: [10/20], Batch Num: [198/600]
Discriminator Loss: 0.4838, Generator Loss: 2.1969
D(x): 0.8379, D(G(z)): 0.1696
Epoch: [10/20], Batch Num: [199/600]
Discriminator Loss: 0.4604, Generator Loss: 2.3174
D(x): 0.8787, D(G(z)): 0.1927
Epoch: 10, Batch Num: [200/600]

Epoch: [10/20], Batch Num: [200/600]
Discriminator Loss: 0.4927, Generator Loss: 2.3886
D(x): 0.8565, D(G(z)): 0.1888
Epoch: [10/20], Batch Num: [201/600]
Discriminator Loss: 0.3267, Generator Loss: 2.7734
D(x): 0.9134, D(G(z)): 0.1436
Epoch: [10/20], Batch Num: [202/600]
Discriminator Loss: 0.4929, Generator Loss: 2.6624
D(x): 0.8079, D(G(z)): 0.1090
Epoch: [10/20], Batch Num: [203/600]
Discriminator Loss: 0.5559, Generator Loss: 2.4216
D(x): 0.7938, D(G(z)): 0.1439
Epoch: [10/20], Batch Num: [204/600]
Discriminator Loss: 0.4953, Generator Loss: 1.9831
D(x): 0.8238, D(G(z)): 0.1678
Epoch: [10/20], Batch Num: [205/600]
Discriminator Loss: 0.4067, Generator Loss: 1.9327
D(x): 0.9214, D(G(z)): 0.2212
Epoch: [10/20], Batch Num: [206/600]
Discriminator Loss: 0.4304, Generator Loss: 1.9867
D(x): 0.8937, D(G(z)): 0.2120
Epoch: [10/20], Batch Num: [207/600]
Discriminator Loss: 0.4252, Generator Loss: 2.3102
D(x): 0.8750, D(G(z)): 0.1856
Epoch: [10/20], Batch Num: [208/600]
Discriminator Loss: 0.4791, Generator Loss: 2.9627
D(x): 0.8672, D(G(z)): 0.1882
Epoch: [10/20], Batch Num: [209/600]
Discriminator Loss: 0.5037, Generator Loss: 2.7365
D(x): 0.8041, D(G(z)): 0.1331
Epoch: [10/20], Batch Num: [210/600]
Discriminator Loss: 0.4356, Generator Loss: 2.5298
D(x): 0.8171, D(G(z)): 0.1142
Epoch: [10/20], Batch Num: [211/600]
Discriminator Loss: 0.4236, Generator Loss: 2.2623
D(x): 0.8588, D(G(z)): 0.1385
Epoch: [10/20], Batch Num: [212/600]
Discriminator Loss: 0.4137, Generator Loss: 2.3489
D(x): 0.8953, D(G(z)): 0.1954
Epoch: [10/20], Batch Num: [213/600]
Discriminator Loss: 0.4777, Generator Loss: 2.2646
D(x): 0.8449, D(G(z)): 0.1695
Epoch: [10/20], Batch Num: [214/600]
Discriminator Loss: 0.6574, Generator Loss: 2.3814
D(x): 0.8253, D(G(z)): 0.2234
Epoch: [10/20], Batch Num: [215/600]
Discriminator Loss: 0.5397, Generator Loss: 2.3859
D(x): 0.8318, D(G(z)): 0.1767
Epoch: [10/20], Batch Num: [216/600]
Discriminator Loss: 0.5658, Generator Loss: 2.3632
D(x): 0.8116, D(G(z)): 0.1623
Epoch: [10/20], Batch Num: [217/600]
Discriminator Loss: 0.5991, Generator Loss: 2.4221
D(x): 0.8083, D(G(z)): 0.1860
Epoch: [10/20], Batch Num: [218/600]
Discriminator Loss: 0.5952, Generator Loss: 2.4223
D(x): 0.8373, D(G(z)): 0.2013
Epoch: [10/20], Batch Num: [219/600]
Discriminator Loss: 0.5086, Generator Loss: 2.5428
D(x): 0.8636, D(G(z)): 0.1905
Epoch: [10/20], Batch Num: [220/600]
Discriminator Loss: 0.6973, Generator Loss: 2.4549
D(x): 0.7577, D(G(z)): 0.1540
Epoch: [10/20], Batch Num: [221/600]
Discriminator Loss: 0.7147, Generator Loss: 2.2015
D(x): 0.8102, D(G(z)): 0.2368
Epoch: [10/20], Batch Num: [222/600]
Discriminator Loss: 0.7523, Generator Loss: 2.6073
D(x): 0.8269, D(G(z)): 0.2429
Epoch: [10/20], Batch Num: [223/600]
Discriminator Loss: 0.8097, Generator Loss: 2.4641
D(x): 0.7499, D(G(z)): 0.1811
Epoch: [10/20], Batch Num: [224/600]
Discriminator Loss: 1.0100, Generator Loss: 2.1576
D(x): 0.7355, D(G(z)): 0.2063
Epoch: [10/20], Batch Num: [225/600]
Discriminator Loss: 1.0236, Generator Loss: 1.9906
D(x): 0.7235, D(G(z)): 0.2321
Epoch: [10/20], Batch Num: [226/600]
Discriminator Loss: 0.9578, Generator Loss: 2.0689
D(x): 0.8161, D(G(z)): 0.3487
Epoch: [10/20], Batch Num: [227/600]
Discriminator Loss: 0.8415, Generator Loss: 2.2489
D(x): 0.7760, D(G(z)): 0.2615
Epoch: [10/20], Batch Num: [228/600]
Discriminator Loss: 0.8526, Generator Loss: 2.1976
D(x): 0.7355, D(G(z)): 0.1997
Epoch: [10/20], Batch Num: [229/600]
Discriminator Loss: 0.7447, Generator Loss: 2.3455
D(x): 0.7811, D(G(z)): 0.2270
Epoch: [10/20], Batch Num: [230/600]
Discriminator Loss: 0.8832, Generator Loss: 2.0167
D(x): 0.6928, D(G(z)): 0.1734
Epoch: [10/20], Batch Num: [231/600]
Discriminator Loss: 0.7372, Generator Loss: 1.9084
D(x): 0.8057, D(G(z)): 0.2169
Epoch: [10/20], Batch Num: [232/600]
Discriminator Loss: 0.8004, Generator Loss: 1.9542
D(x): 0.8079, D(G(z)): 0.2709
Epoch: [10/20], Batch Num: [233/600]
Discriminator Loss: 0.7458, Generator Loss: 1.8957
D(x): 0.7773, D(G(z)): 0.2300
Epoch: [10/20], Batch Num: [234/600]
Discriminator Loss: 0.6333, Generator Loss: 2.1106
D(x): 0.7961, D(G(z)): 0.2196
Epoch: [10/20], Batch Num: [235/600]
Discriminator Loss: 0.6910, Generator Loss: 2.1884
D(x): 0.7812, D(G(z)): 0.2191
Epoch: [10/20], Batch Num: [236/600]
Discriminator Loss: 0.5939, Generator Loss: 2.1347
D(x): 0.7854, D(G(z)): 0.1615
Epoch: [10/20], Batch Num: [237/600]
Discriminator Loss: 0.5579, Generator Loss: 1.8803
D(x): 0.8125, D(G(z)): 0.1946
Epoch: [10/20], Batch Num: [238/600]
Discriminator Loss: 0.5507, Generator Loss: 1.9750
D(x): 0.8434, D(G(z)): 0.2269
Epoch: [10/20], Batch Num: [239/600]
Discriminator Loss: 0.4357, Generator Loss: 1.9927
D(x): 0.8895, D(G(z)): 0.2073
Epoch: [10/20], Batch Num: [240/600]
Discriminator Loss: 0.5706, Generator Loss: 2.3444
D(x): 0.8219, D(G(z)): 0.1759
Epoch: [10/20], Batch Num: [241/600]
Discriminator Loss: 0.4976, Generator Loss: 2.5028
D(x): 0.8346, D(G(z)): 0.1696
Epoch: [10/20], Batch Num: [242/600]
Discriminator Loss: 0.5550, Generator Loss: 2.4272
D(x): 0.8167, D(G(z)): 0.1872
Epoch: [10/20], Batch Num: [243/600]
Discriminator Loss: 0.6746, Generator Loss: 2.2870
D(x): 0.7581, D(G(z)): 0.1637
Epoch: [10/20], Batch Num: [244/600]
Discriminator Loss: 0.5205, Generator Loss: 2.2596
D(x): 0.8446, D(G(z)): 0.1943
Epoch: [10/20], Batch Num: [245/600]
Discriminator Loss: 0.7770, Generator Loss: 2.0088
D(x): 0.8102, D(G(z)): 0.2582
Epoch: [10/20], Batch Num: [246/600]
Discriminator Loss: 0.7359, Generator Loss: 2.0807
D(x): 0.8075, D(G(z)): 0.2389
Epoch: [10/20], Batch Num: [247/600]
Discriminator Loss: 0.4422, Generator Loss: 2.3586
D(x): 0.8507, D(G(z)): 0.1768
Epoch: [10/20], Batch Num: [248/600]
Discriminator Loss: 0.5198, Generator Loss: 2.6111
D(x): 0.8525, D(G(z)): 0.2070
Epoch: [10/20], Batch Num: [249/600]
Discriminator Loss: 0.5429, Generator Loss: 2.5241
D(x): 0.8212, D(G(z)): 0.1881
Epoch: [10/20], Batch Num: [250/600]
Discriminator Loss: 0.8166, Generator Loss: 2.1817
D(x): 0.6826, D(G(z)): 0.1567
Epoch: [10/20], Batch Num: [251/600]
Discriminator Loss: 0.5305, Generator Loss: 1.8838
D(x): 0.8373, D(G(z)): 0.2009
Epoch: [10/20], Batch Num: [252/600]
Discriminator Loss: 0.6936, Generator Loss: 1.7111
D(x): 0.8316, D(G(z)): 0.3011
Epoch: [10/20], Batch Num: [253/600]
Discriminator Loss: 0.6845, Generator Loss: 2.0438
D(x): 0.8405, D(G(z)): 0.2769
Epoch: [10/20], Batch Num: [254/600]
Discriminator Loss: 0.7348, Generator Loss: 2.3531
D(x): 0.7634, D(G(z)): 0.2278
Epoch: [10/20], Batch Num: [255/600]
Discriminator Loss: 0.6656, Generator Loss: 2.3117
D(x): 0.7487, D(G(z)): 0.1562
Epoch: [10/20], Batch Num: [256/600]
Discriminator Loss: 0.7937, Generator Loss: 1.9936
D(x): 0.7249, D(G(z)): 0.1992
Epoch: [10/20], Batch Num: [257/600]
Discriminator Loss: 0.8579, Generator Loss: 1.4576
D(x): 0.7311, D(G(z)): 0.2452
Epoch: [10/20], Batch Num: [258/600]
Discriminator Loss: 0.8876, Generator Loss: 1.6158
D(x): 0.8082, D(G(z)): 0.3500
Epoch: [10/20], Batch Num: [259/600]
Discriminator Loss: 0.9854, Generator Loss: 1.7348
D(x): 0.7294, D(G(z)): 0.2928
Epoch: [10/20], Batch Num: [260/600]
Discriminator Loss: 0.9651, Generator Loss: 2.1696
D(x): 0.6972, D(G(z)): 0.2687
Epoch: [10/20], Batch Num: [261/600]
Discriminator Loss: 1.0890, Generator Loss: 2.2519
D(x): 0.6946, D(G(z)): 0.2829
Epoch: [10/20], Batch Num: [262/600]
Discriminator Loss: 1.0514, Generator Loss: 1.8207
D(x): 0.6221, D(G(z)): 0.2042
Epoch: [10/20], Batch Num: [263/600]
Discriminator Loss: 0.8796, Generator Loss: 1.4052
D(x): 0.7581, D(G(z)): 0.2797
Epoch: [10/20], Batch Num: [264/600]
Discriminator Loss: 0.9637, Generator Loss: 1.4999
D(x): 0.7456, D(G(z)): 0.3187
Epoch: [10/20], Batch Num: [265/600]
Discriminator Loss: 0.9886, Generator Loss: 1.6181
D(x): 0.7564, D(G(z)): 0.3483
Epoch: [10/20], Batch Num: [266/600]
Discriminator Loss: 0.9976, Generator Loss: 1.8967
D(x): 0.7178, D(G(z)): 0.3180
Epoch: [10/20], Batch Num: [267/600]
Discriminator Loss: 0.8907, Generator Loss: 1.9673
D(x): 0.6630, D(G(z)): 0.1823
Epoch: [10/20], Batch Num: [268/600]
Discriminator Loss: 0.9003, Generator Loss: 1.6561
D(x): 0.6689, D(G(z)): 0.2007
Epoch: [10/20], Batch Num: [269/600]
Discriminator Loss: 0.7617, Generator Loss: 1.6144
D(x): 0.7905, D(G(z)): 0.2792
Epoch: [10/20], Batch Num: [270/600]
Discriminator Loss: 0.9319, Generator Loss: 1.8225
D(x): 0.7100, D(G(z)): 0.2842
Epoch: [10/20], Batch Num: [271/600]
Discriminator Loss: 0.6735, Generator Loss: 1.6277
D(x): 0.8026, D(G(z)): 0.2464
Epoch: [10/20], Batch Num: [272/600]
Discriminator Loss: 0.8460, Generator Loss: 1.8442
D(x): 0.7484, D(G(z)): 0.2933
Epoch: [10/20], Batch Num: [273/600]
Discriminator Loss: 0.6211, Generator Loss: 2.1055
D(x): 0.8271, D(G(z)): 0.2680
Epoch: [10/20], Batch Num: [274/600]
Discriminator Loss: 0.5267, Generator Loss: 2.4754
D(x): 0.8167, D(G(z)): 0.2048
Epoch: [10/20], Batch Num: [275/600]
Discriminator Loss: 0.7692, Generator Loss: 2.5889
D(x): 0.7037, D(G(z)): 0.1480
Epoch: [10/20], Batch Num: [276/600]
Discriminator Loss: 0.4280, Generator Loss: 2.6953
D(x): 0.8406, D(G(z)): 0.1362
Epoch: [10/20], Batch Num: [277/600]
Discriminator Loss: 0.4805, Generator Loss: 2.2403
D(x): 0.8152, D(G(z)): 0.1402
Epoch: [10/20], Batch Num: [278/600]
Discriminator Loss: 0.4979, Generator Loss: 2.2218
D(x): 0.8216, D(G(z)): 0.1704
Epoch: [10/20], Batch Num: [279/600]
Discriminator Loss: 0.3814, Generator Loss: 2.1804
D(x): 0.8915, D(G(z)): 0.1758
Epoch: [10/20], Batch Num: [280/600]
Discriminator Loss: 0.3851, Generator Loss: 2.4538
D(x): 0.9273, D(G(z)): 0.1858
Epoch: [10/20], Batch Num: [281/600]
Discriminator Loss: 0.3430, Generator Loss: 2.6053
D(x): 0.8903, D(G(z)): 0.1584
Epoch: [10/20], Batch Num: [282/600]
Discriminator Loss: 0.3429, Generator Loss: 2.6049
D(x): 0.8920, D(G(z)): 0.1508
Epoch: [10/20], Batch Num: [283/600]
Discriminator Loss: 0.3920, Generator Loss: 2.8410
D(x): 0.8798, D(G(z)): 0.1379
Epoch: [10/20], Batch Num: [284/600]
Discriminator Loss: 0.3959, Generator Loss: 3.1981
D(x): 0.8543, D(G(z)): 0.1221
Epoch: [10/20], Batch Num: [285/600]
Discriminator Loss: 0.4682, Generator Loss: 2.8208
D(x): 0.8103, D(G(z)): 0.1094
Epoch: [10/20], Batch Num: [286/600]
Discriminator Loss: 0.3578, Generator Loss: 2.6197
D(x): 0.8897, D(G(z)): 0.1170
Epoch: [10/20], Batch Num: [287/600]
Discriminator Loss: 0.2856, Generator Loss: 2.4672
D(x): 0.9068, D(G(z)): 0.1147
Epoch: [10/20], Batch Num: [288/600]
Discriminator Loss: 0.4387, Generator Loss: 2.3907
D(x): 0.8704, D(G(z)): 0.1666
Epoch: [10/20], Batch Num: [289/600]
Discriminator Loss: 0.3907, Generator Loss: 2.3738
D(x): 0.9008, D(G(z)): 0.1787
Epoch: [10/20], Batch Num: [290/600]
Discriminator Loss: 0.4444, Generator Loss: 2.6598
D(x): 0.9069, D(G(z)): 0.1834
Epoch: [10/20], Batch Num: [291/600]
Discriminator Loss: 0.2977, Generator Loss: 2.8266
D(x): 0.9002, D(G(z)): 0.1329
Epoch: [10/20], Batch Num: [292/600]
Discriminator Loss: 0.4496, Generator Loss: 2.8096
D(x): 0.8543, D(G(z)): 0.1353
Epoch: [10/20], Batch Num: [293/600]
Discriminator Loss: 0.3984, Generator Loss: 2.8016
D(x): 0.8721, D(G(z)): 0.1367
Epoch: [10/20], Batch Num: [294/600]
Discriminator Loss: 0.5907, Generator Loss: 2.8164
D(x): 0.8080, D(G(z)): 0.1707
Epoch: [10/20], Batch Num: [295/600]
Discriminator Loss: 0.3884, Generator Loss: 3.0027
D(x): 0.8896, D(G(z)): 0.1569
Epoch: [10/20], Batch Num: [296/600]
Discriminator Loss: 0.3060, Generator Loss: 2.9160
D(x): 0.9120, D(G(z)): 0.1387
Epoch: [10/20], Batch Num: [297/600]
Discriminator Loss: 0.4437, Generator Loss: 2.7150
D(x): 0.8389, D(G(z)): 0.1309
Epoch: [10/20], Batch Num: [298/600]
Discriminator Loss: 0.6119, Generator Loss: 2.6156
D(x): 0.7673, D(G(z)): 0.1292
Epoch: [10/20], Batch Num: [299/600]
Discriminator Loss: 0.4723, Generator Loss: 2.4356
D(x): 0.8733, D(G(z)): 0.1784
Epoch: 10, Batch Num: [300/600]

Epoch: [10/20], Batch Num: [300/600]
Discriminator Loss: 0.6542, Generator Loss: 2.4584
D(x): 0.8445, D(G(z)): 0.2352
Epoch: [10/20], Batch Num: [301/600]
Discriminator Loss: 0.4369, Generator Loss: 2.6489
D(x): 0.9081, D(G(z)): 0.1854
Epoch: [10/20], Batch Num: [302/600]
Discriminator Loss: 0.6945, Generator Loss: 2.9052
D(x): 0.7882, D(G(z)): 0.1610
Epoch: [10/20], Batch Num: [303/600]
Discriminator Loss: 0.5873, Generator Loss: 2.7385
D(x): 0.7795, D(G(z)): 0.1269
Epoch: [10/20], Batch Num: [304/600]
Discriminator Loss: 0.6456, Generator Loss: 2.3284
D(x): 0.8042, D(G(z)): 0.1455
Epoch: [10/20], Batch Num: [305/600]
Discriminator Loss: 0.7349, Generator Loss: 1.9270
D(x): 0.7827, D(G(z)): 0.1996
Epoch: [10/20], Batch Num: [306/600]
Discriminator Loss: 0.6915, Generator Loss: 1.6965
D(x): 0.8410, D(G(z)): 0.2583
Epoch: [10/20], Batch Num: [307/600]
Discriminator Loss: 0.8437, Generator Loss: 2.5349
D(x): 0.8578, D(G(z)): 0.3281
Epoch: [10/20], Batch Num: [308/600]
Discriminator Loss: 0.8880, Generator Loss: 2.7498
D(x): 0.7694, D(G(z)): 0.2232
Epoch: [10/20], Batch Num: [309/600]
Discriminator Loss: 0.8652, Generator Loss: 2.7804
D(x): 0.7051, D(G(z)): 0.1569
Epoch: [10/20], Batch Num: [310/600]
Discriminator Loss: 0.7497, Generator Loss: 2.4787
D(x): 0.7441, D(G(z)): 0.1427
Epoch: [10/20], Batch Num: [311/600]
Discriminator Loss: 0.7960, Generator Loss: 2.0614
D(x): 0.7471, D(G(z)): 0.1657
Epoch: [10/20], Batch Num: [312/600]
Discriminator Loss: 0.7527, Generator Loss: 1.8439
D(x): 0.8238, D(G(z)): 0.2364
Epoch: [10/20], Batch Num: [313/600]
Discriminator Loss: 0.7070, Generator Loss: 1.9424
D(x): 0.8788, D(G(z)): 0.2796
Epoch: [10/20], Batch Num: [314/600]
Discriminator Loss: 0.7562, Generator Loss: 2.1362
D(x): 0.8300, D(G(z)): 0.2610
Epoch: [10/20], Batch Num: [315/600]
Discriminator Loss: 0.6295, Generator Loss: 2.6230
D(x): 0.8522, D(G(z)): 0.2368
Epoch: [10/20], Batch Num: [316/600]
Discriminator Loss: 0.6459, Generator Loss: 2.8064
D(x): 0.7717, D(G(z)): 0.1338
Epoch: [10/20], Batch Num: [317/600]
Discriminator Loss: 0.6558, Generator Loss: 2.7286
D(x): 0.7651, D(G(z)): 0.1135
Epoch: [10/20], Batch Num: [318/600]
Discriminator Loss: 0.5407, Generator Loss: 2.6758
D(x): 0.8178, D(G(z)): 0.1592
Epoch: [10/20], Batch Num: [319/600]
Discriminator Loss: 0.4634, Generator Loss: 2.1022
D(x): 0.8616, D(G(z)): 0.1677
Epoch: [10/20], Batch Num: [320/600]
Discriminator Loss: 0.6313, Generator Loss: 2.2324
D(x): 0.8361, D(G(z)): 0.2482
Epoch: [10/20], Batch Num: [321/600]
Discriminator Loss: 0.4611, Generator Loss: 2.5011
D(x): 0.8945, D(G(z)): 0.2093
Epoch: [10/20], Batch Num: [322/600]
Discriminator Loss: 0.4047, Generator Loss: 2.5080
D(x): 0.9100, D(G(z)): 0.1947
Epoch: [10/20], Batch Num: [323/600]
Discriminator Loss: 0.4746, Generator Loss: 3.0356
D(x): 0.8634, D(G(z)): 0.1716
Epoch: [10/20], Batch Num: [324/600]
Discriminator Loss: 0.6052, Generator Loss: 2.8180
D(x): 0.7986, D(G(z)): 0.1418
Epoch: [10/20], Batch Num: [325/600]
Discriminator Loss: 0.5822, Generator Loss: 3.0282
D(x): 0.7798, D(G(z)): 0.1360
Epoch: [10/20], Batch Num: [326/600]
Discriminator Loss: 0.5819, Generator Loss: 2.8120
D(x): 0.7538, D(G(z)): 0.1183
Epoch: [10/20], Batch Num: [327/600]
Discriminator Loss: 0.6563, Generator Loss: 1.9815
D(x): 0.7823, D(G(z)): 0.1709
Epoch: [10/20], Batch Num: [328/600]
Discriminator Loss: 0.5568, Generator Loss: 1.6423
D(x): 0.8205, D(G(z)): 0.1892
Epoch: [10/20], Batch Num: [329/600]
Discriminator Loss: 0.6121, Generator Loss: 1.8556
D(x): 0.9335, D(G(z)): 0.3196
Epoch: [10/20], Batch Num: [330/600]
Discriminator Loss: 0.7131, Generator Loss: 2.0566
D(x): 0.9052, D(G(z)): 0.3314
Epoch: [10/20], Batch Num: [331/600]
Discriminator Loss: 0.6437, Generator Loss: 2.5928
D(x): 0.8402, D(G(z)): 0.2476
Epoch: [10/20], Batch Num: [332/600]
Discriminator Loss: 0.6828, Generator Loss: 2.8655
D(x): 0.7319, D(G(z)): 0.1261
Epoch: [10/20], Batch Num: [333/600]
Discriminator Loss: 0.7564, Generator Loss: 2.8301
D(x): 0.6865, D(G(z)): 0.0931
Epoch: [10/20], Batch Num: [334/600]
Discriminator Loss: 0.8322, Generator Loss: 2.2484
D(x): 0.6933, D(G(z)): 0.1121
Epoch: [10/20], Batch Num: [335/600]
Discriminator Loss: 0.6559, Generator Loss: 1.7506
D(x): 0.8218, D(G(z)): 0.2287
Epoch: [10/20], Batch Num: [336/600]
Discriminator Loss: 0.6374, Generator Loss: 1.5573
D(x): 0.8817, D(G(z)): 0.2950
Epoch: [10/20], Batch Num: [337/600]
Discriminator Loss: 0.7515, Generator Loss: 1.8517
D(x): 0.8889, D(G(z)): 0.3577
Epoch: [10/20], Batch Num: [338/600]
Discriminator Loss: 0.8009, Generator Loss: 2.2185
D(x): 0.8378, D(G(z)): 0.3246
Epoch: [10/20], Batch Num: [339/600]
Discriminator Loss: 0.6720, Generator Loss: 2.8572
D(x): 0.7885, D(G(z)): 0.2192
Epoch: [10/20], Batch Num: [340/600]
Discriminator Loss: 0.8495, Generator Loss: 2.8258
D(x): 0.6713, D(G(z)): 0.1054
Epoch: [10/20], Batch Num: [341/600]
Discriminator Loss: 0.8533, Generator Loss: 2.6914
D(x): 0.6350, D(G(z)): 0.1004
Epoch: [10/20], Batch Num: [342/600]
Discriminator Loss: 0.5402, Generator Loss: 1.7785
D(x): 0.8083, D(G(z)): 0.1615
Epoch: [10/20], Batch Num: [343/600]
Discriminator Loss: 0.7345, Generator Loss: 1.5348
D(x): 0.7808, D(G(z)): 0.2603
Epoch: [10/20], Batch Num: [344/600]
Discriminator Loss: 0.9075, Generator Loss: 1.6666
D(x): 0.8713, D(G(z)): 0.3700
Epoch: [10/20], Batch Num: [345/600]
Discriminator Loss: 0.7302, Generator Loss: 1.8150
D(x): 0.8597, D(G(z)): 0.3284
Epoch: [10/20], Batch Num: [346/600]
Discriminator Loss: 0.6393, Generator Loss: 2.3275
D(x): 0.8444, D(G(z)): 0.2558
Epoch: [10/20], Batch Num: [347/600]
Discriminator Loss: 0.5877, Generator Loss: 2.5126
D(x): 0.7797, D(G(z)): 0.1757
Epoch: [10/20], Batch Num: [348/600]
Discriminator Loss: 0.6828, Generator Loss: 2.6495
D(x): 0.7237, D(G(z)): 0.1530
Epoch: [10/20], Batch Num: [349/600]
Discriminator Loss: 0.8008, Generator Loss: 2.2570
D(x): 0.6991, D(G(z)): 0.1631
Epoch: [10/20], Batch Num: [350/600]
Discriminator Loss: 0.8214, Generator Loss: 1.8847
D(x): 0.6783, D(G(z)): 0.1543
Epoch: [10/20], Batch Num: [351/600]
Discriminator Loss: 0.6533, Generator Loss: 1.5021
D(x): 0.8060, D(G(z)): 0.2288
Epoch: [10/20], Batch Num: [352/600]
Discriminator Loss: 0.8082, Generator Loss: 1.4849
D(x): 0.8685, D(G(z)): 0.3726
Epoch: [10/20], Batch Num: [353/600]
Discriminator Loss: 0.7532, Generator Loss: 1.7199
D(x): 0.8601, D(G(z)): 0.3462
Epoch: [10/20], Batch Num: [354/600]
Discriminator Loss: 0.7093, Generator Loss: 2.1231
D(x): 0.7923, D(G(z)): 0.2549
Epoch: [10/20], Batch Num: [355/600]
Discriminator Loss: 0.7934, Generator Loss: 2.4242
D(x): 0.6793, D(G(z)): 0.1614
Epoch: [10/20], Batch Num: [356/600]
Discriminator Loss: 0.9361, Generator Loss: 2.1595
D(x): 0.6465, D(G(z)): 0.1803
Epoch: [10/20], Batch Num: [357/600]
Discriminator Loss: 0.7496, Generator Loss: 2.1000
D(x): 0.7275, D(G(z)): 0.1857
Epoch: [10/20], Batch Num: [358/600]
Discriminator Loss: 0.8033, Generator Loss: 1.8180
D(x): 0.7478, D(G(z)): 0.2505
Epoch: [10/20], Batch Num: [359/600]
Discriminator Loss: 0.7268, Generator Loss: 1.5070
D(x): 0.7467, D(G(z)): 0.2335
Epoch: [10/20], Batch Num: [360/600]
Discriminator Loss: 0.8588, Generator Loss: 1.5377
D(x): 0.8148, D(G(z)): 0.3488
Epoch: [10/20], Batch Num: [361/600]
Discriminator Loss: 0.8235, Generator Loss: 1.8110
D(x): 0.7972, D(G(z)): 0.3173
Epoch: [10/20], Batch Num: [362/600]
Discriminator Loss: 0.6400, Generator Loss: 2.0292
D(x): 0.7647, D(G(z)): 0.1909
Epoch: [10/20], Batch Num: [363/600]
Discriminator Loss: 0.6476, Generator Loss: 2.3047
D(x): 0.7852, D(G(z)): 0.2020
Epoch: [10/20], Batch Num: [364/600]
Discriminator Loss: 0.9275, Generator Loss: 2.2583
D(x): 0.6340, D(G(z)): 0.1758
Epoch: [10/20], Batch Num: [365/600]
Discriminator Loss: 0.6736, Generator Loss: 1.8839
D(x): 0.7158, D(G(z)): 0.1638
Epoch: [10/20], Batch Num: [366/600]
Discriminator Loss: 0.6782, Generator Loss: 1.6532
D(x): 0.7830, D(G(z)): 0.2322
Epoch: [10/20], Batch Num: [367/600]
Discriminator Loss: 0.7265, Generator Loss: 1.4139
D(x): 0.8111, D(G(z)): 0.2900
Epoch: [10/20], Batch Num: [368/600]
Discriminator Loss: 0.7338, Generator Loss: 1.4957
D(x): 0.8398, D(G(z)): 0.3070
Epoch: [10/20], Batch Num: [369/600]
Discriminator Loss: 0.7859, Generator Loss: 1.9400
D(x): 0.8262, D(G(z)): 0.3267
Epoch: [10/20], Batch Num: [370/600]
Discriminator Loss: 0.8042, Generator Loss: 2.3090
D(x): 0.7090, D(G(z)): 0.2010
Epoch: [10/20], Batch Num: [371/600]
Discriminator Loss: 0.7830, Generator Loss: 2.3404
D(x): 0.6903, D(G(z)): 0.1718
Epoch: [10/20], Batch Num: [372/600]
Discriminator Loss: 0.9427, Generator Loss: 2.1840
D(x): 0.6328, D(G(z)): 0.1489
Epoch: [10/20], Batch Num: [373/600]
Discriminator Loss: 0.9195, Generator Loss: 2.0213
D(x): 0.6836, D(G(z)): 0.2164
Epoch: [10/20], Batch Num: [374/600]
Discriminator Loss: 0.7623, Generator Loss: 1.4129
D(x): 0.7149, D(G(z)): 0.2066
Epoch: [10/20], Batch Num: [375/600]
Discriminator Loss: 0.7202, Generator Loss: 1.5073
D(x): 0.8219, D(G(z)): 0.2803
Epoch: [10/20], Batch Num: [376/600]
Discriminator Loss: 0.7560, Generator Loss: 1.6509
D(x): 0.8614, D(G(z)): 0.3449
Epoch: [10/20], Batch Num: [377/600]
Discriminator Loss: 0.7324, Generator Loss: 1.9691
D(x): 0.8024, D(G(z)): 0.2956
Epoch: [10/20], Batch Num: [378/600]
Discriminator Loss: 0.6105, Generator Loss: 2.1212
D(x): 0.7676, D(G(z)): 0.1803
Epoch: [10/20], Batch Num: [379/600]
Discriminator Loss: 0.6186, Generator Loss: 2.3449
D(x): 0.7560, D(G(z)): 0.1578
Epoch: [10/20], Batch Num: [380/600]
Discriminator Loss: 0.6490, Generator Loss: 2.1329
D(x): 0.7111, D(G(z)): 0.1198
Epoch: [10/20], Batch Num: [381/600]
Discriminator Loss: 0.6676, Generator Loss: 1.9742
D(x): 0.7571, D(G(z)): 0.1867
Epoch: [10/20], Batch Num: [382/600]
Discriminator Loss: 0.5220, Generator Loss: 1.6984
D(x): 0.8200, D(G(z)): 0.1895
Epoch: [10/20], Batch Num: [383/600]
Discriminator Loss: 0.7587, Generator Loss: 1.5783
D(x): 0.8329, D(G(z)): 0.2796
Epoch: [10/20], Batch Num: [384/600]
Discriminator Loss: 0.5871, Generator Loss: 1.6982
D(x): 0.8476, D(G(z)): 0.2582
Epoch: [10/20], Batch Num: [385/600]
Discriminator Loss: 0.6961, Generator Loss: 2.0278
D(x): 0.8309, D(G(z)): 0.2855
Epoch: [10/20], Batch Num: [386/600]
Discriminator Loss: 0.5766, Generator Loss: 2.1564
D(x): 0.8057, D(G(z)): 0.1884
Epoch: [10/20], Batch Num: [387/600]
Discriminator Loss: 0.6854, Generator Loss: 2.5237
D(x): 0.7251, D(G(z)): 0.1541
Epoch: [10/20], Batch Num: [388/600]
Discriminator Loss: 0.6328, Generator Loss: 2.3610
D(x): 0.7405, D(G(z)): 0.1450
Epoch: [10/20], Batch Num: [389/600]
Discriminator Loss: 0.6341, Generator Loss: 2.1296
D(x): 0.7384, D(G(z)): 0.1427
Epoch: [10/20], Batch Num: [390/600]
Discriminator Loss: 0.6239, Generator Loss: 1.9114
D(x): 0.7986, D(G(z)): 0.1912
Epoch: [10/20], Batch Num: [391/600]
Discriminator Loss: 0.5855, Generator Loss: 1.8441
D(x): 0.8244, D(G(z)): 0.2284
Epoch: [10/20], Batch Num: [392/600]
Discriminator Loss: 0.5907, Generator Loss: 1.8045
D(x): 0.8350, D(G(z)): 0.2370
Epoch: [10/20], Batch Num: [393/600]
Discriminator Loss: 0.5604, Generator Loss: 2.0139
D(x): 0.8895, D(G(z)): 0.2691
Epoch: [10/20], Batch Num: [394/600]
Discriminator Loss: 0.5254, Generator Loss: 2.4471
D(x): 0.8126, D(G(z)): 0.1832
Epoch: [10/20], Batch Num: [395/600]
Discriminator Loss: 0.5237, Generator Loss: 2.5273
D(x): 0.8104, D(G(z)): 0.1663
Epoch: [10/20], Batch Num: [396/600]
Discriminator Loss: 0.5132, Generator Loss: 2.3986
D(x): 0.8205, D(G(z)): 0.1693
Epoch: [10/20], Batch Num: [397/600]
Discriminator Loss: 0.6193, Generator Loss: 2.1852
D(x): 0.7637, D(G(z)): 0.1325
Epoch: [10/20], Batch Num: [398/600]
Discriminator Loss: 0.5199, Generator Loss: 2.0870
D(x): 0.8310, D(G(z)): 0.1911
Epoch: [10/20], Batch Num: [399/600]
Discriminator Loss: 0.5701, Generator Loss: 2.1900
D(x): 0.8190, D(G(z)): 0.1891
Epoch: 10, Batch Num: [400/600]

Epoch: [10/20], Batch Num: [400/600]
Discriminator Loss: 0.4347, Generator Loss: 2.1671
D(x): 0.8670, D(G(z)): 0.1711
Epoch: [10/20], Batch Num: [401/600]
Discriminator Loss: 0.4226, Generator Loss: 2.1621
D(x): 0.9014, D(G(z)): 0.1922
Epoch: [10/20], Batch Num: [402/600]
Discriminator Loss: 0.4398, Generator Loss: 2.3613
D(x): 0.8570, D(G(z)): 0.1673
Epoch: [10/20], Batch Num: [403/600]
Discriminator Loss: 0.4655, Generator Loss: 2.2714
D(x): 0.8235, D(G(z)): 0.1544
Epoch: [10/20], Batch Num: [404/600]
Discriminator Loss: 0.5361, Generator Loss: 2.2002
D(x): 0.8082, D(G(z)): 0.1785
Epoch: [10/20], Batch Num: [405/600]
Discriminator Loss: 0.7068, Generator Loss: 2.4206
D(x): 0.8049, D(G(z)): 0.2185
Epoch: [10/20], Batch Num: [406/600]
Discriminator Loss: 0.6037, Generator Loss: 2.2958
D(x): 0.8557, D(G(z)): 0.2184
Epoch: [10/20], Batch Num: [407/600]
Discriminator Loss: 0.6247, Generator Loss: 2.3326
D(x): 0.8124, D(G(z)): 0.2055
Epoch: [10/20], Batch Num: [408/600]
Discriminator Loss: 0.5752, Generator Loss: 2.5699
D(x): 0.8190, D(G(z)): 0.1786
Epoch: [10/20], Batch Num: [409/600]
Discriminator Loss: 0.4383, Generator Loss: 2.5451
D(x): 0.8248, D(G(z)): 0.1368
Epoch: [10/20], Batch Num: [410/600]
Discriminator Loss: 0.6509, Generator Loss: 2.1475
D(x): 0.7613, D(G(z)): 0.1481
Epoch: [10/20], Batch Num: [411/600]
Discriminator Loss: 0.6128, Generator Loss: 2.0666
D(x): 0.8682, D(G(z)): 0.2363
Epoch: [10/20], Batch Num: [412/600]
Discriminator Loss: 0.6887, Generator Loss: 2.2881
D(x): 0.8141, D(G(z)): 0.2144
Epoch: [10/20], Batch Num: [413/600]
Discriminator Loss: 0.5093, Generator Loss: 2.3194
D(x): 0.8517, D(G(z)): 0.2132
Epoch: [10/20], Batch Num: [414/600]
Discriminator Loss: 0.7690, Generator Loss: 2.2336
D(x): 0.7286, D(G(z)): 0.1882
Epoch: [10/20], Batch Num: [415/600]
Discriminator Loss: 0.6364, Generator Loss: 1.9814
D(x): 0.8270, D(G(z)): 0.2008
Epoch: [10/20], Batch Num: [416/600]
Discriminator Loss: 0.6727, Generator Loss: 2.1304
D(x): 0.8126, D(G(z)): 0.2354
Epoch: [10/20], Batch Num: [417/600]
Discriminator Loss: 0.5549, Generator Loss: 2.3580
D(x): 0.8341, D(G(z)): 0.1989
Epoch: [10/20], Batch Num: [418/600]
Discriminator Loss: 0.7602, Generator Loss: 2.4213
D(x): 0.7775, D(G(z)): 0.2192
Epoch: [10/20], Batch Num: [419/600]
Discriminator Loss: 0.8418, Generator Loss: 2.2410
D(x): 0.7236, D(G(z)): 0.2201
Epoch: [10/20], Batch Num: [420/600]
Discriminator Loss: 0.8329, Generator Loss: 1.9360
D(x): 0.7319, D(G(z)): 0.1829
Epoch: [10/20], Batch Num: [421/600]
Discriminator Loss: 0.7720, Generator Loss: 1.8758
D(x): 0.7627, D(G(z)): 0.2257
Epoch: [10/20], Batch Num: [422/600]
Discriminator Loss: 0.7402, Generator Loss: 1.9391
D(x): 0.8013, D(G(z)): 0.2670
Epoch: [10/20], Batch Num: [423/600]
Discriminator Loss: 0.7807, Generator Loss: 2.2516
D(x): 0.8352, D(G(z)): 0.2909
Epoch: [10/20], Batch Num: [424/600]
Discriminator Loss: 0.7153, Generator Loss: 2.5062
D(x): 0.8189, D(G(z)): 0.2482
Epoch: [10/20], Batch Num: [425/600]
Discriminator Loss: 0.6411, Generator Loss: 2.5754
D(x): 0.7623, D(G(z)): 0.1655
Epoch: [10/20], Batch Num: [426/600]
Discriminator Loss: 0.8331, Generator Loss: 2.3110
D(x): 0.6318, D(G(z)): 0.1279
Epoch: [10/20], Batch Num: [427/600]
Discriminator Loss: 0.7303, Generator Loss: 1.6863
D(x): 0.7226, D(G(z)): 0.1986
Epoch: [10/20], Batch Num: [428/600]
Discriminator Loss: 0.6726, Generator Loss: 1.4825
D(x): 0.8289, D(G(z)): 0.2793
Epoch: [10/20], Batch Num: [429/600]
Discriminator Loss: 0.5820, Generator Loss: 1.4996
D(x): 0.8710, D(G(z)): 0.2761
Epoch: [10/20], Batch Num: [430/600]
Discriminator Loss: 0.7833, Generator Loss: 1.7906
D(x): 0.8656, D(G(z)): 0.3557
Epoch: [10/20], Batch Num: [431/600]
Discriminator Loss: 0.6074, Generator Loss: 2.1233
D(x): 0.8096, D(G(z)): 0.2370
Epoch: [10/20], Batch Num: [432/600]
Discriminator Loss: 0.5366, Generator Loss: 2.3654
D(x): 0.8127, D(G(z)): 0.1961
Epoch: [10/20], Batch Num: [433/600]
Discriminator Loss: 0.7513, Generator Loss: 2.4344
D(x): 0.7059, D(G(z)): 0.1681
Epoch: [10/20], Batch Num: [434/600]
Discriminator Loss: 0.5593, Generator Loss: 2.2606
D(x): 0.8005, D(G(z)): 0.1524
Epoch: [10/20], Batch Num: [435/600]
Discriminator Loss: 0.6348, Generator Loss: 2.0288
D(x): 0.7989, D(G(z)): 0.2351
Epoch: [10/20], Batch Num: [436/600]
Discriminator Loss: 0.6065, Generator Loss: 1.8872
D(x): 0.8081, D(G(z)): 0.2035
Epoch: [10/20], Batch Num: [437/600]
Discriminator Loss: 0.5500, Generator Loss: 2.0362
D(x): 0.8553, D(G(z)): 0.2417
Epoch: [10/20], Batch Num: [438/600]
Discriminator Loss: 0.5948, Generator Loss: 2.2854
D(x): 0.8346, D(G(z)): 0.2474
Epoch: [10/20], Batch Num: [439/600]
Discriminator Loss: 0.5254, Generator Loss: 2.6284
D(x): 0.8780, D(G(z)): 0.2144
Epoch: [10/20], Batch Num: [440/600]
Discriminator Loss: 0.4666, Generator Loss: 2.7183
D(x): 0.8147, D(G(z)): 0.1432
Epoch: [10/20], Batch Num: [441/600]
Discriminator Loss: 0.5845, Generator Loss: 2.4231
D(x): 0.7846, D(G(z)): 0.1556
Epoch: [10/20], Batch Num: [442/600]
Discriminator Loss: 0.6058, Generator Loss: 2.4601
D(x): 0.7572, D(G(z)): 0.1438
Epoch: [10/20], Batch Num: [443/600]
Discriminator Loss: 0.3986, Generator Loss: 2.2332
D(x): 0.8830, D(G(z)): 0.1629
Epoch: [10/20], Batch Num: [444/600]
Discriminator Loss: 0.5801, Generator Loss: 2.0577
D(x): 0.8241, D(G(z)): 0.2059
Epoch: [10/20], Batch Num: [445/600]
Discriminator Loss: 0.5213, Generator Loss: 1.8470
D(x): 0.8809, D(G(z)): 0.2313
Epoch: [10/20], Batch Num: [446/600]
Discriminator Loss: 0.4960, Generator Loss: 2.4436
D(x): 0.9141, D(G(z)): 0.2436
Epoch: [10/20], Batch Num: [447/600]
Discriminator Loss: 0.5101, Generator Loss: 2.6216
D(x): 0.8490, D(G(z)): 0.1889
Epoch: [10/20], Batch Num: [448/600]
Discriminator Loss: 0.5598, Generator Loss: 2.8303
D(x): 0.8040, D(G(z)): 0.1654
Epoch: [10/20], Batch Num: [449/600]
Discriminator Loss: 0.6891, Generator Loss: 2.5079
D(x): 0.7102, D(G(z)): 0.1219
Epoch: [10/20], Batch Num: [450/600]
Discriminator Loss: 0.5787, Generator Loss: 2.3784
D(x): 0.8325, D(G(z)): 0.2038
Epoch: [10/20], Batch Num: [451/600]
Discriminator Loss: 0.5134, Generator Loss: 2.1521
D(x): 0.8457, D(G(z)): 0.1983
Epoch: [10/20], Batch Num: [452/600]
Discriminator Loss: 0.5825, Generator Loss: 2.0449
D(x): 0.8674, D(G(z)): 0.2580
Epoch: [10/20], Batch Num: [453/600]
Discriminator Loss: 0.7678, Generator Loss: 2.2799
D(x): 0.7918, D(G(z)): 0.2599
Epoch: [10/20], Batch Num: [454/600]
Discriminator Loss: 0.8141, Generator Loss: 2.7418
D(x): 0.7757, D(G(z)): 0.2498
Epoch: [10/20], Batch Num: [455/600]
Discriminator Loss: 0.6796, Generator Loss: 2.3923
D(x): 0.7787, D(G(z)): 0.1787
Epoch: [10/20], Batch Num: [456/600]
Discriminator Loss: 0.8852, Generator Loss: 2.3670
D(x): 0.7635, D(G(z)): 0.2059
Epoch: [10/20], Batch Num: [457/600]
Discriminator Loss: 0.8206, Generator Loss: 2.3610
D(x): 0.7354, D(G(z)): 0.2209
Epoch: [10/20], Batch Num: [458/600]
Discriminator Loss: 0.7555, Generator Loss: 1.9974
D(x): 0.8065, D(G(z)): 0.2608
Epoch: [10/20], Batch Num: [459/600]
Discriminator Loss: 0.6699, Generator Loss: 2.2740
D(x): 0.7906, D(G(z)): 0.2237
Epoch: [10/20], Batch Num: [460/600]
Discriminator Loss: 0.9749, Generator Loss: 2.0215
D(x): 0.7603, D(G(z)): 0.2866
Epoch: [10/20], Batch Num: [461/600]
Discriminator Loss: 0.8061, Generator Loss: 2.0118
D(x): 0.7590, D(G(z)): 0.2346
Epoch: [10/20], Batch Num: [462/600]
Discriminator Loss: 0.9661, Generator Loss: 2.0372
D(x): 0.7212, D(G(z)): 0.2800
Epoch: [10/20], Batch Num: [463/600]
Discriminator Loss: 1.0720, Generator Loss: 1.9772
D(x): 0.6849, D(G(z)): 0.2677
Epoch: [10/20], Batch Num: [464/600]
Discriminator Loss: 0.8466, Generator Loss: 1.9633
D(x): 0.7145, D(G(z)): 0.2348
Epoch: [10/20], Batch Num: [465/600]
Discriminator Loss: 1.0619, Generator Loss: 2.1637
D(x): 0.7335, D(G(z)): 0.3387
Epoch: [10/20], Batch Num: [466/600]
Discriminator Loss: 0.9908, Generator Loss: 2.0120
D(x): 0.6886, D(G(z)): 0.2386
Epoch: [10/20], Batch Num: [467/600]
Discriminator Loss: 0.9040, Generator Loss: 1.9812
D(x): 0.6632, D(G(z)): 0.1757
Epoch: [10/20], Batch Num: [468/600]
Discriminator Loss: 0.6800, Generator Loss: 1.9170
D(x): 0.7736, D(G(z)): 0.2331
Epoch: [10/20], Batch Num: [469/600]
Discriminator Loss: 0.6876, Generator Loss: 2.0690
D(x): 0.8434, D(G(z)): 0.2736
Epoch: [10/20], Batch Num: [470/600]
Discriminator Loss: 0.6733, Generator Loss: 2.2622
D(x): 0.8193, D(G(z)): 0.2432
Epoch: [10/20], Batch Num: [471/600]
Discriminator Loss: 0.8133, Generator Loss: 2.6349
D(x): 0.7694, D(G(z)): 0.2352
Epoch: [10/20], Batch Num: [472/600]
Discriminator Loss: 0.5204, Generator Loss: 2.7803
D(x): 0.7973, D(G(z)): 0.1365
Epoch: [10/20], Batch Num: [473/600]
Discriminator Loss: 0.6710, Generator Loss: 2.6963
D(x): 0.7365, D(G(z)): 0.1221
Epoch: [10/20], Batch Num: [474/600]
Discriminator Loss: 0.5468, Generator Loss: 2.4495
D(x): 0.7966, D(G(z)): 0.1716
Epoch: [10/20], Batch Num: [475/600]
Discriminator Loss: 0.6211, Generator Loss: 2.5140
D(x): 0.8288, D(G(z)): 0.1984
Epoch: [10/20], Batch Num: [476/600]
Discriminator Loss: 0.5663, Generator Loss: 2.4258
D(x): 0.8730, D(G(z)): 0.2136
Epoch: [10/20], Batch Num: [477/600]
Discriminator Loss: 0.6693, Generator Loss: 2.5294
D(x): 0.7504, D(G(z)): 0.1646
Epoch: [10/20], Batch Num: [478/600]
Discriminator Loss: 0.5514, Generator Loss: 2.7264
D(x): 0.8335, D(G(z)): 0.1995
Epoch: [10/20], Batch Num: [479/600]
Discriminator Loss: 0.5685, Generator Loss: 2.8663
D(x): 0.8374, D(G(z)): 0.1887
Epoch: [10/20], Batch Num: [480/600]
Discriminator Loss: 0.5288, Generator Loss: 2.8260
D(x): 0.8179, D(G(z)): 0.1687
Epoch: [10/20], Batch Num: [481/600]
Discriminator Loss: 0.4898, Generator Loss: 2.9014
D(x): 0.8262, D(G(z)): 0.1392
Epoch: [10/20], Batch Num: [482/600]
Discriminator Loss: 0.7127, Generator Loss: 2.3715
D(x): 0.7177, D(G(z)): 0.1506
Epoch: [10/20], Batch Num: [483/600]
Discriminator Loss: 0.5829, Generator Loss: 2.4780
D(x): 0.8095, D(G(z)): 0.1907
Epoch: [10/20], Batch Num: [484/600]
Discriminator Loss: 0.4404, Generator Loss: 2.0749
D(x): 0.8854, D(G(z)): 0.1929
Epoch: [10/20], Batch Num: [485/600]
Discriminator Loss: 0.7125, Generator Loss: 2.3207
D(x): 0.8306, D(G(z)): 0.2535
Epoch: [10/20], Batch Num: [486/600]
Discriminator Loss: 0.7318, Generator Loss: 2.8253
D(x): 0.8249, D(G(z)): 0.2582
Epoch: [10/20], Batch Num: [487/600]
Discriminator Loss: 0.7265, Generator Loss: 2.8968
D(x): 0.7733, D(G(z)): 0.1974
Epoch: [10/20], Batch Num: [488/600]
Discriminator Loss: 0.7875, Generator Loss: 2.9610
D(x): 0.7469, D(G(z)): 0.1567
Epoch: [10/20], Batch Num: [489/600]
Discriminator Loss: 0.8229, Generator Loss: 2.8745
D(x): 0.7145, D(G(z)): 0.1409
Epoch: [10/20], Batch Num: [490/600]
Discriminator Loss: 0.6662, Generator Loss: 2.4789
D(x): 0.7586, D(G(z)): 0.1579
Epoch: [10/20], Batch Num: [491/600]
Discriminator Loss: 0.7609, Generator Loss: 1.9881
D(x): 0.7936, D(G(z)): 0.2285
Epoch: [10/20], Batch Num: [492/600]
Discriminator Loss: 1.0134, Generator Loss: 2.6990
D(x): 0.8242, D(G(z)): 0.3492
Epoch: [10/20], Batch Num: [493/600]
Discriminator Loss: 0.8506, Generator Loss: 2.8949
D(x): 0.7277, D(G(z)): 0.1944
Epoch: [10/20], Batch Num: [494/600]
Discriminator Loss: 0.7516, Generator Loss: 2.5527
D(x): 0.7379, D(G(z)): 0.1582
Epoch: [10/20], Batch Num: [495/600]
Discriminator Loss: 0.9308, Generator Loss: 2.2126
D(x): 0.6895, D(G(z)): 0.1839
Epoch: [10/20], Batch Num: [496/600]
Discriminator Loss: 0.8761, Generator Loss: 1.9083
D(x): 0.7731, D(G(z)): 0.2675
Epoch: [10/20], Batch Num: [497/600]
Discriminator Loss: 0.8802, Generator Loss: 2.2768
D(x): 0.7565, D(G(z)): 0.2773
Epoch: [10/20], Batch Num: [498/600]
Discriminator Loss: 0.8961, Generator Loss: 2.4566
D(x): 0.7594, D(G(z)): 0.2911
Epoch: [10/20], Batch Num: [499/600]
Discriminator Loss: 0.9518, Generator Loss: 2.1791
D(x): 0.6675, D(G(z)): 0.2286
Epoch: 10, Batch Num: [500/600]

Epoch: [10/20], Batch Num: [500/600]
Discriminator Loss: 1.0468, Generator Loss: 2.0711
D(x): 0.6562, D(G(z)): 0.2279
Epoch: [10/20], Batch Num: [501/600]
Discriminator Loss: 0.9427, Generator Loss: 1.8403
D(x): 0.6713, D(G(z)): 0.2182
Epoch: [10/20], Batch Num: [502/600]
Discriminator Loss: 0.9940, Generator Loss: 1.7546
D(x): 0.7319, D(G(z)): 0.3230
Epoch: [10/20], Batch Num: [503/600]
Discriminator Loss: 0.9971, Generator Loss: 1.6715
D(x): 0.7247, D(G(z)): 0.2980
Epoch: [10/20], Batch Num: [504/600]
Discriminator Loss: 1.0889, Generator Loss: 1.6817
D(x): 0.6512, D(G(z)): 0.3033
Epoch: [10/20], Batch Num: [505/600]
Discriminator Loss: 0.9364, Generator Loss: 2.1501
D(x): 0.7547, D(G(z)): 0.3290
Epoch: [10/20], Batch Num: [506/600]
Discriminator Loss: 0.9493, Generator Loss: 2.1446
D(x): 0.6668, D(G(z)): 0.2330
Epoch: [10/20], Batch Num: [507/600]
Discriminator Loss: 0.8073, Generator Loss: 1.9942
D(x): 0.7292, D(G(z)): 0.2276
Epoch: [10/20], Batch Num: [508/600]
Discriminator Loss: 0.9521, Generator Loss: 2.0088
D(x): 0.7073, D(G(z)): 0.2719
Epoch: [10/20], Batch Num: [509/600]
Discriminator Loss: 0.8177, Generator Loss: 2.1146
D(x): 0.7419, D(G(z)): 0.2646
Epoch: [10/20], Batch Num: [510/600]
Discriminator Loss: 0.7689, Generator Loss: 2.0686
D(x): 0.7315, D(G(z)): 0.2094
Epoch: [10/20], Batch Num: [511/600]
Discriminator Loss: 0.8065, Generator Loss: 2.1052
D(x): 0.7294, D(G(z)): 0.2360
Epoch: [10/20], Batch Num: [512/600]
Discriminator Loss: 0.6616, Generator Loss: 1.9152
D(x): 0.8015, D(G(z)): 0.2464
Epoch: [10/20], Batch Num: [513/600]
Discriminator Loss: 0.7721, Generator Loss: 2.0455
D(x): 0.7258, D(G(z)): 0.2055
Epoch: [10/20], Batch Num: [514/600]
Discriminator Loss: 0.7134, Generator Loss: 1.9191
D(x): 0.7756, D(G(z)): 0.2386
Epoch: [10/20], Batch Num: [515/600]
Discriminator Loss: 0.8179, Generator Loss: 1.7094
D(x): 0.7259, D(G(z)): 0.2316
Epoch: [10/20], Batch Num: [516/600]
Discriminator Loss: 0.7926, Generator Loss: 1.5734
D(x): 0.7655, D(G(z)): 0.2855
Epoch: [10/20], Batch Num: [517/600]
Discriminator Loss: 0.7304, Generator Loss: 1.9638
D(x): 0.8147, D(G(z)): 0.2942
Epoch: [10/20], Batch Num: [518/600]
Discriminator Loss: 0.7407, Generator Loss: 2.2749
D(x): 0.7829, D(G(z)): 0.2640
Epoch: [10/20], Batch Num: [519/600]
Discriminator Loss: 0.6392, Generator Loss: 2.4040
D(x): 0.7820, D(G(z)): 0.1886
Epoch: [10/20], Batch Num: [520/600]
Discriminator Loss: 0.5906, Generator Loss: 2.4384
D(x): 0.7723, D(G(z)): 0.1330
Epoch: [10/20], Batch Num: [521/600]
Discriminator Loss: 0.7307, Generator Loss: 2.4152
D(x): 0.7218, D(G(z)): 0.1620
Epoch: [10/20], Batch Num: [522/600]
Discriminator Loss: 0.6609, Generator Loss: 1.9524
D(x): 0.7948, D(G(z)): 0.1918
Epoch: [10/20], Batch Num: [523/600]
Discriminator Loss: 0.5646, Generator Loss: 2.1107
D(x): 0.8525, D(G(z)): 0.2317
Epoch: [10/20], Batch Num: [524/600]
Discriminator Loss: 0.6685, Generator Loss: 2.3270
D(x): 0.8348, D(G(z)): 0.2362
Epoch: [10/20], Batch Num: [525/600]
Discriminator Loss: 0.6440, Generator Loss: 2.4884
D(x): 0.7877, D(G(z)): 0.1968
Epoch: [10/20], Batch Num: [526/600]
Discriminator Loss: 0.7769, Generator Loss: 2.5628
D(x): 0.8127, D(G(z)): 0.2650
Epoch: [10/20], Batch Num: [527/600]
Discriminator Loss: 0.5272, Generator Loss: 2.6757
D(x): 0.8278, D(G(z)): 0.1747
Epoch: [10/20], Batch Num: [528/600]
Discriminator Loss: 0.7311, Generator Loss: 2.8230
D(x): 0.7765, D(G(z)): 0.1695
Epoch: [10/20], Batch Num: [529/600]
Discriminator Loss: 0.8285, Generator Loss: 2.6767
D(x): 0.7259, D(G(z)): 0.1739
Epoch: [10/20], Batch Num: [530/600]
Discriminator Loss: 0.6090, Generator Loss: 2.5606
D(x): 0.8107, D(G(z)): 0.1770
Epoch: [10/20], Batch Num: [531/600]
Discriminator Loss: 0.7174, Generator Loss: 2.3484
D(x): 0.8250, D(G(z)): 0.2340
Epoch: [10/20], Batch Num: [532/600]
Discriminator Loss: 0.7302, Generator Loss: 2.5694
D(x): 0.8154, D(G(z)): 0.2307
Epoch: [10/20], Batch Num: [533/600]
Discriminator Loss: 0.7099, Generator Loss: 2.5246
D(x): 0.8226, D(G(z)): 0.2446
Epoch: [10/20], Batch Num: [534/600]
Discriminator Loss: 0.6328, Generator Loss: 2.3508
D(x): 0.7564, D(G(z)): 0.1218
Epoch: [10/20], Batch Num: [535/600]
Discriminator Loss: 0.6252, Generator Loss: 2.5944
D(x): 0.8213, D(G(z)): 0.1983
Epoch: [10/20], Batch Num: [536/600]
Discriminator Loss: 0.8098, Generator Loss: 2.4033
D(x): 0.7902, D(G(z)): 0.2357
Epoch: [10/20], Batch Num: [537/600]
Discriminator Loss: 0.6808, Generator Loss: 2.2144
D(x): 0.8285, D(G(z)): 0.2377
Epoch: [10/20], Batch Num: [538/600]
Discriminator Loss: 0.7418, Generator Loss: 2.5440
D(x): 0.8324, D(G(z)): 0.2416
Epoch: [10/20], Batch Num: [539/600]
Discriminator Loss: 0.6626, Generator Loss: 2.5729
D(x): 0.8081, D(G(z)): 0.2157
Epoch: [10/20], Batch Num: [540/600]
Discriminator Loss: 0.7902, Generator Loss: 2.4750
D(x): 0.7626, D(G(z)): 0.2337
Epoch: [10/20], Batch Num: [541/600]
Discriminator Loss: 0.6797, Generator Loss: 2.6481
D(x): 0.7496, D(G(z)): 0.1812
Epoch: [10/20], Batch Num: [542/600]
Discriminator Loss: 0.6266, Generator Loss: 2.2477
D(x): 0.7646, D(G(z)): 0.1694
Epoch: [10/20], Batch Num: [543/600]
Discriminator Loss: 0.8534, Generator Loss: 2.1298
D(x): 0.7461, D(G(z)): 0.2322
Epoch: [10/20], Batch Num: [544/600]
Discriminator Loss: 0.6505, Generator Loss: 2.0143
D(x): 0.7960, D(G(z)): 0.2082
Epoch: [10/20], Batch Num: [545/600]
Discriminator Loss: 0.7651, Generator Loss: 2.0859
D(x): 0.7829, D(G(z)): 0.2595
Epoch: [10/20], Batch Num: [546/600]
Discriminator Loss: 0.8389, Generator Loss: 1.9750
D(x): 0.8017, D(G(z)): 0.2900
Epoch: [10/20], Batch Num: [547/600]
Discriminator Loss: 0.7776, Generator Loss: 2.2560
D(x): 0.8345, D(G(z)): 0.2913
Epoch: [10/20], Batch Num: [548/600]
Discriminator Loss: 0.7033, Generator Loss: 2.4797
D(x): 0.7677, D(G(z)): 0.2194
Epoch: [10/20], Batch Num: [549/600]
Discriminator Loss: 0.6503, Generator Loss: 2.0794
D(x): 0.7394, D(G(z)): 0.1717
Epoch: [10/20], Batch Num: [550/600]
Discriminator Loss: 0.6246, Generator Loss: 2.0805
D(x): 0.8078, D(G(z)): 0.2290
Epoch: [10/20], Batch Num: [551/600]
Discriminator Loss: 0.7860, Generator Loss: 2.1393
D(x): 0.7271, D(G(z)): 0.2412
Epoch: [10/20], Batch Num: [552/600]
Discriminator Loss: 0.8408, Generator Loss: 1.9304
D(x): 0.7296, D(G(z)): 0.2114
Epoch: [10/20], Batch Num: [553/600]
Discriminator Loss: 0.6399, Generator Loss: 1.9127
D(x): 0.8404, D(G(z)): 0.2706
Epoch: [10/20], Batch Num: [554/600]
Discriminator Loss: 0.6695, Generator Loss: 1.8570
D(x): 0.8326, D(G(z)): 0.2622
Epoch: [10/20], Batch Num: [555/600]
Discriminator Loss: 0.7089, Generator Loss: 1.6582
D(x): 0.7890, D(G(z)): 0.2672
Epoch: [10/20], Batch Num: [556/600]
Discriminator Loss: 0.7462, Generator Loss: 1.8342
D(x): 0.7692, D(G(z)): 0.2767
Epoch: [10/20], Batch Num: [557/600]
Discriminator Loss: 0.8670, Generator Loss: 1.8985
D(x): 0.7168, D(G(z)): 0.2575
Epoch: [10/20], Batch Num: [558/600]
Discriminator Loss: 0.8020, Generator Loss: 1.8722
D(x): 0.7775, D(G(z)): 0.2721
Epoch: [10/20], Batch Num: [559/600]
Discriminator Loss: 0.8924, Generator Loss: 1.8333
D(x): 0.7487, D(G(z)): 0.3097
Epoch: [10/20], Batch Num: [560/600]
Discriminator Loss: 0.7261, Generator Loss: 1.9499
D(x): 0.7325, D(G(z)): 0.2135
Epoch: [10/20], Batch Num: [561/600]
Discriminator Loss: 0.8147, Generator Loss: 1.9511
D(x): 0.7514, D(G(z)): 0.2611
Epoch: [10/20], Batch Num: [562/600]
Discriminator Loss: 0.7756, Generator Loss: 1.7340
D(x): 0.7320, D(G(z)): 0.2351
Epoch: [10/20], Batch Num: [563/600]
Discriminator Loss: 0.7676, Generator Loss: 1.6717
D(x): 0.7749, D(G(z)): 0.2789
Epoch: [10/20], Batch Num: [564/600]
Discriminator Loss: 0.7572, Generator Loss: 1.7213
D(x): 0.7794, D(G(z)): 0.2774
Epoch: [10/20], Batch Num: [565/600]
Discriminator Loss: 0.6260, Generator Loss: 1.7325
D(x): 0.8434, D(G(z)): 0.2903
Epoch: [10/20], Batch Num: [566/600]
Discriminator Loss: 0.7413, Generator Loss: 1.7547
D(x): 0.7936, D(G(z)): 0.2759
Epoch: [10/20], Batch Num: [567/600]
Discriminator Loss: 0.6901, Generator Loss: 2.0219
D(x): 0.7826, D(G(z)): 0.2616
Epoch: [10/20], Batch Num: [568/600]
Discriminator Loss: 0.9118, Generator Loss: 1.7271
D(x): 0.7434, D(G(z)): 0.2839
Epoch: [10/20], Batch Num: [569/600]
Discriminator Loss: 0.6227, Generator Loss: 1.9299
D(x): 0.7939, D(G(z)): 0.2361
Epoch: [10/20], Batch Num: [570/600]
Discriminator Loss: 0.6923, Generator Loss: 2.0667
D(x): 0.7772, D(G(z)): 0.2271
Epoch: [10/20], Batch Num: [571/600]
Discriminator Loss: 0.6274, Generator Loss: 1.9390
D(x): 0.7738, D(G(z)): 0.1919
Epoch: [10/20], Batch Num: [572/600]
Discriminator Loss: 0.6883, Generator Loss: 1.9309
D(x): 0.7719, D(G(z)): 0.2307
Epoch: [10/20], Batch Num: [573/600]
Discriminator Loss: 0.7139, Generator Loss: 1.7184
D(x): 0.7723, D(G(z)): 0.2317
Epoch: [10/20], Batch Num: [574/600]
Discriminator Loss: 0.6980, Generator Loss: 1.9808
D(x): 0.8688, D(G(z)): 0.3127
Epoch: [10/20], Batch Num: [575/600]
Discriminator Loss: 0.5485, Generator Loss: 2.4793
D(x): 0.8329, D(G(z)): 0.2110
Epoch: [10/20], Batch Num: [576/600]
Discriminator Loss: 0.5467, Generator Loss: 2.7095
D(x): 0.7911, D(G(z)): 0.1659
Epoch: [10/20], Batch Num: [577/600]
Discriminator Loss: 0.4968, Generator Loss: 2.3860
D(x): 0.7907, D(G(z)): 0.1462
Epoch: [10/20], Batch Num: [578/600]
Discriminator Loss: 0.5190, Generator Loss: 2.6302
D(x): 0.8002, D(G(z)): 0.1553
Epoch: [10/20], Batch Num: [579/600]
Discriminator Loss: 0.5615, Generator Loss: 2.4050
D(x): 0.7902, D(G(z)): 0.1608
Epoch: [10/20], Batch Num: [580/600]
Discriminator Loss: 0.5452, Generator Loss: 2.3536
D(x): 0.8463, D(G(z)): 0.2146
Epoch: [10/20], Batch Num: [581/600]
Discriminator Loss: 0.5038, Generator Loss: 2.1757
D(x): 0.8506, D(G(z)): 0.1854
Epoch: [10/20], Batch Num: [582/600]
Discriminator Loss: 0.4679, Generator Loss: 2.3697
D(x): 0.8912, D(G(z)): 0.2072
Epoch: [10/20], Batch Num: [583/600]
Discriminator Loss: 0.4673, Generator Loss: 2.7139
D(x): 0.8601, D(G(z)): 0.1731
Epoch: [10/20], Batch Num: [584/600]
Discriminator Loss: 0.5381, Generator Loss: 2.8936
D(x): 0.8496, D(G(z)): 0.2062
Epoch: [10/20], Batch Num: [585/600]
Discriminator Loss: 0.4344, Generator Loss: 2.9251
D(x): 0.8468, D(G(z)): 0.1546
Epoch: [10/20], Batch Num: [586/600]
Discriminator Loss: 0.4362, Generator Loss: 3.0500
D(x): 0.8316, D(G(z)): 0.1310
Epoch: [10/20], Batch Num: [587/600]
Discriminator Loss: 0.4779, Generator Loss: 2.7298
D(x): 0.8089, D(G(z)): 0.1285
Epoch: [10/20], Batch Num: [588/600]
Discriminator Loss: 0.5401, Generator Loss: 2.4996
D(x): 0.8264, D(G(z)): 0.2001
Epoch: [10/20], Batch Num: [589/600]
Discriminator Loss: 0.4697, Generator Loss: 2.5466
D(x): 0.8554, D(G(z)): 0.1826
Epoch: [10/20], Batch Num: [590/600]
Discriminator Loss: 0.4518, Generator Loss: 2.4604
D(x): 0.8753, D(G(z)): 0.1982
Epoch: [10/20], Batch Num: [591/600]
Discriminator Loss: 0.4596, Generator Loss: 2.6267
D(x): 0.8379, D(G(z)): 0.1719
Epoch: [10/20], Batch Num: [592/600]
Discriminator Loss: 0.4691, Generator Loss: 2.5017
D(x): 0.8313, D(G(z)): 0.1503
Epoch: [10/20], Batch Num: [593/600]
Discriminator Loss: 0.5883, Generator Loss: 2.7628
D(x): 0.8261, D(G(z)): 0.2022
Epoch: [10/20], Batch Num: [594/600]
Discriminator Loss: 0.6361, Generator Loss: 2.6317
D(x): 0.8013, D(G(z)): 0.2065
Epoch: [10/20], Batch Num: [595/600]
Discriminator Loss: 0.7559, Generator Loss: 2.5081
D(x): 0.7472, D(G(z)): 0.2051
Epoch: [10/20], Batch Num: [596/600]
Discriminator Loss: 0.7234, Generator Loss: 2.4920
D(x): 0.8157, D(G(z)): 0.2253
Epoch: [10/20], Batch Num: [597/600]
Discriminator Loss: 0.6287, Generator Loss: 2.4370
D(x): 0.7765, D(G(z)): 0.1877
Epoch: [10/20], Batch Num: [598/600]
Discriminator Loss: 0.7266, Generator Loss: 2.2352
D(x): 0.8040, D(G(z)): 0.2143
Epoch: [10/20], Batch Num: [599/600]
Discriminator Loss: 0.6502, Generator Loss: 2.3173
D(x): 0.8184, D(G(z)): 0.2299
Epoch: 11, Batch Num: [0/600]

Epoch: [11/20], Batch Num: [0/600]
Discriminator Loss: 0.6647, Generator Loss: 2.4376
D(x): 0.7976, D(G(z)): 0.2079
Epoch: [11/20], Batch Num: [1/600]
Discriminator Loss: 0.9287, Generator Loss: 2.0628
D(x): 0.7229, D(G(z)): 0.1963
Epoch: [11/20], Batch Num: [2/600]
Discriminator Loss: 0.8049, Generator Loss: 1.9645
D(x): 0.7593, D(G(z)): 0.2363
Epoch: [11/20], Batch Num: [3/600]
Discriminator Loss: 0.7259, Generator Loss: 1.8717
D(x): 0.8025, D(G(z)): 0.2272
Epoch: [11/20], Batch Num: [4/600]
Discriminator Loss: 0.9079, Generator Loss: 1.7935
D(x): 0.7554, D(G(z)): 0.2594
Epoch: [11/20], Batch Num: [5/600]
Discriminator Loss: 0.9061, Generator Loss: 1.9384
D(x): 0.7432, D(G(z)): 0.2650
Epoch: [11/20], Batch Num: [6/600]
Discriminator Loss: 1.0875, Generator Loss: 1.8146
D(x): 0.6992, D(G(z)): 0.3018
Epoch: [11/20], Batch Num: [7/600]
Discriminator Loss: 1.0357, Generator Loss: 1.8382
D(x): 0.7145, D(G(z)): 0.2875
Epoch: [11/20], Batch Num: [8/600]
Discriminator Loss: 0.9776, Generator Loss: 1.9375
D(x): 0.7095, D(G(z)): 0.2685
Epoch: [11/20], Batch Num: [9/600]
Discriminator Loss: 1.0010, Generator Loss: 1.6214
D(x): 0.6949, D(G(z)): 0.2771
Epoch: [11/20], Batch Num: [10/600]
Discriminator Loss: 0.8696, Generator Loss: 1.7506
D(x): 0.7595, D(G(z)): 0.2663
Epoch: [11/20], Batch Num: [11/600]
Discriminator Loss: 0.8471, Generator Loss: 1.7775
D(x): 0.7788, D(G(z)): 0.3153
Epoch: [11/20], Batch Num: [12/600]
Discriminator Loss: 0.8391, Generator Loss: 2.0692
D(x): 0.7645, D(G(z)): 0.2661
Epoch: [11/20], Batch Num: [13/600]
Discriminator Loss: 0.8732, Generator Loss: 2.0461
D(x): 0.7226, D(G(z)): 0.2525
Epoch: [11/20], Batch Num: [14/600]
Discriminator Loss: 0.8817, Generator Loss: 2.1936
D(x): 0.6905, D(G(z)): 0.2084
Epoch: [11/20], Batch Num: [15/600]
Discriminator Loss: 0.9096, Generator Loss: 1.7702
D(x): 0.6713, D(G(z)): 0.2314
Epoch: [11/20], Batch Num: [16/600]
Discriminator Loss: 0.6966, Generator Loss: 1.8527
D(x): 0.7503, D(G(z)): 0.2089
Epoch: [11/20], Batch Num: [17/600]
Discriminator Loss: 0.8157, Generator Loss: 1.6621
D(x): 0.8087, D(G(z)): 0.3114
Epoch: [11/20], Batch Num: [18/600]
Discriminator Loss: 0.5811, Generator Loss: 1.9393
D(x): 0.8508, D(G(z)): 0.2746
Epoch: [11/20], Batch Num: [19/600]
Discriminator Loss: 0.7039, Generator Loss: 2.1681
D(x): 0.8414, D(G(z)): 0.2697
Epoch: [11/20], Batch Num: [20/600]
Discriminator Loss: 0.6451, Generator Loss: 2.3185
D(x): 0.7730, D(G(z)): 0.1964
Epoch: [11/20], Batch Num: [21/600]
Discriminator Loss: 0.4880, Generator Loss: 2.7098
D(x): 0.8090, D(G(z)): 0.1602
Epoch: [11/20], Batch Num: [22/600]
Discriminator Loss: 0.6491, Generator Loss: 2.2954
D(x): 0.7456, D(G(z)): 0.1437
Epoch: [11/20], Batch Num: [23/600]
Discriminator Loss: 0.5473, Generator Loss: 2.3042
D(x): 0.7990, D(G(z)): 0.1504
Epoch: [11/20], Batch Num: [24/600]
Discriminator Loss: 0.4934, Generator Loss: 2.0648
D(x): 0.8012, D(G(z)): 0.1549
Epoch: [11/20], Batch Num: [25/600]
Discriminator Loss: 0.5262, Generator Loss: 2.0839
D(x): 0.8468, D(G(z)): 0.1874
Epoch: [11/20], Batch Num: [26/600]
Discriminator Loss: 0.4340, Generator Loss: 2.1401
D(x): 0.8911, D(G(z)): 0.1951
Epoch: [11/20], Batch Num: [27/600]
Discriminator Loss: 0.4101, Generator Loss: 2.2193
D(x): 0.8812, D(G(z)): 0.1779
Epoch: [11/20], Batch Num: [28/600]
Discriminator Loss: 0.4074, Generator Loss: 2.3702
D(x): 0.8951, D(G(z)): 0.1881
Epoch: [11/20], Batch Num: [29/600]
Discriminator Loss: 0.5613, Generator Loss: 2.8136
D(x): 0.8953, D(G(z)): 0.1904
Epoch: [11/20], Batch Num: [30/600]
Discriminator Loss: 0.5031, Generator Loss: 2.9159
D(x): 0.8299, D(G(z)): 0.1298
Epoch: [11/20], Batch Num: [31/600]
Discriminator Loss: 0.4779, Generator Loss: 3.1137
D(x): 0.8054, D(G(z)): 0.0980
Epoch: [11/20], Batch Num: [32/600]
Discriminator Loss: 0.4239, Generator Loss: 2.9393
D(x): 0.8191, D(G(z)): 0.0991
Epoch: [11/20], Batch Num: [33/600]
Discriminator Loss: 0.3654, Generator Loss: 2.5325
D(x): 0.8560, D(G(z)): 0.1068
Epoch: [11/20], Batch Num: [34/600]
Discriminator Loss: 0.4643, Generator Loss: 2.4304
D(x): 0.8696, D(G(z)): 0.1746
Epoch: [11/20], Batch Num: [35/600]
Discriminator Loss: 0.6391, Generator Loss: 2.2929
D(x): 0.8577, D(G(z)): 0.2143
Epoch: [11/20], Batch Num: [36/600]
Discriminator Loss: 0.5668, Generator Loss: 2.6735
D(x): 0.8956, D(G(z)): 0.2382
Epoch: [11/20], Batch Num: [37/600]
Discriminator Loss: 0.6022, Generator Loss: 2.9315
D(x): 0.8112, D(G(z)): 0.1524
Epoch: [11/20], Batch Num: [38/600]
Discriminator Loss: 0.6086, Generator Loss: 2.9625
D(x): 0.7733, D(G(z)): 0.1410
Epoch: [11/20], Batch Num: [39/600]
Discriminator Loss: 0.4830, Generator Loss: 2.8520
D(x): 0.8161, D(G(z)): 0.1333
Epoch: [11/20], Batch Num: [40/600]
Discriminator Loss: 0.6291, Generator Loss: 2.7695
D(x): 0.8045, D(G(z)): 0.1852
Epoch: [11/20], Batch Num: [41/600]
Discriminator Loss: 0.6322, Generator Loss: 2.3642
D(x): 0.8424, D(G(z)): 0.2084
Epoch: [11/20], Batch Num: [42/600]
Discriminator Loss: 0.6985, Generator Loss: 2.4340
D(x): 0.7835, D(G(z)): 0.1915
Epoch: [11/20], Batch Num: [43/600]
Discriminator Loss: 0.5541, Generator Loss: 2.3396
D(x): 0.8184, D(G(z)): 0.1999
Epoch: [11/20], Batch Num: [44/600]
Discriminator Loss: 0.6516, Generator Loss: 2.2116
D(x): 0.8072, D(G(z)): 0.2148
Epoch: [11/20], Batch Num: [45/600]
Discriminator Loss: 0.9225, Generator Loss: 2.4917
D(x): 0.8081, D(G(z)): 0.2855
Epoch: [11/20], Batch Num: [46/600]
Discriminator Loss: 0.9586, Generator Loss: 2.3786
D(x): 0.6816, D(G(z)): 0.2064
Epoch: [11/20], Batch Num: [47/600]
Discriminator Loss: 0.7444, Generator Loss: 2.3296
D(x): 0.7581, D(G(z)): 0.1956
Epoch: [11/20], Batch Num: [48/600]
Discriminator Loss: 0.8304, Generator Loss: 1.9851
D(x): 0.7536, D(G(z)): 0.1819
Epoch: [11/20], Batch Num: [49/600]
Discriminator Loss: 0.9201, Generator Loss: 1.7721
D(x): 0.7661, D(G(z)): 0.2771
Epoch: [11/20], Batch Num: [50/600]
Discriminator Loss: 0.9540, Generator Loss: 1.7261
D(x): 0.7825, D(G(z)): 0.2946
Epoch: [11/20], Batch Num: [51/600]
Discriminator Loss: 1.0995, Generator Loss: 2.0270
D(x): 0.7625, D(G(z)): 0.3307
Epoch: [11/20], Batch Num: [52/600]
Discriminator Loss: 0.8963, Generator Loss: 1.8508
D(x): 0.6893, D(G(z)): 0.2171
Epoch: [11/20], Batch Num: [53/600]
Discriminator Loss: 0.8767, Generator Loss: 1.9288
D(x): 0.7452, D(G(z)): 0.2695
Epoch: [11/20], Batch Num: [54/600]
Discriminator Loss: 0.8468, Generator Loss: 1.7825
D(x): 0.6958, D(G(z)): 0.2054
Epoch: [11/20], Batch Num: [55/600]
Discriminator Loss: 0.9011, Generator Loss: 1.6144
D(x): 0.7247, D(G(z)): 0.2660
Epoch: [11/20], Batch Num: [56/600]
Discriminator Loss: 0.9979, Generator Loss: 1.6809
D(x): 0.7722, D(G(z)): 0.3232
Epoch: [11/20], Batch Num: [57/600]
Discriminator Loss: 1.1628, Generator Loss: 1.6449
D(x): 0.7061, D(G(z)): 0.3519
Epoch: [11/20], Batch Num: [58/600]
Discriminator Loss: 1.0633, Generator Loss: 1.8086
D(x): 0.7023, D(G(z)): 0.3100
Epoch: [11/20], Batch Num: [59/600]
Discriminator Loss: 0.9123, Generator Loss: 1.7468
D(x): 0.6766, D(G(z)): 0.2280
Epoch: [11/20], Batch Num: [60/600]
Discriminator Loss: 1.1105, Generator Loss: 1.5726
D(x): 0.6173, D(G(z)): 0.2329
Epoch: [11/20], Batch Num: [61/600]
Discriminator Loss: 0.9041, Generator Loss: 1.5077
D(x): 0.6898, D(G(z)): 0.2448
Epoch: [11/20], Batch Num: [62/600]
Discriminator Loss: 1.0150, Generator Loss: 1.3615
D(x): 0.7275, D(G(z)): 0.3540
Epoch: [11/20], Batch Num: [63/600]
Discriminator Loss: 0.9018, Generator Loss: 1.3565
D(x): 0.7635, D(G(z)): 0.3361
Epoch: [11/20], Batch Num: [64/600]
Discriminator Loss: 0.8076, Generator Loss: 1.5652
D(x): 0.7822, D(G(z)): 0.3164
Epoch: [11/20], Batch Num: [65/600]
Discriminator Loss: 0.8866, Generator Loss: 1.8736
D(x): 0.7309, D(G(z)): 0.3111
Epoch: [11/20], Batch Num: [66/600]
Discriminator Loss: 0.7018, Generator Loss: 1.8892
D(x): 0.7225, D(G(z)): 0.2099
Epoch: [11/20], Batch Num: [67/600]
Discriminator Loss: 0.7917, Generator Loss: 1.9081
D(x): 0.7139, D(G(z)): 0.2119
Epoch: [11/20], Batch Num: [68/600]
Discriminator Loss: 0.7824, Generator Loss: 1.8656
D(x): 0.7177, D(G(z)): 0.2269
Epoch: [11/20], Batch Num: [69/600]
Discriminator Loss: 0.6807, Generator Loss: 1.7135
D(x): 0.7512, D(G(z)): 0.2015
Epoch: [11/20], Batch Num: [70/600]
Discriminator Loss: 0.5678, Generator Loss: 1.8685
D(x): 0.8138, D(G(z)): 0.2244
Epoch: [11/20], Batch Num: [71/600]
Discriminator Loss: 0.7273, Generator Loss: 1.7078
D(x): 0.7019, D(G(z)): 0.2137
Epoch: [11/20], Batch Num: [72/600]
Discriminator Loss: 0.6407, Generator Loss: 1.5405
D(x): 0.8048, D(G(z)): 0.2502
Epoch: [11/20], Batch Num: [73/600]
Discriminator Loss: 0.6139, Generator Loss: 1.5982
D(x): 0.8130, D(G(z)): 0.2690
Epoch: [11/20], Batch Num: [74/600]
Discriminator Loss: 0.6120, Generator Loss: 1.7126
D(x): 0.8283, D(G(z)): 0.2746
Epoch: [11/20], Batch Num: [75/600]
Discriminator Loss: 0.5270, Generator Loss: 1.8707
D(x): 0.8479, D(G(z)): 0.2417
Epoch: [11/20], Batch Num: [76/600]
Discriminator Loss: 0.4764, Generator Loss: 2.0657
D(x): 0.8196, D(G(z)): 0.1809
Epoch: [11/20], Batch Num: [77/600]
Discriminator Loss: 0.4514, Generator Loss: 2.0885
D(x): 0.8465, D(G(z)): 0.1958
Epoch: [11/20], Batch Num: [78/600]
Discriminator Loss: 0.4539, Generator Loss: 2.1095
D(x): 0.8257, D(G(z)): 0.1589
Epoch: [11/20], Batch Num: [79/600]
Discriminator Loss: 0.4279, Generator Loss: 2.1167
D(x): 0.8324, D(G(z)): 0.1404
Epoch: [11/20], Batch Num: [80/600]
Discriminator Loss: 0.4420, Generator Loss: 2.2303
D(x): 0.8656, D(G(z)): 0.1948
Epoch: [11/20], Batch Num: [81/600]
Discriminator Loss: 0.4275, Generator Loss: 2.1391
D(x): 0.8609, D(G(z)): 0.1718
Epoch: [11/20], Batch Num: [82/600]
Discriminator Loss: 0.4055, Generator Loss: 2.1513
D(x): 0.8787, D(G(z)): 0.1727
Epoch: [11/20], Batch Num: [83/600]
Discriminator Loss: 0.4290, Generator Loss: 2.1263
D(x): 0.8688, D(G(z)): 0.1816
Epoch: [11/20], Batch Num: [84/600]
Discriminator Loss: 0.3324, Generator Loss: 2.2267
D(x): 0.8920, D(G(z)): 0.1512
Epoch: [11/20], Batch Num: [85/600]
Discriminator Loss: 0.4221, Generator Loss: 2.4858
D(x): 0.8925, D(G(z)): 0.1856
Epoch: [11/20], Batch Num: [86/600]
Discriminator Loss: 0.4831, Generator Loss: 2.7493
D(x): 0.8426, D(G(z)): 0.1591
Epoch: [11/20], Batch Num: [87/600]
Discriminator Loss: 0.3198, Generator Loss: 2.5063
D(x): 0.8677, D(G(z)): 0.1151
Epoch: [11/20], Batch Num: [88/600]
Discriminator Loss: 0.4312, Generator Loss: 2.6645
D(x): 0.8543, D(G(z)): 0.1686
Epoch: [11/20], Batch Num: [89/600]
Discriminator Loss: 0.3552, Generator Loss: 2.6931
D(x): 0.8901, D(G(z)): 0.1503
Epoch: [11/20], Batch Num: [90/600]
Discriminator Loss: 0.3523, Generator Loss: 2.5598
D(x): 0.9044, D(G(z)): 0.1556
Epoch: [11/20], Batch Num: [91/600]
Discriminator Loss: 0.3462, Generator Loss: 2.6361
D(x): 0.9013, D(G(z)): 0.1615
Epoch: [11/20], Batch Num: [92/600]
Discriminator Loss: 0.4626, Generator Loss: 2.8781
D(x): 0.8431, D(G(z)): 0.1432
Epoch: [11/20], Batch Num: [93/600]
Discriminator Loss: 0.5104, Generator Loss: 2.7197
D(x): 0.8225, D(G(z)): 0.1404
Epoch: [11/20], Batch Num: [94/600]
Discriminator Loss: 0.4137, Generator Loss: 2.5499
D(x): 0.8507, D(G(z)): 0.1179
Epoch: [11/20], Batch Num: [95/600]
Discriminator Loss: 0.4851, Generator Loss: 2.1837
D(x): 0.8235, D(G(z)): 0.1596
Epoch: [11/20], Batch Num: [96/600]
Discriminator Loss: 0.3652, Generator Loss: 2.0755
D(x): 0.9055, D(G(z)): 0.1725
Epoch: [11/20], Batch Num: [97/600]
Discriminator Loss: 0.4824, Generator Loss: 2.2919
D(x): 0.9072, D(G(z)): 0.2316
Epoch: [11/20], Batch Num: [98/600]
Discriminator Loss: 0.5818, Generator Loss: 2.6348
D(x): 0.8512, D(G(z)): 0.2435
Epoch: [11/20], Batch Num: [99/600]
Discriminator Loss: 0.5259, Generator Loss: 2.8699
D(x): 0.7927, D(G(z)): 0.1229
Epoch: 11, Batch Num: [100/600]

Epoch: [11/20], Batch Num: [100/600]
Discriminator Loss: 0.7273, Generator Loss: 2.5740
D(x): 0.7382, D(G(z)): 0.1486
Epoch: [11/20], Batch Num: [101/600]
Discriminator Loss: 0.7016, Generator Loss: 2.3493
D(x): 0.8127, D(G(z)): 0.2153
Epoch: [11/20], Batch Num: [102/600]
Discriminator Loss: 0.7406, Generator Loss: 2.1240
D(x): 0.7974, D(G(z)): 0.2253
Epoch: [11/20], Batch Num: [103/600]
Discriminator Loss: 0.8578, Generator Loss: 1.9001
D(x): 0.7615, D(G(z)): 0.2362
Epoch: [11/20], Batch Num: [104/600]
Discriminator Loss: 0.7950, Generator Loss: 1.9040
D(x): 0.7790, D(G(z)): 0.2274
Epoch: [11/20], Batch Num: [105/600]
Discriminator Loss: 0.7353, Generator Loss: 1.8224
D(x): 0.8164, D(G(z)): 0.2516
Epoch: [11/20], Batch Num: [106/600]
Discriminator Loss: 0.7508, Generator Loss: 2.1929
D(x): 0.7885, D(G(z)): 0.2224
Epoch: [11/20], Batch Num: [107/600]
Discriminator Loss: 0.7776, Generator Loss: 2.2218
D(x): 0.7882, D(G(z)): 0.2229
Epoch: [11/20], Batch Num: [108/600]
Discriminator Loss: 0.8674, Generator Loss: 1.8576
D(x): 0.7508, D(G(z)): 0.2447
Epoch: [11/20], Batch Num: [109/600]
Discriminator Loss: 0.8494, Generator Loss: 2.0330
D(x): 0.7712, D(G(z)): 0.2512
Epoch: [11/20], Batch Num: [110/600]
Discriminator Loss: 0.9222, Generator Loss: 2.1181
D(x): 0.7550, D(G(z)): 0.2886
Epoch: [11/20], Batch Num: [111/600]
Discriminator Loss: 1.0289, Generator Loss: 2.0149
D(x): 0.6945, D(G(z)): 0.2579
Epoch: [11/20], Batch Num: [112/600]
Discriminator Loss: 0.8695, Generator Loss: 2.0437
D(x): 0.7381, D(G(z)): 0.2458
Epoch: [11/20], Batch Num: [113/600]
Discriminator Loss: 0.9103, Generator Loss: 1.9003
D(x): 0.7157, D(G(z)): 0.2337
Epoch: [11/20], Batch Num: [114/600]
Discriminator Loss: 0.9714, Generator Loss: 2.0781
D(x): 0.7616, D(G(z)): 0.3040
Epoch: [11/20], Batch Num: [115/600]
Discriminator Loss: 0.9829, Generator Loss: 1.7236
D(x): 0.7355, D(G(z)): 0.2771
Epoch: [11/20], Batch Num: [116/600]
Discriminator Loss: 0.7585, Generator Loss: 1.9328
D(x): 0.7924, D(G(z)): 0.2688
Epoch: [11/20], Batch Num: [117/600]
Discriminator Loss: 0.9295, Generator Loss: 2.0149
D(x): 0.7320, D(G(z)): 0.2805
Epoch: [11/20], Batch Num: [118/600]
Discriminator Loss: 0.7432, Generator Loss: 2.1875
D(x): 0.7431, D(G(z)): 0.1996
Epoch: [11/20], Batch Num: [119/600]
Discriminator Loss: 0.8529, Generator Loss: 2.2289
D(x): 0.7086, D(G(z)): 0.2191
Epoch: [11/20], Batch Num: [120/600]
Discriminator Loss: 0.8123, Generator Loss: 2.0925
D(x): 0.7044, D(G(z)): 0.2055
Epoch: [11/20], Batch Num: [121/600]
Discriminator Loss: 0.6798, Generator Loss: 1.8838
D(x): 0.7843, D(G(z)): 0.2069
Epoch: [11/20], Batch Num: [122/600]
Discriminator Loss: 0.6269, Generator Loss: 1.9322
D(x): 0.8244, D(G(z)): 0.2424
Epoch: [11/20], Batch Num: [123/600]
Discriminator Loss: 0.5397, Generator Loss: 1.9452
D(x): 0.8404, D(G(z)): 0.2016
Epoch: [11/20], Batch Num: [124/600]
Discriminator Loss: 0.6171, Generator Loss: 1.8735
D(x): 0.8257, D(G(z)): 0.2144
Epoch: [11/20], Batch Num: [125/600]
Discriminator Loss: 0.5948, Generator Loss: 2.1090
D(x): 0.8548, D(G(z)): 0.2449
Epoch: [11/20], Batch Num: [126/600]
Discriminator Loss: 0.6186, Generator Loss: 2.3453
D(x): 0.8451, D(G(z)): 0.2144
Epoch: [11/20], Batch Num: [127/600]
Discriminator Loss: 0.5330, Generator Loss: 2.5655
D(x): 0.8081, D(G(z)): 0.1702
Epoch: [11/20], Batch Num: [128/600]
Discriminator Loss: 0.6132, Generator Loss: 2.4753
D(x): 0.7674, D(G(z)): 0.1674
Epoch: [11/20], Batch Num: [129/600]
Discriminator Loss: 0.4840, Generator Loss: 2.3426
D(x): 0.8016, D(G(z)): 0.1257
Epoch: [11/20], Batch Num: [130/600]
Discriminator Loss: 0.4507, Generator Loss: 2.2564
D(x): 0.8465, D(G(z)): 0.1770
Epoch: [11/20], Batch Num: [131/600]
Discriminator Loss: 0.5454, Generator Loss: 2.0136
D(x): 0.8325, D(G(z)): 0.1878
Epoch: [11/20], Batch Num: [132/600]
Discriminator Loss: 0.4212, Generator Loss: 1.9745
D(x): 0.9038, D(G(z)): 0.2015
Epoch: [11/20], Batch Num: [133/600]
Discriminator Loss: 0.4426, Generator Loss: 1.9759
D(x): 0.9011, D(G(z)): 0.2115
Epoch: [11/20], Batch Num: [134/600]
Discriminator Loss: 0.4478, Generator Loss: 2.4403
D(x): 0.8744, D(G(z)): 0.1998
Epoch: [11/20], Batch Num: [135/600]
Discriminator Loss: 0.5544, Generator Loss: 2.4950
D(x): 0.8258, D(G(z)): 0.1835
Epoch: [11/20], Batch Num: [136/600]
Discriminator Loss: 0.4257, Generator Loss: 2.5979
D(x): 0.8609, D(G(z)): 0.1531
Epoch: [11/20], Batch Num: [137/600]
Discriminator Loss: 0.4080, Generator Loss: 2.4570
D(x): 0.8497, D(G(z)): 0.1440
Epoch: [11/20], Batch Num: [138/600]
Discriminator Loss: 0.3927, Generator Loss: 2.4108
D(x): 0.8605, D(G(z)): 0.1242
Epoch: [11/20], Batch Num: [139/600]
Discriminator Loss: 0.4712, Generator Loss: 2.4937
D(x): 0.8438, D(G(z)): 0.1596
Epoch: [11/20], Batch Num: [140/600]
Discriminator Loss: 0.6069, Generator Loss: 2.4583
D(x): 0.8501, D(G(z)): 0.2102
Epoch: [11/20], Batch Num: [141/600]
Discriminator Loss: 0.4507, Generator Loss: 2.5484
D(x): 0.8573, D(G(z)): 0.1824
Epoch: [11/20], Batch Num: [142/600]
Discriminator Loss: 0.5037, Generator Loss: 2.5468
D(x): 0.8455, D(G(z)): 0.1764
Epoch: [11/20], Batch Num: [143/600]
Discriminator Loss: 0.5428, Generator Loss: 2.3580
D(x): 0.7862, D(G(z)): 0.1352
Epoch: [11/20], Batch Num: [144/600]
Discriminator Loss: 0.5739, Generator Loss: 2.4939
D(x): 0.8466, D(G(z)): 0.1960
Epoch: [11/20], Batch Num: [145/600]
Discriminator Loss: 0.6642, Generator Loss: 2.4290
D(x): 0.7648, D(G(z)): 0.1637
Epoch: [11/20], Batch Num: [146/600]
Discriminator Loss: 0.6978, Generator Loss: 2.0877
D(x): 0.8388, D(G(z)): 0.2251
Epoch: [11/20], Batch Num: [147/600]
Discriminator Loss: 0.6451, Generator Loss: 2.2410
D(x): 0.8275, D(G(z)): 0.2317
Epoch: [11/20], Batch Num: [148/600]
Discriminator Loss: 0.7253, Generator Loss: 2.4385
D(x): 0.8083, D(G(z)): 0.2391
Epoch: [11/20], Batch Num: [149/600]
Discriminator Loss: 0.7248, Generator Loss: 2.4557
D(x): 0.7847, D(G(z)): 0.2052
Epoch: [11/20], Batch Num: [150/600]
Discriminator Loss: 0.7434, Generator Loss: 2.3119
D(x): 0.7332, D(G(z)): 0.1795
Epoch: [11/20], Batch Num: [151/600]
Discriminator Loss: 0.7431, Generator Loss: 1.9806
D(x): 0.7688, D(G(z)): 0.1963
Epoch: [11/20], Batch Num: [152/600]
Discriminator Loss: 0.8831, Generator Loss: 1.6591
D(x): 0.7541, D(G(z)): 0.2602
Epoch: [11/20], Batch Num: [153/600]
Discriminator Loss: 0.7998, Generator Loss: 1.6934
D(x): 0.8400, D(G(z)): 0.3274
Epoch: [11/20], Batch Num: [154/600]
Discriminator Loss: 0.8783, Generator Loss: 1.9882
D(x): 0.7831, D(G(z)): 0.2935
Epoch: [11/20], Batch Num: [155/600]
Discriminator Loss: 0.7340, Generator Loss: 2.3938
D(x): 0.8262, D(G(z)): 0.2645
Epoch: [11/20], Batch Num: [156/600]
Discriminator Loss: 0.8437, Generator Loss: 2.5385
D(x): 0.7103, D(G(z)): 0.1951
Epoch: [11/20], Batch Num: [157/600]
Discriminator Loss: 1.0176, Generator Loss: 2.1852
D(x): 0.6393, D(G(z)): 0.1627
Epoch: [11/20], Batch Num: [158/600]
Discriminator Loss: 1.0048, Generator Loss: 1.9565
D(x): 0.7109, D(G(z)): 0.2478
Epoch: [11/20], Batch Num: [159/600]
Discriminator Loss: 0.9816, Generator Loss: 1.5057
D(x): 0.7082, D(G(z)): 0.2736
Epoch: [11/20], Batch Num: [160/600]
Discriminator Loss: 0.7484, Generator Loss: 1.4545
D(x): 0.8038, D(G(z)): 0.3053
Epoch: [11/20], Batch Num: [161/600]
Discriminator Loss: 0.9943, Generator Loss: 1.7867
D(x): 0.7919, D(G(z)): 0.3546
Epoch: [11/20], Batch Num: [162/600]
Discriminator Loss: 0.8308, Generator Loss: 2.0870
D(x): 0.7689, D(G(z)): 0.2738
Epoch: [11/20], Batch Num: [163/600]
Discriminator Loss: 0.6886, Generator Loss: 2.2749
D(x): 0.7287, D(G(z)): 0.1757
Epoch: [11/20], Batch Num: [164/600]
Discriminator Loss: 0.7060, Generator Loss: 2.0228
D(x): 0.7115, D(G(z)): 0.1790
Epoch: [11/20], Batch Num: [165/600]
Discriminator Loss: 0.7956, Generator Loss: 2.0069
D(x): 0.6961, D(G(z)): 0.1715
Epoch: [11/20], Batch Num: [166/600]
Discriminator Loss: 0.6874, Generator Loss: 1.5764
D(x): 0.7808, D(G(z)): 0.2496
Epoch: [11/20], Batch Num: [167/600]
Discriminator Loss: 0.7120, Generator Loss: 1.5832
D(x): 0.8307, D(G(z)): 0.3104
Epoch: [11/20], Batch Num: [168/600]
Discriminator Loss: 0.5955, Generator Loss: 1.9274
D(x): 0.8412, D(G(z)): 0.2621
Epoch: [11/20], Batch Num: [169/600]
Discriminator Loss: 0.6661, Generator Loss: 2.2929
D(x): 0.8085, D(G(z)): 0.2612
Epoch: [11/20], Batch Num: [170/600]
Discriminator Loss: 0.6111, Generator Loss: 2.3909
D(x): 0.7762, D(G(z)): 0.1770
Epoch: [11/20], Batch Num: [171/600]
Discriminator Loss: 0.6591, Generator Loss: 2.5605
D(x): 0.7328, D(G(z)): 0.1540
Epoch: [11/20], Batch Num: [172/600]
Discriminator Loss: 0.7303, Generator Loss: 2.4573
D(x): 0.6864, D(G(z)): 0.1217
Epoch: [11/20], Batch Num: [173/600]
Discriminator Loss: 0.6167, Generator Loss: 1.6904
D(x): 0.7384, D(G(z)): 0.1657
Epoch: [11/20], Batch Num: [174/600]
Discriminator Loss: 0.5367, Generator Loss: 1.4542
D(x): 0.8743, D(G(z)): 0.2492
Epoch: [11/20], Batch Num: [175/600]
Discriminator Loss: 0.6219, Generator Loss: 1.8670
D(x): 0.9159, D(G(z)): 0.3246
Epoch: [11/20], Batch Num: [176/600]
Discriminator Loss: 0.5890, Generator Loss: 2.0709
D(x): 0.9042, D(G(z)): 0.2758
Epoch: [11/20], Batch Num: [177/600]
Discriminator Loss: 0.5453, Generator Loss: 2.8436
D(x): 0.8177, D(G(z)): 0.2022
Epoch: [11/20], Batch Num: [178/600]
Discriminator Loss: 0.6216, Generator Loss: 2.8909
D(x): 0.7172, D(G(z)): 0.1115
Epoch: [11/20], Batch Num: [179/600]
Discriminator Loss: 0.5850, Generator Loss: 2.6513
D(x): 0.7486, D(G(z)): 0.1117
Epoch: [11/20], Batch Num: [180/600]
Discriminator Loss: 0.4462, Generator Loss: 2.1390
D(x): 0.8111, D(G(z)): 0.1257
Epoch: [11/20], Batch Num: [181/600]
Discriminator Loss: 0.5014, Generator Loss: 1.8564
D(x): 0.8433, D(G(z)): 0.1722
Epoch: [11/20], Batch Num: [182/600]
Discriminator Loss: 0.4945, Generator Loss: 1.5903
D(x): 0.8873, D(G(z)): 0.2357
Epoch: [11/20], Batch Num: [183/600]
Discriminator Loss: 0.6063, Generator Loss: 1.8901
D(x): 0.8628, D(G(z)): 0.2577
Epoch: [11/20], Batch Num: [184/600]
Discriminator Loss: 0.6041, Generator Loss: 2.2415
D(x): 0.8673, D(G(z)): 0.2572
Epoch: [11/20], Batch Num: [185/600]
Discriminator Loss: 0.6336, Generator Loss: 2.6189
D(x): 0.8009, D(G(z)): 0.1827
Epoch: [11/20], Batch Num: [186/600]
Discriminator Loss: 0.5859, Generator Loss: 2.4096
D(x): 0.7642, D(G(z)): 0.1420
Epoch: [11/20], Batch Num: [187/600]
Discriminator Loss: 0.7932, Generator Loss: 2.1804
D(x): 0.7027, D(G(z)): 0.1605
Epoch: [11/20], Batch Num: [188/600]
Discriminator Loss: 0.5803, Generator Loss: 1.7328
D(x): 0.8096, D(G(z)): 0.1912
Epoch: [11/20], Batch Num: [189/600]
Discriminator Loss: 0.6009, Generator Loss: 1.9242
D(x): 0.8710, D(G(z)): 0.2451
Epoch: [11/20], Batch Num: [190/600]
Discriminator Loss: 0.7253, Generator Loss: 2.0726
D(x): 0.8229, D(G(z)): 0.2597
Epoch: [11/20], Batch Num: [191/600]
Discriminator Loss: 0.6881, Generator Loss: 2.3476
D(x): 0.8000, D(G(z)): 0.2186
Epoch: [11/20], Batch Num: [192/600]
Discriminator Loss: 0.6350, Generator Loss: 2.3368
D(x): 0.7487, D(G(z)): 0.1604
Epoch: [11/20], Batch Num: [193/600]
Discriminator Loss: 0.6086, Generator Loss: 2.3433
D(x): 0.7833, D(G(z)): 0.1736
Epoch: [11/20], Batch Num: [194/600]
Discriminator Loss: 0.7142, Generator Loss: 1.9705
D(x): 0.7564, D(G(z)): 0.1942
Epoch: [11/20], Batch Num: [195/600]
Discriminator Loss: 0.7050, Generator Loss: 1.8082
D(x): 0.8100, D(G(z)): 0.2642
Epoch: [11/20], Batch Num: [196/600]
Discriminator Loss: 0.7815, Generator Loss: 1.9308
D(x): 0.8077, D(G(z)): 0.2745
Epoch: [11/20], Batch Num: [197/600]
Discriminator Loss: 0.7357, Generator Loss: 2.0639
D(x): 0.7524, D(G(z)): 0.2241
Epoch: [11/20], Batch Num: [198/600]
Discriminator Loss: 0.6640, Generator Loss: 2.3083
D(x): 0.8030, D(G(z)): 0.2235
Epoch: [11/20], Batch Num: [199/600]
Discriminator Loss: 0.8673, Generator Loss: 2.1445
D(x): 0.7024, D(G(z)): 0.2127
Epoch: 11, Batch Num: [200/600]

Epoch: [11/20], Batch Num: [200/600]
Discriminator Loss: 0.7082, Generator Loss: 1.8718
D(x): 0.7541, D(G(z)): 0.1971
Epoch: [11/20], Batch Num: [201/600]
Discriminator Loss: 0.8504, Generator Loss: 1.6625
D(x): 0.7399, D(G(z)): 0.2353
Epoch: [11/20], Batch Num: [202/600]
Discriminator Loss: 0.7066, Generator Loss: 1.7424
D(x): 0.8174, D(G(z)): 0.2749
Epoch: [11/20], Batch Num: [203/600]
Discriminator Loss: 0.8291, Generator Loss: 2.0351
D(x): 0.7673, D(G(z)): 0.2755
Epoch: [11/20], Batch Num: [204/600]
Discriminator Loss: 0.7754, Generator Loss: 2.1046
D(x): 0.7447, D(G(z)): 0.2074
Epoch: [11/20], Batch Num: [205/600]
Discriminator Loss: 0.9337, Generator Loss: 1.5807
D(x): 0.7158, D(G(z)): 0.2265
Epoch: [11/20], Batch Num: [206/600]
Discriminator Loss: 0.6962, Generator Loss: 1.8472
D(x): 0.8049, D(G(z)): 0.2571
Epoch: [11/20], Batch Num: [207/600]
Discriminator Loss: 0.8216, Generator Loss: 2.0347
D(x): 0.7528, D(G(z)): 0.2548
Epoch: [11/20], Batch Num: [208/600]
Discriminator Loss: 0.8669, Generator Loss: 2.0172
D(x): 0.7223, D(G(z)): 0.2270
Epoch: [11/20], Batch Num: [209/600]
Discriminator Loss: 0.7848, Generator Loss: 1.8838
D(x): 0.7424, D(G(z)): 0.2462
Epoch: [11/20], Batch Num: [210/600]
Discriminator Loss: 0.8542, Generator Loss: 1.8519
D(x): 0.7135, D(G(z)): 0.2430
Epoch: [11/20], Batch Num: [211/600]
Discriminator Loss: 0.8429, Generator Loss: 1.8190
D(x): 0.7631, D(G(z)): 0.2711
Epoch: [11/20], Batch Num: [212/600]
Discriminator Loss: 0.6428, Generator Loss: 1.9074
D(x): 0.7892, D(G(z)): 0.2264
Epoch: [11/20], Batch Num: [213/600]
Discriminator Loss: 0.7459, Generator Loss: 2.1200
D(x): 0.8088, D(G(z)): 0.2887
Epoch: [11/20], Batch Num: [214/600]
Discriminator Loss: 0.6725, Generator Loss: 2.3135
D(x): 0.7792, D(G(z)): 0.1921
Epoch: [11/20], Batch Num: [215/600]
Discriminator Loss: 0.7637, Generator Loss: 2.1758
D(x): 0.7057, D(G(z)): 0.1690
Epoch: [11/20], Batch Num: [216/600]
Discriminator Loss: 0.6800, Generator Loss: 1.8648
D(x): 0.7632, D(G(z)): 0.2032
Epoch: [11/20], Batch Num: [217/600]
Discriminator Loss: 0.6437, Generator Loss: 1.7286
D(x): 0.7765, D(G(z)): 0.2022
Epoch: [11/20], Batch Num: [218/600]
Discriminator Loss: 0.7949, Generator Loss: 1.7670
D(x): 0.7850, D(G(z)): 0.2892
Epoch: [11/20], Batch Num: [219/600]
Discriminator Loss: 0.6392, Generator Loss: 1.7885
D(x): 0.8431, D(G(z)): 0.2714
Epoch: [11/20], Batch Num: [220/600]
Discriminator Loss: 0.8375, Generator Loss: 1.8602
D(x): 0.7727, D(G(z)): 0.2783
Epoch: [11/20], Batch Num: [221/600]
Discriminator Loss: 0.7263, Generator Loss: 2.1034
D(x): 0.7889, D(G(z)): 0.2474
Epoch: [11/20], Batch Num: [222/600]
Discriminator Loss: 0.7723, Generator Loss: 2.2584
D(x): 0.7131, D(G(z)): 0.1967
Epoch: [11/20], Batch Num: [223/600]
Discriminator Loss: 0.6846, Generator Loss: 1.9120
D(x): 0.7481, D(G(z)): 0.1994
Epoch: [11/20], Batch Num: [224/600]
Discriminator Loss: 0.7530, Generator Loss: 1.9741
D(x): 0.7920, D(G(z)): 0.2561
Epoch: [11/20], Batch Num: [225/600]
Discriminator Loss: 0.7771, Generator Loss: 2.1938
D(x): 0.7835, D(G(z)): 0.2437
Epoch: [11/20], Batch Num: [226/600]
Discriminator Loss: 0.7445, Generator Loss: 2.2228
D(x): 0.7550, D(G(z)): 0.1860
Epoch: [11/20], Batch Num: [227/600]
Discriminator Loss: 0.6423, Generator Loss: 2.2327
D(x): 0.7803, D(G(z)): 0.2171
Epoch: [11/20], Batch Num: [228/600]
Discriminator Loss: 0.6328, Generator Loss: 1.9399
D(x): 0.7834, D(G(z)): 0.2018
Epoch: [11/20], Batch Num: [229/600]
Discriminator Loss: 0.8121, Generator Loss: 1.8353
D(x): 0.8084, D(G(z)): 0.2968
Epoch: [11/20], Batch Num: [230/600]
Discriminator Loss: 0.6112, Generator Loss: 2.0538
D(x): 0.8063, D(G(z)): 0.2113
Epoch: [11/20], Batch Num: [231/600]
Discriminator Loss: 0.6151, Generator Loss: 1.9657
D(x): 0.7491, D(G(z)): 0.1660
Epoch: [11/20], Batch Num: [232/600]
Discriminator Loss: 0.7219, Generator Loss: 1.7443
D(x): 0.7725, D(G(z)): 0.2472
Epoch: [11/20], Batch Num: [233/600]
Discriminator Loss: 0.7959, Generator Loss: 1.9130
D(x): 0.7371, D(G(z)): 0.2412
Epoch: [11/20], Batch Num: [234/600]
Discriminator Loss: 0.7565, Generator Loss: 1.7874
D(x): 0.8009, D(G(z)): 0.2932
Epoch: [11/20], Batch Num: [235/600]
Discriminator Loss: 0.8609, Generator Loss: 2.0246
D(x): 0.7645, D(G(z)): 0.2657
Epoch: [11/20], Batch Num: [236/600]
Discriminator Loss: 0.7111, Generator Loss: 2.1250
D(x): 0.7649, D(G(z)): 0.2187
Epoch: [11/20], Batch Num: [237/600]
Discriminator Loss: 0.7919, Generator Loss: 2.1002
D(x): 0.7429, D(G(z)): 0.2269
Epoch: [11/20], Batch Num: [238/600]
Discriminator Loss: 0.7098, Generator Loss: 2.1160
D(x): 0.7442, D(G(z)): 0.1931
Epoch: [11/20], Batch Num: [239/600]
Discriminator Loss: 0.8995, Generator Loss: 1.8979
D(x): 0.6992, D(G(z)): 0.2250
Epoch: [11/20], Batch Num: [240/600]
Discriminator Loss: 0.7404, Generator Loss: 1.7140
D(x): 0.7908, D(G(z)): 0.2674
Epoch: [11/20], Batch Num: [241/600]
Discriminator Loss: 0.7497, Generator Loss: 1.9003
D(x): 0.7955, D(G(z)): 0.2643
Epoch: [11/20], Batch Num: [242/600]
Discriminator Loss: 0.8232, Generator Loss: 1.8404
D(x): 0.7435, D(G(z)): 0.2468
Epoch: [11/20], Batch Num: [243/600]
Discriminator Loss: 0.8109, Generator Loss: 1.8529
D(x): 0.7568, D(G(z)): 0.2490
Epoch: [11/20], Batch Num: [244/600]
Discriminator Loss: 0.8961, Generator Loss: 1.8027
D(x): 0.6884, D(G(z)): 0.2178
Epoch: [11/20], Batch Num: [245/600]
Discriminator Loss: 0.8839, Generator Loss: 2.0564
D(x): 0.7215, D(G(z)): 0.2503
Epoch: [11/20], Batch Num: [246/600]
Discriminator Loss: 0.8609, Generator Loss: 1.9128
D(x): 0.7340, D(G(z)): 0.2412
Epoch: [11/20], Batch Num: [247/600]
Discriminator Loss: 0.7509, Generator Loss: 1.8936
D(x): 0.7905, D(G(z)): 0.2668
Epoch: [11/20], Batch Num: [248/600]
Discriminator Loss: 0.8356, Generator Loss: 2.1736
D(x): 0.7489, D(G(z)): 0.2628
Epoch: [11/20], Batch Num: [249/600]
Discriminator Loss: 0.8291, Generator Loss: 1.9223
D(x): 0.7157, D(G(z)): 0.2463
Epoch: [11/20], Batch Num: [250/600]
Discriminator Loss: 0.8088, Generator Loss: 1.9177
D(x): 0.7296, D(G(z)): 0.2235
Epoch: [11/20], Batch Num: [251/600]
Discriminator Loss: 0.7658, Generator Loss: 1.8702
D(x): 0.7541, D(G(z)): 0.2494
Epoch: [11/20], Batch Num: [252/600]
Discriminator Loss: 0.9216, Generator Loss: 1.8140
D(x): 0.6902, D(G(z)): 0.2775
Epoch: [11/20], Batch Num: [253/600]
Discriminator Loss: 0.8943, Generator Loss: 1.7760
D(x): 0.7329, D(G(z)): 0.2924
Epoch: [11/20], Batch Num: [254/600]
Discriminator Loss: 0.9515, Generator Loss: 1.9396
D(x): 0.7575, D(G(z)): 0.3251
Epoch: [11/20], Batch Num: [255/600]
Discriminator Loss: 0.8831, Generator Loss: 1.8696
D(x): 0.7417, D(G(z)): 0.2794
Epoch: [11/20], Batch Num: [256/600]
Discriminator Loss: 0.8160, Generator Loss: 2.1945
D(x): 0.7819, D(G(z)): 0.2756
Epoch: [11/20], Batch Num: [257/600]
Discriminator Loss: 0.7358, Generator Loss: 2.1111
D(x): 0.7105, D(G(z)): 0.2041
Epoch: [11/20], Batch Num: [258/600]
Discriminator Loss: 0.9204, Generator Loss: 2.0058
D(x): 0.6928, D(G(z)): 0.2399
Epoch: [11/20], Batch Num: [259/600]
Discriminator Loss: 0.8291, Generator Loss: 1.8568
D(x): 0.6988, D(G(z)): 0.2268
Epoch: [11/20], Batch Num: [260/600]
Discriminator Loss: 0.8604, Generator Loss: 1.5766
D(x): 0.7317, D(G(z)): 0.2678
Epoch: [11/20], Batch Num: [261/600]
Discriminator Loss: 0.8755, Generator Loss: 1.7845
D(x): 0.7955, D(G(z)): 0.3543
Epoch: [11/20], Batch Num: [262/600]
Discriminator Loss: 0.6545, Generator Loss: 1.8923
D(x): 0.8460, D(G(z)): 0.3135
Epoch: [11/20], Batch Num: [263/600]
Discriminator Loss: 0.7714, Generator Loss: 1.9763
D(x): 0.7479, D(G(z)): 0.2479
Epoch: [11/20], Batch Num: [264/600]
Discriminator Loss: 0.7013, Generator Loss: 2.0556
D(x): 0.7258, D(G(z)): 0.1823
Epoch: [11/20], Batch Num: [265/600]
Discriminator Loss: 0.8861, Generator Loss: 1.9962
D(x): 0.6507, D(G(z)): 0.1834
Epoch: [11/20], Batch Num: [266/600]
Discriminator Loss: 0.7089, Generator Loss: 1.7763
D(x): 0.7758, D(G(z)): 0.2455
Epoch: [11/20], Batch Num: [267/600]
Discriminator Loss: 0.8545, Generator Loss: 1.8334
D(x): 0.7657, D(G(z)): 0.3003
Epoch: [11/20], Batch Num: [268/600]
Discriminator Loss: 0.7609, Generator Loss: 1.8516
D(x): 0.8267, D(G(z)): 0.3056
Epoch: [11/20], Batch Num: [269/600]
Discriminator Loss: 0.9852, Generator Loss: 2.0285
D(x): 0.6851, D(G(z)): 0.2700
Epoch: [11/20], Batch Num: [270/600]
Discriminator Loss: 0.8597, Generator Loss: 2.1332
D(x): 0.6940, D(G(z)): 0.2186
Epoch: [11/20], Batch Num: [271/600]
Discriminator Loss: 0.9227, Generator Loss: 1.8853
D(x): 0.7065, D(G(z)): 0.2310
Epoch: [11/20], Batch Num: [272/600]
Discriminator Loss: 0.9610, Generator Loss: 1.5793
D(x): 0.7023, D(G(z)): 0.2796
Epoch: [11/20], Batch Num: [273/600]
Discriminator Loss: 0.8487, Generator Loss: 1.6182
D(x): 0.7860, D(G(z)): 0.2977
Epoch: [11/20], Batch Num: [274/600]
Discriminator Loss: 0.7219, Generator Loss: 1.7746
D(x): 0.8216, D(G(z)): 0.3038
Epoch: [11/20], Batch Num: [275/600]
Discriminator Loss: 0.7827, Generator Loss: 2.1419
D(x): 0.8198, D(G(z)): 0.3107
Epoch: [11/20], Batch Num: [276/600]
Discriminator Loss: 0.7762, Generator Loss: 2.1171
D(x): 0.7071, D(G(z)): 0.1920
Epoch: [11/20], Batch Num: [277/600]
Discriminator Loss: 0.8931, Generator Loss: 2.0471
D(x): 0.6679, D(G(z)): 0.2049
Epoch: [11/20], Batch Num: [278/600]
Discriminator Loss: 0.8001, Generator Loss: 2.0193
D(x): 0.7287, D(G(z)): 0.2601
Epoch: [11/20], Batch Num: [279/600]
Discriminator Loss: 0.7738, Generator Loss: 1.7446
D(x): 0.7705, D(G(z)): 0.2576
Epoch: [11/20], Batch Num: [280/600]
Discriminator Loss: 0.8687, Generator Loss: 1.8734
D(x): 0.7217, D(G(z)): 0.2451
Epoch: [11/20], Batch Num: [281/600]
Discriminator Loss: 0.7203, Generator Loss: 1.7321
D(x): 0.7781, D(G(z)): 0.2606
Epoch: [11/20], Batch Num: [282/600]
Discriminator Loss: 0.6553, Generator Loss: 1.8625
D(x): 0.8226, D(G(z)): 0.2674
Epoch: [11/20], Batch Num: [283/600]
Discriminator Loss: 0.8155, Generator Loss: 1.8996
D(x): 0.7468, D(G(z)): 0.2663
Epoch: [11/20], Batch Num: [284/600]
Discriminator Loss: 0.6834, Generator Loss: 2.3522
D(x): 0.7800, D(G(z)): 0.2248
Epoch: [11/20], Batch Num: [285/600]
Discriminator Loss: 0.7530, Generator Loss: 2.1396
D(x): 0.7190, D(G(z)): 0.1979
Epoch: [11/20], Batch Num: [286/600]
Discriminator Loss: 0.7930, Generator Loss: 2.2475
D(x): 0.7343, D(G(z)): 0.2266
Epoch: [11/20], Batch Num: [287/600]
Discriminator Loss: 0.7859, Generator Loss: 2.1213
D(x): 0.7774, D(G(z)): 0.2643
Epoch: [11/20], Batch Num: [288/600]
Discriminator Loss: 0.6364, Generator Loss: 2.0797
D(x): 0.8119, D(G(z)): 0.2317
Epoch: [11/20], Batch Num: [289/600]
Discriminator Loss: 0.6220, Generator Loss: 1.9958
D(x): 0.7879, D(G(z)): 0.2269
Epoch: [11/20], Batch Num: [290/600]
Discriminator Loss: 0.6538, Generator Loss: 2.1703
D(x): 0.7633, D(G(z)): 0.2021
Epoch: [11/20], Batch Num: [291/600]
Discriminator Loss: 0.7842, Generator Loss: 1.9857
D(x): 0.7129, D(G(z)): 0.2169
Epoch: [11/20], Batch Num: [292/600]
Discriminator Loss: 0.7068, Generator Loss: 2.0462
D(x): 0.7626, D(G(z)): 0.2232
Epoch: [11/20], Batch Num: [293/600]
Discriminator Loss: 0.7538, Generator Loss: 2.1055
D(x): 0.8303, D(G(z)): 0.2792
Epoch: [11/20], Batch Num: [294/600]
Discriminator Loss: 0.6780, Generator Loss: 2.2822
D(x): 0.8162, D(G(z)): 0.2409
Epoch: [11/20], Batch Num: [295/600]
Discriminator Loss: 0.6746, Generator Loss: 2.3747
D(x): 0.7903, D(G(z)): 0.2394
Epoch: [11/20], Batch Num: [296/600]
Discriminator Loss: 0.6687, Generator Loss: 2.4578
D(x): 0.7759, D(G(z)): 0.1979
Epoch: [11/20], Batch Num: [297/600]
Discriminator Loss: 0.5158, Generator Loss: 2.3285
D(x): 0.8164, D(G(z)): 0.1836
Epoch: [11/20], Batch Num: [298/600]
Discriminator Loss: 0.7309, Generator Loss: 2.1869
D(x): 0.7671, D(G(z)): 0.2235
Epoch: [11/20], Batch Num: [299/600]
Discriminator Loss: 0.6954, Generator Loss: 2.1637
D(x): 0.7552, D(G(z)): 0.1849
Epoch: 11, Batch Num: [300/600]

Epoch: [11/20], Batch Num: [300/600]
Discriminator Loss: 0.5531, Generator Loss: 2.0452
D(x): 0.8493, D(G(z)): 0.2140
Epoch: [11/20], Batch Num: [301/600]
Discriminator Loss: 0.7822, Generator Loss: 1.8982
D(x): 0.7684, D(G(z)): 0.2493
Epoch: [11/20], Batch Num: [302/600]
Discriminator Loss: 0.8508, Generator Loss: 1.9826
D(x): 0.7713, D(G(z)): 0.2898
Epoch: [11/20], Batch Num: [303/600]
Discriminator Loss: 0.7893, Generator Loss: 2.4055
D(x): 0.8047, D(G(z)): 0.2718
Epoch: [11/20], Batch Num: [304/600]
Discriminator Loss: 0.7822, Generator Loss: 2.0308
D(x): 0.7293, D(G(z)): 0.2083
Epoch: [11/20], Batch Num: [305/600]
Discriminator Loss: 0.8537, Generator Loss: 2.1334
D(x): 0.7094, D(G(z)): 0.2284
Epoch: [11/20], Batch Num: [306/600]
Discriminator Loss: 0.8059, Generator Loss: 1.9263
D(x): 0.7332, D(G(z)): 0.2356
Epoch: [11/20], Batch Num: [307/600]
Discriminator Loss: 0.8532, Generator Loss: 1.8573
D(x): 0.7687, D(G(z)): 0.2828
Epoch: [11/20], Batch Num: [308/600]
Discriminator Loss: 0.9715, Generator Loss: 1.7226
D(x): 0.7141, D(G(z)): 0.2857
Epoch: [11/20], Batch Num: [309/600]
Discriminator Loss: 1.1368, Generator Loss: 1.9462
D(x): 0.6650, D(G(z)): 0.3416
Epoch: [11/20], Batch Num: [310/600]
Discriminator Loss: 1.1365, Generator Loss: 1.8668
D(x): 0.7061, D(G(z)): 0.3079
Epoch: [11/20], Batch Num: [311/600]
Discriminator Loss: 0.9636, Generator Loss: 1.7882
D(x): 0.6747, D(G(z)): 0.2389
Epoch: [11/20], Batch Num: [312/600]
Discriminator Loss: 1.0961, Generator Loss: 1.5543
D(x): 0.6859, D(G(z)): 0.3078
Epoch: [11/20], Batch Num: [313/600]
Discriminator Loss: 1.0167, Generator Loss: 1.6912
D(x): 0.6934, D(G(z)): 0.2800
Epoch: [11/20], Batch Num: [314/600]
Discriminator Loss: 1.0118, Generator Loss: 1.6723
D(x): 0.7328, D(G(z)): 0.2894
Epoch: [11/20], Batch Num: [315/600]
Discriminator Loss: 1.1332, Generator Loss: 1.6336
D(x): 0.6616, D(G(z)): 0.2922
Epoch: [11/20], Batch Num: [316/600]
Discriminator Loss: 1.0701, Generator Loss: 1.7057
D(x): 0.7051, D(G(z)): 0.2999
Epoch: [11/20], Batch Num: [317/600]
Discriminator Loss: 1.1276, Generator Loss: 1.8774
D(x): 0.6667, D(G(z)): 0.3161
Epoch: [11/20], Batch Num: [318/600]
Discriminator Loss: 1.1119, Generator Loss: 1.6281
D(x): 0.6281, D(G(z)): 0.2740
Epoch: [11/20], Batch Num: [319/600]
Discriminator Loss: 0.9334, Generator Loss: 1.6853
D(x): 0.7259, D(G(z)): 0.2877
Epoch: [11/20], Batch Num: [320/600]
Discriminator Loss: 1.0581, Generator Loss: 1.8833
D(x): 0.6947, D(G(z)): 0.2836
Epoch: [11/20], Batch Num: [321/600]
Discriminator Loss: 0.9065, Generator Loss: 1.6927
D(x): 0.7017, D(G(z)): 0.2677
Epoch: [11/20], Batch Num: [322/600]
Discriminator Loss: 0.8569, Generator Loss: 1.8604
D(x): 0.7056, D(G(z)): 0.2607
Epoch: [11/20], Batch Num: [323/600]
Discriminator Loss: 0.9106, Generator Loss: 1.6661
D(x): 0.7023, D(G(z)): 0.2361
Epoch: [11/20], Batch Num: [324/600]
Discriminator Loss: 0.8838, Generator Loss: 1.9156
D(x): 0.7270, D(G(z)): 0.2741
Epoch: [11/20], Batch Num: [325/600]
Discriminator Loss: 0.8871, Generator Loss: 1.6361
D(x): 0.7144, D(G(z)): 0.2678
Epoch: [11/20], Batch Num: [326/600]
Discriminator Loss: 0.9231, Generator Loss: 1.7748
D(x): 0.7340, D(G(z)): 0.2944
Epoch: [11/20], Batch Num: [327/600]
Discriminator Loss: 0.8337, Generator Loss: 1.9124
D(x): 0.7151, D(G(z)): 0.2775
Epoch: [11/20], Batch Num: [328/600]
Discriminator Loss: 0.8159, Generator Loss: 1.6467
D(x): 0.7071, D(G(z)): 0.2609
Epoch: [11/20], Batch Num: [329/600]
Discriminator Loss: 0.8155, Generator Loss: 1.8835
D(x): 0.7552, D(G(z)): 0.2867
Epoch: [11/20], Batch Num: [330/600]
Discriminator Loss: 0.8736, Generator Loss: 1.9142
D(x): 0.7322, D(G(z)): 0.2787
Epoch: [11/20], Batch Num: [331/600]
Discriminator Loss: 0.8797, Generator Loss: 2.1288
D(x): 0.6935, D(G(z)): 0.2489
Epoch: [11/20], Batch Num: [332/600]
Discriminator Loss: 0.7655, Generator Loss: 1.8286
D(x): 0.7415, D(G(z)): 0.2489
Epoch: [11/20], Batch Num: [333/600]
Discriminator Loss: 0.7365, Generator Loss: 1.8576
D(x): 0.7495, D(G(z)): 0.2458
Epoch: [11/20], Batch Num: [334/600]
Discriminator Loss: 0.7834, Generator Loss: 1.8790
D(x): 0.7227, D(G(z)): 0.2388
Epoch: [11/20], Batch Num: [335/600]
Discriminator Loss: 0.5969, Generator Loss: 1.8789
D(x): 0.7948, D(G(z)): 0.2109
Epoch: [11/20], Batch Num: [336/600]
Discriminator Loss: 0.7319, Generator Loss: 1.7637
D(x): 0.7544, D(G(z)): 0.2508
Epoch: [11/20], Batch Num: [337/600]
Discriminator Loss: 0.6932, Generator Loss: 1.8527
D(x): 0.7744, D(G(z)): 0.2459
Epoch: [11/20], Batch Num: [338/600]
Discriminator Loss: 0.7107, Generator Loss: 1.7437
D(x): 0.7496, D(G(z)): 0.2386
Epoch: [11/20], Batch Num: [339/600]
Discriminator Loss: 0.5134, Generator Loss: 1.8988
D(x): 0.8502, D(G(z)): 0.2271
Epoch: [11/20], Batch Num: [340/600]
Discriminator Loss: 0.5274, Generator Loss: 2.0794
D(x): 0.8079, D(G(z)): 0.1984
Epoch: [11/20], Batch Num: [341/600]
Discriminator Loss: 0.5497, Generator Loss: 2.0959
D(x): 0.8231, D(G(z)): 0.2361
Epoch: [11/20], Batch Num: [342/600]
Discriminator Loss: 0.6219, Generator Loss: 2.2426
D(x): 0.7743, D(G(z)): 0.1925
Epoch: [11/20], Batch Num: [343/600]
Discriminator Loss: 0.7627, Generator Loss: 2.0033
D(x): 0.7313, D(G(z)): 0.2281
Epoch: [11/20], Batch Num: [344/600]
Discriminator Loss: 0.5583, Generator Loss: 1.8390
D(x): 0.8109, D(G(z)): 0.2136
Epoch: [11/20], Batch Num: [345/600]
Discriminator Loss: 0.6643, Generator Loss: 2.1912
D(x): 0.8537, D(G(z)): 0.2719
Epoch: [11/20], Batch Num: [346/600]
Discriminator Loss: 0.6811, Generator Loss: 2.2307
D(x): 0.8238, D(G(z)): 0.2457
Epoch: [11/20], Batch Num: [347/600]
Discriminator Loss: 0.8984, Generator Loss: 2.2531
D(x): 0.7149, D(G(z)): 0.2441
Epoch: [11/20], Batch Num: [348/600]
Discriminator Loss: 0.7225, Generator Loss: 2.2670
D(x): 0.8112, D(G(z)): 0.2615
Epoch: [11/20], Batch Num: [349/600]
Discriminator Loss: 0.8401, Generator Loss: 2.3572
D(x): 0.7081, D(G(z)): 0.2153
Epoch: [11/20], Batch Num: [350/600]
Discriminator Loss: 0.8726, Generator Loss: 2.0701
D(x): 0.7202, D(G(z)): 0.2012
Epoch: [11/20], Batch Num: [351/600]
Discriminator Loss: 1.0830, Generator Loss: 1.9583
D(x): 0.7526, D(G(z)): 0.3259
Epoch: [11/20], Batch Num: [352/600]
Discriminator Loss: 0.8917, Generator Loss: 1.7738
D(x): 0.7245, D(G(z)): 0.2530
Epoch: [11/20], Batch Num: [353/600]
Discriminator Loss: 0.8565, Generator Loss: 1.7432
D(x): 0.7965, D(G(z)): 0.3143
Epoch: [11/20], Batch Num: [354/600]
Discriminator Loss: 0.7438, Generator Loss: 1.7550
D(x): 0.7557, D(G(z)): 0.2516
Epoch: [11/20], Batch Num: [355/600]
Discriminator Loss: 0.7104, Generator Loss: 1.9246
D(x): 0.8030, D(G(z)): 0.2773
Epoch: [11/20], Batch Num: [356/600]
Discriminator Loss: 0.8137, Generator Loss: 2.0127
D(x): 0.7407, D(G(z)): 0.2703
Epoch: [11/20], Batch Num: [357/600]
Discriminator Loss: 1.1034, Generator Loss: 2.0041
D(x): 0.6841, D(G(z)): 0.2759
Epoch: [11/20], Batch Num: [358/600]
Discriminator Loss: 1.0020, Generator Loss: 1.7087
D(x): 0.6657, D(G(z)): 0.2435
Epoch: [11/20], Batch Num: [359/600]
Discriminator Loss: 0.9904, Generator Loss: 1.5141
D(x): 0.6806, D(G(z)): 0.2702
Epoch: [11/20], Batch Num: [360/600]
Discriminator Loss: 0.9735, Generator Loss: 1.3827
D(x): 0.7570, D(G(z)): 0.3384
Epoch: [11/20], Batch Num: [361/600]
Discriminator Loss: 1.0083, Generator Loss: 1.6017
D(x): 0.7466, D(G(z)): 0.3537
Epoch: [11/20], Batch Num: [362/600]
Discriminator Loss: 0.7677, Generator Loss: 1.6816
D(x): 0.7718, D(G(z)): 0.2809
Epoch: [11/20], Batch Num: [363/600]
Discriminator Loss: 0.8532, Generator Loss: 1.6557
D(x): 0.7060, D(G(z)): 0.2443
Epoch: [11/20], Batch Num: [364/600]
Discriminator Loss: 0.9269, Generator Loss: 1.8053
D(x): 0.6705, D(G(z)): 0.2487
Epoch: [11/20], Batch Num: [365/600]
Discriminator Loss: 0.9179, Generator Loss: 1.6138
D(x): 0.6794, D(G(z)): 0.2387
Epoch: [11/20], Batch Num: [366/600]
Discriminator Loss: 1.0163, Generator Loss: 1.6871
D(x): 0.6656, D(G(z)): 0.3000
Epoch: [11/20], Batch Num: [367/600]
Discriminator Loss: 0.7207, Generator Loss: 1.5677
D(x): 0.7823, D(G(z)): 0.2696
Epoch: [11/20], Batch Num: [368/600]
Discriminator Loss: 0.8393, Generator Loss: 1.6359
D(x): 0.7738, D(G(z)): 0.3224
Epoch: [11/20], Batch Num: [369/600]
Discriminator Loss: 0.8885, Generator Loss: 1.8177
D(x): 0.7621, D(G(z)): 0.3190
Epoch: [11/20], Batch Num: [370/600]
Discriminator Loss: 0.7665, Generator Loss: 1.6949
D(x): 0.7176, D(G(z)): 0.2250
Epoch: [11/20], Batch Num: [371/600]
Discriminator Loss: 0.7107, Generator Loss: 1.8411
D(x): 0.7548, D(G(z)): 0.2443
Epoch: [11/20], Batch Num: [372/600]
Discriminator Loss: 0.9512, Generator Loss: 1.8131
D(x): 0.6467, D(G(z)): 0.2407
Epoch: [11/20], Batch Num: [373/600]
Discriminator Loss: 0.7993, Generator Loss: 1.4672
D(x): 0.7056, D(G(z)): 0.2441
Epoch: [11/20], Batch Num: [374/600]
Discriminator Loss: 0.7740, Generator Loss: 1.4657
D(x): 0.7521, D(G(z)): 0.2708
Epoch: [11/20], Batch Num: [375/600]
Discriminator Loss: 0.9060, Generator Loss: 1.5028
D(x): 0.7401, D(G(z)): 0.3376
Epoch: [11/20], Batch Num: [376/600]
Discriminator Loss: 0.7227, Generator Loss: 1.5129
D(x): 0.8270, D(G(z)): 0.3038
Epoch: [11/20], Batch Num: [377/600]
Discriminator Loss: 0.6877, Generator Loss: 1.7022
D(x): 0.7775, D(G(z)): 0.2664
Epoch: [11/20], Batch Num: [378/600]
Discriminator Loss: 0.6761, Generator Loss: 1.8395
D(x): 0.7928, D(G(z)): 0.2638
Epoch: [11/20], Batch Num: [379/600]
Discriminator Loss: 0.7301, Generator Loss: 1.6890
D(x): 0.7425, D(G(z)): 0.2452
Epoch: [11/20], Batch Num: [380/600]
Discriminator Loss: 0.7689, Generator Loss: 1.8615
D(x): 0.7684, D(G(z)): 0.2624
Epoch: [11/20], Batch Num: [381/600]
Discriminator Loss: 0.7625, Generator Loss: 1.9881
D(x): 0.7530, D(G(z)): 0.2734
Epoch: [11/20], Batch Num: [382/600]
Discriminator Loss: 0.7192, Generator Loss: 1.9673
D(x): 0.7527, D(G(z)): 0.2395
Epoch: [11/20], Batch Num: [383/600]
Discriminator Loss: 0.6609, Generator Loss: 1.8384
D(x): 0.7439, D(G(z)): 0.1940
Epoch: [11/20], Batch Num: [384/600]
Discriminator Loss: 0.6951, Generator Loss: 1.5858
D(x): 0.7970, D(G(z)): 0.2682
Epoch: [11/20], Batch Num: [385/600]
Discriminator Loss: 0.8047, Generator Loss: 1.5651
D(x): 0.7691, D(G(z)): 0.3049
Epoch: [11/20], Batch Num: [386/600]
Discriminator Loss: 0.7255, Generator Loss: 1.9034
D(x): 0.7866, D(G(z)): 0.2803
Epoch: [11/20], Batch Num: [387/600]
Discriminator Loss: 0.6923, Generator Loss: 1.8723
D(x): 0.7687, D(G(z)): 0.2403
Epoch: [11/20], Batch Num: [388/600]
Discriminator Loss: 0.7984, Generator Loss: 1.9159
D(x): 0.7385, D(G(z)): 0.2744
Epoch: [11/20], Batch Num: [389/600]
Discriminator Loss: 0.7177, Generator Loss: 1.8180
D(x): 0.7773, D(G(z)): 0.2674
Epoch: [11/20], Batch Num: [390/600]
Discriminator Loss: 0.8340, Generator Loss: 1.8653
D(x): 0.7270, D(G(z)): 0.2681
Epoch: [11/20], Batch Num: [391/600]
Discriminator Loss: 0.7134, Generator Loss: 1.8874
D(x): 0.7577, D(G(z)): 0.2590
Epoch: [11/20], Batch Num: [392/600]
Discriminator Loss: 0.8142, Generator Loss: 1.8938
D(x): 0.7468, D(G(z)): 0.2688
Epoch: [11/20], Batch Num: [393/600]
Discriminator Loss: 0.8107, Generator Loss: 1.8422
D(x): 0.7315, D(G(z)): 0.2541
Epoch: [11/20], Batch Num: [394/600]
Discriminator Loss: 0.7833, Generator Loss: 1.8330
D(x): 0.7481, D(G(z)): 0.2562
Epoch: [11/20], Batch Num: [395/600]
Discriminator Loss: 0.8134, Generator Loss: 1.8725
D(x): 0.7692, D(G(z)): 0.2755
Epoch: [11/20], Batch Num: [396/600]
Discriminator Loss: 0.6726, Generator Loss: 1.9331
D(x): 0.8286, D(G(z)): 0.2736
Epoch: [11/20], Batch Num: [397/600]
Discriminator Loss: 0.8914, Generator Loss: 2.0732
D(x): 0.6951, D(G(z)): 0.2578
Epoch: [11/20], Batch Num: [398/600]
Discriminator Loss: 0.7975, Generator Loss: 1.8387
D(x): 0.7380, D(G(z)): 0.2680
Epoch: [11/20], Batch Num: [399/600]
Discriminator Loss: 0.9032, Generator Loss: 1.7707
D(x): 0.6986, D(G(z)): 0.2535
Epoch: 11, Batch Num: [400/600]

Epoch: [11/20], Batch Num: [400/600]
Discriminator Loss: 0.8100, Generator Loss: 1.7036
D(x): 0.7325, D(G(z)): 0.2682
Epoch: [11/20], Batch Num: [401/600]
Discriminator Loss: 1.0026, Generator Loss: 1.7363
D(x): 0.7150, D(G(z)): 0.3291
Epoch: [11/20], Batch Num: [402/600]
Discriminator Loss: 0.8045, Generator Loss: 1.5776
D(x): 0.7527, D(G(z)): 0.2711
Epoch: [11/20], Batch Num: [403/600]
Discriminator Loss: 0.9200, Generator Loss: 1.6292
D(x): 0.7049, D(G(z)): 0.2869
Epoch: [11/20], Batch Num: [404/600]
Discriminator Loss: 0.9474, Generator Loss: 1.6546
D(x): 0.7056, D(G(z)): 0.2910
Epoch: [11/20], Batch Num: [405/600]
Discriminator Loss: 0.8329, Generator Loss: 1.5445
D(x): 0.7470, D(G(z)): 0.2955
Epoch: [11/20], Batch Num: [406/600]
Discriminator Loss: 0.7642, Generator Loss: 1.7638
D(x): 0.8008, D(G(z)): 0.3144
Epoch: [11/20], Batch Num: [407/600]
Discriminator Loss: 0.7859, Generator Loss: 1.6806
D(x): 0.7328, D(G(z)): 0.2465
Epoch: [11/20], Batch Num: [408/600]
Discriminator Loss: 0.9017, Generator Loss: 1.8586
D(x): 0.7151, D(G(z)): 0.2914
Epoch: [11/20], Batch Num: [409/600]
Discriminator Loss: 0.8704, Generator Loss: 1.6237
D(x): 0.7070, D(G(z)): 0.2626
Epoch: [11/20], Batch Num: [410/600]
Discriminator Loss: 0.8340, Generator Loss: 1.6447
D(x): 0.7057, D(G(z)): 0.2603
Epoch: [11/20], Batch Num: [411/600]
Discriminator Loss: 0.9780, Generator Loss: 1.3318
D(x): 0.6305, D(G(z)): 0.2515
Epoch: [11/20], Batch Num: [412/600]
Discriminator Loss: 1.0267, Generator Loss: 1.3212
D(x): 0.6736, D(G(z)): 0.3167
Epoch: [11/20], Batch Num: [413/600]
Discriminator Loss: 1.0002, Generator Loss: 1.3017
D(x): 0.7611, D(G(z)): 0.3965
Epoch: [11/20], Batch Num: [414/600]
Discriminator Loss: 0.8571, Generator Loss: 1.4309
D(x): 0.7879, D(G(z)): 0.3559
Epoch: [11/20], Batch Num: [415/600]
Discriminator Loss: 0.9666, Generator Loss: 1.6455
D(x): 0.6873, D(G(z)): 0.3109
Epoch: [11/20], Batch Num: [416/600]
Discriminator Loss: 0.8516, Generator Loss: 1.8710
D(x): 0.7208, D(G(z)): 0.2734
Epoch: [11/20], Batch Num: [417/600]
Discriminator Loss: 0.7522, Generator Loss: 1.6831
D(x): 0.7164, D(G(z)): 0.2432
Epoch: [11/20], Batch Num: [418/600]
Discriminator Loss: 0.9017, Generator Loss: 1.6848
D(x): 0.6338, D(G(z)): 0.2148
Epoch: [11/20], Batch Num: [419/600]
Discriminator Loss: 0.9552, Generator Loss: 1.5260
D(x): 0.6502, D(G(z)): 0.2626
Epoch: [11/20], Batch Num: [420/600]
Discriminator Loss: 0.9133, Generator Loss: 1.4725
D(x): 0.6941, D(G(z)): 0.3016
Epoch: [11/20], Batch Num: [421/600]
Discriminator Loss: 0.7859, Generator Loss: 1.4661
D(x): 0.7878, D(G(z)): 0.3113
Epoch: [11/20], Batch Num: [422/600]
Discriminator Loss: 0.7762, Generator Loss: 1.5292
D(x): 0.7666, D(G(z)): 0.2852
Epoch: [11/20], Batch Num: [423/600]
Discriminator Loss: 0.7849, Generator Loss: 1.8304
D(x): 0.7935, D(G(z)): 0.3149
Epoch: [11/20], Batch Num: [424/600]
Discriminator Loss: 0.8487, Generator Loss: 1.9418
D(x): 0.7338, D(G(z)): 0.2915
Epoch: [11/20], Batch Num: [425/600]
Discriminator Loss: 0.7246, Generator Loss: 2.0811
D(x): 0.7504, D(G(z)): 0.2538
Epoch: [11/20], Batch Num: [426/600]
Discriminator Loss: 0.7151, Generator Loss: 2.0152
D(x): 0.7209, D(G(z)): 0.2098
Epoch: [11/20], Batch Num: [427/600]
Discriminator Loss: 0.7295, Generator Loss: 1.9867
D(x): 0.7265, D(G(z)): 0.1894
Epoch: [11/20], Batch Num: [428/600]
Discriminator Loss: 0.6903, Generator Loss: 1.9377
D(x): 0.7447, D(G(z)): 0.2112
Epoch: [11/20], Batch Num: [429/600]
Discriminator Loss: 0.7725, Generator Loss: 1.8123
D(x): 0.7718, D(G(z)): 0.2702
Epoch: [11/20], Batch Num: [430/600]
Discriminator Loss: 0.7011, Generator Loss: 1.7044
D(x): 0.7842, D(G(z)): 0.2534
Epoch: [11/20], Batch Num: [431/600]
Discriminator Loss: 0.6544, Generator Loss: 1.8156
D(x): 0.8370, D(G(z)): 0.2849
Epoch: [11/20], Batch Num: [432/600]
Discriminator Loss: 0.6867, Generator Loss: 2.0168
D(x): 0.8338, D(G(z)): 0.2837
Epoch: [11/20], Batch Num: [433/600]
Discriminator Loss: 0.6554, Generator Loss: 2.1820
D(x): 0.7690, D(G(z)): 0.1940
Epoch: [11/20], Batch Num: [434/600]
Discriminator Loss: 0.5631, Generator Loss: 2.4820
D(x): 0.8088, D(G(z)): 0.1914
Epoch: [11/20], Batch Num: [435/600]
Discriminator Loss: 0.6859, Generator Loss: 2.1670
D(x): 0.7122, D(G(z)): 0.1576
Epoch: [11/20], Batch Num: [436/600]
Discriminator Loss: 0.8080, Generator Loss: 2.2258
D(x): 0.7108, D(G(z)): 0.2130
Epoch: [11/20], Batch Num: [437/600]
Discriminator Loss: 0.7276, Generator Loss: 1.8626
D(x): 0.7766, D(G(z)): 0.2428
Epoch: [11/20], Batch Num: [438/600]
Discriminator Loss: 0.6359, Generator Loss: 2.1964
D(x): 0.8466, D(G(z)): 0.2746
Epoch: [11/20], Batch Num: [439/600]
Discriminator Loss: 0.5665, Generator Loss: 2.4545
D(x): 0.8624, D(G(z)): 0.2455
Epoch: [11/20], Batch Num: [440/600]
Discriminator Loss: 0.7113, Generator Loss: 2.6718
D(x): 0.7584, D(G(z)): 0.2068
Epoch: [11/20], Batch Num: [441/600]
Discriminator Loss: 0.6987, Generator Loss: 2.3331
D(x): 0.7491, D(G(z)): 0.1853
Epoch: [11/20], Batch Num: [442/600]
Discriminator Loss: 0.7022, Generator Loss: 2.2714
D(x): 0.7350, D(G(z)): 0.1815
Epoch: [11/20], Batch Num: [443/600]
Discriminator Loss: 0.7110, Generator Loss: 1.8603
D(x): 0.7820, D(G(z)): 0.2305
Epoch: [11/20], Batch Num: [444/600]
Discriminator Loss: 0.6806, Generator Loss: 1.9948
D(x): 0.8489, D(G(z)): 0.2858
Epoch: [11/20], Batch Num: [445/600]
Discriminator Loss: 0.6033, Generator Loss: 1.9410
D(x): 0.8364, D(G(z)): 0.2239
Epoch: [11/20], Batch Num: [446/600]
Discriminator Loss: 0.7817, Generator Loss: 2.0870
D(x): 0.7653, D(G(z)): 0.2482
Epoch: [11/20], Batch Num: [447/600]
Discriminator Loss: 0.7683, Generator Loss: 2.2552
D(x): 0.7232, D(G(z)): 0.1962
Epoch: [11/20], Batch Num: [448/600]
Discriminator Loss: 0.7262, Generator Loss: 2.2722
D(x): 0.8110, D(G(z)): 0.2528
Epoch: [11/20], Batch Num: [449/600]
Discriminator Loss: 0.8323, Generator Loss: 2.1164
D(x): 0.6949, D(G(z)): 0.2177
Epoch: [11/20], Batch Num: [450/600]
Discriminator Loss: 0.8842, Generator Loss: 2.1717
D(x): 0.7400, D(G(z)): 0.2696
Epoch: [11/20], Batch Num: [451/600]
Discriminator Loss: 0.7641, Generator Loss: 1.7983
D(x): 0.7413, D(G(z)): 0.2291
Epoch: [11/20], Batch Num: [452/600]
Discriminator Loss: 0.9232, Generator Loss: 1.8947
D(x): 0.7687, D(G(z)): 0.3261
Epoch: [11/20], Batch Num: [453/600]
Discriminator Loss: 0.7729, Generator Loss: 2.1004
D(x): 0.7482, D(G(z)): 0.2368
Epoch: [11/20], Batch Num: [454/600]
Discriminator Loss: 0.6253, Generator Loss: 1.9392
D(x): 0.8131, D(G(z)): 0.2299
Epoch: [11/20], Batch Num: [455/600]
Discriminator Loss: 0.9282, Generator Loss: 1.9724
D(x): 0.7186, D(G(z)): 0.2441
Epoch: [11/20], Batch Num: [456/600]
Discriminator Loss: 0.8601, Generator Loss: 1.8246
D(x): 0.6866, D(G(z)): 0.2236
Epoch: [11/20], Batch Num: [457/600]
Discriminator Loss: 0.8018, Generator Loss: 1.4157
D(x): 0.7409, D(G(z)): 0.2477
Epoch: [11/20], Batch Num: [458/600]
Discriminator Loss: 0.7775, Generator Loss: 1.6311
D(x): 0.8323, D(G(z)): 0.3381
Epoch: [11/20], Batch Num: [459/600]
Discriminator Loss: 0.8844, Generator Loss: 1.6998
D(x): 0.7956, D(G(z)): 0.3467
Epoch: [11/20], Batch Num: [460/600]
Discriminator Loss: 0.7554, Generator Loss: 2.1808
D(x): 0.7670, D(G(z)): 0.2582
Epoch: [11/20], Batch Num: [461/600]
Discriminator Loss: 0.9105, Generator Loss: 2.1560
D(x): 0.6955, D(G(z)): 0.2247
Epoch: [11/20], Batch Num: [462/600]
Discriminator Loss: 0.7021, Generator Loss: 2.0240
D(x): 0.7151, D(G(z)): 0.1672
Epoch: [11/20], Batch Num: [463/600]
Discriminator Loss: 0.7517, Generator Loss: 1.9388
D(x): 0.7174, D(G(z)): 0.2089
Epoch: [11/20], Batch Num: [464/600]
Discriminator Loss: 0.7354, Generator Loss: 1.7811
D(x): 0.7711, D(G(z)): 0.2605
Epoch: [11/20], Batch Num: [465/600]
Discriminator Loss: 0.8395, Generator Loss: 1.8084
D(x): 0.7979, D(G(z)): 0.3309
Epoch: [11/20], Batch Num: [466/600]
Discriminator Loss: 0.8391, Generator Loss: 1.8950
D(x): 0.7711, D(G(z)): 0.3036
Epoch: [11/20], Batch Num: [467/600]
Discriminator Loss: 0.7611, Generator Loss: 2.1101
D(x): 0.7696, D(G(z)): 0.2506
Epoch: [11/20], Batch Num: [468/600]
Discriminator Loss: 0.7604, Generator Loss: 2.2014
D(x): 0.7375, D(G(z)): 0.2283
Epoch: [11/20], Batch Num: [469/600]
Discriminator Loss: 0.8147, Generator Loss: 2.0634
D(x): 0.6808, D(G(z)): 0.1912
Epoch: [11/20], Batch Num: [470/600]
Discriminator Loss: 0.7063, Generator Loss: 2.0738
D(x): 0.7747, D(G(z)): 0.2280
Epoch: [11/20], Batch Num: [471/600]
Discriminator Loss: 0.6463, Generator Loss: 1.8891
D(x): 0.7939, D(G(z)): 0.2303
Epoch: [11/20], Batch Num: [472/600]
Discriminator Loss: 0.6977, Generator Loss: 2.0996
D(x): 0.7936, D(G(z)): 0.2397
Epoch: [11/20], Batch Num: [473/600]
Discriminator Loss: 0.6198, Generator Loss: 1.7506
D(x): 0.7780, D(G(z)): 0.2083
Epoch: [11/20], Batch Num: [474/600]
Discriminator Loss: 0.7327, Generator Loss: 1.7926
D(x): 0.8133, D(G(z)): 0.2743
Epoch: [11/20], Batch Num: [475/600]
Discriminator Loss: 0.7248, Generator Loss: 1.8937
D(x): 0.8262, D(G(z)): 0.3098
Epoch: [11/20], Batch Num: [476/600]
Discriminator Loss: 0.6587, Generator Loss: 2.5225
D(x): 0.8306, D(G(z)): 0.2643
Epoch: [11/20], Batch Num: [477/600]
Discriminator Loss: 0.6143, Generator Loss: 2.4111
D(x): 0.7471, D(G(z)): 0.1446
Epoch: [11/20], Batch Num: [478/600]
Discriminator Loss: 0.7681, Generator Loss: 2.3130
D(x): 0.6885, D(G(z)): 0.1382
Epoch: [11/20], Batch Num: [479/600]
Discriminator Loss: 0.6411, Generator Loss: 2.0779
D(x): 0.7644, D(G(z)): 0.2006
Epoch: [11/20], Batch Num: [480/600]
Discriminator Loss: 0.6832, Generator Loss: 1.9278
D(x): 0.7705, D(G(z)): 0.2158
Epoch: [11/20], Batch Num: [481/600]
Discriminator Loss: 0.6888, Generator Loss: 1.9057
D(x): 0.7949, D(G(z)): 0.2549
Epoch: [11/20], Batch Num: [482/600]
Discriminator Loss: 0.5219, Generator Loss: 1.9873
D(x): 0.8714, D(G(z)): 0.2389
Epoch: [11/20], Batch Num: [483/600]
Discriminator Loss: 0.7112, Generator Loss: 2.1334
D(x): 0.8291, D(G(z)): 0.2788
Epoch: [11/20], Batch Num: [484/600]
Discriminator Loss: 0.6439, Generator Loss: 2.3232
D(x): 0.7532, D(G(z)): 0.1838
Epoch: [11/20], Batch Num: [485/600]
Discriminator Loss: 0.7526, Generator Loss: 2.2603
D(x): 0.6840, D(G(z)): 0.1661
Epoch: [11/20], Batch Num: [486/600]
Discriminator Loss: 0.7320, Generator Loss: 1.7230
D(x): 0.7632, D(G(z)): 0.2147
Epoch: [11/20], Batch Num: [487/600]
Discriminator Loss: 0.6632, Generator Loss: 1.7561
D(x): 0.7769, D(G(z)): 0.2120
Epoch: [11/20], Batch Num: [488/600]
Discriminator Loss: 0.7296, Generator Loss: 1.7520
D(x): 0.8143, D(G(z)): 0.3005
Epoch: [11/20], Batch Num: [489/600]
Discriminator Loss: 0.6898, Generator Loss: 1.9892
D(x): 0.8466, D(G(z)): 0.2864
Epoch: [11/20], Batch Num: [490/600]
Discriminator Loss: 0.6776, Generator Loss: 2.5305
D(x): 0.8213, D(G(z)): 0.2824
Epoch: [11/20], Batch Num: [491/600]
Discriminator Loss: 0.6296, Generator Loss: 2.5991
D(x): 0.7663, D(G(z)): 0.1629
Epoch: [11/20], Batch Num: [492/600]
Discriminator Loss: 0.7626, Generator Loss: 2.7179
D(x): 0.6799, D(G(z)): 0.1373
Epoch: [11/20], Batch Num: [493/600]
Discriminator Loss: 0.7217, Generator Loss: 1.9943
D(x): 0.7054, D(G(z)): 0.1498
Epoch: [11/20], Batch Num: [494/600]
Discriminator Loss: 0.7277, Generator Loss: 1.4006
D(x): 0.7449, D(G(z)): 0.2003
Epoch: [11/20], Batch Num: [495/600]
Discriminator Loss: 0.9073, Generator Loss: 1.3090
D(x): 0.8302, D(G(z)): 0.3955
Epoch: [11/20], Batch Num: [496/600]
Discriminator Loss: 0.8091, Generator Loss: 1.6690
D(x): 0.8397, D(G(z)): 0.3416
Epoch: [11/20], Batch Num: [497/600]
Discriminator Loss: 0.7040, Generator Loss: 2.2603
D(x): 0.7805, D(G(z)): 0.2724
Epoch: [11/20], Batch Num: [498/600]
Discriminator Loss: 0.8176, Generator Loss: 2.1417
D(x): 0.6903, D(G(z)): 0.1826
Epoch: [11/20], Batch Num: [499/600]
Discriminator Loss: 0.7058, Generator Loss: 2.0577
D(x): 0.7332, D(G(z)): 0.1921
Epoch: 11, Batch Num: [500/600]

Epoch: [11/20], Batch Num: [500/600]
Discriminator Loss: 0.7991, Generator Loss: 1.8217
D(x): 0.7117, D(G(z)): 0.1920
Epoch: [11/20], Batch Num: [501/600]
Discriminator Loss: 0.7759, Generator Loss: 1.6114
D(x): 0.7675, D(G(z)): 0.2562
Epoch: [11/20], Batch Num: [502/600]
Discriminator Loss: 0.6907, Generator Loss: 1.7614
D(x): 0.8432, D(G(z)): 0.2966
Epoch: [11/20], Batch Num: [503/600]
Discriminator Loss: 0.6868, Generator Loss: 1.7351
D(x): 0.7968, D(G(z)): 0.2738
Epoch: [11/20], Batch Num: [504/600]
Discriminator Loss: 0.8167, Generator Loss: 1.8377
D(x): 0.7059, D(G(z)): 0.2260
Epoch: [11/20], Batch Num: [505/600]
Discriminator Loss: 0.9773, Generator Loss: 1.9627
D(x): 0.7014, D(G(z)): 0.2775
Epoch: [11/20], Batch Num: [506/600]
Discriminator Loss: 0.7319, Generator Loss: 2.0728
D(x): 0.7705, D(G(z)): 0.2410
Epoch: [11/20], Batch Num: [507/600]
Discriminator Loss: 0.8898, Generator Loss: 1.7619
D(x): 0.6811, D(G(z)): 0.2221
Epoch: [11/20], Batch Num: [508/600]
Discriminator Loss: 0.7108, Generator Loss: 1.8206
D(x): 0.7931, D(G(z)): 0.2668
Epoch: [11/20], Batch Num: [509/600]
Discriminator Loss: 0.8403, Generator Loss: 1.8018
D(x): 0.7128, D(G(z)): 0.2437
Epoch: [11/20], Batch Num: [510/600]
Discriminator Loss: 0.9291, Generator Loss: 1.6788
D(x): 0.7146, D(G(z)): 0.2738
Epoch: [11/20], Batch Num: [511/600]
Discriminator Loss: 0.8074, Generator Loss: 1.6931
D(x): 0.7866, D(G(z)): 0.2888
Epoch: [11/20], Batch Num: [512/600]
Discriminator Loss: 0.7422, Generator Loss: 1.6347
D(x): 0.7221, D(G(z)): 0.2249
Epoch: [11/20], Batch Num: [513/600]
Discriminator Loss: 0.7569, Generator Loss: 1.7567
D(x): 0.7620, D(G(z)): 0.2527
Epoch: [11/20], Batch Num: [514/600]
Discriminator Loss: 0.8203, Generator Loss: 1.6420
D(x): 0.7410, D(G(z)): 0.2825
Epoch: [11/20], Batch Num: [515/600]
Discriminator Loss: 0.7842, Generator Loss: 1.6767
D(x): 0.7458, D(G(z)): 0.2504
Epoch: [11/20], Batch Num: [516/600]
Discriminator Loss: 0.6775, Generator Loss: 1.6950
D(x): 0.7633, D(G(z)): 0.2354
Epoch: [11/20], Batch Num: [517/600]
Discriminator Loss: 0.7463, Generator Loss: 1.6215
D(x): 0.7491, D(G(z)): 0.2447
Epoch: [11/20], Batch Num: [518/600]
Discriminator Loss: 0.8012, Generator Loss: 1.6253
D(x): 0.7488, D(G(z)): 0.2575
Epoch: [11/20], Batch Num: [519/600]
Discriminator Loss: 0.7728, Generator Loss: 1.5782
D(x): 0.7934, D(G(z)): 0.2837
Epoch: [11/20], Batch Num: [520/600]
Discriminator Loss: 0.7285, Generator Loss: 1.8778
D(x): 0.7962, D(G(z)): 0.2771
Epoch: [11/20], Batch Num: [521/600]
Discriminator Loss: 0.7179, Generator Loss: 1.7809
D(x): 0.7539, D(G(z)): 0.2208
Epoch: [11/20], Batch Num: [522/600]
Discriminator Loss: 0.8061, Generator Loss: 2.0405
D(x): 0.7538, D(G(z)): 0.2562
Epoch: [11/20], Batch Num: [523/600]
Discriminator Loss: 0.6923, Generator Loss: 1.5881
D(x): 0.7401, D(G(z)): 0.2056
Epoch: [11/20], Batch Num: [524/600]
Discriminator Loss: 0.8594, Generator Loss: 1.6460
D(x): 0.7087, D(G(z)): 0.2432
Epoch: [11/20], Batch Num: [525/600]
Discriminator Loss: 0.7140, Generator Loss: 1.6847
D(x): 0.8050, D(G(z)): 0.2948
Epoch: [11/20], Batch Num: [526/600]
Discriminator Loss: 0.7553, Generator Loss: 1.6982
D(x): 0.7869, D(G(z)): 0.2880
Epoch: [11/20], Batch Num: [527/600]
Discriminator Loss: 0.7641, Generator Loss: 1.9394
D(x): 0.7868, D(G(z)): 0.2688
Epoch: [11/20], Batch Num: [528/600]
Discriminator Loss: 0.7436, Generator Loss: 1.8713
D(x): 0.6994, D(G(z)): 0.1754
Epoch: [11/20], Batch Num: [529/600]
Discriminator Loss: 0.7709, Generator Loss: 1.7777
D(x): 0.7735, D(G(z)): 0.2574
Epoch: [11/20], Batch Num: [530/600]
Discriminator Loss: 0.8709, Generator Loss: 1.7061
D(x): 0.7230, D(G(z)): 0.2579
Epoch: [11/20], Batch Num: [531/600]
Discriminator Loss: 0.8281, Generator Loss: 1.5968
D(x): 0.7213, D(G(z)): 0.2698
Epoch: [11/20], Batch Num: [532/600]
Discriminator Loss: 0.8051, Generator Loss: 1.8712
D(x): 0.7870, D(G(z)): 0.3055
Epoch: [11/20], Batch Num: [533/600]
Discriminator Loss: 0.6767, Generator Loss: 1.8969
D(x): 0.7609, D(G(z)): 0.2150
Epoch: [11/20], Batch Num: [534/600]
Discriminator Loss: 0.6452, Generator Loss: 1.9054
D(x): 0.7713, D(G(z)): 0.2239
Epoch: [11/20], Batch Num: [535/600]
Discriminator Loss: 0.7346, Generator Loss: 1.7275
D(x): 0.7485, D(G(z)): 0.2429
Epoch: [11/20], Batch Num: [536/600]
Discriminator Loss: 0.7177, Generator Loss: 1.6127
D(x): 0.7359, D(G(z)): 0.1955
Epoch: [11/20], Batch Num: [537/600]
Discriminator Loss: 0.8110, Generator Loss: 1.4487
D(x): 0.7809, D(G(z)): 0.3220
Epoch: [11/20], Batch Num: [538/600]
Discriminator Loss: 0.7435, Generator Loss: 1.5779
D(x): 0.7707, D(G(z)): 0.2641
Epoch: [11/20], Batch Num: [539/600]
Discriminator Loss: 0.7391, Generator Loss: 1.7973
D(x): 0.7625, D(G(z)): 0.2583
Epoch: [11/20], Batch Num: [540/600]
Discriminator Loss: 0.8124, Generator Loss: 1.9372
D(x): 0.7749, D(G(z)): 0.3090
Epoch: [11/20], Batch Num: [541/600]
Discriminator Loss: 0.7976, Generator Loss: 1.8668
D(x): 0.7107, D(G(z)): 0.2164
Epoch: [11/20], Batch Num: [542/600]
Discriminator Loss: 0.7999, Generator Loss: 1.7425
D(x): 0.7410, D(G(z)): 0.2523
Epoch: [11/20], Batch Num: [543/600]
Discriminator Loss: 0.8180, Generator Loss: 1.7179
D(x): 0.7190, D(G(z)): 0.2450
Epoch: [11/20], Batch Num: [544/600]
Discriminator Loss: 1.0497, Generator Loss: 1.7985
D(x): 0.7632, D(G(z)): 0.3706
Epoch: [11/20], Batch Num: [545/600]
Discriminator Loss: 0.8054, Generator Loss: 1.7657
D(x): 0.7320, D(G(z)): 0.2517
Epoch: [11/20], Batch Num: [546/600]
Discriminator Loss: 0.7823, Generator Loss: 1.8074
D(x): 0.7322, D(G(z)): 0.2348
Epoch: [11/20], Batch Num: [547/600]
Discriminator Loss: 0.8313, Generator Loss: 1.5204
D(x): 0.6830, D(G(z)): 0.2172
Epoch: [11/20], Batch Num: [548/600]
Discriminator Loss: 0.8838, Generator Loss: 1.4921
D(x): 0.7498, D(G(z)): 0.3103
Epoch: [11/20], Batch Num: [549/600]
Discriminator Loss: 0.8887, Generator Loss: 1.5208
D(x): 0.8031, D(G(z)): 0.3661
Epoch: [11/20], Batch Num: [550/600]
Discriminator Loss: 0.9749, Generator Loss: 1.9058
D(x): 0.7147, D(G(z)): 0.3234
Epoch: [11/20], Batch Num: [551/600]
Discriminator Loss: 0.8887, Generator Loss: 1.9399
D(x): 0.6921, D(G(z)): 0.2534
Epoch: [11/20], Batch Num: [552/600]
Discriminator Loss: 0.9062, Generator Loss: 1.7251
D(x): 0.6528, D(G(z)): 0.2247
Epoch: [11/20], Batch Num: [553/600]
Discriminator Loss: 0.9200, Generator Loss: 1.4192
D(x): 0.6933, D(G(z)): 0.2667
Epoch: [11/20], Batch Num: [554/600]
Discriminator Loss: 0.9572, Generator Loss: 1.3270
D(x): 0.7431, D(G(z)): 0.3387
Epoch: [11/20], Batch Num: [555/600]
Discriminator Loss: 0.9059, Generator Loss: 1.5649
D(x): 0.7621, D(G(z)): 0.3614
Epoch: [11/20], Batch Num: [556/600]
Discriminator Loss: 0.9216, Generator Loss: 1.5888
D(x): 0.7110, D(G(z)): 0.3030
Epoch: [11/20], Batch Num: [557/600]
Discriminator Loss: 0.9504, Generator Loss: 1.4761
D(x): 0.6972, D(G(z)): 0.3111
Epoch: [11/20], Batch Num: [558/600]
Discriminator Loss: 0.7935, Generator Loss: 1.7147
D(x): 0.7369, D(G(z)): 0.2800
Epoch: [11/20], Batch Num: [559/600]
Discriminator Loss: 0.8902, Generator Loss: 1.6316
D(x): 0.7306, D(G(z)): 0.2757
Epoch: [11/20], Batch Num: [560/600]
Discriminator Loss: 0.8688, Generator Loss: 1.6147
D(x): 0.7149, D(G(z)): 0.2726
Epoch: [11/20], Batch Num: [561/600]
Discriminator Loss: 0.8483, Generator Loss: 1.7752
D(x): 0.7467, D(G(z)): 0.3115
Epoch: [11/20], Batch Num: [562/600]
Discriminator Loss: 0.9333, Generator Loss: 1.6391
D(x): 0.7074, D(G(z)): 0.3096
Epoch: [11/20], Batch Num: [563/600]
Discriminator Loss: 0.7326, Generator Loss: 1.6551
D(x): 0.7617, D(G(z)): 0.2688
Epoch: [11/20], Batch Num: [564/600]
Discriminator Loss: 0.6853, Generator Loss: 1.8358
D(x): 0.7647, D(G(z)): 0.2544
Epoch: [11/20], Batch Num: [565/600]
Discriminator Loss: 0.6047, Generator Loss: 1.9715
D(x): 0.7873, D(G(z)): 0.2297
Epoch: [11/20], Batch Num: [566/600]
Discriminator Loss: 0.7156, Generator Loss: 1.7593
D(x): 0.7655, D(G(z)): 0.2662
Epoch: [11/20], Batch Num: [567/600]
Discriminator Loss: 0.7605, Generator Loss: 1.6814
D(x): 0.7370, D(G(z)): 0.2552
Epoch: [11/20], Batch Num: [568/600]
Discriminator Loss: 0.6583, Generator Loss: 1.6408
D(x): 0.7882, D(G(z)): 0.2697
Epoch: [11/20], Batch Num: [569/600]
Discriminator Loss: 0.6300, Generator Loss: 1.8473
D(x): 0.7863, D(G(z)): 0.2398
Epoch: [11/20], Batch Num: [570/600]
Discriminator Loss: 0.5609, Generator Loss: 2.1583
D(x): 0.8600, D(G(z)): 0.2511
Epoch: [11/20], Batch Num: [571/600]
Discriminator Loss: 0.6357, Generator Loss: 2.0812
D(x): 0.7884, D(G(z)): 0.2066
Epoch: [11/20], Batch Num: [572/600]
Discriminator Loss: 0.6677, Generator Loss: 2.2035
D(x): 0.7317, D(G(z)): 0.1965
Epoch: [11/20], Batch Num: [573/600]
Discriminator Loss: 0.6604, Generator Loss: 1.9891
D(x): 0.7334, D(G(z)): 0.1833
Epoch: [11/20], Batch Num: [574/600]
Discriminator Loss: 0.5359, Generator Loss: 1.9075
D(x): 0.8360, D(G(z)): 0.2145
Epoch: [11/20], Batch Num: [575/600]
Discriminator Loss: 0.5605, Generator Loss: 2.0375
D(x): 0.8487, D(G(z)): 0.2350
Epoch: [11/20], Batch Num: [576/600]
Discriminator Loss: 0.5627, Generator Loss: 2.1398
D(x): 0.7871, D(G(z)): 0.1956
Epoch: [11/20], Batch Num: [577/600]
Discriminator Loss: 0.5975, Generator Loss: 2.0314
D(x): 0.8388, D(G(z)): 0.2487
Epoch: [11/20], Batch Num: [578/600]
Discriminator Loss: 0.6459, Generator Loss: 2.1301
D(x): 0.7991, D(G(z)): 0.2189
Epoch: [11/20], Batch Num: [579/600]
Discriminator Loss: 0.5183, Generator Loss: 2.1782
D(x): 0.8114, D(G(z)): 0.1802
Epoch: [11/20], Batch Num: [580/600]
Discriminator Loss: 0.5394, Generator Loss: 2.0423
D(x): 0.8098, D(G(z)): 0.1883
Epoch: [11/20], Batch Num: [581/600]
Discriminator Loss: 0.6245, Generator Loss: 2.0591
D(x): 0.7852, D(G(z)): 0.2205
Epoch: [11/20], Batch Num: [582/600]
Discriminator Loss: 0.5948, Generator Loss: 1.9359
D(x): 0.8567, D(G(z)): 0.2586
Epoch: [11/20], Batch Num: [583/600]
Discriminator Loss: 0.7681, Generator Loss: 2.2508
D(x): 0.7863, D(G(z)): 0.2504
Epoch: [11/20], Batch Num: [584/600]
Discriminator Loss: 0.7197, Generator Loss: 2.1428
D(x): 0.7892, D(G(z)): 0.2194
Epoch: [11/20], Batch Num: [585/600]
Discriminator Loss: 0.5939, Generator Loss: 2.2444
D(x): 0.7927, D(G(z)): 0.1985
Epoch: [11/20], Batch Num: [586/600]
Discriminator Loss: 0.6224, Generator Loss: 1.7497
D(x): 0.7930, D(G(z)): 0.2036
Epoch: [11/20], Batch Num: [587/600]
Discriminator Loss: 0.6652, Generator Loss: 2.0635
D(x): 0.8458, D(G(z)): 0.2670
Epoch: [11/20], Batch Num: [588/600]
Discriminator Loss: 0.8305, Generator Loss: 2.1399
D(x): 0.7860, D(G(z)): 0.2482
Epoch: [11/20], Batch Num: [589/600]
Discriminator Loss: 0.7569, Generator Loss: 1.8719
D(x): 0.7390, D(G(z)): 0.2155
Epoch: [11/20], Batch Num: [590/600]
Discriminator Loss: 0.7142, Generator Loss: 1.8280
D(x): 0.7720, D(G(z)): 0.2335
Epoch: [11/20], Batch Num: [591/600]
Discriminator Loss: 0.8587, Generator Loss: 1.8915
D(x): 0.7630, D(G(z)): 0.2618
Epoch: [11/20], Batch Num: [592/600]
Discriminator Loss: 0.9331, Generator Loss: 1.8684
D(x): 0.7384, D(G(z)): 0.2645
Epoch: [11/20], Batch Num: [593/600]
Discriminator Loss: 0.9724, Generator Loss: 1.9500
D(x): 0.7285, D(G(z)): 0.3134
Epoch: [11/20], Batch Num: [594/600]
Discriminator Loss: 0.9572, Generator Loss: 2.0539
D(x): 0.7101, D(G(z)): 0.2635
Epoch: [11/20], Batch Num: [595/600]
Discriminator Loss: 0.8605, Generator Loss: 1.8916
D(x): 0.6844, D(G(z)): 0.2147
Epoch: [11/20], Batch Num: [596/600]
Discriminator Loss: 0.7884, Generator Loss: 1.3154
D(x): 0.7477, D(G(z)): 0.2701
Epoch: [11/20], Batch Num: [597/600]
Discriminator Loss: 0.9467, Generator Loss: 1.3641
D(x): 0.7539, D(G(z)): 0.3393
Epoch: [11/20], Batch Num: [598/600]
Discriminator Loss: 0.9843, Generator Loss: 1.5756
D(x): 0.7470, D(G(z)): 0.3654
Epoch: [11/20], Batch Num: [599/600]
Discriminator Loss: 0.8486, Generator Loss: 1.7855
D(x): 0.7343, D(G(z)): 0.2670
Epoch: 12, Batch Num: [0/600]

Epoch: [12/20], Batch Num: [0/600]
Discriminator Loss: 0.9191, Generator Loss: 1.7381
D(x): 0.6946, D(G(z)): 0.2825
Epoch: [12/20], Batch Num: [1/600]
Discriminator Loss: 0.8100, Generator Loss: 1.8386
D(x): 0.7435, D(G(z)): 0.2696
Epoch: [12/20], Batch Num: [2/600]
Discriminator Loss: 0.8761, Generator Loss: 1.8893
D(x): 0.7419, D(G(z)): 0.2761
Epoch: [12/20], Batch Num: [3/600]
Discriminator Loss: 0.7353, Generator Loss: 1.9979
D(x): 0.7859, D(G(z)): 0.2601
Epoch: [12/20], Batch Num: [4/600]
Discriminator Loss: 0.7543, Generator Loss: 2.0915
D(x): 0.7378, D(G(z)): 0.2266
Epoch: [12/20], Batch Num: [5/600]
Discriminator Loss: 0.6675, Generator Loss: 1.9495
D(x): 0.7296, D(G(z)): 0.1853
Epoch: [12/20], Batch Num: [6/600]
Discriminator Loss: 0.7600, Generator Loss: 1.4974
D(x): 0.7275, D(G(z)): 0.2160
Epoch: [12/20], Batch Num: [7/600]
Discriminator Loss: 0.6545, Generator Loss: 1.7925
D(x): 0.8116, D(G(z)): 0.2627
Epoch: [12/20], Batch Num: [8/600]
Discriminator Loss: 0.6043, Generator Loss: 1.9680
D(x): 0.9244, D(G(z)): 0.3231
Epoch: [12/20], Batch Num: [9/600]
Discriminator Loss: 0.4780, Generator Loss: 2.5310
D(x): 0.8301, D(G(z)): 0.1845
Epoch: [12/20], Batch Num: [10/600]
Discriminator Loss: 0.4472, Generator Loss: 2.7498
D(x): 0.8239, D(G(z)): 0.1472
Epoch: [12/20], Batch Num: [11/600]
Discriminator Loss: 0.5926, Generator Loss: 2.2996
D(x): 0.7301, D(G(z)): 0.1328
Epoch: [12/20], Batch Num: [12/600]
Discriminator Loss: 0.5447, Generator Loss: 2.1405
D(x): 0.7874, D(G(z)): 0.1503
Epoch: [12/20], Batch Num: [13/600]
Discriminator Loss: 0.4112, Generator Loss: 2.0352
D(x): 0.8737, D(G(z)): 0.1791
Epoch: [12/20], Batch Num: [14/600]
Discriminator Loss: 0.4159, Generator Loss: 2.1362
D(x): 0.9186, D(G(z)): 0.2119
Epoch: [12/20], Batch Num: [15/600]
Discriminator Loss: 0.5559, Generator Loss: 2.4095
D(x): 0.8723, D(G(z)): 0.2493
Epoch: [12/20], Batch Num: [16/600]
Discriminator Loss: 0.5085, Generator Loss: 2.8218
D(x): 0.8433, D(G(z)): 0.1735
Epoch: [12/20], Batch Num: [17/600]
Discriminator Loss: 0.5209, Generator Loss: 2.7721
D(x): 0.8202, D(G(z)): 0.1585
Epoch: [12/20], Batch Num: [18/600]
Discriminator Loss: 0.3896, Generator Loss: 2.8844
D(x): 0.8407, D(G(z)): 0.1260
Epoch: [12/20], Batch Num: [19/600]
Discriminator Loss: 0.5380, Generator Loss: 2.6622
D(x): 0.8216, D(G(z)): 0.1299
Epoch: [12/20], Batch Num: [20/600]
Discriminator Loss: 0.6193, Generator Loss: 2.4720
D(x): 0.8078, D(G(z)): 0.1774
Epoch: [12/20], Batch Num: [21/600]
Discriminator Loss: 0.5849, Generator Loss: 2.1594
D(x): 0.8379, D(G(z)): 0.2167
Epoch: [12/20], Batch Num: [22/600]
Discriminator Loss: 0.7029, Generator Loss: 2.1949
D(x): 0.8476, D(G(z)): 0.2518
Epoch: [12/20], Batch Num: [23/600]
Discriminator Loss: 0.6344, Generator Loss: 2.2421
D(x): 0.8627, D(G(z)): 0.2365
Epoch: [12/20], Batch Num: [24/600]
Discriminator Loss: 0.6903, Generator Loss: 2.4781
D(x): 0.7762, D(G(z)): 0.1844
Epoch: [12/20], Batch Num: [25/600]
Discriminator Loss: 0.4634, Generator Loss: 2.6189
D(x): 0.8292, D(G(z)): 0.1579
Epoch: [12/20], Batch Num: [26/600]
Discriminator Loss: 0.8178, Generator Loss: 1.9735
D(x): 0.7584, D(G(z)): 0.2346
Epoch: [12/20], Batch Num: [27/600]
Discriminator Loss: 0.8219, Generator Loss: 1.8875
D(x): 0.8111, D(G(z)): 0.2558
Epoch: [12/20], Batch Num: [28/600]
Discriminator Loss: 1.0107, Generator Loss: 2.2939
D(x): 0.7868, D(G(z)): 0.3582
Epoch: [12/20], Batch Num: [29/600]
Discriminator Loss: 0.8050, Generator Loss: 2.2826
D(x): 0.7638, D(G(z)): 0.2292
Epoch: [12/20], Batch Num: [30/600]
Discriminator Loss: 0.8481, Generator Loss: 2.0784
D(x): 0.7082, D(G(z)): 0.2067
Epoch: [12/20], Batch Num: [31/600]
Discriminator Loss: 0.8494, Generator Loss: 1.8871
D(x): 0.7695, D(G(z)): 0.2654
Epoch: [12/20], Batch Num: [32/600]
Discriminator Loss: 0.8961, Generator Loss: 1.8475
D(x): 0.7765, D(G(z)): 0.2979
Epoch: [12/20], Batch Num: [33/600]
Discriminator Loss: 1.1403, Generator Loss: 1.9792
D(x): 0.7161, D(G(z)): 0.2912
Epoch: [12/20], Batch Num: [34/600]
Discriminator Loss: 0.7874, Generator Loss: 2.0401
D(x): 0.7680, D(G(z)): 0.2538
Epoch: [12/20], Batch Num: [35/600]
Discriminator Loss: 0.8590, Generator Loss: 1.8005
D(x): 0.7284, D(G(z)): 0.2716
Epoch: [12/20], Batch Num: [36/600]
Discriminator Loss: 1.2510, Generator Loss: 1.6695
D(x): 0.6555, D(G(z)): 0.3007
Epoch: [12/20], Batch Num: [37/600]
Discriminator Loss: 1.0768, Generator Loss: 1.7296
D(x): 0.7148, D(G(z)): 0.3193
Epoch: [12/20], Batch Num: [38/600]
Discriminator Loss: 0.9908, Generator Loss: 1.6708
D(x): 0.7267, D(G(z)): 0.3002
Epoch: [12/20], Batch Num: [39/600]
Discriminator Loss: 1.0121, Generator Loss: 1.9190
D(x): 0.7690, D(G(z)): 0.3258
Epoch: [12/20], Batch Num: [40/600]
Discriminator Loss: 0.8313, Generator Loss: 2.1548
D(x): 0.7411, D(G(z)): 0.2451
Epoch: [12/20], Batch Num: [41/600]
Discriminator Loss: 0.7931, Generator Loss: 2.0582
D(x): 0.7320, D(G(z)): 0.2433
Epoch: [12/20], Batch Num: [42/600]
Discriminator Loss: 0.8055, Generator Loss: 2.0770
D(x): 0.7457, D(G(z)): 0.2454
Epoch: [12/20], Batch Num: [43/600]
Discriminator Loss: 0.7575, Generator Loss: 1.9702
D(x): 0.7083, D(G(z)): 0.2057
Epoch: [12/20], Batch Num: [44/600]
Discriminator Loss: 0.6122, Generator Loss: 1.6568
D(x): 0.7974, D(G(z)): 0.2279
Epoch: [12/20], Batch Num: [45/600]
Discriminator Loss: 0.8158, Generator Loss: 2.0564
D(x): 0.8101, D(G(z)): 0.3311
Epoch: [12/20], Batch Num: [46/600]
Discriminator Loss: 0.8427, Generator Loss: 2.0116
D(x): 0.7561, D(G(z)): 0.2666
Epoch: [12/20], Batch Num: [47/600]
Discriminator Loss: 0.6889, Generator Loss: 2.3194
D(x): 0.7655, D(G(z)): 0.2217
Epoch: [12/20], Batch Num: [48/600]
Discriminator Loss: 0.6235, Generator Loss: 2.2693
D(x): 0.7798, D(G(z)): 0.1995
Epoch: [12/20], Batch Num: [49/600]
Discriminator Loss: 0.6839, Generator Loss: 1.9662
D(x): 0.7166, D(G(z)): 0.1674
Epoch: [12/20], Batch Num: [50/600]
Discriminator Loss: 0.6714, Generator Loss: 1.9406
D(x): 0.7444, D(G(z)): 0.1711
Epoch: [12/20], Batch Num: [51/600]
Discriminator Loss: 0.6133, Generator Loss: 1.8821
D(x): 0.8608, D(G(z)): 0.2637
Epoch: [12/20], Batch Num: [52/600]
Discriminator Loss: 0.7297, Generator Loss: 2.0196
D(x): 0.7759, D(G(z)): 0.2514
Epoch: [12/20], Batch Num: [53/600]
Discriminator Loss: 0.5405, Generator Loss: 2.2473
D(x): 0.8725, D(G(z)): 0.2454
Epoch: [12/20], Batch Num: [54/600]
Discriminator Loss: 0.5553, Generator Loss: 2.5161
D(x): 0.7994, D(G(z)): 0.1833
Epoch: [12/20], Batch Num: [55/600]
Discriminator Loss: 0.6930, Generator Loss: 2.6564
D(x): 0.7326, D(G(z)): 0.1742
Epoch: [12/20], Batch Num: [56/600]
Discriminator Loss: 0.6483, Generator Loss: 2.2254
D(x): 0.7371, D(G(z)): 0.1596
Epoch: [12/20], Batch Num: [57/600]
Discriminator Loss: 0.6222, Generator Loss: 1.9913
D(x): 0.7778, D(G(z)): 0.1832
Epoch: [12/20], Batch Num: [58/600]
Discriminator Loss: 0.6746, Generator Loss: 1.8896
D(x): 0.8288, D(G(z)): 0.2506
Epoch: [12/20], Batch Num: [59/600]
Discriminator Loss: 0.7082, Generator Loss: 2.1621
D(x): 0.8538, D(G(z)): 0.3180
Epoch: [12/20], Batch Num: [60/600]
Discriminator Loss: 0.7522, Generator Loss: 2.3222
D(x): 0.7621, D(G(z)): 0.2143
Epoch: [12/20], Batch Num: [61/600]
Discriminator Loss: 0.7765, Generator Loss: 2.5650
D(x): 0.7558, D(G(z)): 0.2328
Epoch: [12/20], Batch Num: [62/600]
Discriminator Loss: 0.6596, Generator Loss: 2.5111
D(x): 0.7685, D(G(z)): 0.1722
Epoch: [12/20], Batch Num: [63/600]
Discriminator Loss: 0.8126, Generator Loss: 2.3201
D(x): 0.7083, D(G(z)): 0.2125
Epoch: [12/20], Batch Num: [64/600]
Discriminator Loss: 0.8943, Generator Loss: 1.8912
D(x): 0.7071, D(G(z)): 0.2261
Epoch: [12/20], Batch Num: [65/600]
Discriminator Loss: 1.1198, Generator Loss: 1.6115
D(x): 0.6846, D(G(z)): 0.3195
Epoch: [12/20], Batch Num: [66/600]
Discriminator Loss: 0.9998, Generator Loss: 1.8569
D(x): 0.7596, D(G(z)): 0.3392
Epoch: [12/20], Batch Num: [67/600]
Discriminator Loss: 0.9566, Generator Loss: 1.6364
D(x): 0.6989, D(G(z)): 0.2407
Epoch: [12/20], Batch Num: [68/600]
Discriminator Loss: 1.2275, Generator Loss: 1.8113
D(x): 0.6537, D(G(z)): 0.3006
Epoch: [12/20], Batch Num: [69/600]
Discriminator Loss: 1.0487, Generator Loss: 1.8395
D(x): 0.6746, D(G(z)): 0.3058
Epoch: [12/20], Batch Num: [70/600]
Discriminator Loss: 1.4139, Generator Loss: 1.7697
D(x): 0.6561, D(G(z)): 0.3541
Epoch: [12/20], Batch Num: [71/600]
Discriminator Loss: 1.2834, Generator Loss: 1.6167
D(x): 0.6080, D(G(z)): 0.2973
Epoch: [12/20], Batch Num: [72/600]
Discriminator Loss: 1.1666, Generator Loss: 1.5006
D(x): 0.6056, D(G(z)): 0.2912
Epoch: [12/20], Batch Num: [73/600]
Discriminator Loss: 1.3073, Generator Loss: 1.4782
D(x): 0.6549, D(G(z)): 0.3505
Epoch: [12/20], Batch Num: [74/600]
Discriminator Loss: 1.0515, Generator Loss: 1.4680
D(x): 0.6902, D(G(z)): 0.3434
Epoch: [12/20], Batch Num: [75/600]
Discriminator Loss: 1.2102, Generator Loss: 1.4986
D(x): 0.6179, D(G(z)): 0.3340
Epoch: [12/20], Batch Num: [76/600]
Discriminator Loss: 1.2611, Generator Loss: 1.4291
D(x): 0.5834, D(G(z)): 0.3232
Epoch: [12/20], Batch Num: [77/600]
Discriminator Loss: 1.1498, Generator Loss: 1.3737
D(x): 0.6377, D(G(z)): 0.3035
Epoch: [12/20], Batch Num: [78/600]
Discriminator Loss: 1.0079, Generator Loss: 1.4273
D(x): 0.6880, D(G(z)): 0.3175
Epoch: [12/20], Batch Num: [79/600]
Discriminator Loss: 1.1048, Generator Loss: 1.6559
D(x): 0.6626, D(G(z)): 0.3590
Epoch: [12/20], Batch Num: [80/600]
Discriminator Loss: 1.0053, Generator Loss: 1.7366
D(x): 0.6446, D(G(z)): 0.2887
Epoch: [12/20], Batch Num: [81/600]
Discriminator Loss: 0.9505, Generator Loss: 1.5786
D(x): 0.6498, D(G(z)): 0.2657
Epoch: [12/20], Batch Num: [82/600]
Discriminator Loss: 0.8123, Generator Loss: 1.6915
D(x): 0.6579, D(G(z)): 0.2213
Epoch: [12/20], Batch Num: [83/600]
Discriminator Loss: 0.8420, Generator Loss: 1.6284
D(x): 0.6742, D(G(z)): 0.2531
Epoch: [12/20], Batch Num: [84/600]
Discriminator Loss: 0.7259, Generator Loss: 1.4567
D(x): 0.7679, D(G(z)): 0.2913
Epoch: [12/20], Batch Num: [85/600]
Discriminator Loss: 0.7871, Generator Loss: 1.6351
D(x): 0.7637, D(G(z)): 0.3097
Epoch: [12/20], Batch Num: [86/600]
Discriminator Loss: 0.7479, Generator Loss: 1.5124
D(x): 0.7299, D(G(z)): 0.2747
Epoch: [12/20], Batch Num: [87/600]
Discriminator Loss: 0.6444, Generator Loss: 1.6485
D(x): 0.7507, D(G(z)): 0.2348
Epoch: [12/20], Batch Num: [88/600]
Discriminator Loss: 0.7352, Generator Loss: 1.6751
D(x): 0.7541, D(G(z)): 0.2854
Epoch: [12/20], Batch Num: [89/600]
Discriminator Loss: 0.7202, Generator Loss: 1.7057
D(x): 0.7299, D(G(z)): 0.2455
Epoch: [12/20], Batch Num: [90/600]
Discriminator Loss: 0.6509, Generator Loss: 1.8143
D(x): 0.7863, D(G(z)): 0.2684
Epoch: [12/20], Batch Num: [91/600]
Discriminator Loss: 0.5470, Generator Loss: 1.7979
D(x): 0.8034, D(G(z)): 0.2296
Epoch: [12/20], Batch Num: [92/600]
Discriminator Loss: 0.6666, Generator Loss: 2.0020
D(x): 0.7526, D(G(z)): 0.2167
Epoch: [12/20], Batch Num: [93/600]
Discriminator Loss: 0.5041, Generator Loss: 1.8243
D(x): 0.7976, D(G(z)): 0.1922
Epoch: [12/20], Batch Num: [94/600]
Discriminator Loss: 0.4754, Generator Loss: 1.9273
D(x): 0.8378, D(G(z)): 0.2019
Epoch: [12/20], Batch Num: [95/600]
Discriminator Loss: 0.5060, Generator Loss: 1.9965
D(x): 0.8556, D(G(z)): 0.2349
Epoch: [12/20], Batch Num: [96/600]
Discriminator Loss: 0.5194, Generator Loss: 2.1437
D(x): 0.8096, D(G(z)): 0.2109
Epoch: [12/20], Batch Num: [97/600]
Discriminator Loss: 0.4567, Generator Loss: 2.3385
D(x): 0.8201, D(G(z)): 0.1744
Epoch: [12/20], Batch Num: [98/600]
Discriminator Loss: 0.5039, Generator Loss: 2.2269
D(x): 0.7930, D(G(z)): 0.1725
Epoch: [12/20], Batch Num: [99/600]
Discriminator Loss: 0.6389, Generator Loss: 2.2531
D(x): 0.7674, D(G(z)): 0.2115
Epoch: 12, Batch Num: [100/600]

Epoch: [12/20], Batch Num: [100/600]
Discriminator Loss: 0.4498, Generator Loss: 2.1647
D(x): 0.8750, D(G(z)): 0.2080
Epoch: [12/20], Batch Num: [101/600]
Discriminator Loss: 0.6163, Generator Loss: 2.1229
D(x): 0.8249, D(G(z)): 0.2433
Epoch: [12/20], Batch Num: [102/600]
Discriminator Loss: 0.6539, Generator Loss: 2.2204
D(x): 0.8013, D(G(z)): 0.2307
Epoch: [12/20], Batch Num: [103/600]
Discriminator Loss: 0.6641, Generator Loss: 2.3236
D(x): 0.8279, D(G(z)): 0.2712
Epoch: [12/20], Batch Num: [104/600]
Discriminator Loss: 0.7536, Generator Loss: 2.4364
D(x): 0.7480, D(G(z)): 0.2240
Epoch: [12/20], Batch Num: [105/600]
Discriminator Loss: 0.7315, Generator Loss: 2.2828
D(x): 0.7346, D(G(z)): 0.1955
Epoch: [12/20], Batch Num: [106/600]
Discriminator Loss: 0.7674, Generator Loss: 2.0556
D(x): 0.7781, D(G(z)): 0.2500
Epoch: [12/20], Batch Num: [107/600]
Discriminator Loss: 0.8710, Generator Loss: 1.8701
D(x): 0.7742, D(G(z)): 0.3151
Epoch: [12/20], Batch Num: [108/600]
Discriminator Loss: 0.8488, Generator Loss: 1.7131
D(x): 0.7724, D(G(z)): 0.3033
Epoch: [12/20], Batch Num: [109/600]
Discriminator Loss: 0.9413, Generator Loss: 1.7328
D(x): 0.7580, D(G(z)): 0.3164
Epoch: [12/20], Batch Num: [110/600]
Discriminator Loss: 0.8042, Generator Loss: 2.0915
D(x): 0.8217, D(G(z)): 0.3223
Epoch: [12/20], Batch Num: [111/600]
Discriminator Loss: 0.9806, Generator Loss: 2.0593
D(x): 0.7147, D(G(z)): 0.2533
Epoch: [12/20], Batch Num: [112/600]
Discriminator Loss: 1.0378, Generator Loss: 1.6842
D(x): 0.6563, D(G(z)): 0.2386
Epoch: [12/20], Batch Num: [113/600]
Discriminator Loss: 1.2633, Generator Loss: 1.3707
D(x): 0.6429, D(G(z)): 0.3357
Epoch: [12/20], Batch Num: [114/600]
Discriminator Loss: 1.3586, Generator Loss: 1.7141
D(x): 0.7465, D(G(z)): 0.4333
Epoch: [12/20], Batch Num: [115/600]
Discriminator Loss: 1.2180, Generator Loss: 2.4305
D(x): 0.8096, D(G(z)): 0.4391
Epoch: [12/20], Batch Num: [116/600]
Discriminator Loss: 1.0183, Generator Loss: 2.3334
D(x): 0.7205, D(G(z)): 0.3196
Epoch: [12/20], Batch Num: [117/600]
Discriminator Loss: 1.1657, Generator Loss: 3.0194
D(x): 0.6650, D(G(z)): 0.3122
Epoch: [12/20], Batch Num: [118/600]
Discriminator Loss: 1.2466, Generator Loss: 2.3834
D(x): 0.6365, D(G(z)): 0.3089
Epoch: [12/20], Batch Num: [119/600]
Discriminator Loss: 1.1172, Generator Loss: 1.8669
D(x): 0.7201, D(G(z)): 0.3303
Epoch: [12/20], Batch Num: [120/600]
Discriminator Loss: 1.0532, Generator Loss: 2.2910
D(x): 0.7486, D(G(z)): 0.3665
Epoch: [12/20], Batch Num: [121/600]
Discriminator Loss: 1.1735, Generator Loss: 2.0180
D(x): 0.7079, D(G(z)): 0.3868
Epoch: [12/20], Batch Num: [122/600]
Discriminator Loss: 1.2690, Generator Loss: 1.8904
D(x): 0.6367, D(G(z)): 0.3703
Epoch: [12/20], Batch Num: [123/600]
Discriminator Loss: 0.9971, Generator Loss: 1.7649
D(x): 0.6693, D(G(z)): 0.2981
Epoch: [12/20], Batch Num: [124/600]
Discriminator Loss: 0.9639, Generator Loss: 1.8292
D(x): 0.6787, D(G(z)): 0.3211
Epoch: [12/20], Batch Num: [125/600]
Discriminator Loss: 1.0371, Generator Loss: 1.5309
D(x): 0.6492, D(G(z)): 0.3043
Epoch: [12/20], Batch Num: [126/600]
Discriminator Loss: 0.8099, Generator Loss: 1.2203
D(x): 0.7454, D(G(z)): 0.2911
Epoch: [12/20], Batch Num: [127/600]
Discriminator Loss: 0.8524, Generator Loss: 1.2827
D(x): 0.7398, D(G(z)): 0.3215
Epoch: [12/20], Batch Num: [128/600]
Discriminator Loss: 1.0517, Generator Loss: 1.6265
D(x): 0.7094, D(G(z)): 0.3913
Epoch: [12/20], Batch Num: [129/600]
Discriminator Loss: 0.9837, Generator Loss: 1.6428
D(x): 0.6805, D(G(z)): 0.3277
Epoch: [12/20], Batch Num: [130/600]
Discriminator Loss: 0.8559, Generator Loss: 1.8059
D(x): 0.7152, D(G(z)): 0.2912
Epoch: [12/20], Batch Num: [131/600]
Discriminator Loss: 0.8810, Generator Loss: 1.9273
D(x): 0.6923, D(G(z)): 0.2568
Epoch: [12/20], Batch Num: [132/600]
Discriminator Loss: 0.9662, Generator Loss: 1.8210
D(x): 0.6565, D(G(z)): 0.2787
Epoch: [12/20], Batch Num: [133/600]
Discriminator Loss: 0.7552, Generator Loss: 1.5899
D(x): 0.7517, D(G(z)): 0.2811
Epoch: [12/20], Batch Num: [134/600]
Discriminator Loss: 0.6594, Generator Loss: 1.7436
D(x): 0.7893, D(G(z)): 0.2713
Epoch: [12/20], Batch Num: [135/600]
Discriminator Loss: 0.7174, Generator Loss: 1.5604
D(x): 0.7487, D(G(z)): 0.2452
Epoch: [12/20], Batch Num: [136/600]
Discriminator Loss: 0.6978, Generator Loss: 1.6247
D(x): 0.7704, D(G(z)): 0.2652
Epoch: [12/20], Batch Num: [137/600]
Discriminator Loss: 0.6939, Generator Loss: 1.8435
D(x): 0.7477, D(G(z)): 0.2477
Epoch: [12/20], Batch Num: [138/600]
Discriminator Loss: 0.6114, Generator Loss: 1.8951
D(x): 0.7787, D(G(z)): 0.2248
Epoch: [12/20], Batch Num: [139/600]
Discriminator Loss: 0.7157, Generator Loss: 2.1346
D(x): 0.8020, D(G(z)): 0.2954
Epoch: [12/20], Batch Num: [140/600]
Discriminator Loss: 0.6158, Generator Loss: 2.3918
D(x): 0.7693, D(G(z)): 0.2134
Epoch: [12/20], Batch Num: [141/600]
Discriminator Loss: 0.6711, Generator Loss: 2.3238
D(x): 0.7438, D(G(z)): 0.2024
Epoch: [12/20], Batch Num: [142/600]
Discriminator Loss: 0.7366, Generator Loss: 2.2519
D(x): 0.7058, D(G(z)): 0.1966
Epoch: [12/20], Batch Num: [143/600]
Discriminator Loss: 0.5063, Generator Loss: 2.2394
D(x): 0.8281, D(G(z)): 0.1855
Epoch: [12/20], Batch Num: [144/600]
Discriminator Loss: 0.6949, Generator Loss: 2.1020
D(x): 0.8181, D(G(z)): 0.2677
Epoch: [12/20], Batch Num: [145/600]
Discriminator Loss: 0.7173, Generator Loss: 2.2394
D(x): 0.8025, D(G(z)): 0.2644
Epoch: [12/20], Batch Num: [146/600]
Discriminator Loss: 0.5533, Generator Loss: 2.7215
D(x): 0.8035, D(G(z)): 0.1929
Epoch: [12/20], Batch Num: [147/600]
Discriminator Loss: 0.8123, Generator Loss: 2.4253
D(x): 0.6765, D(G(z)): 0.1682
Epoch: [12/20], Batch Num: [148/600]
Discriminator Loss: 0.7687, Generator Loss: 2.1184
D(x): 0.7406, D(G(z)): 0.2245
Epoch: [12/20], Batch Num: [149/600]
Discriminator Loss: 0.7339, Generator Loss: 1.8908
D(x): 0.8025, D(G(z)): 0.2332
Epoch: [12/20], Batch Num: [150/600]
Discriminator Loss: 0.7392, Generator Loss: 2.3328
D(x): 0.8490, D(G(z)): 0.3357
Epoch: [12/20], Batch Num: [151/600]
Discriminator Loss: 0.7945, Generator Loss: 2.7363
D(x): 0.7525, D(G(z)): 0.2211
Epoch: [12/20], Batch Num: [152/600]
Discriminator Loss: 0.8271, Generator Loss: 2.7648
D(x): 0.7274, D(G(z)): 0.2100
Epoch: [12/20], Batch Num: [153/600]
Discriminator Loss: 0.9510, Generator Loss: 2.0792
D(x): 0.6715, D(G(z)): 0.2009
Epoch: [12/20], Batch Num: [154/600]
Discriminator Loss: 0.7793, Generator Loss: 1.6772
D(x): 0.7986, D(G(z)): 0.2603
Epoch: [12/20], Batch Num: [155/600]
Discriminator Loss: 0.9368, Generator Loss: 1.9276
D(x): 0.7881, D(G(z)): 0.3144
Epoch: [12/20], Batch Num: [156/600]
Discriminator Loss: 1.2210, Generator Loss: 2.0288
D(x): 0.6320, D(G(z)): 0.3179
Epoch: [12/20], Batch Num: [157/600]
Discriminator Loss: 1.1003, Generator Loss: 1.8622
D(x): 0.7185, D(G(z)): 0.3258
Epoch: [12/20], Batch Num: [158/600]
Discriminator Loss: 1.0349, Generator Loss: 1.7922
D(x): 0.7220, D(G(z)): 0.2979
Epoch: [12/20], Batch Num: [159/600]
Discriminator Loss: 1.0536, Generator Loss: 1.8508
D(x): 0.7102, D(G(z)): 0.2812
Epoch: [12/20], Batch Num: [160/600]
Discriminator Loss: 0.9027, Generator Loss: 1.8679
D(x): 0.7357, D(G(z)): 0.2590
Epoch: [12/20], Batch Num: [161/600]
Discriminator Loss: 0.8446, Generator Loss: 1.7434
D(x): 0.7408, D(G(z)): 0.2470
Epoch: [12/20], Batch Num: [162/600]
Discriminator Loss: 0.8464, Generator Loss: 1.8413
D(x): 0.7379, D(G(z)): 0.2821
Epoch: [12/20], Batch Num: [163/600]
Discriminator Loss: 0.8860, Generator Loss: 1.6339
D(x): 0.6779, D(G(z)): 0.2260
Epoch: [12/20], Batch Num: [164/600]
Discriminator Loss: 0.7391, Generator Loss: 1.4687
D(x): 0.7816, D(G(z)): 0.2730
Epoch: [12/20], Batch Num: [165/600]
Discriminator Loss: 0.8690, Generator Loss: 1.5177
D(x): 0.7508, D(G(z)): 0.3068
Epoch: [12/20], Batch Num: [166/600]
Discriminator Loss: 0.6659, Generator Loss: 1.6899
D(x): 0.8170, D(G(z)): 0.2990
Epoch: [12/20], Batch Num: [167/600]
Discriminator Loss: 0.8161, Generator Loss: 2.0122
D(x): 0.7690, D(G(z)): 0.2736
Epoch: [12/20], Batch Num: [168/600]
Discriminator Loss: 0.6794, Generator Loss: 2.0785
D(x): 0.7534, D(G(z)): 0.2115
Epoch: [12/20], Batch Num: [169/600]
Discriminator Loss: 0.6477, Generator Loss: 2.1045
D(x): 0.7211, D(G(z)): 0.1686
Epoch: [12/20], Batch Num: [170/600]
Discriminator Loss: 0.6990, Generator Loss: 2.0447
D(x): 0.7314, D(G(z)): 0.1983
Epoch: [12/20], Batch Num: [171/600]
Discriminator Loss: 0.5981, Generator Loss: 1.8375
D(x): 0.8069, D(G(z)): 0.2021
Epoch: [12/20], Batch Num: [172/600]
Discriminator Loss: 0.4711, Generator Loss: 2.0725
D(x): 0.8684, D(G(z)): 0.2144
Epoch: [12/20], Batch Num: [173/600]
Discriminator Loss: 0.6191, Generator Loss: 2.1604
D(x): 0.8576, D(G(z)): 0.2650
Epoch: [12/20], Batch Num: [174/600]
Discriminator Loss: 0.6076, Generator Loss: 2.2763
D(x): 0.7961, D(G(z)): 0.2022
Epoch: [12/20], Batch Num: [175/600]
Discriminator Loss: 0.5036, Generator Loss: 2.7934
D(x): 0.8408, D(G(z)): 0.1778
Epoch: [12/20], Batch Num: [176/600]
Discriminator Loss: 0.6116, Generator Loss: 2.5817
D(x): 0.7506, D(G(z)): 0.1657
Epoch: [12/20], Batch Num: [177/600]
Discriminator Loss: 0.5459, Generator Loss: 2.3608
D(x): 0.7761, D(G(z)): 0.1407
Epoch: [12/20], Batch Num: [178/600]
Discriminator Loss: 0.4172, Generator Loss: 2.3124
D(x): 0.8602, D(G(z)): 0.1526
Epoch: [12/20], Batch Num: [179/600]
Discriminator Loss: 0.5546, Generator Loss: 2.1074
D(x): 0.8622, D(G(z)): 0.2245
Epoch: [12/20], Batch Num: [180/600]
Discriminator Loss: 0.5249, Generator Loss: 2.2658
D(x): 0.8302, D(G(z)): 0.1775
Epoch: [12/20], Batch Num: [181/600]
Discriminator Loss: 0.4737, Generator Loss: 2.2650
D(x): 0.8698, D(G(z)): 0.1865
Epoch: [12/20], Batch Num: [182/600]
Discriminator Loss: 0.4859, Generator Loss: 2.5196
D(x): 0.8860, D(G(z)): 0.2067
Epoch: [12/20], Batch Num: [183/600]
Discriminator Loss: 0.6361, Generator Loss: 2.7435
D(x): 0.7938, D(G(z)): 0.2046
Epoch: [12/20], Batch Num: [184/600]
Discriminator Loss: 0.5406, Generator Loss: 2.6953
D(x): 0.8173, D(G(z)): 0.1331
Epoch: [12/20], Batch Num: [185/600]
Discriminator Loss: 0.5829, Generator Loss: 2.4708
D(x): 0.7772, D(G(z)): 0.1196
Epoch: [12/20], Batch Num: [186/600]
Discriminator Loss: 0.5210, Generator Loss: 1.9882
D(x): 0.8025, D(G(z)): 0.1501
Epoch: [12/20], Batch Num: [187/600]
Discriminator Loss: 0.5119, Generator Loss: 1.8558
D(x): 0.8612, D(G(z)): 0.2263
Epoch: [12/20], Batch Num: [188/600]
Discriminator Loss: 0.6222, Generator Loss: 2.2667
D(x): 0.8585, D(G(z)): 0.2674
Epoch: [12/20], Batch Num: [189/600]
Discriminator Loss: 0.6516, Generator Loss: 2.5346
D(x): 0.8502, D(G(z)): 0.2578
Epoch: [12/20], Batch Num: [190/600]
Discriminator Loss: 0.7769, Generator Loss: 3.2368
D(x): 0.7639, D(G(z)): 0.2136
Epoch: [12/20], Batch Num: [191/600]
Discriminator Loss: 0.7565, Generator Loss: 2.4857
D(x): 0.6721, D(G(z)): 0.1257
Epoch: [12/20], Batch Num: [192/600]
Discriminator Loss: 0.6754, Generator Loss: 2.1301
D(x): 0.7291, D(G(z)): 0.1453
Epoch: [12/20], Batch Num: [193/600]
Discriminator Loss: 0.7576, Generator Loss: 1.6833
D(x): 0.8146, D(G(z)): 0.2490
Epoch: [12/20], Batch Num: [194/600]
Discriminator Loss: 0.8430, Generator Loss: 1.5715
D(x): 0.8083, D(G(z)): 0.3359
Epoch: [12/20], Batch Num: [195/600]
Discriminator Loss: 0.8384, Generator Loss: 2.2375
D(x): 0.8601, D(G(z)): 0.3523
Epoch: [12/20], Batch Num: [196/600]
Discriminator Loss: 0.8998, Generator Loss: 2.8418
D(x): 0.7292, D(G(z)): 0.2277
Epoch: [12/20], Batch Num: [197/600]
Discriminator Loss: 1.0524, Generator Loss: 2.4964
D(x): 0.6023, D(G(z)): 0.1487
Epoch: [12/20], Batch Num: [198/600]
Discriminator Loss: 0.9247, Generator Loss: 2.0791
D(x): 0.6804, D(G(z)): 0.2069
Epoch: [12/20], Batch Num: [199/600]
Discriminator Loss: 0.9644, Generator Loss: 1.4034
D(x): 0.7031, D(G(z)): 0.2638
Epoch: 12, Batch Num: [200/600]

Epoch: [12/20], Batch Num: [200/600]
Discriminator Loss: 1.0241, Generator Loss: 1.4520
D(x): 0.8303, D(G(z)): 0.3773
Epoch: [12/20], Batch Num: [201/600]
Discriminator Loss: 0.8270, Generator Loss: 1.6288
D(x): 0.8308, D(G(z)): 0.3552
Epoch: [12/20], Batch Num: [202/600]
Discriminator Loss: 0.7742, Generator Loss: 2.0896
D(x): 0.8108, D(G(z)): 0.2928
Epoch: [12/20], Batch Num: [203/600]
Discriminator Loss: 0.8469, Generator Loss: 2.2795
D(x): 0.6946, D(G(z)): 0.2054
Epoch: [12/20], Batch Num: [204/600]
Discriminator Loss: 0.8630, Generator Loss: 1.9880
D(x): 0.6721, D(G(z)): 0.1822
Epoch: [12/20], Batch Num: [205/600]
Discriminator Loss: 0.8606, Generator Loss: 1.8322
D(x): 0.6891, D(G(z)): 0.2008
Epoch: [12/20], Batch Num: [206/600]
Discriminator Loss: 0.6416, Generator Loss: 1.4402
D(x): 0.7879, D(G(z)): 0.2368
Epoch: [12/20], Batch Num: [207/600]
Discriminator Loss: 0.8648, Generator Loss: 1.3998
D(x): 0.8096, D(G(z)): 0.3471
Epoch: [12/20], Batch Num: [208/600]
Discriminator Loss: 0.8023, Generator Loss: 1.6405
D(x): 0.8266, D(G(z)): 0.3420
Epoch: [12/20], Batch Num: [209/600]
Discriminator Loss: 0.7494, Generator Loss: 1.8220
D(x): 0.8078, D(G(z)): 0.2879
Epoch: [12/20], Batch Num: [210/600]
Discriminator Loss: 0.6796, Generator Loss: 2.2857
D(x): 0.7895, D(G(z)): 0.2379
Epoch: [12/20], Batch Num: [211/600]
Discriminator Loss: 0.6759, Generator Loss: 2.2573
D(x): 0.7191, D(G(z)): 0.1499
Epoch: [12/20], Batch Num: [212/600]
Discriminator Loss: 0.7668, Generator Loss: 1.9683
D(x): 0.6925, D(G(z)): 0.1795
Epoch: [12/20], Batch Num: [213/600]
Discriminator Loss: 0.6378, Generator Loss: 1.8694
D(x): 0.7695, D(G(z)): 0.1950
Epoch: [12/20], Batch Num: [214/600]
Discriminator Loss: 0.5561, Generator Loss: 1.8196
D(x): 0.8624, D(G(z)): 0.2436
Epoch: [12/20], Batch Num: [215/600]
Discriminator Loss: 0.6722, Generator Loss: 1.7792
D(x): 0.8106, D(G(z)): 0.2805
Epoch: [12/20], Batch Num: [216/600]
Discriminator Loss: 0.6510, Generator Loss: 1.9137
D(x): 0.8105, D(G(z)): 0.2425
Epoch: [12/20], Batch Num: [217/600]
Discriminator Loss: 0.5350, Generator Loss: 2.0453
D(x): 0.8332, D(G(z)): 0.2174
Epoch: [12/20], Batch Num: [218/600]
Discriminator Loss: 0.5561, Generator Loss: 2.2662
D(x): 0.8203, D(G(z)): 0.2059
Epoch: [12/20], Batch Num: [219/600]
Discriminator Loss: 0.6937, Generator Loss: 2.3292
D(x): 0.7470, D(G(z)): 0.1765
Epoch: [12/20], Batch Num: [220/600]
Discriminator Loss: 0.5695, Generator Loss: 2.0758
D(x): 0.7936, D(G(z)): 0.1722
Epoch: [12/20], Batch Num: [221/600]
Discriminator Loss: 0.6334, Generator Loss: 2.0000
D(x): 0.7583, D(G(z)): 0.1936
Epoch: [12/20], Batch Num: [222/600]
Discriminator Loss: 0.5946, Generator Loss: 1.7544
D(x): 0.8119, D(G(z)): 0.2318
Epoch: [12/20], Batch Num: [223/600]
Discriminator Loss: 0.6340, Generator Loss: 1.9187
D(x): 0.8746, D(G(z)): 0.3071
Epoch: [12/20], Batch Num: [224/600]
Discriminator Loss: 0.7129, Generator Loss: 2.0269
D(x): 0.8119, D(G(z)): 0.2545
Epoch: [12/20], Batch Num: [225/600]
Discriminator Loss: 0.6660, Generator Loss: 2.1901
D(x): 0.7952, D(G(z)): 0.2391
Epoch: [12/20], Batch Num: [226/600]
Discriminator Loss: 0.7725, Generator Loss: 2.4090
D(x): 0.7170, D(G(z)): 0.2031
Epoch: [12/20], Batch Num: [227/600]
Discriminator Loss: 0.6861, Generator Loss: 2.2950
D(x): 0.7430, D(G(z)): 0.1940
Epoch: [12/20], Batch Num: [228/600]
Discriminator Loss: 0.5409, Generator Loss: 2.1522
D(x): 0.8011, D(G(z)): 0.1751
Epoch: [12/20], Batch Num: [229/600]
Discriminator Loss: 0.7420, Generator Loss: 1.8114
D(x): 0.7586, D(G(z)): 0.2345
Epoch: [12/20], Batch Num: [230/600]
Discriminator Loss: 0.6640, Generator Loss: 1.6292
D(x): 0.8012, D(G(z)): 0.2522
Epoch: [12/20], Batch Num: [231/600]
Discriminator Loss: 0.6934, Generator Loss: 1.6832
D(x): 0.8247, D(G(z)): 0.2780
Epoch: [12/20], Batch Num: [232/600]
Discriminator Loss: 0.7132, Generator Loss: 2.0157
D(x): 0.8057, D(G(z)): 0.2733
Epoch: [12/20], Batch Num: [233/600]
Discriminator Loss: 0.8149, Generator Loss: 2.1071
D(x): 0.7369, D(G(z)): 0.2487
Epoch: [12/20], Batch Num: [234/600]
Discriminator Loss: 0.6305, Generator Loss: 2.0743
D(x): 0.7527, D(G(z)): 0.1866
Epoch: [12/20], Batch Num: [235/600]
Discriminator Loss: 0.9240, Generator Loss: 1.9963
D(x): 0.6933, D(G(z)): 0.2462
Epoch: [12/20], Batch Num: [236/600]
Discriminator Loss: 0.7084, Generator Loss: 1.9541
D(x): 0.8113, D(G(z)): 0.2590
Epoch: [12/20], Batch Num: [237/600]
Discriminator Loss: 0.7999, Generator Loss: 2.3443
D(x): 0.7616, D(G(z)): 0.2482
Epoch: [12/20], Batch Num: [238/600]
Discriminator Loss: 0.8735, Generator Loss: 2.1102
D(x): 0.7329, D(G(z)): 0.2030
Epoch: [12/20], Batch Num: [239/600]
Discriminator Loss: 0.7929, Generator Loss: 2.2438
D(x): 0.7749, D(G(z)): 0.2658
Epoch: [12/20], Batch Num: [240/600]
Discriminator Loss: 0.8476, Generator Loss: 2.3123
D(x): 0.7441, D(G(z)): 0.2629
Epoch: [12/20], Batch Num: [241/600]
Discriminator Loss: 0.9400, Generator Loss: 2.3283
D(x): 0.6971, D(G(z)): 0.2641
Epoch: [12/20], Batch Num: [242/600]
Discriminator Loss: 0.6421, Generator Loss: 2.2086
D(x): 0.7677, D(G(z)): 0.2121
Epoch: [12/20], Batch Num: [243/600]
Discriminator Loss: 0.8877, Generator Loss: 2.2705
D(x): 0.7298, D(G(z)): 0.2878
Epoch: [12/20], Batch Num: [244/600]
Discriminator Loss: 0.6660, Generator Loss: 2.2013
D(x): 0.7642, D(G(z)): 0.1890
Epoch: [12/20], Batch Num: [245/600]
Discriminator Loss: 0.6890, Generator Loss: 2.0059
D(x): 0.7332, D(G(z)): 0.1794
Epoch: [12/20], Batch Num: [246/600]
Discriminator Loss: 0.7223, Generator Loss: 2.2877
D(x): 0.8099, D(G(z)): 0.2768
Epoch: [12/20], Batch Num: [247/600]
Discriminator Loss: 0.7628, Generator Loss: 2.1338
D(x): 0.7432, D(G(z)): 0.2257
Epoch: [12/20], Batch Num: [248/600]
Discriminator Loss: 0.5209, Generator Loss: 2.1511
D(x): 0.8244, D(G(z)): 0.1712
Epoch: [12/20], Batch Num: [249/600]
Discriminator Loss: 0.6637, Generator Loss: 2.3502
D(x): 0.7926, D(G(z)): 0.2439
Epoch: [12/20], Batch Num: [250/600]
Discriminator Loss: 0.4793, Generator Loss: 2.5338
D(x): 0.8512, D(G(z)): 0.1931
Epoch: [12/20], Batch Num: [251/600]
Discriminator Loss: 0.6457, Generator Loss: 2.2780
D(x): 0.7568, D(G(z)): 0.1770
Epoch: [12/20], Batch Num: [252/600]
Discriminator Loss: 0.5741, Generator Loss: 2.1105
D(x): 0.7557, D(G(z)): 0.1700
Epoch: [12/20], Batch Num: [253/600]
Discriminator Loss: 0.6184, Generator Loss: 2.1368
D(x): 0.8118, D(G(z)): 0.2279
Epoch: [12/20], Batch Num: [254/600]
Discriminator Loss: 0.6057, Generator Loss: 2.0760
D(x): 0.8108, D(G(z)): 0.2296
Epoch: [12/20], Batch Num: [255/600]
Discriminator Loss: 0.5336, Generator Loss: 2.1237
D(x): 0.8262, D(G(z)): 0.2277
Epoch: [12/20], Batch Num: [256/600]
Discriminator Loss: 0.5875, Generator Loss: 2.1661
D(x): 0.8257, D(G(z)): 0.2265
Epoch: [12/20], Batch Num: [257/600]
Discriminator Loss: 0.8061, Generator Loss: 2.8463
D(x): 0.7341, D(G(z)): 0.2239
Epoch: [12/20], Batch Num: [258/600]
Discriminator Loss: 0.8131, Generator Loss: 2.4620
D(x): 0.7175, D(G(z)): 0.2100
Epoch: [12/20], Batch Num: [259/600]
Discriminator Loss: 0.6124, Generator Loss: 2.6297
D(x): 0.7822, D(G(z)): 0.1701
Epoch: [12/20], Batch Num: [260/600]
Discriminator Loss: 0.6641, Generator Loss: 2.5246
D(x): 0.8273, D(G(z)): 0.2436
Epoch: [12/20], Batch Num: [261/600]
Discriminator Loss: 0.7835, Generator Loss: 2.2012
D(x): 0.7348, D(G(z)): 0.2166
Epoch: [12/20], Batch Num: [262/600]
Discriminator Loss: 0.8148, Generator Loss: 2.1366
D(x): 0.7233, D(G(z)): 0.2095
Epoch: [12/20], Batch Num: [263/600]
Discriminator Loss: 0.7848, Generator Loss: 1.8258
D(x): 0.7469, D(G(z)): 0.2815
Epoch: [12/20], Batch Num: [264/600]
Discriminator Loss: 0.8359, Generator Loss: 1.9737
D(x): 0.8063, D(G(z)): 0.3234
Epoch: [12/20], Batch Num: [265/600]
Discriminator Loss: 0.8379, Generator Loss: 1.9736
D(x): 0.7451, D(G(z)): 0.2681
Epoch: [12/20], Batch Num: [266/600]
Discriminator Loss: 0.7996, Generator Loss: 2.3185
D(x): 0.7666, D(G(z)): 0.2679
Epoch: [12/20], Batch Num: [267/600]
Discriminator Loss: 1.2181, Generator Loss: 2.1372
D(x): 0.6066, D(G(z)): 0.2239
Epoch: [12/20], Batch Num: [268/600]
Discriminator Loss: 1.1216, Generator Loss: 1.5706
D(x): 0.6706, D(G(z)): 0.2639
Epoch: [12/20], Batch Num: [269/600]
Discriminator Loss: 1.2033, Generator Loss: 1.2182
D(x): 0.6838, D(G(z)): 0.3693
Epoch: [12/20], Batch Num: [270/600]
Discriminator Loss: 1.3619, Generator Loss: 1.6591
D(x): 0.6957, D(G(z)): 0.4148
Epoch: [12/20], Batch Num: [271/600]
Discriminator Loss: 1.1758, Generator Loss: 1.7603
D(x): 0.6741, D(G(z)): 0.3562
Epoch: [12/20], Batch Num: [272/600]
Discriminator Loss: 1.0275, Generator Loss: 1.8143
D(x): 0.6614, D(G(z)): 0.2932
Epoch: [12/20], Batch Num: [273/600]
Discriminator Loss: 1.0231, Generator Loss: 1.6898
D(x): 0.6417, D(G(z)): 0.2547
Epoch: [12/20], Batch Num: [274/600]
Discriminator Loss: 1.2018, Generator Loss: 1.4807
D(x): 0.5712, D(G(z)): 0.2539
Epoch: [12/20], Batch Num: [275/600]
Discriminator Loss: 1.0020, Generator Loss: 1.3917
D(x): 0.7114, D(G(z)): 0.3265
Epoch: [12/20], Batch Num: [276/600]
Discriminator Loss: 0.9551, Generator Loss: 1.4173
D(x): 0.7757, D(G(z)): 0.3994
Epoch: [12/20], Batch Num: [277/600]
Discriminator Loss: 0.7363, Generator Loss: 1.8809
D(x): 0.8012, D(G(z)): 0.3141
Epoch: [12/20], Batch Num: [278/600]
Discriminator Loss: 0.8197, Generator Loss: 2.0887
D(x): 0.6762, D(G(z)): 0.2124
Epoch: [12/20], Batch Num: [279/600]
Discriminator Loss: 0.7608, Generator Loss: 2.2102
D(x): 0.6825, D(G(z)): 0.1691
Epoch: [12/20], Batch Num: [280/600]
Discriminator Loss: 0.7177, Generator Loss: 1.9907
D(x): 0.7120, D(G(z)): 0.1821
Epoch: [12/20], Batch Num: [281/600]
Discriminator Loss: 0.7209, Generator Loss: 1.6932
D(x): 0.7342, D(G(z)): 0.2011
Epoch: [12/20], Batch Num: [282/600]
Discriminator Loss: 0.6876, Generator Loss: 1.5286
D(x): 0.7860, D(G(z)): 0.2589
Epoch: [12/20], Batch Num: [283/600]
Discriminator Loss: 0.6108, Generator Loss: 1.5059
D(x): 0.8490, D(G(z)): 0.2811
Epoch: [12/20], Batch Num: [284/600]
Discriminator Loss: 0.5794, Generator Loss: 1.9411
D(x): 0.8988, D(G(z)): 0.2940
Epoch: [12/20], Batch Num: [285/600]
Discriminator Loss: 0.5668, Generator Loss: 2.4593
D(x): 0.8377, D(G(z)): 0.2347
Epoch: [12/20], Batch Num: [286/600]
Discriminator Loss: 0.5298, Generator Loss: 2.8148
D(x): 0.7858, D(G(z)): 0.1463
Epoch: [12/20], Batch Num: [287/600]
Discriminator Loss: 0.5325, Generator Loss: 2.8338
D(x): 0.7645, D(G(z)): 0.1213
Epoch: [12/20], Batch Num: [288/600]
Discriminator Loss: 0.5395, Generator Loss: 2.6519
D(x): 0.7464, D(G(z)): 0.1163
Epoch: [12/20], Batch Num: [289/600]
Discriminator Loss: 0.5505, Generator Loss: 2.3775
D(x): 0.8011, D(G(z)): 0.1445
Epoch: [12/20], Batch Num: [290/600]
Discriminator Loss: 0.4993, Generator Loss: 2.1081
D(x): 0.8550, D(G(z)): 0.1987
Epoch: [12/20], Batch Num: [291/600]
Discriminator Loss: 0.4800, Generator Loss: 2.2629
D(x): 0.8991, D(G(z)): 0.2360
Epoch: [12/20], Batch Num: [292/600]
Discriminator Loss: 0.4667, Generator Loss: 2.4991
D(x): 0.9037, D(G(z)): 0.2105
Epoch: [12/20], Batch Num: [293/600]
Discriminator Loss: 0.5153, Generator Loss: 2.6038
D(x): 0.8663, D(G(z)): 0.1780
Epoch: [12/20], Batch Num: [294/600]
Discriminator Loss: 0.5148, Generator Loss: 3.1176
D(x): 0.8122, D(G(z)): 0.1498
Epoch: [12/20], Batch Num: [295/600]
Discriminator Loss: 0.5246, Generator Loss: 2.7318
D(x): 0.7972, D(G(z)): 0.1383
Epoch: [12/20], Batch Num: [296/600]
Discriminator Loss: 0.5653, Generator Loss: 2.4927
D(x): 0.8244, D(G(z)): 0.1545
Epoch: [12/20], Batch Num: [297/600]
Discriminator Loss: 0.5454, Generator Loss: 2.3806
D(x): 0.8407, D(G(z)): 0.1968
Epoch: [12/20], Batch Num: [298/600]
Discriminator Loss: 0.6898, Generator Loss: 2.0548
D(x): 0.8064, D(G(z)): 0.2017
Epoch: [12/20], Batch Num: [299/600]
Discriminator Loss: 0.9041, Generator Loss: 2.1677
D(x): 0.7685, D(G(z)): 0.2590
Epoch: 12, Batch Num: [300/600]

Epoch: [12/20], Batch Num: [300/600]
Discriminator Loss: 0.6319, Generator Loss: 2.2783
D(x): 0.8429, D(G(z)): 0.2345
Epoch: [12/20], Batch Num: [301/600]
Discriminator Loss: 0.8171, Generator Loss: 2.3726
D(x): 0.7419, D(G(z)): 0.2483
Epoch: [12/20], Batch Num: [302/600]
Discriminator Loss: 0.6769, Generator Loss: 2.2288
D(x): 0.7870, D(G(z)): 0.2098
Epoch: [12/20], Batch Num: [303/600]
Discriminator Loss: 0.7979, Generator Loss: 2.4811
D(x): 0.7603, D(G(z)): 0.2063
Epoch: [12/20], Batch Num: [304/600]
Discriminator Loss: 0.8967, Generator Loss: 2.1345
D(x): 0.7402, D(G(z)): 0.2271
Epoch: [12/20], Batch Num: [305/600]
Discriminator Loss: 0.6353, Generator Loss: 1.9585
D(x): 0.8075, D(G(z)): 0.1839
Epoch: [12/20], Batch Num: [306/600]
Discriminator Loss: 0.8551, Generator Loss: 1.9262
D(x): 0.7978, D(G(z)): 0.2687
Epoch: [12/20], Batch Num: [307/600]
Discriminator Loss: 0.9097, Generator Loss: 1.8457
D(x): 0.7682, D(G(z)): 0.2949
Epoch: [12/20], Batch Num: [308/600]
Discriminator Loss: 1.0464, Generator Loss: 2.0253
D(x): 0.7732, D(G(z)): 0.3242
Epoch: [12/20], Batch Num: [309/600]
Discriminator Loss: 0.9076, Generator Loss: 2.1490
D(x): 0.7466, D(G(z)): 0.2472
Epoch: [12/20], Batch Num: [310/600]
Discriminator Loss: 0.8141, Generator Loss: 2.1710
D(x): 0.7456, D(G(z)): 0.2298
Epoch: [12/20], Batch Num: [311/600]
Discriminator Loss: 0.9243, Generator Loss: 2.0653
D(x): 0.7182, D(G(z)): 0.2334
Epoch: [12/20], Batch Num: [312/600]
Discriminator Loss: 1.0000, Generator Loss: 1.6606
D(x): 0.6871, D(G(z)): 0.2582
Epoch: [12/20], Batch Num: [313/600]
Discriminator Loss: 0.9411, Generator Loss: 1.5351
D(x): 0.7803, D(G(z)): 0.3114
Epoch: [12/20], Batch Num: [314/600]
Discriminator Loss: 0.7824, Generator Loss: 1.8863
D(x): 0.8180, D(G(z)): 0.3338
Epoch: [12/20], Batch Num: [315/600]
Discriminator Loss: 0.7763, Generator Loss: 2.1204
D(x): 0.7532, D(G(z)): 0.2411
Epoch: [12/20], Batch Num: [316/600]
Discriminator Loss: 0.7015, Generator Loss: 2.3288
D(x): 0.7597, D(G(z)): 0.1949
Epoch: [12/20], Batch Num: [317/600]
Discriminator Loss: 0.6782, Generator Loss: 2.1829
D(x): 0.7756, D(G(z)): 0.2113
Epoch: [12/20], Batch Num: [318/600]
Discriminator Loss: 0.7695, Generator Loss: 2.0154
D(x): 0.7171, D(G(z)): 0.2122
Epoch: [12/20], Batch Num: [319/600]
Discriminator Loss: 0.5541, Generator Loss: 2.0555
D(x): 0.8115, D(G(z)): 0.2118
Epoch: [12/20], Batch Num: [320/600]
Discriminator Loss: 0.6753, Generator Loss: 2.2558
D(x): 0.7806, D(G(z)): 0.2356
Epoch: [12/20], Batch Num: [321/600]
Discriminator Loss: 0.6053, Generator Loss: 1.9839
D(x): 0.8409, D(G(z)): 0.2563
Epoch: [12/20], Batch Num: [322/600]
Discriminator Loss: 0.5733, Generator Loss: 2.1596
D(x): 0.8183, D(G(z)): 0.2358
Epoch: [12/20], Batch Num: [323/600]
Discriminator Loss: 0.6490, Generator Loss: 1.9440
D(x): 0.7785, D(G(z)): 0.1807
Epoch: [12/20], Batch Num: [324/600]
Discriminator Loss: 0.5450, Generator Loss: 2.0718
D(x): 0.8212, D(G(z)): 0.1939
Epoch: [12/20], Batch Num: [325/600]
Discriminator Loss: 0.5451, Generator Loss: 2.1812
D(x): 0.8125, D(G(z)): 0.2041
Epoch: [12/20], Batch Num: [326/600]
Discriminator Loss: 0.4315, Generator Loss: 2.5871
D(x): 0.8437, D(G(z)): 0.1741
Epoch: [12/20], Batch Num: [327/600]
Discriminator Loss: 0.6053, Generator Loss: 2.5828
D(x): 0.8163, D(G(z)): 0.2346
Epoch: [12/20], Batch Num: [328/600]
Discriminator Loss: 0.5012, Generator Loss: 2.6196
D(x): 0.8221, D(G(z)): 0.1767
Epoch: [12/20], Batch Num: [329/600]
Discriminator Loss: 0.6306, Generator Loss: 2.8099
D(x): 0.8454, D(G(z)): 0.2307
Epoch: [12/20], Batch Num: [330/600]
Discriminator Loss: 0.6136, Generator Loss: 2.7908
D(x): 0.7834, D(G(z)): 0.1583
Epoch: [12/20], Batch Num: [331/600]
Discriminator Loss: 0.5853, Generator Loss: 2.6828
D(x): 0.7675, D(G(z)): 0.1300
Epoch: [12/20], Batch Num: [332/600]
Discriminator Loss: 0.4953, Generator Loss: 2.2717
D(x): 0.8400, D(G(z)): 0.1732
Epoch: [12/20], Batch Num: [333/600]
Discriminator Loss: 0.5226, Generator Loss: 2.3551
D(x): 0.8584, D(G(z)): 0.2102
Epoch: [12/20], Batch Num: [334/600]
Discriminator Loss: 0.7053, Generator Loss: 2.4339
D(x): 0.8062, D(G(z)): 0.2361
Epoch: [12/20], Batch Num: [335/600]
Discriminator Loss: 0.5597, Generator Loss: 2.4448
D(x): 0.8185, D(G(z)): 0.2069
Epoch: [12/20], Batch Num: [336/600]
Discriminator Loss: 0.5185, Generator Loss: 2.7607
D(x): 0.8504, D(G(z)): 0.2052
Epoch: [12/20], Batch Num: [337/600]
Discriminator Loss: 0.6069, Generator Loss: 3.2642
D(x): 0.8582, D(G(z)): 0.2119
Epoch: [12/20], Batch Num: [338/600]
Discriminator Loss: 0.8035, Generator Loss: 3.1124
D(x): 0.7028, D(G(z)): 0.1616
Epoch: [12/20], Batch Num: [339/600]
Discriminator Loss: 0.6867, Generator Loss: 2.5798
D(x): 0.7505, D(G(z)): 0.1451
Epoch: [12/20], Batch Num: [340/600]
Discriminator Loss: 0.6338, Generator Loss: 2.5999
D(x): 0.8249, D(G(z)): 0.2064
Epoch: [12/20], Batch Num: [341/600]
Discriminator Loss: 0.6831, Generator Loss: 2.3900
D(x): 0.8189, D(G(z)): 0.2410
Epoch: [12/20], Batch Num: [342/600]
Discriminator Loss: 0.7267, Generator Loss: 2.6035
D(x): 0.8039, D(G(z)): 0.2214
Epoch: [12/20], Batch Num: [343/600]
Discriminator Loss: 0.8748, Generator Loss: 2.2860
D(x): 0.7258, D(G(z)): 0.2455
Epoch: [12/20], Batch Num: [344/600]
Discriminator Loss: 0.8573, Generator Loss: 2.0488
D(x): 0.7398, D(G(z)): 0.2088
Epoch: [12/20], Batch Num: [345/600]
Discriminator Loss: 0.9432, Generator Loss: 2.1154
D(x): 0.7912, D(G(z)): 0.3066
Epoch: [12/20], Batch Num: [346/600]
Discriminator Loss: 0.7825, Generator Loss: 2.1343
D(x): 0.8030, D(G(z)): 0.2640
Epoch: [12/20], Batch Num: [347/600]
Discriminator Loss: 1.0448, Generator Loss: 2.4471
D(x): 0.6979, D(G(z)): 0.2440
Epoch: [12/20], Batch Num: [348/600]
Discriminator Loss: 0.8355, Generator Loss: 2.4287
D(x): 0.7039, D(G(z)): 0.2306
Epoch: [12/20], Batch Num: [349/600]
Discriminator Loss: 1.0050, Generator Loss: 1.9890
D(x): 0.6653, D(G(z)): 0.2601
Epoch: [12/20], Batch Num: [350/600]
Discriminator Loss: 0.9834, Generator Loss: 1.9511
D(x): 0.7756, D(G(z)): 0.3293
Epoch: [12/20], Batch Num: [351/600]
Discriminator Loss: 0.8959, Generator Loss: 2.0080
D(x): 0.7670, D(G(z)): 0.3336
Epoch: [12/20], Batch Num: [352/600]
Discriminator Loss: 0.8345, Generator Loss: 1.9269
D(x): 0.7512, D(G(z)): 0.2616
Epoch: [12/20], Batch Num: [353/600]
Discriminator Loss: 0.9239, Generator Loss: 1.9966
D(x): 0.7159, D(G(z)): 0.2513
Epoch: [12/20], Batch Num: [354/600]
Discriminator Loss: 0.7629, Generator Loss: 2.2996
D(x): 0.7550, D(G(z)): 0.2760
Epoch: [12/20], Batch Num: [355/600]
Discriminator Loss: 0.8619, Generator Loss: 1.9870
D(x): 0.7146, D(G(z)): 0.2694
Epoch: [12/20], Batch Num: [356/600]
Discriminator Loss: 0.8216, Generator Loss: 1.9086
D(x): 0.7479, D(G(z)): 0.2786
Epoch: [12/20], Batch Num: [357/600]
Discriminator Loss: 0.8509, Generator Loss: 1.9692
D(x): 0.7137, D(G(z)): 0.2409
Epoch: [12/20], Batch Num: [358/600]
Discriminator Loss: 0.9927, Generator Loss: 1.8874
D(x): 0.6704, D(G(z)): 0.2821
Epoch: [12/20], Batch Num: [359/600]
Discriminator Loss: 0.8817, Generator Loss: 1.7568
D(x): 0.7180, D(G(z)): 0.2800
Epoch: [12/20], Batch Num: [360/600]
Discriminator Loss: 0.8478, Generator Loss: 1.6926
D(x): 0.7448, D(G(z)): 0.3216
Epoch: [12/20], Batch Num: [361/600]
Discriminator Loss: 0.8742, Generator Loss: 1.6649
D(x): 0.7407, D(G(z)): 0.3109
Epoch: [12/20], Batch Num: [362/600]
Discriminator Loss: 0.9965, Generator Loss: 1.7067
D(x): 0.7092, D(G(z)): 0.3154
Epoch: [12/20], Batch Num: [363/600]
Discriminator Loss: 0.9221, Generator Loss: 1.8712
D(x): 0.7287, D(G(z)): 0.3215
Epoch: [12/20], Batch Num: [364/600]
Discriminator Loss: 0.8421, Generator Loss: 1.7076
D(x): 0.7407, D(G(z)): 0.2841
Epoch: [12/20], Batch Num: [365/600]
Discriminator Loss: 0.9052, Generator Loss: 1.9911
D(x): 0.6956, D(G(z)): 0.2646
Epoch: [12/20], Batch Num: [366/600]
Discriminator Loss: 0.9104, Generator Loss: 1.6592
D(x): 0.6678, D(G(z)): 0.2477
Epoch: [12/20], Batch Num: [367/600]
Discriminator Loss: 0.8618, Generator Loss: 1.5879
D(x): 0.6528, D(G(z)): 0.2172
Epoch: [12/20], Batch Num: [368/600]
Discriminator Loss: 0.8055, Generator Loss: 1.0824
D(x): 0.7591, D(G(z)): 0.3198
Epoch: [12/20], Batch Num: [369/600]
Discriminator Loss: 1.1877, Generator Loss: 1.1232
D(x): 0.7499, D(G(z)): 0.4619
Epoch: [12/20], Batch Num: [370/600]
Discriminator Loss: 0.8668, Generator Loss: 1.5787
D(x): 0.8054, D(G(z)): 0.3792
Epoch: [12/20], Batch Num: [371/600]
Discriminator Loss: 0.8151, Generator Loss: 2.0852
D(x): 0.7401, D(G(z)): 0.3007
Epoch: [12/20], Batch Num: [372/600]
Discriminator Loss: 0.8008, Generator Loss: 2.0343
D(x): 0.6771, D(G(z)): 0.2163
Epoch: [12/20], Batch Num: [373/600]
Discriminator Loss: 0.9786, Generator Loss: 2.0749
D(x): 0.6189, D(G(z)): 0.2108
Epoch: [12/20], Batch Num: [374/600]
Discriminator Loss: 0.7688, Generator Loss: 1.8423
D(x): 0.6760, D(G(z)): 0.1878
Epoch: [12/20], Batch Num: [375/600]
Discriminator Loss: 0.8645, Generator Loss: 1.5814
D(x): 0.7036, D(G(z)): 0.2735
Epoch: [12/20], Batch Num: [376/600]
Discriminator Loss: 0.7684, Generator Loss: 1.3906
D(x): 0.7819, D(G(z)): 0.3090
Epoch: [12/20], Batch Num: [377/600]
Discriminator Loss: 0.7552, Generator Loss: 1.3900
D(x): 0.8120, D(G(z)): 0.3333
Epoch: [12/20], Batch Num: [378/600]
Discriminator Loss: 0.7710, Generator Loss: 1.7570
D(x): 0.8248, D(G(z)): 0.3524
Epoch: [12/20], Batch Num: [379/600]
Discriminator Loss: 0.6811, Generator Loss: 1.7939
D(x): 0.7959, D(G(z)): 0.2817
Epoch: [12/20], Batch Num: [380/600]
Discriminator Loss: 0.7042, Generator Loss: 2.1629
D(x): 0.7237, D(G(z)): 0.2034
Epoch: [12/20], Batch Num: [381/600]
Discriminator Loss: 0.6943, Generator Loss: 2.3443
D(x): 0.7193, D(G(z)): 0.1892
Epoch: [12/20], Batch Num: [382/600]
Discriminator Loss: 0.8533, Generator Loss: 2.0672
D(x): 0.6697, D(G(z)): 0.1859
Epoch: [12/20], Batch Num: [383/600]
Discriminator Loss: 0.7469, Generator Loss: 1.7338
D(x): 0.7000, D(G(z)): 0.1892
Epoch: [12/20], Batch Num: [384/600]
Discriminator Loss: 0.5693, Generator Loss: 1.5059
D(x): 0.8227, D(G(z)): 0.2342
Epoch: [12/20], Batch Num: [385/600]
Discriminator Loss: 0.7006, Generator Loss: 1.5388
D(x): 0.8286, D(G(z)): 0.3015
Epoch: [12/20], Batch Num: [386/600]
Discriminator Loss: 0.9254, Generator Loss: 1.6253
D(x): 0.8046, D(G(z)): 0.3554
Epoch: [12/20], Batch Num: [387/600]
Discriminator Loss: 0.7776, Generator Loss: 1.9473
D(x): 0.7502, D(G(z)): 0.2577
Epoch: [12/20], Batch Num: [388/600]
Discriminator Loss: 0.6438, Generator Loss: 2.1339
D(x): 0.7757, D(G(z)): 0.2250
Epoch: [12/20], Batch Num: [389/600]
Discriminator Loss: 0.6816, Generator Loss: 2.3228
D(x): 0.7808, D(G(z)): 0.2173
Epoch: [12/20], Batch Num: [390/600]
Discriminator Loss: 0.8408, Generator Loss: 2.0591
D(x): 0.6918, D(G(z)): 0.2234
Epoch: [12/20], Batch Num: [391/600]
Discriminator Loss: 0.9048, Generator Loss: 2.2122
D(x): 0.6861, D(G(z)): 0.2205
Epoch: [12/20], Batch Num: [392/600]
Discriminator Loss: 0.7979, Generator Loss: 1.8480
D(x): 0.7167, D(G(z)): 0.2347
Epoch: [12/20], Batch Num: [393/600]
Discriminator Loss: 0.7847, Generator Loss: 1.6047
D(x): 0.7678, D(G(z)): 0.2767
Epoch: [12/20], Batch Num: [394/600]
Discriminator Loss: 0.8790, Generator Loss: 1.8887
D(x): 0.7619, D(G(z)): 0.2971
Epoch: [12/20], Batch Num: [395/600]
Discriminator Loss: 0.8232, Generator Loss: 1.7559
D(x): 0.7898, D(G(z)): 0.3316
Epoch: [12/20], Batch Num: [396/600]
Discriminator Loss: 0.8488, Generator Loss: 1.9659
D(x): 0.7180, D(G(z)): 0.2710
Epoch: [12/20], Batch Num: [397/600]
Discriminator Loss: 0.7854, Generator Loss: 1.9445
D(x): 0.7099, D(G(z)): 0.2183
Epoch: [12/20], Batch Num: [398/600]
Discriminator Loss: 0.8607, Generator Loss: 1.7607
D(x): 0.7003, D(G(z)): 0.2412
Epoch: [12/20], Batch Num: [399/600]
Discriminator Loss: 0.8458, Generator Loss: 1.6335
D(x): 0.7419, D(G(z)): 0.2611
Epoch: 12, Batch Num: [400/600]

Epoch: [12/20], Batch Num: [400/600]
Discriminator Loss: 0.9269, Generator Loss: 1.5301
D(x): 0.7033, D(G(z)): 0.2866
Epoch: [12/20], Batch Num: [401/600]
Discriminator Loss: 0.8515, Generator Loss: 1.6665
D(x): 0.8181, D(G(z)): 0.3414
Epoch: [12/20], Batch Num: [402/600]
Discriminator Loss: 1.0764, Generator Loss: 1.5787
D(x): 0.7062, D(G(z)): 0.3437
Epoch: [12/20], Batch Num: [403/600]
Discriminator Loss: 0.9649, Generator Loss: 1.8656
D(x): 0.7214, D(G(z)): 0.3059
Epoch: [12/20], Batch Num: [404/600]
Discriminator Loss: 0.7796, Generator Loss: 2.0488
D(x): 0.7198, D(G(z)): 0.2353
Epoch: [12/20], Batch Num: [405/600]
Discriminator Loss: 0.9006, Generator Loss: 1.6834
D(x): 0.6585, D(G(z)): 0.2089
Epoch: [12/20], Batch Num: [406/600]
Discriminator Loss: 0.8504, Generator Loss: 1.7217
D(x): 0.7068, D(G(z)): 0.2251
Epoch: [12/20], Batch Num: [407/600]
Discriminator Loss: 0.8149, Generator Loss: 1.5541
D(x): 0.7368, D(G(z)): 0.2613
Epoch: [12/20], Batch Num: [408/600]
Discriminator Loss: 1.0822, Generator Loss: 1.4156
D(x): 0.7334, D(G(z)): 0.3395
Epoch: [12/20], Batch Num: [409/600]
Discriminator Loss: 0.8650, Generator Loss: 1.4795
D(x): 0.7732, D(G(z)): 0.3101
Epoch: [12/20], Batch Num: [410/600]
Discriminator Loss: 0.8616, Generator Loss: 1.5460
D(x): 0.7772, D(G(z)): 0.3331
Epoch: [12/20], Batch Num: [411/600]
Discriminator Loss: 0.8696, Generator Loss: 1.8808
D(x): 0.7502, D(G(z)): 0.3115
Epoch: [12/20], Batch Num: [412/600]
Discriminator Loss: 0.6955, Generator Loss: 1.8072
D(x): 0.7429, D(G(z)): 0.2262
Epoch: [12/20], Batch Num: [413/600]
Discriminator Loss: 0.9780, Generator Loss: 2.1047
D(x): 0.6644, D(G(z)): 0.2517
Epoch: [12/20], Batch Num: [414/600]
Discriminator Loss: 0.8325, Generator Loss: 1.8939
D(x): 0.6845, D(G(z)): 0.1906
Epoch: [12/20], Batch Num: [415/600]
Discriminator Loss: 0.7378, Generator Loss: 1.9845
D(x): 0.7590, D(G(z)): 0.2391
Epoch: [12/20], Batch Num: [416/600]
Discriminator Loss: 0.7473, Generator Loss: 1.6421
D(x): 0.7491, D(G(z)): 0.2424
Epoch: [12/20], Batch Num: [417/600]
Discriminator Loss: 0.7228, Generator Loss: 1.7648
D(x): 0.8107, D(G(z)): 0.3004
Epoch: [12/20], Batch Num: [418/600]
Discriminator Loss: 0.5914, Generator Loss: 1.9064
D(x): 0.8542, D(G(z)): 0.2671
Epoch: [12/20], Batch Num: [419/600]
Discriminator Loss: 0.6380, Generator Loss: 2.0379
D(x): 0.7717, D(G(z)): 0.1977
Epoch: [12/20], Batch Num: [420/600]
Discriminator Loss: 0.6168, Generator Loss: 2.1072
D(x): 0.7943, D(G(z)): 0.2103
Epoch: [12/20], Batch Num: [421/600]
Discriminator Loss: 0.6513, Generator Loss: 2.3862
D(x): 0.7771, D(G(z)): 0.1961
Epoch: [12/20], Batch Num: [422/600]
Discriminator Loss: 0.4820, Generator Loss: 2.0979
D(x): 0.8007, D(G(z)): 0.1594
Epoch: [12/20], Batch Num: [423/600]
Discriminator Loss: 0.6088, Generator Loss: 1.9348
D(x): 0.7439, D(G(z)): 0.1668
Epoch: [12/20], Batch Num: [424/600]
Discriminator Loss: 0.6240, Generator Loss: 1.9484
D(x): 0.8004, D(G(z)): 0.2308
Epoch: [12/20], Batch Num: [425/600]
Discriminator Loss: 0.7476, Generator Loss: 1.7997
D(x): 0.7974, D(G(z)): 0.2807
Epoch: [12/20], Batch Num: [426/600]
Discriminator Loss: 0.6472, Generator Loss: 2.0203
D(x): 0.8513, D(G(z)): 0.2746
Epoch: [12/20], Batch Num: [427/600]
Discriminator Loss: 0.5952, Generator Loss: 2.4145
D(x): 0.8228, D(G(z)): 0.2233
Epoch: [12/20], Batch Num: [428/600]
Discriminator Loss: 0.5660, Generator Loss: 2.5407
D(x): 0.8156, D(G(z)): 0.2071
Epoch: [12/20], Batch Num: [429/600]
Discriminator Loss: 0.5915, Generator Loss: 2.3419
D(x): 0.8014, D(G(z)): 0.2032
Epoch: [12/20], Batch Num: [430/600]
Discriminator Loss: 0.6333, Generator Loss: 2.2062
D(x): 0.7620, D(G(z)): 0.1722
Epoch: [12/20], Batch Num: [431/600]
Discriminator Loss: 0.6783, Generator Loss: 2.0737
D(x): 0.7499, D(G(z)): 0.1803
Epoch: [12/20], Batch Num: [432/600]
Discriminator Loss: 0.5914, Generator Loss: 1.9602
D(x): 0.7767, D(G(z)): 0.1684
Epoch: [12/20], Batch Num: [433/600]
Discriminator Loss: 0.6397, Generator Loss: 1.7622
D(x): 0.8434, D(G(z)): 0.2506
Epoch: [12/20], Batch Num: [434/600]
Discriminator Loss: 0.8130, Generator Loss: 2.1798
D(x): 0.8401, D(G(z)): 0.3312
Epoch: [12/20], Batch Num: [435/600]
Discriminator Loss: 0.6269, Generator Loss: 2.5492
D(x): 0.8548, D(G(z)): 0.2553
Epoch: [12/20], Batch Num: [436/600]
Discriminator Loss: 0.6773, Generator Loss: 2.3911
D(x): 0.7817, D(G(z)): 0.2161
Epoch: [12/20], Batch Num: [437/600]
Discriminator Loss: 0.6244, Generator Loss: 2.7814
D(x): 0.7807, D(G(z)): 0.1820
Epoch: [12/20], Batch Num: [438/600]
Discriminator Loss: 0.7206, Generator Loss: 2.7445
D(x): 0.7607, D(G(z)): 0.1789
Epoch: [12/20], Batch Num: [439/600]
Discriminator Loss: 0.8425, Generator Loss: 2.1358
D(x): 0.6542, D(G(z)): 0.1540
Epoch: [12/20], Batch Num: [440/600]
Discriminator Loss: 0.8606, Generator Loss: 1.6724
D(x): 0.6977, D(G(z)): 0.2052
Epoch: [12/20], Batch Num: [441/600]
Discriminator Loss: 0.8354, Generator Loss: 1.3619
D(x): 0.8591, D(G(z)): 0.3286
Epoch: [12/20], Batch Num: [442/600]
Discriminator Loss: 0.8659, Generator Loss: 1.5898
D(x): 0.8310, D(G(z)): 0.3517
Epoch: [12/20], Batch Num: [443/600]
Discriminator Loss: 0.7876, Generator Loss: 2.0483
D(x): 0.8450, D(G(z)): 0.3298
Epoch: [12/20], Batch Num: [444/600]
Discriminator Loss: 0.7400, Generator Loss: 2.4342
D(x): 0.7710, D(G(z)): 0.2314
Epoch: [12/20], Batch Num: [445/600]
Discriminator Loss: 1.0256, Generator Loss: 2.2515
D(x): 0.6151, D(G(z)): 0.1772
Epoch: [12/20], Batch Num: [446/600]
Discriminator Loss: 0.8937, Generator Loss: 1.8957
D(x): 0.6372, D(G(z)): 0.1610
Epoch: [12/20], Batch Num: [447/600]
Discriminator Loss: 0.8183, Generator Loss: 1.5643
D(x): 0.7438, D(G(z)): 0.2678
Epoch: [12/20], Batch Num: [448/600]
Discriminator Loss: 0.8293, Generator Loss: 1.4913
D(x): 0.8229, D(G(z)): 0.3292
Epoch: [12/20], Batch Num: [449/600]
Discriminator Loss: 0.9647, Generator Loss: 1.5552
D(x): 0.8255, D(G(z)): 0.3900
Epoch: [12/20], Batch Num: [450/600]
Discriminator Loss: 0.8740, Generator Loss: 2.0019
D(x): 0.7967, D(G(z)): 0.3529
Epoch: [12/20], Batch Num: [451/600]
Discriminator Loss: 0.7570, Generator Loss: 2.0914
D(x): 0.7486, D(G(z)): 0.2295
Epoch: [12/20], Batch Num: [452/600]
Discriminator Loss: 1.0648, Generator Loss: 2.0499
D(x): 0.6268, D(G(z)): 0.2372
Epoch: [12/20], Batch Num: [453/600]
Discriminator Loss: 1.0251, Generator Loss: 1.8497
D(x): 0.6269, D(G(z)): 0.2002
Epoch: [12/20], Batch Num: [454/600]
Discriminator Loss: 1.0597, Generator Loss: 1.6994
D(x): 0.6617, D(G(z)): 0.2729
Epoch: [12/20], Batch Num: [455/600]
Discriminator Loss: 0.9726, Generator Loss: 1.7122
D(x): 0.7433, D(G(z)): 0.3230
Epoch: [12/20], Batch Num: [456/600]
Discriminator Loss: 0.8829, Generator Loss: 1.5922
D(x): 0.7716, D(G(z)): 0.3172
Epoch: [12/20], Batch Num: [457/600]
Discriminator Loss: 0.8744, Generator Loss: 1.4756
D(x): 0.7703, D(G(z)): 0.3203
Epoch: [12/20], Batch Num: [458/600]
Discriminator Loss: 0.9659, Generator Loss: 1.8068
D(x): 0.7222, D(G(z)): 0.3379
Epoch: [12/20], Batch Num: [459/600]
Discriminator Loss: 0.8762, Generator Loss: 1.6246
D(x): 0.7399, D(G(z)): 0.3048
Epoch: [12/20], Batch Num: [460/600]
Discriminator Loss: 0.8587, Generator Loss: 2.0462
D(x): 0.6942, D(G(z)): 0.2336
Epoch: [12/20], Batch Num: [461/600]
Discriminator Loss: 0.9359, Generator Loss: 1.6314
D(x): 0.6297, D(G(z)): 0.2427
Epoch: [12/20], Batch Num: [462/600]
Discriminator Loss: 0.8480, Generator Loss: 1.5701
D(x): 0.6941, D(G(z)): 0.2645
Epoch: [12/20], Batch Num: [463/600]
Discriminator Loss: 1.0597, Generator Loss: 1.4563
D(x): 0.7330, D(G(z)): 0.3773
Epoch: [12/20], Batch Num: [464/600]
Discriminator Loss: 0.8627, Generator Loss: 1.6769
D(x): 0.7433, D(G(z)): 0.2978
Epoch: [12/20], Batch Num: [465/600]
Discriminator Loss: 0.8199, Generator Loss: 1.5264
D(x): 0.7186, D(G(z)): 0.2856
Epoch: [12/20], Batch Num: [466/600]
Discriminator Loss: 0.8845, Generator Loss: 1.6990
D(x): 0.7226, D(G(z)): 0.3106
Epoch: [12/20], Batch Num: [467/600]
Discriminator Loss: 0.8850, Generator Loss: 1.8948
D(x): 0.7074, D(G(z)): 0.3008
Epoch: [12/20], Batch Num: [468/600]
Discriminator Loss: 0.7148, Generator Loss: 1.9897
D(x): 0.7268, D(G(z)): 0.2007
Epoch: [12/20], Batch Num: [469/600]
Discriminator Loss: 0.7351, Generator Loss: 1.7503
D(x): 0.6996, D(G(z)): 0.2025
Epoch: [12/20], Batch Num: [470/600]
Discriminator Loss: 0.7894, Generator Loss: 1.6776
D(x): 0.7567, D(G(z)): 0.2601
Epoch: [12/20], Batch Num: [471/600]
Discriminator Loss: 0.6882, Generator Loss: 1.8259
D(x): 0.7912, D(G(z)): 0.2633
Epoch: [12/20], Batch Num: [472/600]
Discriminator Loss: 0.6421, Generator Loss: 1.7727
D(x): 0.8077, D(G(z)): 0.2565
Epoch: [12/20], Batch Num: [473/600]
Discriminator Loss: 0.6930, Generator Loss: 1.8170
D(x): 0.7870, D(G(z)): 0.2663
Epoch: [12/20], Batch Num: [474/600]
Discriminator Loss: 0.5801, Generator Loss: 2.0006
D(x): 0.8017, D(G(z)): 0.2362
Epoch: [12/20], Batch Num: [475/600]
Discriminator Loss: 0.5480, Generator Loss: 2.1444
D(x): 0.7780, D(G(z)): 0.1741
Epoch: [12/20], Batch Num: [476/600]
Discriminator Loss: 0.6267, Generator Loss: 1.7490
D(x): 0.7248, D(G(z)): 0.1725
Epoch: [12/20], Batch Num: [477/600]
Discriminator Loss: 0.6684, Generator Loss: 1.7008
D(x): 0.7604, D(G(z)): 0.2213
Epoch: [12/20], Batch Num: [478/600]
Discriminator Loss: 0.7061, Generator Loss: 1.8064
D(x): 0.7757, D(G(z)): 0.2603
Epoch: [12/20], Batch Num: [479/600]
Discriminator Loss: 0.6353, Generator Loss: 1.5445
D(x): 0.8149, D(G(z)): 0.2557
Epoch: [12/20], Batch Num: [480/600]
Discriminator Loss: 0.7443, Generator Loss: 1.8153
D(x): 0.8076, D(G(z)): 0.2987
Epoch: [12/20], Batch Num: [481/600]
Discriminator Loss: 0.7695, Generator Loss: 2.1695
D(x): 0.7917, D(G(z)): 0.2826
Epoch: [12/20], Batch Num: [482/600]
Discriminator Loss: 0.7068, Generator Loss: 2.2637
D(x): 0.7592, D(G(z)): 0.2162
Epoch: [12/20], Batch Num: [483/600]
Discriminator Loss: 0.8142, Generator Loss: 2.0984
D(x): 0.6867, D(G(z)): 0.2131
Epoch: [12/20], Batch Num: [484/600]
Discriminator Loss: 0.8700, Generator Loss: 1.7693
D(x): 0.6682, D(G(z)): 0.1964
Epoch: [12/20], Batch Num: [485/600]
Discriminator Loss: 0.8923, Generator Loss: 1.7135
D(x): 0.7360, D(G(z)): 0.2687
Epoch: [12/20], Batch Num: [486/600]
Discriminator Loss: 0.8612, Generator Loss: 1.4289
D(x): 0.7683, D(G(z)): 0.3106
Epoch: [12/20], Batch Num: [487/600]
Discriminator Loss: 0.9307, Generator Loss: 1.6161
D(x): 0.7832, D(G(z)): 0.3633
Epoch: [12/20], Batch Num: [488/600]
Discriminator Loss: 1.1852, Generator Loss: 1.9645
D(x): 0.6759, D(G(z)): 0.3411
Epoch: [12/20], Batch Num: [489/600]
Discriminator Loss: 1.1468, Generator Loss: 1.8335
D(x): 0.6004, D(G(z)): 0.2324
Epoch: [12/20], Batch Num: [490/600]
Discriminator Loss: 1.1336, Generator Loss: 1.6789
D(x): 0.6276, D(G(z)): 0.3215
Epoch: [12/20], Batch Num: [491/600]
Discriminator Loss: 1.3036, Generator Loss: 1.5123
D(x): 0.6775, D(G(z)): 0.3998
Epoch: [12/20], Batch Num: [492/600]
Discriminator Loss: 1.0452, Generator Loss: 1.5453
D(x): 0.6703, D(G(z)): 0.3060
Epoch: [12/20], Batch Num: [493/600]
Discriminator Loss: 1.0666, Generator Loss: 1.6102
D(x): 0.6906, D(G(z)): 0.3576
Epoch: [12/20], Batch Num: [494/600]
Discriminator Loss: 1.0771, Generator Loss: 1.5217
D(x): 0.6828, D(G(z)): 0.3197
Epoch: [12/20], Batch Num: [495/600]
Discriminator Loss: 1.0878, Generator Loss: 1.5596
D(x): 0.6339, D(G(z)): 0.2928
Epoch: [12/20], Batch Num: [496/600]
Discriminator Loss: 1.1994, Generator Loss: 1.3419
D(x): 0.6448, D(G(z)): 0.3807
Epoch: [12/20], Batch Num: [497/600]
Discriminator Loss: 1.2416, Generator Loss: 1.4188
D(x): 0.6291, D(G(z)): 0.3777
Epoch: [12/20], Batch Num: [498/600]
Discriminator Loss: 1.1631, Generator Loss: 1.4148
D(x): 0.6386, D(G(z)): 0.3551
Epoch: [12/20], Batch Num: [499/600]
Discriminator Loss: 1.0701, Generator Loss: 1.2829
D(x): 0.6373, D(G(z)): 0.3364
Epoch: 12, Batch Num: [500/600]

Epoch: [12/20], Batch Num: [500/600]
Discriminator Loss: 1.1141, Generator Loss: 1.1755
D(x): 0.6398, D(G(z)): 0.3314
Epoch: [12/20], Batch Num: [501/600]
Discriminator Loss: 1.1678, Generator Loss: 1.3680
D(x): 0.6889, D(G(z)): 0.4059
Epoch: [12/20], Batch Num: [502/600]
Discriminator Loss: 1.0477, Generator Loss: 1.2909
D(x): 0.6652, D(G(z)): 0.3339
Epoch: [12/20], Batch Num: [503/600]
Discriminator Loss: 0.8514, Generator Loss: 1.4035
D(x): 0.7023, D(G(z)): 0.2841
Epoch: [12/20], Batch Num: [504/600]
Discriminator Loss: 0.9761, Generator Loss: 1.4277
D(x): 0.6502, D(G(z)): 0.2955
Epoch: [12/20], Batch Num: [505/600]
Discriminator Loss: 0.8171, Generator Loss: 1.5085
D(x): 0.7456, D(G(z)): 0.3207
Epoch: [12/20], Batch Num: [506/600]
Discriminator Loss: 0.8747, Generator Loss: 1.5417
D(x): 0.6908, D(G(z)): 0.2910
Epoch: [12/20], Batch Num: [507/600]
Discriminator Loss: 0.8909, Generator Loss: 1.5102
D(x): 0.7031, D(G(z)): 0.3179
Epoch: [12/20], Batch Num: [508/600]
Discriminator Loss: 0.7185, Generator Loss: 1.7396
D(x): 0.7420, D(G(z)): 0.2674
Epoch: [12/20], Batch Num: [509/600]
Discriminator Loss: 0.8680, Generator Loss: 1.6479
D(x): 0.6892, D(G(z)): 0.2814
Epoch: [12/20], Batch Num: [510/600]
Discriminator Loss: 0.6268, Generator Loss: 1.7170
D(x): 0.7592, D(G(z)): 0.2272
Epoch: [12/20], Batch Num: [511/600]
Discriminator Loss: 0.5066, Generator Loss: 1.6426
D(x): 0.8395, D(G(z)): 0.2341
Epoch: [12/20], Batch Num: [512/600]
Discriminator Loss: 0.6312, Generator Loss: 1.8283
D(x): 0.7895, D(G(z)): 0.2554
Epoch: [12/20], Batch Num: [513/600]
Discriminator Loss: 0.6271, Generator Loss: 1.9136
D(x): 0.7761, D(G(z)): 0.2275
Epoch: [12/20], Batch Num: [514/600]
Discriminator Loss: 0.6202, Generator Loss: 2.0643
D(x): 0.7896, D(G(z)): 0.2224
Epoch: [12/20], Batch Num: [515/600]
Discriminator Loss: 0.6071, Generator Loss: 2.0937
D(x): 0.7687, D(G(z)): 0.2006
Epoch: [12/20], Batch Num: [516/600]
Discriminator Loss: 0.5740, Generator Loss: 2.0956
D(x): 0.8098, D(G(z)): 0.2106
Epoch: [12/20], Batch Num: [517/600]
Discriminator Loss: 0.5038, Generator Loss: 2.1951
D(x): 0.8134, D(G(z)): 0.1872
Epoch: [12/20], Batch Num: [518/600]
Discriminator Loss: 0.5326, Generator Loss: 2.3784
D(x): 0.8050, D(G(z)): 0.1904
Epoch: [12/20], Batch Num: [519/600]
Discriminator Loss: 0.6018, Generator Loss: 2.3283
D(x): 0.7936, D(G(z)): 0.2087
Epoch: [12/20], Batch Num: [520/600]
Discriminator Loss: 0.5585, Generator Loss: 2.3394
D(x): 0.8459, D(G(z)): 0.2214
Epoch: [12/20], Batch Num: [521/600]
Discriminator Loss: 0.6025, Generator Loss: 2.4826
D(x): 0.7688, D(G(z)): 0.1719
Epoch: [12/20], Batch Num: [522/600]
Discriminator Loss: 0.5165, Generator Loss: 2.5852
D(x): 0.8190, D(G(z)): 0.1778
Epoch: [12/20], Batch Num: [523/600]
Discriminator Loss: 0.4814, Generator Loss: 2.4918
D(x): 0.8253, D(G(z)): 0.1645
Epoch: [12/20], Batch Num: [524/600]
Discriminator Loss: 0.6543, Generator Loss: 2.3264
D(x): 0.7521, D(G(z)): 0.1842
Epoch: [12/20], Batch Num: [525/600]
Discriminator Loss: 0.6289, Generator Loss: 2.0658
D(x): 0.8051, D(G(z)): 0.2307
Epoch: [12/20], Batch Num: [526/600]
Discriminator Loss: 0.5360, Generator Loss: 2.4193
D(x): 0.8683, D(G(z)): 0.2320
Epoch: [12/20], Batch Num: [527/600]
Discriminator Loss: 0.5226, Generator Loss: 2.5208
D(x): 0.8416, D(G(z)): 0.2032
Epoch: [12/20], Batch Num: [528/600]
Discriminator Loss: 0.8114, Generator Loss: 2.3603
D(x): 0.7718, D(G(z)): 0.2449
Epoch: [12/20], Batch Num: [529/600]
Discriminator Loss: 0.6766, Generator Loss: 3.0123
D(x): 0.7999, D(G(z)): 0.2271
Epoch: [12/20], Batch Num: [530/600]
Discriminator Loss: 0.7778, Generator Loss: 2.3926
D(x): 0.7349, D(G(z)): 0.1781
Epoch: [12/20], Batch Num: [531/600]
Discriminator Loss: 0.7546, Generator Loss: 2.1044
D(x): 0.7340, D(G(z)): 0.2010
Epoch: [12/20], Batch Num: [532/600]
Discriminator Loss: 0.7453, Generator Loss: 1.9259
D(x): 0.7920, D(G(z)): 0.2337
Epoch: [12/20], Batch Num: [533/600]
Discriminator Loss: 0.9765, Generator Loss: 1.9546
D(x): 0.7587, D(G(z)): 0.3013
Epoch: [12/20], Batch Num: [534/600]
Discriminator Loss: 1.1846, Generator Loss: 1.6618
D(x): 0.6574, D(G(z)): 0.3204
Epoch: [12/20], Batch Num: [535/600]
Discriminator Loss: 0.8817, Generator Loss: 1.7993
D(x): 0.7355, D(G(z)): 0.2765
Epoch: [12/20], Batch Num: [536/600]
Discriminator Loss: 0.8896, Generator Loss: 1.8177
D(x): 0.7414, D(G(z)): 0.3065
Epoch: [12/20], Batch Num: [537/600]
Discriminator Loss: 0.9177, Generator Loss: 1.9823
D(x): 0.7309, D(G(z)): 0.2506
Epoch: [12/20], Batch Num: [538/600]
Discriminator Loss: 1.0588, Generator Loss: 1.7782
D(x): 0.6596, D(G(z)): 0.2944
Epoch: [12/20], Batch Num: [539/600]
Discriminator Loss: 0.9888, Generator Loss: 1.5515
D(x): 0.6923, D(G(z)): 0.2925
Epoch: [12/20], Batch Num: [540/600]
Discriminator Loss: 1.1433, Generator Loss: 1.5485
D(x): 0.6759, D(G(z)): 0.3708
Epoch: [12/20], Batch Num: [541/600]
Discriminator Loss: 1.0654, Generator Loss: 1.4913
D(x): 0.6941, D(G(z)): 0.3423
Epoch: [12/20], Batch Num: [542/600]
Discriminator Loss: 1.1160, Generator Loss: 1.7074
D(x): 0.6642, D(G(z)): 0.3245
Epoch: [12/20], Batch Num: [543/600]
Discriminator Loss: 1.2064, Generator Loss: 1.3439
D(x): 0.5772, D(G(z)): 0.2830
Epoch: [12/20], Batch Num: [544/600]
Discriminator Loss: 1.1538, Generator Loss: 1.4519
D(x): 0.7053, D(G(z)): 0.3874
Epoch: [12/20], Batch Num: [545/600]
Discriminator Loss: 0.9716, Generator Loss: 1.5578
D(x): 0.7155, D(G(z)): 0.3311
Epoch: [12/20], Batch Num: [546/600]
Discriminator Loss: 1.0063, Generator Loss: 1.7491
D(x): 0.6439, D(G(z)): 0.3015
Epoch: [12/20], Batch Num: [547/600]
Discriminator Loss: 1.0379, Generator Loss: 1.4917
D(x): 0.6857, D(G(z)): 0.3186
Epoch: [12/20], Batch Num: [548/600]
Discriminator Loss: 0.9208, Generator Loss: 1.5607
D(x): 0.7010, D(G(z)): 0.3005
Epoch: [12/20], Batch Num: [549/600]
Discriminator Loss: 0.7206, Generator Loss: 1.5725
D(x): 0.7710, D(G(z)): 0.2796
Epoch: [12/20], Batch Num: [550/600]
Discriminator Loss: 0.7595, Generator Loss: 1.6276
D(x): 0.7615, D(G(z)): 0.2712
Epoch: [12/20], Batch Num: [551/600]
Discriminator Loss: 0.7766, Generator Loss: 1.9347
D(x): 0.7170, D(G(z)): 0.2493
Epoch: [12/20], Batch Num: [552/600]
Discriminator Loss: 0.6435, Generator Loss: 1.8031
D(x): 0.7653, D(G(z)): 0.2241
Epoch: [12/20], Batch Num: [553/600]
Discriminator Loss: 0.5915, Generator Loss: 1.6410
D(x): 0.7856, D(G(z)): 0.2096
Epoch: [12/20], Batch Num: [554/600]
Discriminator Loss: 0.6931, Generator Loss: 1.7688
D(x): 0.7627, D(G(z)): 0.2473
Epoch: [12/20], Batch Num: [555/600]
Discriminator Loss: 0.6884, Generator Loss: 2.1008
D(x): 0.7805, D(G(z)): 0.2573
Epoch: [12/20], Batch Num: [556/600]
Discriminator Loss: 0.6417, Generator Loss: 2.0472
D(x): 0.8062, D(G(z)): 0.2299
Epoch: [12/20], Batch Num: [557/600]
Discriminator Loss: 0.5223, Generator Loss: 2.4659
D(x): 0.8401, D(G(z)): 0.2142
Epoch: [12/20], Batch Num: [558/600]
Discriminator Loss: 0.5335, Generator Loss: 2.4753
D(x): 0.7741, D(G(z)): 0.1644
Epoch: [12/20], Batch Num: [559/600]
Discriminator Loss: 0.5827, Generator Loss: 2.1368
D(x): 0.7486, D(G(z)): 0.1318
Epoch: [12/20], Batch Num: [560/600]
Discriminator Loss: 0.5153, Generator Loss: 2.1472
D(x): 0.8117, D(G(z)): 0.1824
Epoch: [12/20], Batch Num: [561/600]
Discriminator Loss: 0.4658, Generator Loss: 1.9394
D(x): 0.8815, D(G(z)): 0.2178
Epoch: [12/20], Batch Num: [562/600]
Discriminator Loss: 0.5243, Generator Loss: 2.3708
D(x): 0.8686, D(G(z)): 0.2282
Epoch: [12/20], Batch Num: [563/600]
Discriminator Loss: 0.5486, Generator Loss: 2.6174
D(x): 0.8731, D(G(z)): 0.2430
Epoch: [12/20], Batch Num: [564/600]
Discriminator Loss: 0.5977, Generator Loss: 3.1893
D(x): 0.7694, D(G(z)): 0.1580
Epoch: [12/20], Batch Num: [565/600]
Discriminator Loss: 0.5843, Generator Loss: 2.8035
D(x): 0.7447, D(G(z)): 0.0954
Epoch: [12/20], Batch Num: [566/600]
Discriminator Loss: 0.5151, Generator Loss: 2.3413
D(x): 0.8020, D(G(z)): 0.1424
Epoch: [12/20], Batch Num: [567/600]
Discriminator Loss: 0.5101, Generator Loss: 2.0211
D(x): 0.8531, D(G(z)): 0.1921
Epoch: [12/20], Batch Num: [568/600]
Discriminator Loss: 0.5989, Generator Loss: 1.9934
D(x): 0.8494, D(G(z)): 0.2296
Epoch: [12/20], Batch Num: [569/600]
Discriminator Loss: 0.5879, Generator Loss: 2.5065
D(x): 0.8810, D(G(z)): 0.2548
Epoch: [12/20], Batch Num: [570/600]
Discriminator Loss: 0.5582, Generator Loss: 2.6174
D(x): 0.8469, D(G(z)): 0.2128
Epoch: [12/20], Batch Num: [571/600]
Discriminator Loss: 0.6983, Generator Loss: 2.6751
D(x): 0.7306, D(G(z)): 0.1495
Epoch: [12/20], Batch Num: [572/600]
Discriminator Loss: 0.6504, Generator Loss: 2.1384
D(x): 0.7250, D(G(z)): 0.1344
Epoch: [12/20], Batch Num: [573/600]
Discriminator Loss: 0.7454, Generator Loss: 1.7509
D(x): 0.7543, D(G(z)): 0.2092
Epoch: [12/20], Batch Num: [574/600]
Discriminator Loss: 0.6702, Generator Loss: 1.6754
D(x): 0.8612, D(G(z)): 0.2968
Epoch: [12/20], Batch Num: [575/600]
Discriminator Loss: 1.1440, Generator Loss: 2.3501
D(x): 0.8033, D(G(z)): 0.4246
Epoch: [12/20], Batch Num: [576/600]
Discriminator Loss: 0.8601, Generator Loss: 2.5849
D(x): 0.7056, D(G(z)): 0.2231
Epoch: [12/20], Batch Num: [577/600]
Discriminator Loss: 1.1812, Generator Loss: 2.2336
D(x): 0.5855, D(G(z)): 0.1908
Epoch: [12/20], Batch Num: [578/600]
Discriminator Loss: 1.0553, Generator Loss: 1.5779
D(x): 0.6327, D(G(z)): 0.2496
Epoch: [12/20], Batch Num: [579/600]
Discriminator Loss: 1.3030, Generator Loss: 1.2572
D(x): 0.6957, D(G(z)): 0.3868
Epoch: [12/20], Batch Num: [580/600]
Discriminator Loss: 1.1362, Generator Loss: 1.5075
D(x): 0.7877, D(G(z)): 0.4402
Epoch: [12/20], Batch Num: [581/600]
Discriminator Loss: 1.2020, Generator Loss: 1.7172
D(x): 0.6596, D(G(z)): 0.3671
Epoch: [12/20], Batch Num: [582/600]
Discriminator Loss: 1.1255, Generator Loss: 1.9861
D(x): 0.6672, D(G(z)): 0.3270
Epoch: [12/20], Batch Num: [583/600]
Discriminator Loss: 1.2107, Generator Loss: 1.7347
D(x): 0.5508, D(G(z)): 0.2431
Epoch: [12/20], Batch Num: [584/600]
Discriminator Loss: 1.0900, Generator Loss: 1.3390
D(x): 0.6139, D(G(z)): 0.2567
Epoch: [12/20], Batch Num: [585/600]
Discriminator Loss: 1.0674, Generator Loss: 1.2789
D(x): 0.7008, D(G(z)): 0.3765
Epoch: [12/20], Batch Num: [586/600]
Discriminator Loss: 1.0800, Generator Loss: 1.1557
D(x): 0.7377, D(G(z)): 0.4055
Epoch: [12/20], Batch Num: [587/600]
Discriminator Loss: 1.1653, Generator Loss: 1.4896
D(x): 0.7296, D(G(z)): 0.4226
Epoch: [12/20], Batch Num: [588/600]
Discriminator Loss: 0.9568, Generator Loss: 1.4804
D(x): 0.6869, D(G(z)): 0.3202
Epoch: [12/20], Batch Num: [589/600]
Discriminator Loss: 1.0505, Generator Loss: 1.7429
D(x): 0.6357, D(G(z)): 0.3264
Epoch: [12/20], Batch Num: [590/600]
Discriminator Loss: 0.9816, Generator Loss: 1.6066
D(x): 0.6440, D(G(z)): 0.2481
Epoch: [12/20], Batch Num: [591/600]
Discriminator Loss: 0.9919, Generator Loss: 1.6490
D(x): 0.6259, D(G(z)): 0.2547
Epoch: [12/20], Batch Num: [592/600]
Discriminator Loss: 0.8993, Generator Loss: 1.5748
D(x): 0.6983, D(G(z)): 0.2939
Epoch: [12/20], Batch Num: [593/600]
Discriminator Loss: 0.8083, Generator Loss: 1.4018
D(x): 0.7216, D(G(z)): 0.2910
Epoch: [12/20], Batch Num: [594/600]
Discriminator Loss: 0.7446, Generator Loss: 1.3522
D(x): 0.7670, D(G(z)): 0.2987
Epoch: [12/20], Batch Num: [595/600]
Discriminator Loss: 0.7007, Generator Loss: 1.3058
D(x): 0.8003, D(G(z)): 0.3119
Epoch: [12/20], Batch Num: [596/600]
Discriminator Loss: 0.7176, Generator Loss: 1.5477
D(x): 0.8463, D(G(z)): 0.3355
Epoch: [12/20], Batch Num: [597/600]
Discriminator Loss: 0.7466, Generator Loss: 1.7326
D(x): 0.8009, D(G(z)): 0.3077
Epoch: [12/20], Batch Num: [598/600]
Discriminator Loss: 0.5999, Generator Loss: 1.9229
D(x): 0.7570, D(G(z)): 0.1931
Epoch: [12/20], Batch Num: [599/600]
Discriminator Loss: 0.6829, Generator Loss: 2.0628
D(x): 0.7255, D(G(z)): 0.2041
Epoch: 13, Batch Num: [0/600]

Epoch: [13/20], Batch Num: [0/600]
Discriminator Loss: 0.7309, Generator Loss: 1.9369
D(x): 0.6823, D(G(z)): 0.1875
Epoch: [13/20], Batch Num: [1/600]
Discriminator Loss: 0.6599, Generator Loss: 1.7224
D(x): 0.7306, D(G(z)): 0.1910
Epoch: [13/20], Batch Num: [2/600]
Discriminator Loss: 0.5599, Generator Loss: 1.4337
D(x): 0.8548, D(G(z)): 0.2680
Epoch: [13/20], Batch Num: [3/600]
Discriminator Loss: 0.5996, Generator Loss: 1.6253
D(x): 0.8439, D(G(z)): 0.2797
Epoch: [13/20], Batch Num: [4/600]
Discriminator Loss: 0.6018, Generator Loss: 1.8687
D(x): 0.8475, D(G(z)): 0.2852
Epoch: [13/20], Batch Num: [5/600]
Discriminator Loss: 0.5725, Generator Loss: 2.0601
D(x): 0.8044, D(G(z)): 0.2170
Epoch: [13/20], Batch Num: [6/600]
Discriminator Loss: 0.5556, Generator Loss: 2.3274
D(x): 0.8311, D(G(z)): 0.2263
Epoch: [13/20], Batch Num: [7/600]
Discriminator Loss: 0.5464, Generator Loss: 2.2025
D(x): 0.7711, D(G(z)): 0.1691
Epoch: [13/20], Batch Num: [8/600]
Discriminator Loss: 0.5532, Generator Loss: 2.2729
D(x): 0.7410, D(G(z)): 0.1406
Epoch: [13/20], Batch Num: [9/600]
Discriminator Loss: 0.5887, Generator Loss: 2.0146
D(x): 0.8069, D(G(z)): 0.2059
Epoch: [13/20], Batch Num: [10/600]
Discriminator Loss: 0.5093, Generator Loss: 1.9267
D(x): 0.8245, D(G(z)): 0.2063
Epoch: [13/20], Batch Num: [11/600]
Discriminator Loss: 0.5551, Generator Loss: 1.9964
D(x): 0.8832, D(G(z)): 0.2730
Epoch: [13/20], Batch Num: [12/600]
Discriminator Loss: 0.5483, Generator Loss: 2.0530
D(x): 0.8464, D(G(z)): 0.2236
Epoch: [13/20], Batch Num: [13/600]
Discriminator Loss: 0.5726, Generator Loss: 2.7412
D(x): 0.8789, D(G(z)): 0.2596
Epoch: [13/20], Batch Num: [14/600]
Discriminator Loss: 0.5980, Generator Loss: 2.8560
D(x): 0.7512, D(G(z)): 0.1402
Epoch: [13/20], Batch Num: [15/600]
Discriminator Loss: 0.7170, Generator Loss: 2.6920
D(x): 0.6774, D(G(z)): 0.1278
Epoch: [13/20], Batch Num: [16/600]
Discriminator Loss: 0.6705, Generator Loss: 2.0299
D(x): 0.7578, D(G(z)): 0.1930
Epoch: [13/20], Batch Num: [17/600]
Discriminator Loss: 0.6616, Generator Loss: 2.0807
D(x): 0.8370, D(G(z)): 0.2651
Epoch: [13/20], Batch Num: [18/600]
Discriminator Loss: 0.6771, Generator Loss: 1.6449
D(x): 0.8461, D(G(z)): 0.2830
Epoch: [13/20], Batch Num: [19/600]
Discriminator Loss: 0.7746, Generator Loss: 2.5474
D(x): 0.8379, D(G(z)): 0.3147
Epoch: [13/20], Batch Num: [20/600]
Discriminator Loss: 0.6614, Generator Loss: 2.8351
D(x): 0.7831, D(G(z)): 0.2064
Epoch: [13/20], Batch Num: [21/600]
Discriminator Loss: 0.7795, Generator Loss: 2.6221
D(x): 0.7071, D(G(z)): 0.1593
Epoch: [13/20], Batch Num: [22/600]
Discriminator Loss: 0.9851, Generator Loss: 2.1510
D(x): 0.6576, D(G(z)): 0.1893
Epoch: [13/20], Batch Num: [23/600]
Discriminator Loss: 0.9567, Generator Loss: 1.8969
D(x): 0.7509, D(G(z)): 0.2967
Epoch: [13/20], Batch Num: [24/600]
Discriminator Loss: 1.0182, Generator Loss: 1.7388
D(x): 0.7922, D(G(z)): 0.3633
Epoch: [13/20], Batch Num: [25/600]
Discriminator Loss: 1.1441, Generator Loss: 1.9578
D(x): 0.7013, D(G(z)): 0.3529
Epoch: [13/20], Batch Num: [26/600]
Discriminator Loss: 0.8116, Generator Loss: 2.1854
D(x): 0.7499, D(G(z)): 0.2352
Epoch: [13/20], Batch Num: [27/600]
Discriminator Loss: 1.1357, Generator Loss: 1.8482
D(x): 0.6313, D(G(z)): 0.2619
Epoch: [13/20], Batch Num: [28/600]
Discriminator Loss: 1.0705, Generator Loss: 1.6664
D(x): 0.7197, D(G(z)): 0.3534
Epoch: [13/20], Batch Num: [29/600]
Discriminator Loss: 1.0997, Generator Loss: 1.4620
D(x): 0.7220, D(G(z)): 0.3569
Epoch: [13/20], Batch Num: [30/600]
Discriminator Loss: 1.2655, Generator Loss: 1.8029
D(x): 0.6717, D(G(z)): 0.3640
Epoch: [13/20], Batch Num: [31/600]
Discriminator Loss: 1.2400, Generator Loss: 1.5873
D(x): 0.6190, D(G(z)): 0.3102
Epoch: [13/20], Batch Num: [32/600]
Discriminator Loss: 1.2111, Generator Loss: 1.4112
D(x): 0.6386, D(G(z)): 0.2978
Epoch: [13/20], Batch Num: [33/600]
Discriminator Loss: 1.2386, Generator Loss: 1.3436
D(x): 0.6965, D(G(z)): 0.3712
Epoch: [13/20], Batch Num: [34/600]
Discriminator Loss: 1.2862, Generator Loss: 1.5029
D(x): 0.6636, D(G(z)): 0.3893
Epoch: [13/20], Batch Num: [35/600]
Discriminator Loss: 1.3044, Generator Loss: 1.4553
D(x): 0.6289, D(G(z)): 0.3758
Epoch: [13/20], Batch Num: [36/600]
Discriminator Loss: 1.1712, Generator Loss: 1.1806
D(x): 0.6306, D(G(z)): 0.3355
Epoch: [13/20], Batch Num: [37/600]
Discriminator Loss: 1.1579, Generator Loss: 1.2077
D(x): 0.6598, D(G(z)): 0.3472
Epoch: [13/20], Batch Num: [38/600]
Discriminator Loss: 1.3065, Generator Loss: 1.2151
D(x): 0.6308, D(G(z)): 0.3812
Epoch: [13/20], Batch Num: [39/600]
Discriminator Loss: 1.1470, Generator Loss: 1.3772
D(x): 0.6588, D(G(z)): 0.3551
Epoch: [13/20], Batch Num: [40/600]
Discriminator Loss: 1.0175, Generator Loss: 1.3817
D(x): 0.7130, D(G(z)): 0.3791
Epoch: [13/20], Batch Num: [41/600]
Discriminator Loss: 0.9226, Generator Loss: 1.4961
D(x): 0.7114, D(G(z)): 0.3278
Epoch: [13/20], Batch Num: [42/600]
Discriminator Loss: 0.8852, Generator Loss: 1.5592
D(x): 0.7047, D(G(z)): 0.2914
Epoch: [13/20], Batch Num: [43/600]
Discriminator Loss: 1.0001, Generator Loss: 1.4756
D(x): 0.6513, D(G(z)): 0.2903
Epoch: [13/20], Batch Num: [44/600]
Discriminator Loss: 0.9055, Generator Loss: 1.5300
D(x): 0.6903, D(G(z)): 0.3108
Epoch: [13/20], Batch Num: [45/600]
Discriminator Loss: 0.8643, Generator Loss: 1.3567
D(x): 0.6805, D(G(z)): 0.2843
Epoch: [13/20], Batch Num: [46/600]
Discriminator Loss: 0.8356, Generator Loss: 1.4027
D(x): 0.7247, D(G(z)): 0.3185
Epoch: [13/20], Batch Num: [47/600]
Discriminator Loss: 0.7588, Generator Loss: 1.3412
D(x): 0.7971, D(G(z)): 0.3362
Epoch: [13/20], Batch Num: [48/600]
Discriminator Loss: 0.7845, Generator Loss: 1.5497
D(x): 0.7657, D(G(z)): 0.3236
Epoch: [13/20], Batch Num: [49/600]
Discriminator Loss: 0.7946, Generator Loss: 1.6624
D(x): 0.7180, D(G(z)): 0.2838
Epoch: [13/20], Batch Num: [50/600]
Discriminator Loss: 0.6369, Generator Loss: 1.8499
D(x): 0.7786, D(G(z)): 0.2510
Epoch: [13/20], Batch Num: [51/600]
Discriminator Loss: 0.6318, Generator Loss: 1.9375
D(x): 0.7485, D(G(z)): 0.2112
Epoch: [13/20], Batch Num: [52/600]
Discriminator Loss: 0.6254, Generator Loss: 1.7932
D(x): 0.7624, D(G(z)): 0.2300
Epoch: [13/20], Batch Num: [53/600]
Discriminator Loss: 0.5545, Generator Loss: 1.9158
D(x): 0.7900, D(G(z)): 0.2089
Epoch: [13/20], Batch Num: [54/600]
Discriminator Loss: 0.5818, Generator Loss: 1.8366
D(x): 0.7827, D(G(z)): 0.2034
Epoch: [13/20], Batch Num: [55/600]
Discriminator Loss: 0.6960, Generator Loss: 1.7097
D(x): 0.7465, D(G(z)): 0.2612
Epoch: [13/20], Batch Num: [56/600]
Discriminator Loss: 0.6199, Generator Loss: 1.5924
D(x): 0.7965, D(G(z)): 0.2599
Epoch: [13/20], Batch Num: [57/600]
Discriminator Loss: 0.4978, Generator Loss: 1.7187
D(x): 0.8689, D(G(z)): 0.2506
Epoch: [13/20], Batch Num: [58/600]
Discriminator Loss: 0.5190, Generator Loss: 1.9549
D(x): 0.8627, D(G(z)): 0.2442
Epoch: [13/20], Batch Num: [59/600]
Discriminator Loss: 0.5864, Generator Loss: 2.2955
D(x): 0.8292, D(G(z)): 0.2583
Epoch: [13/20], Batch Num: [60/600]
Discriminator Loss: 0.5537, Generator Loss: 2.3210
D(x): 0.7467, D(G(z)): 0.1417
Epoch: [13/20], Batch Num: [61/600]
Discriminator Loss: 0.6742, Generator Loss: 2.1647
D(x): 0.7062, D(G(z)): 0.1508
Epoch: [13/20], Batch Num: [62/600]
Discriminator Loss: 0.5332, Generator Loss: 1.9563
D(x): 0.8096, D(G(z)): 0.1735
Epoch: [13/20], Batch Num: [63/600]
Discriminator Loss: 0.5956, Generator Loss: 1.9087
D(x): 0.8262, D(G(z)): 0.2437
Epoch: [13/20], Batch Num: [64/600]
Discriminator Loss: 0.5949, Generator Loss: 1.9539
D(x): 0.8657, D(G(z)): 0.2756
Epoch: [13/20], Batch Num: [65/600]
Discriminator Loss: 0.6300, Generator Loss: 2.1953
D(x): 0.8312, D(G(z)): 0.2468
Epoch: [13/20], Batch Num: [66/600]
Discriminator Loss: 0.6084, Generator Loss: 2.3692
D(x): 0.7970, D(G(z)): 0.2082
Epoch: [13/20], Batch Num: [67/600]
Discriminator Loss: 0.6724, Generator Loss: 2.5844
D(x): 0.7447, D(G(z)): 0.1948
Epoch: [13/20], Batch Num: [68/600]
Discriminator Loss: 0.7502, Generator Loss: 2.0603
D(x): 0.7235, D(G(z)): 0.1947
Epoch: [13/20], Batch Num: [69/600]
Discriminator Loss: 0.6552, Generator Loss: 1.9133
D(x): 0.7946, D(G(z)): 0.2491
Epoch: [13/20], Batch Num: [70/600]
Discriminator Loss: 0.6248, Generator Loss: 1.9085
D(x): 0.7860, D(G(z)): 0.2173
Epoch: [13/20], Batch Num: [71/600]
Discriminator Loss: 0.8647, Generator Loss: 2.0230
D(x): 0.8165, D(G(z)): 0.3452
Epoch: [13/20], Batch Num: [72/600]
Discriminator Loss: 0.8145, Generator Loss: 2.4738
D(x): 0.7563, D(G(z)): 0.2710
Epoch: [13/20], Batch Num: [73/600]
Discriminator Loss: 0.9767, Generator Loss: 2.5374
D(x): 0.6450, D(G(z)): 0.2268
Epoch: [13/20], Batch Num: [74/600]
Discriminator Loss: 0.8392, Generator Loss: 1.7496
D(x): 0.6759, D(G(z)): 0.1592
Epoch: [13/20], Batch Num: [75/600]
Discriminator Loss: 1.0757, Generator Loss: 1.3473
D(x): 0.6643, D(G(z)): 0.2621
Epoch: [13/20], Batch Num: [76/600]
Discriminator Loss: 1.0387, Generator Loss: 1.4665
D(x): 0.8168, D(G(z)): 0.4294
Epoch: [13/20], Batch Num: [77/600]
Discriminator Loss: 1.1549, Generator Loss: 2.1913
D(x): 0.7733, D(G(z)): 0.4246
Epoch: [13/20], Batch Num: [78/600]
Discriminator Loss: 0.9545, Generator Loss: 2.6667
D(x): 0.6400, D(G(z)): 0.2147
Epoch: [13/20], Batch Num: [79/600]
Discriminator Loss: 1.0309, Generator Loss: 2.4124
D(x): 0.6006, D(G(z)): 0.1439
Epoch: [13/20], Batch Num: [80/600]
Discriminator Loss: 0.9766, Generator Loss: 1.6412
D(x): 0.5983, D(G(z)): 0.1729
Epoch: [13/20], Batch Num: [81/600]
Discriminator Loss: 1.0393, Generator Loss: 1.1944
D(x): 0.7061, D(G(z)): 0.3127
Epoch: [13/20], Batch Num: [82/600]
Discriminator Loss: 1.1784, Generator Loss: 1.5096
D(x): 0.8149, D(G(z)): 0.4683
Epoch: [13/20], Batch Num: [83/600]
Discriminator Loss: 0.9188, Generator Loss: 1.8747
D(x): 0.7479, D(G(z)): 0.3330
Epoch: [13/20], Batch Num: [84/600]
Discriminator Loss: 0.9068, Generator Loss: 2.0509
D(x): 0.6412, D(G(z)): 0.2079
Epoch: [13/20], Batch Num: [85/600]
Discriminator Loss: 0.9097, Generator Loss: 1.6813
D(x): 0.6562, D(G(z)): 0.2153
Epoch: [13/20], Batch Num: [86/600]
Discriminator Loss: 0.6735, Generator Loss: 1.8062
D(x): 0.7581, D(G(z)): 0.2249
Epoch: [13/20], Batch Num: [87/600]
Discriminator Loss: 0.8679, Generator Loss: 1.6912
D(x): 0.7693, D(G(z)): 0.3042
Epoch: [13/20], Batch Num: [88/600]
Discriminator Loss: 0.6903, Generator Loss: 1.9175
D(x): 0.8193, D(G(z)): 0.2859
Epoch: [13/20], Batch Num: [89/600]
Discriminator Loss: 0.7855, Generator Loss: 1.8839
D(x): 0.7338, D(G(z)): 0.2636
Epoch: [13/20], Batch Num: [90/600]
Discriminator Loss: 0.7750, Generator Loss: 2.1543
D(x): 0.7398, D(G(z)): 0.2296
Epoch: [13/20], Batch Num: [91/600]
Discriminator Loss: 0.7092, Generator Loss: 2.1271
D(x): 0.7147, D(G(z)): 0.1767
Epoch: [13/20], Batch Num: [92/600]
Discriminator Loss: 0.8386, Generator Loss: 1.8826
D(x): 0.7138, D(G(z)): 0.2443
Epoch: [13/20], Batch Num: [93/600]
Discriminator Loss: 0.8106, Generator Loss: 1.8554
D(x): 0.7328, D(G(z)): 0.2531
Epoch: [13/20], Batch Num: [94/600]
Discriminator Loss: 0.7056, Generator Loss: 1.4562
D(x): 0.7778, D(G(z)): 0.2527
Epoch: [13/20], Batch Num: [95/600]
Discriminator Loss: 0.7992, Generator Loss: 1.8707
D(x): 0.8391, D(G(z)): 0.3424
Epoch: [13/20], Batch Num: [96/600]
Discriminator Loss: 0.7349, Generator Loss: 1.9867
D(x): 0.7572, D(G(z)): 0.2506
Epoch: [13/20], Batch Num: [97/600]
Discriminator Loss: 0.7548, Generator Loss: 2.4534
D(x): 0.7676, D(G(z)): 0.2712
Epoch: [13/20], Batch Num: [98/600]
Discriminator Loss: 0.7320, Generator Loss: 2.3441
D(x): 0.6870, D(G(z)): 0.1487
Epoch: [13/20], Batch Num: [99/600]
Discriminator Loss: 0.7362, Generator Loss: 1.9674
D(x): 0.6885, D(G(z)): 0.1741
Epoch: 13, Batch Num: [100/600]

Epoch: [13/20], Batch Num: [100/600]
Discriminator Loss: 0.7463, Generator Loss: 1.6247
D(x): 0.7355, D(G(z)): 0.2268
Epoch: [13/20], Batch Num: [101/600]
Discriminator Loss: 0.7620, Generator Loss: 1.6551
D(x): 0.8438, D(G(z)): 0.3586
Epoch: [13/20], Batch Num: [102/600]
Discriminator Loss: 0.7437, Generator Loss: 1.9338
D(x): 0.8473, D(G(z)): 0.3135
Epoch: [13/20], Batch Num: [103/600]
Discriminator Loss: 0.6733, Generator Loss: 2.2018
D(x): 0.7540, D(G(z)): 0.2214
Epoch: [13/20], Batch Num: [104/600]
Discriminator Loss: 0.7663, Generator Loss: 2.0756
D(x): 0.7032, D(G(z)): 0.1839
Epoch: [13/20], Batch Num: [105/600]
Discriminator Loss: 0.7533, Generator Loss: 1.9783
D(x): 0.7150, D(G(z)): 0.1923
Epoch: [13/20], Batch Num: [106/600]
Discriminator Loss: 0.6801, Generator Loss: 1.7042
D(x): 0.7658, D(G(z)): 0.2251
Epoch: [13/20], Batch Num: [107/600]
Discriminator Loss: 0.6594, Generator Loss: 1.8234
D(x): 0.8192, D(G(z)): 0.2664
Epoch: [13/20], Batch Num: [108/600]
Discriminator Loss: 0.6208, Generator Loss: 1.9950
D(x): 0.8054, D(G(z)): 0.2212
Epoch: [13/20], Batch Num: [109/600]
Discriminator Loss: 0.7042, Generator Loss: 2.0118
D(x): 0.8089, D(G(z)): 0.2854
Epoch: [13/20], Batch Num: [110/600]
Discriminator Loss: 0.7895, Generator Loss: 2.4188
D(x): 0.7756, D(G(z)): 0.2489
Epoch: [13/20], Batch Num: [111/600]
Discriminator Loss: 0.7157, Generator Loss: 2.1262
D(x): 0.7522, D(G(z)): 0.2234
Epoch: [13/20], Batch Num: [112/600]
Discriminator Loss: 0.6419, Generator Loss: 2.2060
D(x): 0.7457, D(G(z)): 0.1637
Epoch: [13/20], Batch Num: [113/600]
Discriminator Loss: 0.7715, Generator Loss: 1.9205
D(x): 0.7413, D(G(z)): 0.2189
Epoch: [13/20], Batch Num: [114/600]
Discriminator Loss: 0.7307, Generator Loss: 1.7110
D(x): 0.7769, D(G(z)): 0.2457
Epoch: [13/20], Batch Num: [115/600]
Discriminator Loss: 0.8588, Generator Loss: 1.9134
D(x): 0.7616, D(G(z)): 0.2713
Epoch: [13/20], Batch Num: [116/600]
Discriminator Loss: 0.6334, Generator Loss: 2.1341
D(x): 0.8385, D(G(z)): 0.2822
Epoch: [13/20], Batch Num: [117/600]
Discriminator Loss: 0.8155, Generator Loss: 2.1569
D(x): 0.7441, D(G(z)): 0.2317
Epoch: [13/20], Batch Num: [118/600]
Discriminator Loss: 0.6830, Generator Loss: 2.2755
D(x): 0.7544, D(G(z)): 0.1932
Epoch: [13/20], Batch Num: [119/600]
Discriminator Loss: 0.6666, Generator Loss: 2.2874
D(x): 0.7879, D(G(z)): 0.2195
Epoch: [13/20], Batch Num: [120/600]
Discriminator Loss: 0.8608, Generator Loss: 1.9638
D(x): 0.7145, D(G(z)): 0.2089
Epoch: [13/20], Batch Num: [121/600]
Discriminator Loss: 0.6861, Generator Loss: 1.9632
D(x): 0.7819, D(G(z)): 0.2126
Epoch: [13/20], Batch Num: [122/600]
Discriminator Loss: 0.8357, Generator Loss: 2.0833
D(x): 0.7707, D(G(z)): 0.2744
Epoch: [13/20], Batch Num: [123/600]
Discriminator Loss: 0.6940, Generator Loss: 1.9655
D(x): 0.8255, D(G(z)): 0.2530
Epoch: [13/20], Batch Num: [124/600]
Discriminator Loss: 0.8291, Generator Loss: 2.1244
D(x): 0.7656, D(G(z)): 0.2588
Epoch: [13/20], Batch Num: [125/600]
Discriminator Loss: 0.6804, Generator Loss: 2.3184
D(x): 0.8020, D(G(z)): 0.2530
Epoch: [13/20], Batch Num: [126/600]
Discriminator Loss: 0.8252, Generator Loss: 2.3797
D(x): 0.7199, D(G(z)): 0.2149
Epoch: [13/20], Batch Num: [127/600]
Discriminator Loss: 0.7734, Generator Loss: 2.2889
D(x): 0.7317, D(G(z)): 0.2025
Epoch: [13/20], Batch Num: [128/600]
Discriminator Loss: 0.5838, Generator Loss: 2.2500
D(x): 0.8091, D(G(z)): 0.2019
Epoch: [13/20], Batch Num: [129/600]
Discriminator Loss: 0.5274, Generator Loss: 2.0320
D(x): 0.8137, D(G(z)): 0.1856
Epoch: [13/20], Batch Num: [130/600]
Discriminator Loss: 0.9185, Generator Loss: 2.0611
D(x): 0.7838, D(G(z)): 0.2701
Epoch: [13/20], Batch Num: [131/600]
Discriminator Loss: 0.7181, Generator Loss: 2.0978
D(x): 0.8062, D(G(z)): 0.2540
Epoch: [13/20], Batch Num: [132/600]
Discriminator Loss: 0.8242, Generator Loss: 2.1773
D(x): 0.7456, D(G(z)): 0.2188
Epoch: [13/20], Batch Num: [133/600]
Discriminator Loss: 0.6831, Generator Loss: 2.2895
D(x): 0.8095, D(G(z)): 0.2496
Epoch: [13/20], Batch Num: [134/600]
Discriminator Loss: 0.6918, Generator Loss: 2.2412
D(x): 0.7893, D(G(z)): 0.2254
Epoch: [13/20], Batch Num: [135/600]
Discriminator Loss: 0.8376, Generator Loss: 2.2186
D(x): 0.7579, D(G(z)): 0.2568
Epoch: [13/20], Batch Num: [136/600]
Discriminator Loss: 0.8638, Generator Loss: 2.1425
D(x): 0.7220, D(G(z)): 0.2301
Epoch: [13/20], Batch Num: [137/600]
Discriminator Loss: 0.7565, Generator Loss: 2.1959
D(x): 0.7463, D(G(z)): 0.2276
Epoch: [13/20], Batch Num: [138/600]
Discriminator Loss: 0.8012, Generator Loss: 1.9797
D(x): 0.7842, D(G(z)): 0.2636
Epoch: [13/20], Batch Num: [139/600]
Discriminator Loss: 0.8725, Generator Loss: 2.1643
D(x): 0.7847, D(G(z)): 0.2950
Epoch: [13/20], Batch Num: [140/600]
Discriminator Loss: 0.8622, Generator Loss: 1.8982
D(x): 0.7551, D(G(z)): 0.2550
Epoch: [13/20], Batch Num: [141/600]
Discriminator Loss: 0.8716, Generator Loss: 2.1515
D(x): 0.7217, D(G(z)): 0.2423
Epoch: [13/20], Batch Num: [142/600]
Discriminator Loss: 1.1069, Generator Loss: 1.9562
D(x): 0.6205, D(G(z)): 0.2651
Epoch: [13/20], Batch Num: [143/600]
Discriminator Loss: 0.8192, Generator Loss: 1.7592
D(x): 0.7504, D(G(z)): 0.2788
Epoch: [13/20], Batch Num: [144/600]
Discriminator Loss: 0.9362, Generator Loss: 1.7045
D(x): 0.7423, D(G(z)): 0.3015
Epoch: [13/20], Batch Num: [145/600]
Discriminator Loss: 1.0074, Generator Loss: 1.5376
D(x): 0.7385, D(G(z)): 0.3184
Epoch: [13/20], Batch Num: [146/600]
Discriminator Loss: 1.0736, Generator Loss: 1.6062
D(x): 0.7509, D(G(z)): 0.3704
Epoch: [13/20], Batch Num: [147/600]
Discriminator Loss: 0.8869, Generator Loss: 1.9783
D(x): 0.7116, D(G(z)): 0.2583
Epoch: [13/20], Batch Num: [148/600]
Discriminator Loss: 0.8630, Generator Loss: 2.0910
D(x): 0.7076, D(G(z)): 0.2387
Epoch: [13/20], Batch Num: [149/600]
Discriminator Loss: 0.9061, Generator Loss: 2.0839
D(x): 0.7054, D(G(z)): 0.2299
Epoch: [13/20], Batch Num: [150/600]
Discriminator Loss: 0.9601, Generator Loss: 1.9373
D(x): 0.6454, D(G(z)): 0.2103
Epoch: [13/20], Batch Num: [151/600]
Discriminator Loss: 0.8914, Generator Loss: 1.5676
D(x): 0.6757, D(G(z)): 0.2543
Epoch: [13/20], Batch Num: [152/600]
Discriminator Loss: 0.8657, Generator Loss: 1.5828
D(x): 0.7906, D(G(z)): 0.3236
Epoch: [13/20], Batch Num: [153/600]
Discriminator Loss: 0.8357, Generator Loss: 1.5784
D(x): 0.8222, D(G(z)): 0.3529
Epoch: [13/20], Batch Num: [154/600]
Discriminator Loss: 0.8193, Generator Loss: 1.9901
D(x): 0.7750, D(G(z)): 0.3027
Epoch: [13/20], Batch Num: [155/600]
Discriminator Loss: 1.0445, Generator Loss: 1.9365
D(x): 0.6383, D(G(z)): 0.2833
Epoch: [13/20], Batch Num: [156/600]
Discriminator Loss: 0.7058, Generator Loss: 1.9193
D(x): 0.6974, D(G(z)): 0.2002
Epoch: [13/20], Batch Num: [157/600]
Discriminator Loss: 0.8139, Generator Loss: 2.0958
D(x): 0.6826, D(G(z)): 0.2373
Epoch: [13/20], Batch Num: [158/600]
Discriminator Loss: 0.8292, Generator Loss: 1.8093
D(x): 0.7443, D(G(z)): 0.2868
Epoch: [13/20], Batch Num: [159/600]
Discriminator Loss: 0.6912, Generator Loss: 1.6423
D(x): 0.7488, D(G(z)): 0.2288
Epoch: [13/20], Batch Num: [160/600]
Discriminator Loss: 0.8118, Generator Loss: 1.5561
D(x): 0.7721, D(G(z)): 0.2930
Epoch: [13/20], Batch Num: [161/600]
Discriminator Loss: 0.7725, Generator Loss: 1.7896
D(x): 0.7836, D(G(z)): 0.2882
Epoch: [13/20], Batch Num: [162/600]
Discriminator Loss: 0.8817, Generator Loss: 2.1060
D(x): 0.7608, D(G(z)): 0.3221
Epoch: [13/20], Batch Num: [163/600]
Discriminator Loss: 0.6495, Generator Loss: 2.2804
D(x): 0.7777, D(G(z)): 0.2372
Epoch: [13/20], Batch Num: [164/600]
Discriminator Loss: 0.6298, Generator Loss: 2.4147
D(x): 0.7712, D(G(z)): 0.2049
Epoch: [13/20], Batch Num: [165/600]
Discriminator Loss: 0.5963, Generator Loss: 2.4196
D(x): 0.7489, D(G(z)): 0.1794
Epoch: [13/20], Batch Num: [166/600]
Discriminator Loss: 0.5974, Generator Loss: 2.4771
D(x): 0.7738, D(G(z)): 0.1820
Epoch: [13/20], Batch Num: [167/600]
Discriminator Loss: 0.5196, Generator Loss: 2.2577
D(x): 0.8076, D(G(z)): 0.1959
Epoch: [13/20], Batch Num: [168/600]
Discriminator Loss: 0.5584, Generator Loss: 2.3617
D(x): 0.8046, D(G(z)): 0.2071
Epoch: [13/20], Batch Num: [169/600]
Discriminator Loss: 0.5927, Generator Loss: 2.2755
D(x): 0.8165, D(G(z)): 0.2270
Epoch: [13/20], Batch Num: [170/600]
Discriminator Loss: 0.6918, Generator Loss: 2.2372
D(x): 0.7760, D(G(z)): 0.2260
Epoch: [13/20], Batch Num: [171/600]
Discriminator Loss: 0.6610, Generator Loss: 2.1800
D(x): 0.7880, D(G(z)): 0.2305
Epoch: [13/20], Batch Num: [172/600]
Discriminator Loss: 0.5703, Generator Loss: 2.3882
D(x): 0.8298, D(G(z)): 0.2157
Epoch: [13/20], Batch Num: [173/600]
Discriminator Loss: 0.6688, Generator Loss: 2.4159
D(x): 0.7924, D(G(z)): 0.2508
Epoch: [13/20], Batch Num: [174/600]
Discriminator Loss: 0.6212, Generator Loss: 2.4501
D(x): 0.7511, D(G(z)): 0.1685
Epoch: [13/20], Batch Num: [175/600]
Discriminator Loss: 0.8287, Generator Loss: 2.2563
D(x): 0.7284, D(G(z)): 0.2441
Epoch: [13/20], Batch Num: [176/600]
Discriminator Loss: 0.5187, Generator Loss: 2.2332
D(x): 0.7865, D(G(z)): 0.1644
Epoch: [13/20], Batch Num: [177/600]
Discriminator Loss: 0.7661, Generator Loss: 1.9660
D(x): 0.7655, D(G(z)): 0.2463
Epoch: [13/20], Batch Num: [178/600]
Discriminator Loss: 0.7445, Generator Loss: 1.9715
D(x): 0.7679, D(G(z)): 0.2470
Epoch: [13/20], Batch Num: [179/600]
Discriminator Loss: 0.9389, Generator Loss: 1.6522
D(x): 0.7034, D(G(z)): 0.2888
Epoch: [13/20], Batch Num: [180/600]
Discriminator Loss: 0.8852, Generator Loss: 2.0805
D(x): 0.7758, D(G(z)): 0.3124
Epoch: [13/20], Batch Num: [181/600]
Discriminator Loss: 0.8534, Generator Loss: 2.3257
D(x): 0.7751, D(G(z)): 0.2901
Epoch: [13/20], Batch Num: [182/600]
Discriminator Loss: 0.9633, Generator Loss: 2.3156
D(x): 0.6446, D(G(z)): 0.2084
Epoch: [13/20], Batch Num: [183/600]
Discriminator Loss: 1.1209, Generator Loss: 1.8190
D(x): 0.6283, D(G(z)): 0.2516
Epoch: [13/20], Batch Num: [184/600]
Discriminator Loss: 1.0105, Generator Loss: 1.7983
D(x): 0.7415, D(G(z)): 0.3315
Epoch: [13/20], Batch Num: [185/600]
Discriminator Loss: 1.0798, Generator Loss: 1.9512
D(x): 0.7182, D(G(z)): 0.3680
Epoch: [13/20], Batch Num: [186/600]
Discriminator Loss: 1.0904, Generator Loss: 1.8320
D(x): 0.6836, D(G(z)): 0.3111
Epoch: [13/20], Batch Num: [187/600]
Discriminator Loss: 1.0598, Generator Loss: 1.9233
D(x): 0.6563, D(G(z)): 0.2534
Epoch: [13/20], Batch Num: [188/600]
Discriminator Loss: 1.2745, Generator Loss: 1.5563
D(x): 0.6033, D(G(z)): 0.2698
Epoch: [13/20], Batch Num: [189/600]
Discriminator Loss: 1.1030, Generator Loss: 1.3089
D(x): 0.6725, D(G(z)): 0.3316
Epoch: [13/20], Batch Num: [190/600]
Discriminator Loss: 1.1161, Generator Loss: 1.2645
D(x): 0.7412, D(G(z)): 0.3913
Epoch: [13/20], Batch Num: [191/600]
Discriminator Loss: 1.2123, Generator Loss: 1.6141
D(x): 0.7060, D(G(z)): 0.4292
Epoch: [13/20], Batch Num: [192/600]
Discriminator Loss: 1.1190, Generator Loss: 1.9265
D(x): 0.6491, D(G(z)): 0.3183
Epoch: [13/20], Batch Num: [193/600]
Discriminator Loss: 1.0974, Generator Loss: 1.8797
D(x): 0.5960, D(G(z)): 0.2449
Epoch: [13/20], Batch Num: [194/600]
Discriminator Loss: 1.0211, Generator Loss: 1.7224
D(x): 0.6263, D(G(z)): 0.2391
Epoch: [13/20], Batch Num: [195/600]
Discriminator Loss: 1.1472, Generator Loss: 1.5096
D(x): 0.6152, D(G(z)): 0.2928
Epoch: [13/20], Batch Num: [196/600]
Discriminator Loss: 0.9056, Generator Loss: 1.3254
D(x): 0.7087, D(G(z)): 0.2983
Epoch: [13/20], Batch Num: [197/600]
Discriminator Loss: 0.9007, Generator Loss: 1.5691
D(x): 0.7980, D(G(z)): 0.3760
Epoch: [13/20], Batch Num: [198/600]
Discriminator Loss: 1.0489, Generator Loss: 1.6209
D(x): 0.6981, D(G(z)): 0.3724
Epoch: [13/20], Batch Num: [199/600]
Discriminator Loss: 0.8058, Generator Loss: 1.7533
D(x): 0.7305, D(G(z)): 0.2903
Epoch: 13, Batch Num: [200/600]

Epoch: [13/20], Batch Num: [200/600]
Discriminator Loss: 0.8855, Generator Loss: 1.8121
D(x): 0.6537, D(G(z)): 0.2479
Epoch: [13/20], Batch Num: [201/600]
Discriminator Loss: 0.8559, Generator Loss: 1.8300
D(x): 0.6724, D(G(z)): 0.2402
Epoch: [13/20], Batch Num: [202/600]
Discriminator Loss: 0.8055, Generator Loss: 1.6491
D(x): 0.7388, D(G(z)): 0.2906
Epoch: [13/20], Batch Num: [203/600]
Discriminator Loss: 0.6968, Generator Loss: 1.8158
D(x): 0.7678, D(G(z)): 0.2556
Epoch: [13/20], Batch Num: [204/600]
Discriminator Loss: 0.7072, Generator Loss: 1.7265
D(x): 0.7527, D(G(z)): 0.2468
Epoch: [13/20], Batch Num: [205/600]
Discriminator Loss: 0.5633, Generator Loss: 1.9321
D(x): 0.8273, D(G(z)): 0.2427
Epoch: [13/20], Batch Num: [206/600]
Discriminator Loss: 0.5267, Generator Loss: 1.8224
D(x): 0.8311, D(G(z)): 0.2303
Epoch: [13/20], Batch Num: [207/600]
Discriminator Loss: 0.6445, Generator Loss: 2.2529
D(x): 0.8152, D(G(z)): 0.2482
Epoch: [13/20], Batch Num: [208/600]
Discriminator Loss: 0.5972, Generator Loss: 1.9293
D(x): 0.7179, D(G(z)): 0.1494
Epoch: [13/20], Batch Num: [209/600]
Discriminator Loss: 0.6955, Generator Loss: 2.1865
D(x): 0.7599, D(G(z)): 0.2370
Epoch: [13/20], Batch Num: [210/600]
Discriminator Loss: 0.5561, Generator Loss: 2.1419
D(x): 0.8002, D(G(z)): 0.2054
Epoch: [13/20], Batch Num: [211/600]
Discriminator Loss: 0.5787, Generator Loss: 2.1863
D(x): 0.8224, D(G(z)): 0.2410
Epoch: [13/20], Batch Num: [212/600]
Discriminator Loss: 0.5557, Generator Loss: 2.3829
D(x): 0.8187, D(G(z)): 0.2158
Epoch: [13/20], Batch Num: [213/600]
Discriminator Loss: 0.6115, Generator Loss: 2.4633
D(x): 0.8127, D(G(z)): 0.2214
Epoch: [13/20], Batch Num: [214/600]
Discriminator Loss: 0.6854, Generator Loss: 2.4100
D(x): 0.7510, D(G(z)): 0.2036
Epoch: [13/20], Batch Num: [215/600]
Discriminator Loss: 0.5414, Generator Loss: 2.2930
D(x): 0.7893, D(G(z)): 0.1783
Epoch: [13/20], Batch Num: [216/600]
Discriminator Loss: 0.5512, Generator Loss: 2.3562
D(x): 0.8319, D(G(z)): 0.2062
Epoch: [13/20], Batch Num: [217/600]
Discriminator Loss: 0.5707, Generator Loss: 2.2905
D(x): 0.7706, D(G(z)): 0.1617
Epoch: [13/20], Batch Num: [218/600]
Discriminator Loss: 0.5954, Generator Loss: 2.2537
D(x): 0.8173, D(G(z)): 0.2240
Epoch: [13/20], Batch Num: [219/600]
Discriminator Loss: 0.6896, Generator Loss: 2.2786
D(x): 0.8271, D(G(z)): 0.2692
Epoch: [13/20], Batch Num: [220/600]
Discriminator Loss: 0.6043, Generator Loss: 2.4722
D(x): 0.8067, D(G(z)): 0.2135
Epoch: [13/20], Batch Num: [221/600]
Discriminator Loss: 0.7086, Generator Loss: 2.5964
D(x): 0.7436, D(G(z)): 0.1692
Epoch: [13/20], Batch Num: [222/600]
Discriminator Loss: 0.6658, Generator Loss: 2.2951
D(x): 0.7520, D(G(z)): 0.1711
Epoch: [13/20], Batch Num: [223/600]
Discriminator Loss: 0.8021, Generator Loss: 2.0782
D(x): 0.7834, D(G(z)): 0.2576
Epoch: [13/20], Batch Num: [224/600]
Discriminator Loss: 0.7045, Generator Loss: 1.8990
D(x): 0.7992, D(G(z)): 0.2391
Epoch: [13/20], Batch Num: [225/600]
Discriminator Loss: 0.6965, Generator Loss: 2.2066
D(x): 0.8150, D(G(z)): 0.2435
Epoch: [13/20], Batch Num: [226/600]
Discriminator Loss: 0.8681, Generator Loss: 2.5555
D(x): 0.7743, D(G(z)): 0.2777
Epoch: [13/20], Batch Num: [227/600]
Discriminator Loss: 0.8227, Generator Loss: 2.5176
D(x): 0.7182, D(G(z)): 0.1807
Epoch: [13/20], Batch Num: [228/600]
Discriminator Loss: 0.8520, Generator Loss: 2.3541
D(x): 0.7133, D(G(z)): 0.2163
Epoch: [13/20], Batch Num: [229/600]
Discriminator Loss: 0.7433, Generator Loss: 1.6741
D(x): 0.7724, D(G(z)): 0.2728
Epoch: [13/20], Batch Num: [230/600]
Discriminator Loss: 0.7726, Generator Loss: 1.6816
D(x): 0.7822, D(G(z)): 0.2786
Epoch: [13/20], Batch Num: [231/600]
Discriminator Loss: 0.7735, Generator Loss: 2.2239
D(x): 0.8123, D(G(z)): 0.2698
Epoch: [13/20], Batch Num: [232/600]
Discriminator Loss: 0.8421, Generator Loss: 2.6595
D(x): 0.7694, D(G(z)): 0.2722
Epoch: [13/20], Batch Num: [233/600]
Discriminator Loss: 0.9163, Generator Loss: 2.2369
D(x): 0.6467, D(G(z)): 0.1794
Epoch: [13/20], Batch Num: [234/600]
Discriminator Loss: 1.0732, Generator Loss: 1.6086
D(x): 0.6232, D(G(z)): 0.2121
Epoch: [13/20], Batch Num: [235/600]
Discriminator Loss: 0.8601, Generator Loss: 1.3047
D(x): 0.7419, D(G(z)): 0.2754
Epoch: [13/20], Batch Num: [236/600]
Discriminator Loss: 1.1542, Generator Loss: 1.2112
D(x): 0.8032, D(G(z)): 0.4346
Epoch: [13/20], Batch Num: [237/600]
Discriminator Loss: 0.8953, Generator Loss: 1.8313
D(x): 0.8632, D(G(z)): 0.4018
Epoch: [13/20], Batch Num: [238/600]
Discriminator Loss: 0.9489, Generator Loss: 2.4534
D(x): 0.6831, D(G(z)): 0.2585
Epoch: [13/20], Batch Num: [239/600]
Discriminator Loss: 0.9832, Generator Loss: 2.0320
D(x): 0.6279, D(G(z)): 0.1816
Epoch: [13/20], Batch Num: [240/600]
Discriminator Loss: 1.1152, Generator Loss: 1.4793
D(x): 0.5758, D(G(z)): 0.1828
Epoch: [13/20], Batch Num: [241/600]
Discriminator Loss: 0.8998, Generator Loss: 1.1798
D(x): 0.6846, D(G(z)): 0.2604
Epoch: [13/20], Batch Num: [242/600]
Discriminator Loss: 1.0236, Generator Loss: 1.1887
D(x): 0.7748, D(G(z)): 0.3942
Epoch: [13/20], Batch Num: [243/600]
Discriminator Loss: 1.1248, Generator Loss: 1.4033
D(x): 0.8177, D(G(z)): 0.4376
Epoch: [13/20], Batch Num: [244/600]
Discriminator Loss: 0.9594, Generator Loss: 1.8094
D(x): 0.7768, D(G(z)): 0.3753
Epoch: [13/20], Batch Num: [245/600]
Discriminator Loss: 0.8332, Generator Loss: 2.0934
D(x): 0.7143, D(G(z)): 0.2687
Epoch: [13/20], Batch Num: [246/600]
Discriminator Loss: 0.8906, Generator Loss: 2.2374
D(x): 0.6047, D(G(z)): 0.1921
Epoch: [13/20], Batch Num: [247/600]
Discriminator Loss: 0.9290, Generator Loss: 1.9827
D(x): 0.6305, D(G(z)): 0.2223
Epoch: [13/20], Batch Num: [248/600]
Discriminator Loss: 0.7593, Generator Loss: 1.8653
D(x): 0.6905, D(G(z)): 0.1984
Epoch: [13/20], Batch Num: [249/600]
Discriminator Loss: 0.9391, Generator Loss: 1.4143
D(x): 0.6777, D(G(z)): 0.2720
Epoch: [13/20], Batch Num: [250/600]
Discriminator Loss: 0.8601, Generator Loss: 1.4190
D(x): 0.7773, D(G(z)): 0.3655
Epoch: [13/20], Batch Num: [251/600]
Discriminator Loss: 0.8927, Generator Loss: 1.3681
D(x): 0.7766, D(G(z)): 0.3575
Epoch: [13/20], Batch Num: [252/600]
Discriminator Loss: 0.9408, Generator Loss: 1.3384
D(x): 0.7952, D(G(z)): 0.3954
Epoch: [13/20], Batch Num: [253/600]
Discriminator Loss: 0.7865, Generator Loss: 1.7121
D(x): 0.7586, D(G(z)): 0.2918
Epoch: [13/20], Batch Num: [254/600]
Discriminator Loss: 0.7753, Generator Loss: 1.9350
D(x): 0.7045, D(G(z)): 0.2438
Epoch: [13/20], Batch Num: [255/600]
Discriminator Loss: 0.8019, Generator Loss: 1.8111
D(x): 0.6688, D(G(z)): 0.2038
Epoch: [13/20], Batch Num: [256/600]
Discriminator Loss: 0.7908, Generator Loss: 1.7989
D(x): 0.6910, D(G(z)): 0.2381
Epoch: [13/20], Batch Num: [257/600]
Discriminator Loss: 0.7433, Generator Loss: 1.6932
D(x): 0.7075, D(G(z)): 0.2300
Epoch: [13/20], Batch Num: [258/600]
Discriminator Loss: 0.7627, Generator Loss: 1.4788
D(x): 0.7415, D(G(z)): 0.2853
Epoch: [13/20], Batch Num: [259/600]
Discriminator Loss: 0.7478, Generator Loss: 1.4404
D(x): 0.8228, D(G(z)): 0.3234
Epoch: [13/20], Batch Num: [260/600]
Discriminator Loss: 0.7355, Generator Loss: 1.4986
D(x): 0.8134, D(G(z)): 0.3198
Epoch: [13/20], Batch Num: [261/600]
Discriminator Loss: 0.6438, Generator Loss: 1.6966
D(x): 0.8181, D(G(z)): 0.2691
Epoch: [13/20], Batch Num: [262/600]
Discriminator Loss: 0.7518, Generator Loss: 1.7680
D(x): 0.7716, D(G(z)): 0.2969
Epoch: [13/20], Batch Num: [263/600]
Discriminator Loss: 0.6700, Generator Loss: 2.1702
D(x): 0.7661, D(G(z)): 0.2560
Epoch: [13/20], Batch Num: [264/600]
Discriminator Loss: 0.7051, Generator Loss: 2.3424
D(x): 0.7332, D(G(z)): 0.2052
Epoch: [13/20], Batch Num: [265/600]
Discriminator Loss: 0.7326, Generator Loss: 1.9754
D(x): 0.6744, D(G(z)): 0.1757
Epoch: [13/20], Batch Num: [266/600]
Discriminator Loss: 0.5812, Generator Loss: 1.7775
D(x): 0.8003, D(G(z)): 0.2125
Epoch: [13/20], Batch Num: [267/600]
Discriminator Loss: 0.6504, Generator Loss: 1.9687
D(x): 0.8229, D(G(z)): 0.2674
Epoch: [13/20], Batch Num: [268/600]
Discriminator Loss: 0.6076, Generator Loss: 1.8656
D(x): 0.8146, D(G(z)): 0.2368
Epoch: [13/20], Batch Num: [269/600]
Discriminator Loss: 0.6197, Generator Loss: 2.0465
D(x): 0.7905, D(G(z)): 0.2241
Epoch: [13/20], Batch Num: [270/600]
Discriminator Loss: 0.5391, Generator Loss: 1.9559
D(x): 0.8177, D(G(z)): 0.2219
Epoch: [13/20], Batch Num: [271/600]
Discriminator Loss: 0.6197, Generator Loss: 2.0236
D(x): 0.7732, D(G(z)): 0.2125
Epoch: [13/20], Batch Num: [272/600]
Discriminator Loss: 0.5691, Generator Loss: 2.3686
D(x): 0.8314, D(G(z)): 0.2357
Epoch: [13/20], Batch Num: [273/600]
Discriminator Loss: 0.7096, Generator Loss: 2.2022
D(x): 0.7321, D(G(z)): 0.1954
Epoch: [13/20], Batch Num: [274/600]
Discriminator Loss: 0.5701, Generator Loss: 2.3262
D(x): 0.8139, D(G(z)): 0.2131
Epoch: [13/20], Batch Num: [275/600]
Discriminator Loss: 0.6596, Generator Loss: 2.1177
D(x): 0.7230, D(G(z)): 0.1666
Epoch: [13/20], Batch Num: [276/600]
Discriminator Loss: 0.5695, Generator Loss: 1.7898
D(x): 0.8048, D(G(z)): 0.1958
Epoch: [13/20], Batch Num: [277/600]
Discriminator Loss: 0.7250, Generator Loss: 1.7222
D(x): 0.7679, D(G(z)): 0.2630
Epoch: [13/20], Batch Num: [278/600]
Discriminator Loss: 0.7215, Generator Loss: 1.8178
D(x): 0.8435, D(G(z)): 0.3179
Epoch: [13/20], Batch Num: [279/600]
Discriminator Loss: 0.6827, Generator Loss: 2.2641
D(x): 0.8255, D(G(z)): 0.2679
Epoch: [13/20], Batch Num: [280/600]
Discriminator Loss: 0.6571, Generator Loss: 2.2800
D(x): 0.7650, D(G(z)): 0.1969
Epoch: [13/20], Batch Num: [281/600]
Discriminator Loss: 0.6524, Generator Loss: 2.2813
D(x): 0.8000, D(G(z)): 0.2184
Epoch: [13/20], Batch Num: [282/600]
Discriminator Loss: 0.8268, Generator Loss: 2.2751
D(x): 0.7036, D(G(z)): 0.1774
Epoch: [13/20], Batch Num: [283/600]
Discriminator Loss: 0.7716, Generator Loss: 2.1109
D(x): 0.7720, D(G(z)): 0.2543
Epoch: [13/20], Batch Num: [284/600]
Discriminator Loss: 0.6280, Generator Loss: 1.9795
D(x): 0.8024, D(G(z)): 0.2389
Epoch: [13/20], Batch Num: [285/600]
Discriminator Loss: 0.6185, Generator Loss: 2.0974
D(x): 0.7644, D(G(z)): 0.1772
Epoch: [13/20], Batch Num: [286/600]
Discriminator Loss: 0.7724, Generator Loss: 1.7285
D(x): 0.7745, D(G(z)): 0.2859
Epoch: [13/20], Batch Num: [287/600]
Discriminator Loss: 0.7747, Generator Loss: 2.0395
D(x): 0.8099, D(G(z)): 0.3042
Epoch: [13/20], Batch Num: [288/600]
Discriminator Loss: 0.7880, Generator Loss: 2.1483
D(x): 0.7239, D(G(z)): 0.2293
Epoch: [13/20], Batch Num: [289/600]
Discriminator Loss: 0.9177, Generator Loss: 1.8542
D(x): 0.6507, D(G(z)): 0.2044
Epoch: [13/20], Batch Num: [290/600]
Discriminator Loss: 0.7384, Generator Loss: 1.7990
D(x): 0.8051, D(G(z)): 0.2764
Epoch: [13/20], Batch Num: [291/600]
Discriminator Loss: 0.8517, Generator Loss: 1.8330
D(x): 0.7659, D(G(z)): 0.3075
Epoch: [13/20], Batch Num: [292/600]
Discriminator Loss: 0.9969, Generator Loss: 1.8780
D(x): 0.7166, D(G(z)): 0.2917
Epoch: [13/20], Batch Num: [293/600]
Discriminator Loss: 1.0209, Generator Loss: 2.0172
D(x): 0.7106, D(G(z)): 0.3038
Epoch: [13/20], Batch Num: [294/600]
Discriminator Loss: 0.8924, Generator Loss: 2.0749
D(x): 0.7134, D(G(z)): 0.2477
Epoch: [13/20], Batch Num: [295/600]
Discriminator Loss: 0.9710, Generator Loss: 1.6679
D(x): 0.6662, D(G(z)): 0.2244
Epoch: [13/20], Batch Num: [296/600]
Discriminator Loss: 0.8297, Generator Loss: 1.6721
D(x): 0.7943, D(G(z)): 0.2937
Epoch: [13/20], Batch Num: [297/600]
Discriminator Loss: 0.8826, Generator Loss: 1.6826
D(x): 0.7528, D(G(z)): 0.3058
Epoch: [13/20], Batch Num: [298/600]
Discriminator Loss: 1.0500, Generator Loss: 1.8538
D(x): 0.7124, D(G(z)): 0.3472
Epoch: [13/20], Batch Num: [299/600]
Discriminator Loss: 0.7978, Generator Loss: 2.0520
D(x): 0.7681, D(G(z)): 0.2644
Epoch: 13, Batch Num: [300/600]

Epoch: [13/20], Batch Num: [300/600]
Discriminator Loss: 1.0699, Generator Loss: 1.8763
D(x): 0.6568, D(G(z)): 0.2674
Epoch: [13/20], Batch Num: [301/600]
Discriminator Loss: 1.1025, Generator Loss: 1.8324
D(x): 0.6272, D(G(z)): 0.2488
Epoch: [13/20], Batch Num: [302/600]
Discriminator Loss: 0.7692, Generator Loss: 1.7580
D(x): 0.7656, D(G(z)): 0.2791
Epoch: [13/20], Batch Num: [303/600]
Discriminator Loss: 0.8696, Generator Loss: 1.8240
D(x): 0.7647, D(G(z)): 0.3288
Epoch: [13/20], Batch Num: [304/600]
Discriminator Loss: 0.8386, Generator Loss: 1.8043
D(x): 0.7617, D(G(z)): 0.2833
Epoch: [13/20], Batch Num: [305/600]
Discriminator Loss: 0.8681, Generator Loss: 1.7862
D(x): 0.7368, D(G(z)): 0.2603
Epoch: [13/20], Batch Num: [306/600]
Discriminator Loss: 0.8072, Generator Loss: 1.8866
D(x): 0.6909, D(G(z)): 0.2148
Epoch: [13/20], Batch Num: [307/600]
Discriminator Loss: 0.7996, Generator Loss: 1.8239
D(x): 0.7364, D(G(z)): 0.2523
Epoch: [13/20], Batch Num: [308/600]
Discriminator Loss: 0.8191, Generator Loss: 1.8001
D(x): 0.7450, D(G(z)): 0.2758
Epoch: [13/20], Batch Num: [309/600]
Discriminator Loss: 0.9446, Generator Loss: 1.7131
D(x): 0.6954, D(G(z)): 0.2628
Epoch: [13/20], Batch Num: [310/600]
Discriminator Loss: 0.7271, Generator Loss: 1.8290
D(x): 0.7853, D(G(z)): 0.2691
Epoch: [13/20], Batch Num: [311/600]
Discriminator Loss: 0.7228, Generator Loss: 1.8117
D(x): 0.7367, D(G(z)): 0.2272
Epoch: [13/20], Batch Num: [312/600]
Discriminator Loss: 0.7365, Generator Loss: 1.8352
D(x): 0.8024, D(G(z)): 0.2993
Epoch: [13/20], Batch Num: [313/600]
Discriminator Loss: 0.7107, Generator Loss: 1.8982
D(x): 0.7844, D(G(z)): 0.2644
Epoch: [13/20], Batch Num: [314/600]
Discriminator Loss: 0.5567, Generator Loss: 2.2711
D(x): 0.8216, D(G(z)): 0.2216
Epoch: [13/20], Batch Num: [315/600]
Discriminator Loss: 0.7310, Generator Loss: 2.3446
D(x): 0.7659, D(G(z)): 0.2246
Epoch: [13/20], Batch Num: [316/600]
Discriminator Loss: 0.6315, Generator Loss: 2.4496
D(x): 0.7454, D(G(z)): 0.1615
Epoch: [13/20], Batch Num: [317/600]
Discriminator Loss: 0.7057, Generator Loss: 2.0554
D(x): 0.7186, D(G(z)): 0.1659
Epoch: [13/20], Batch Num: [318/600]
Discriminator Loss: 0.6441, Generator Loss: 1.9576
D(x): 0.8043, D(G(z)): 0.2064
Epoch: [13/20], Batch Num: [319/600]
Discriminator Loss: 0.6044, Generator Loss: 1.9278
D(x): 0.8528, D(G(z)): 0.2640
Epoch: [13/20], Batch Num: [320/600]
Discriminator Loss: 0.6184, Generator Loss: 2.1029
D(x): 0.8540, D(G(z)): 0.2591
Epoch: [13/20], Batch Num: [321/600]
Discriminator Loss: 0.5351, Generator Loss: 2.3209
D(x): 0.8547, D(G(z)): 0.2327
Epoch: [13/20], Batch Num: [322/600]
Discriminator Loss: 0.5503, Generator Loss: 2.6277
D(x): 0.8291, D(G(z)): 0.2088
Epoch: [13/20], Batch Num: [323/600]
Discriminator Loss: 0.4434, Generator Loss: 2.8608
D(x): 0.8236, D(G(z)): 0.1422
Epoch: [13/20], Batch Num: [324/600]
Discriminator Loss: 0.7228, Generator Loss: 2.4028
D(x): 0.7181, D(G(z)): 0.1491
Epoch: [13/20], Batch Num: [325/600]
Discriminator Loss: 0.5741, Generator Loss: 2.2859
D(x): 0.7826, D(G(z)): 0.1579
Epoch: [13/20], Batch Num: [326/600]
Discriminator Loss: 0.6723, Generator Loss: 2.1603
D(x): 0.8264, D(G(z)): 0.2371
Epoch: [13/20], Batch Num: [327/600]
Discriminator Loss: 0.4751, Generator Loss: 2.0877
D(x): 0.8724, D(G(z)): 0.2239
Epoch: [13/20], Batch Num: [328/600]
Discriminator Loss: 0.5890, Generator Loss: 1.9585
D(x): 0.8623, D(G(z)): 0.2388
Epoch: [13/20], Batch Num: [329/600]
Discriminator Loss: 0.6480, Generator Loss: 2.4098
D(x): 0.8146, D(G(z)): 0.2076
Epoch: [13/20], Batch Num: [330/600]
Discriminator Loss: 0.5259, Generator Loss: 2.4340
D(x): 0.8332, D(G(z)): 0.1995
Epoch: [13/20], Batch Num: [331/600]
Discriminator Loss: 0.7417, Generator Loss: 2.6276
D(x): 0.7568, D(G(z)): 0.2054
Epoch: [13/20], Batch Num: [332/600]
Discriminator Loss: 0.7047, Generator Loss: 2.4380
D(x): 0.7427, D(G(z)): 0.1470
Epoch: [13/20], Batch Num: [333/600]
Discriminator Loss: 0.6508, Generator Loss: 2.1252
D(x): 0.8002, D(G(z)): 0.1978
Epoch: [13/20], Batch Num: [334/600]
Discriminator Loss: 0.6916, Generator Loss: 1.8319
D(x): 0.7555, D(G(z)): 0.1966
Epoch: [13/20], Batch Num: [335/600]
Discriminator Loss: 0.6251, Generator Loss: 1.8083
D(x): 0.8705, D(G(z)): 0.2864
Epoch: [13/20], Batch Num: [336/600]
Discriminator Loss: 0.6818, Generator Loss: 2.0430
D(x): 0.8641, D(G(z)): 0.2807
Epoch: [13/20], Batch Num: [337/600]
Discriminator Loss: 0.9521, Generator Loss: 2.3881
D(x): 0.7908, D(G(z)): 0.3206
Epoch: [13/20], Batch Num: [338/600]
Discriminator Loss: 0.8021, Generator Loss: 2.6058
D(x): 0.7364, D(G(z)): 0.2051
Epoch: [13/20], Batch Num: [339/600]
Discriminator Loss: 0.8794, Generator Loss: 2.5946
D(x): 0.6833, D(G(z)): 0.1864
Epoch: [13/20], Batch Num: [340/600]
Discriminator Loss: 1.0153, Generator Loss: 2.1674
D(x): 0.6628, D(G(z)): 0.2316
Epoch: [13/20], Batch Num: [341/600]
Discriminator Loss: 0.7942, Generator Loss: 1.6033
D(x): 0.7423, D(G(z)): 0.2503
Epoch: [13/20], Batch Num: [342/600]
Discriminator Loss: 0.9151, Generator Loss: 1.4644
D(x): 0.8029, D(G(z)): 0.3651
Epoch: [13/20], Batch Num: [343/600]
Discriminator Loss: 0.8336, Generator Loss: 1.4301
D(x): 0.8052, D(G(z)): 0.3080
Epoch: [13/20], Batch Num: [344/600]
Discriminator Loss: 1.0621, Generator Loss: 1.8467
D(x): 0.7335, D(G(z)): 0.3375
Epoch: [13/20], Batch Num: [345/600]
Discriminator Loss: 1.0086, Generator Loss: 1.9572
D(x): 0.6893, D(G(z)): 0.2489
Epoch: [13/20], Batch Num: [346/600]
Discriminator Loss: 1.0468, Generator Loss: 1.8756
D(x): 0.6153, D(G(z)): 0.2293
Epoch: [13/20], Batch Num: [347/600]
Discriminator Loss: 0.9436, Generator Loss: 1.5852
D(x): 0.6694, D(G(z)): 0.2599
Epoch: [13/20], Batch Num: [348/600]
Discriminator Loss: 0.7245, Generator Loss: 1.4982
D(x): 0.7500, D(G(z)): 0.2499
Epoch: [13/20], Batch Num: [349/600]
Discriminator Loss: 0.8273, Generator Loss: 1.6214
D(x): 0.7986, D(G(z)): 0.3304
Epoch: [13/20], Batch Num: [350/600]
Discriminator Loss: 0.9042, Generator Loss: 1.5619
D(x): 0.7626, D(G(z)): 0.3294
Epoch: [13/20], Batch Num: [351/600]
Discriminator Loss: 0.8794, Generator Loss: 1.7782
D(x): 0.7609, D(G(z)): 0.3166
Epoch: [13/20], Batch Num: [352/600]
Discriminator Loss: 0.8639, Generator Loss: 2.0251
D(x): 0.6974, D(G(z)): 0.2625
Epoch: [13/20], Batch Num: [353/600]
Discriminator Loss: 0.7316, Generator Loss: 1.9593
D(x): 0.7204, D(G(z)): 0.2140
Epoch: [13/20], Batch Num: [354/600]
Discriminator Loss: 0.6030, Generator Loss: 1.8265
D(x): 0.7614, D(G(z)): 0.1805
Epoch: [13/20], Batch Num: [355/600]
Discriminator Loss: 0.7519, Generator Loss: 1.8613
D(x): 0.7656, D(G(z)): 0.2528
Epoch: [13/20], Batch Num: [356/600]
Discriminator Loss: 0.7662, Generator Loss: 1.9012
D(x): 0.7658, D(G(z)): 0.2691
Epoch: [13/20], Batch Num: [357/600]
Discriminator Loss: 0.6166, Generator Loss: 2.0688
D(x): 0.8327, D(G(z)): 0.2726
Epoch: [13/20], Batch Num: [358/600]
Discriminator Loss: 0.7229, Generator Loss: 2.1116
D(x): 0.7651, D(G(z)): 0.2517
Epoch: [13/20], Batch Num: [359/600]
Discriminator Loss: 0.5636, Generator Loss: 2.4048
D(x): 0.8327, D(G(z)): 0.2210
Epoch: [13/20], Batch Num: [360/600]
Discriminator Loss: 0.5365, Generator Loss: 2.3481
D(x): 0.7871, D(G(z)): 0.1760
Epoch: [13/20], Batch Num: [361/600]
Discriminator Loss: 0.8148, Generator Loss: 2.4673
D(x): 0.6880, D(G(z)): 0.2082
Epoch: [13/20], Batch Num: [362/600]
Discriminator Loss: 0.6210, Generator Loss: 2.2978
D(x): 0.7708, D(G(z)): 0.1900
Epoch: [13/20], Batch Num: [363/600]
Discriminator Loss: 0.5738, Generator Loss: 2.3142
D(x): 0.7885, D(G(z)): 0.1798
Epoch: [13/20], Batch Num: [364/600]
Discriminator Loss: 0.4961, Generator Loss: 2.1873
D(x): 0.8623, D(G(z)): 0.2061
Epoch: [13/20], Batch Num: [365/600]
Discriminator Loss: 0.6739, Generator Loss: 2.2022
D(x): 0.8118, D(G(z)): 0.2538
Epoch: [13/20], Batch Num: [366/600]
Discriminator Loss: 0.5672, Generator Loss: 2.2840
D(x): 0.8347, D(G(z)): 0.2261
Epoch: [13/20], Batch Num: [367/600]
Discriminator Loss: 0.5230, Generator Loss: 2.6210
D(x): 0.8342, D(G(z)): 0.1918
Epoch: [13/20], Batch Num: [368/600]
Discriminator Loss: 0.6221, Generator Loss: 2.5831
D(x): 0.8174, D(G(z)): 0.2515
Epoch: [13/20], Batch Num: [369/600]
Discriminator Loss: 0.6839, Generator Loss: 2.7891
D(x): 0.7114, D(G(z)): 0.1466
Epoch: [13/20], Batch Num: [370/600]
Discriminator Loss: 0.5602, Generator Loss: 2.4369
D(x): 0.7624, D(G(z)): 0.1336
Epoch: [13/20], Batch Num: [371/600]
Discriminator Loss: 0.5439, Generator Loss: 2.2670
D(x): 0.8090, D(G(z)): 0.1656
Epoch: [13/20], Batch Num: [372/600]
Discriminator Loss: 0.6480, Generator Loss: 1.9394
D(x): 0.8285, D(G(z)): 0.2177
Epoch: [13/20], Batch Num: [373/600]
Discriminator Loss: 0.6260, Generator Loss: 2.0253
D(x): 0.8605, D(G(z)): 0.2587
Epoch: [13/20], Batch Num: [374/600]
Discriminator Loss: 0.6716, Generator Loss: 2.2376
D(x): 0.8304, D(G(z)): 0.2707
Epoch: [13/20], Batch Num: [375/600]
Discriminator Loss: 0.7130, Generator Loss: 2.4456
D(x): 0.7776, D(G(z)): 0.2341
Epoch: [13/20], Batch Num: [376/600]
Discriminator Loss: 0.6737, Generator Loss: 2.6232
D(x): 0.7320, D(G(z)): 0.1626
Epoch: [13/20], Batch Num: [377/600]
Discriminator Loss: 0.7272, Generator Loss: 2.3375
D(x): 0.7356, D(G(z)): 0.1792
Epoch: [13/20], Batch Num: [378/600]
Discriminator Loss: 0.6857, Generator Loss: 2.0241
D(x): 0.7689, D(G(z)): 0.2340
Epoch: [13/20], Batch Num: [379/600]
Discriminator Loss: 0.6851, Generator Loss: 1.9997
D(x): 0.8218, D(G(z)): 0.2746
Epoch: [13/20], Batch Num: [380/600]
Discriminator Loss: 0.7427, Generator Loss: 2.2312
D(x): 0.8156, D(G(z)): 0.2818
Epoch: [13/20], Batch Num: [381/600]
Discriminator Loss: 0.8563, Generator Loss: 2.1698
D(x): 0.7146, D(G(z)): 0.2290
Epoch: [13/20], Batch Num: [382/600]
Discriminator Loss: 0.6874, Generator Loss: 2.1089
D(x): 0.7690, D(G(z)): 0.2186
Epoch: [13/20], Batch Num: [383/600]
Discriminator Loss: 0.7749, Generator Loss: 1.9098
D(x): 0.7738, D(G(z)): 0.2474
Epoch: [13/20], Batch Num: [384/600]
Discriminator Loss: 0.8729, Generator Loss: 1.8646
D(x): 0.7396, D(G(z)): 0.2530
Epoch: [13/20], Batch Num: [385/600]
Discriminator Loss: 0.9667, Generator Loss: 1.8299
D(x): 0.7894, D(G(z)): 0.3649
Epoch: [13/20], Batch Num: [386/600]
Discriminator Loss: 0.9283, Generator Loss: 1.9893
D(x): 0.6667, D(G(z)): 0.2239
Epoch: [13/20], Batch Num: [387/600]
Discriminator Loss: 0.9082, Generator Loss: 1.7223
D(x): 0.7109, D(G(z)): 0.2294
Epoch: [13/20], Batch Num: [388/600]
Discriminator Loss: 1.1030, Generator Loss: 1.3996
D(x): 0.6667, D(G(z)): 0.2927
Epoch: [13/20], Batch Num: [389/600]
Discriminator Loss: 1.0021, Generator Loss: 1.6662
D(x): 0.7932, D(G(z)): 0.3930
Epoch: [13/20], Batch Num: [390/600]
Discriminator Loss: 0.8918, Generator Loss: 1.8636
D(x): 0.7382, D(G(z)): 0.2923
Epoch: [13/20], Batch Num: [391/600]
Discriminator Loss: 0.9632, Generator Loss: 1.7771
D(x): 0.6614, D(G(z)): 0.2222
Epoch: [13/20], Batch Num: [392/600]
Discriminator Loss: 0.8667, Generator Loss: 1.6359
D(x): 0.6834, D(G(z)): 0.2295
Epoch: [13/20], Batch Num: [393/600]
Discriminator Loss: 0.9920, Generator Loss: 1.5429
D(x): 0.6720, D(G(z)): 0.2908
Epoch: [13/20], Batch Num: [394/600]
Discriminator Loss: 1.0891, Generator Loss: 1.2797
D(x): 0.7244, D(G(z)): 0.3711
Epoch: [13/20], Batch Num: [395/600]
Discriminator Loss: 0.9283, Generator Loss: 1.4862
D(x): 0.7881, D(G(z)): 0.3839
Epoch: [13/20], Batch Num: [396/600]
Discriminator Loss: 1.0492, Generator Loss: 1.7242
D(x): 0.7252, D(G(z)): 0.3638
Epoch: [13/20], Batch Num: [397/600]
Discriminator Loss: 0.9147, Generator Loss: 1.9359
D(x): 0.7101, D(G(z)): 0.2919
Epoch: [13/20], Batch Num: [398/600]
Discriminator Loss: 1.0053, Generator Loss: 1.8145
D(x): 0.6236, D(G(z)): 0.2037
Epoch: [13/20], Batch Num: [399/600]
Discriminator Loss: 1.0228, Generator Loss: 1.5575
D(x): 0.6221, D(G(z)): 0.2376
Epoch: 13, Batch Num: [400/600]

Epoch: [13/20], Batch Num: [400/600]
Discriminator Loss: 1.0152, Generator Loss: 1.2225
D(x): 0.6632, D(G(z)): 0.3151
Epoch: [13/20], Batch Num: [401/600]
Discriminator Loss: 0.8344, Generator Loss: 1.3701
D(x): 0.8073, D(G(z)): 0.3672
Epoch: [13/20], Batch Num: [402/600]
Discriminator Loss: 0.8279, Generator Loss: 1.3824
D(x): 0.8149, D(G(z)): 0.3737
Epoch: [13/20], Batch Num: [403/600]
Discriminator Loss: 0.9578, Generator Loss: 1.8329
D(x): 0.7201, D(G(z)): 0.3457
Epoch: [13/20], Batch Num: [404/600]
Discriminator Loss: 0.8687, Generator Loss: 1.9682
D(x): 0.7181, D(G(z)): 0.3017
Epoch: [13/20], Batch Num: [405/600]
Discriminator Loss: 0.7331, Generator Loss: 2.0636
D(x): 0.6920, D(G(z)): 0.2014
Epoch: [13/20], Batch Num: [406/600]
Discriminator Loss: 0.8166, Generator Loss: 2.1211
D(x): 0.6740, D(G(z)): 0.2054
Epoch: [13/20], Batch Num: [407/600]
Discriminator Loss: 0.7457, Generator Loss: 1.9304
D(x): 0.7297, D(G(z)): 0.2320
Epoch: [13/20], Batch Num: [408/600]
Discriminator Loss: 0.7826, Generator Loss: 1.7567
D(x): 0.6880, D(G(z)): 0.2162
Epoch: [13/20], Batch Num: [409/600]
Discriminator Loss: 0.7191, Generator Loss: 1.5497
D(x): 0.7987, D(G(z)): 0.3015
Epoch: [13/20], Batch Num: [410/600]
Discriminator Loss: 0.6606, Generator Loss: 1.8269
D(x): 0.8239, D(G(z)): 0.3026
Epoch: [13/20], Batch Num: [411/600]
Discriminator Loss: 0.5213, Generator Loss: 1.7795
D(x): 0.8535, D(G(z)): 0.2449
Epoch: [13/20], Batch Num: [412/600]
Discriminator Loss: 0.6368, Generator Loss: 1.8963
D(x): 0.8207, D(G(z)): 0.2670
Epoch: [13/20], Batch Num: [413/600]
Discriminator Loss: 0.5966, Generator Loss: 2.0653
D(x): 0.8018, D(G(z)): 0.2209
Epoch: [13/20], Batch Num: [414/600]
Discriminator Loss: 0.6402, Generator Loss: 2.1956
D(x): 0.7589, D(G(z)): 0.2073
Epoch: [13/20], Batch Num: [415/600]
Discriminator Loss: 0.4871, Generator Loss: 2.1721
D(x): 0.8134, D(G(z)): 0.1688
Epoch: [13/20], Batch Num: [416/600]
Discriminator Loss: 0.6120, Generator Loss: 2.3303
D(x): 0.7846, D(G(z)): 0.2174
Epoch: [13/20], Batch Num: [417/600]
Discriminator Loss: 0.5214, Generator Loss: 2.2027
D(x): 0.8134, D(G(z)): 0.1906
Epoch: [13/20], Batch Num: [418/600]
Discriminator Loss: 0.5165, Generator Loss: 2.2442
D(x): 0.8229, D(G(z)): 0.1824
Epoch: [13/20], Batch Num: [419/600]
Discriminator Loss: 0.6021, Generator Loss: 2.0133
D(x): 0.7996, D(G(z)): 0.2094
Epoch: [13/20], Batch Num: [420/600]
Discriminator Loss: 0.4680, Generator Loss: 1.9365
D(x): 0.8476, D(G(z)): 0.1911
Epoch: [13/20], Batch Num: [421/600]
Discriminator Loss: 0.6010, Generator Loss: 2.3655
D(x): 0.8294, D(G(z)): 0.2274
Epoch: [13/20], Batch Num: [422/600]
Discriminator Loss: 0.4582, Generator Loss: 2.3824
D(x): 0.8573, D(G(z)): 0.1979
Epoch: [13/20], Batch Num: [423/600]
Discriminator Loss: 0.4623, Generator Loss: 2.4510
D(x): 0.8398, D(G(z)): 0.1755
Epoch: [13/20], Batch Num: [424/600]
Discriminator Loss: 0.4306, Generator Loss: 2.6002
D(x): 0.8617, D(G(z)): 0.1567
Epoch: [13/20], Batch Num: [425/600]
Discriminator Loss: 0.4382, Generator Loss: 2.8020
D(x): 0.8351, D(G(z)): 0.1417
Epoch: [13/20], Batch Num: [426/600]
Discriminator Loss: 0.4345, Generator Loss: 2.6617
D(x): 0.8512, D(G(z)): 0.1618
Epoch: [13/20], Batch Num: [427/600]
Discriminator Loss: 0.4789, Generator Loss: 2.5244
D(x): 0.8220, D(G(z)): 0.1414
Epoch: [13/20], Batch Num: [428/600]
Discriminator Loss: 0.5292, Generator Loss: 2.4204
D(x): 0.8187, D(G(z)): 0.1775
Epoch: [13/20], Batch Num: [429/600]
Discriminator Loss: 0.5992, Generator Loss: 2.4249
D(x): 0.8158, D(G(z)): 0.2189
Epoch: [13/20], Batch Num: [430/600]
Discriminator Loss: 0.5745, Generator Loss: 2.5450
D(x): 0.8610, D(G(z)): 0.2518
Epoch: [13/20], Batch Num: [431/600]
Discriminator Loss: 0.5528, Generator Loss: 2.7960
D(x): 0.7881, D(G(z)): 0.1434
Epoch: [13/20], Batch Num: [432/600]
Discriminator Loss: 0.6861, Generator Loss: 2.4653
D(x): 0.7355, D(G(z)): 0.1740
Epoch: [13/20], Batch Num: [433/600]
Discriminator Loss: 0.6105, Generator Loss: 1.9340
D(x): 0.8145, D(G(z)): 0.2041
Epoch: [13/20], Batch Num: [434/600]
Discriminator Loss: 0.8343, Generator Loss: 2.3295
D(x): 0.8080, D(G(z)): 0.2961
Epoch: [13/20], Batch Num: [435/600]
Discriminator Loss: 0.8318, Generator Loss: 2.6139
D(x): 0.8019, D(G(z)): 0.2490
Epoch: [13/20], Batch Num: [436/600]
Discriminator Loss: 0.8130, Generator Loss: 2.5725
D(x): 0.7044, D(G(z)): 0.1787
Epoch: [13/20], Batch Num: [437/600]
Discriminator Loss: 0.7733, Generator Loss: 2.0197
D(x): 0.7431, D(G(z)): 0.1881
Epoch: [13/20], Batch Num: [438/600]
Discriminator Loss: 0.7821, Generator Loss: 1.7628
D(x): 0.7738, D(G(z)): 0.2509
Epoch: [13/20], Batch Num: [439/600]
Discriminator Loss: 0.9222, Generator Loss: 1.9505
D(x): 0.7973, D(G(z)): 0.3126
Epoch: [13/20], Batch Num: [440/600]
Discriminator Loss: 1.0128, Generator Loss: 1.9927
D(x): 0.7474, D(G(z)): 0.3144
Epoch: [13/20], Batch Num: [441/600]
Discriminator Loss: 0.9445, Generator Loss: 2.0606
D(x): 0.6898, D(G(z)): 0.2255
Epoch: [13/20], Batch Num: [442/600]
Discriminator Loss: 0.9621, Generator Loss: 1.7491
D(x): 0.6549, D(G(z)): 0.2183
Epoch: [13/20], Batch Num: [443/600]
Discriminator Loss: 1.1868, Generator Loss: 1.6592
D(x): 0.7131, D(G(z)): 0.3720
Epoch: [13/20], Batch Num: [444/600]
Discriminator Loss: 1.0933, Generator Loss: 1.8412
D(x): 0.6946, D(G(z)): 0.3294
Epoch: [13/20], Batch Num: [445/600]
Discriminator Loss: 1.1942, Generator Loss: 1.7297
D(x): 0.6675, D(G(z)): 0.3087
Epoch: [13/20], Batch Num: [446/600]
Discriminator Loss: 0.9809, Generator Loss: 1.7396
D(x): 0.6775, D(G(z)): 0.2602
Epoch: [13/20], Batch Num: [447/600]
Discriminator Loss: 1.2112, Generator Loss: 1.7168
D(x): 0.6161, D(G(z)): 0.2654
Epoch: [13/20], Batch Num: [448/600]
Discriminator Loss: 1.0863, Generator Loss: 1.5032
D(x): 0.6545, D(G(z)): 0.3047
Epoch: [13/20], Batch Num: [449/600]
Discriminator Loss: 1.1562, Generator Loss: 1.4374
D(x): 0.6787, D(G(z)): 0.3397
Epoch: [13/20], Batch Num: [450/600]
Discriminator Loss: 1.2597, Generator Loss: 1.4474
D(x): 0.6858, D(G(z)): 0.4085
Epoch: [13/20], Batch Num: [451/600]
Discriminator Loss: 1.0040, Generator Loss: 1.8029
D(x): 0.7292, D(G(z)): 0.3406
Epoch: [13/20], Batch Num: [452/600]
Discriminator Loss: 1.1970, Generator Loss: 1.8986
D(x): 0.6177, D(G(z)): 0.2447
Epoch: [13/20], Batch Num: [453/600]
Discriminator Loss: 1.2591, Generator Loss: 1.5842
D(x): 0.5428, D(G(z)): 0.2494
Epoch: [13/20], Batch Num: [454/600]
Discriminator Loss: 1.1709, Generator Loss: 1.3411
D(x): 0.5733, D(G(z)): 0.2623
Epoch: [13/20], Batch Num: [455/600]
Discriminator Loss: 1.1001, Generator Loss: 1.2161
D(x): 0.6855, D(G(z)): 0.3767
Epoch: [13/20], Batch Num: [456/600]
Discriminator Loss: 0.9246, Generator Loss: 1.2509
D(x): 0.7713, D(G(z)): 0.3613
Epoch: [13/20], Batch Num: [457/600]
Discriminator Loss: 0.8182, Generator Loss: 1.5056
D(x): 0.7875, D(G(z)): 0.3418
Epoch: [13/20], Batch Num: [458/600]
Discriminator Loss: 0.9107, Generator Loss: 1.6047
D(x): 0.7147, D(G(z)): 0.3152
Epoch: [13/20], Batch Num: [459/600]
Discriminator Loss: 0.8600, Generator Loss: 1.8415
D(x): 0.6970, D(G(z)): 0.2778
Epoch: [13/20], Batch Num: [460/600]
Discriminator Loss: 0.8440, Generator Loss: 1.7945
D(x): 0.6405, D(G(z)): 0.2005
Epoch: [13/20], Batch Num: [461/600]
Discriminator Loss: 0.7584, Generator Loss: 1.7292
D(x): 0.6997, D(G(z)): 0.2092
Epoch: [13/20], Batch Num: [462/600]
Discriminator Loss: 0.8141, Generator Loss: 1.6434
D(x): 0.6902, D(G(z)): 0.2495
Epoch: [13/20], Batch Num: [463/600]
Discriminator Loss: 0.7002, Generator Loss: 1.6810
D(x): 0.7528, D(G(z)): 0.2651
Epoch: [13/20], Batch Num: [464/600]
Discriminator Loss: 0.5663, Generator Loss: 1.5451
D(x): 0.8159, D(G(z)): 0.2533
Epoch: [13/20], Batch Num: [465/600]
Discriminator Loss: 0.6802, Generator Loss: 1.6167
D(x): 0.7928, D(G(z)): 0.2872
Epoch: [13/20], Batch Num: [466/600]
Discriminator Loss: 0.5480, Generator Loss: 1.5373
D(x): 0.8229, D(G(z)): 0.2418
Epoch: [13/20], Batch Num: [467/600]
Discriminator Loss: 0.6556, Generator Loss: 1.8785
D(x): 0.8181, D(G(z)): 0.2770
Epoch: [13/20], Batch Num: [468/600]
Discriminator Loss: 0.4713, Generator Loss: 2.1121
D(x): 0.8531, D(G(z)): 0.2164
Epoch: [13/20], Batch Num: [469/600]
Discriminator Loss: 0.5842, Generator Loss: 2.4642
D(x): 0.7637, D(G(z)): 0.1740
Epoch: [13/20], Batch Num: [470/600]
Discriminator Loss: 0.4314, Generator Loss: 2.4585
D(x): 0.8205, D(G(z)): 0.1506
Epoch: [13/20], Batch Num: [471/600]
Discriminator Loss: 0.4544, Generator Loss: 2.4813
D(x): 0.8211, D(G(z)): 0.1547
Epoch: [13/20], Batch Num: [472/600]
Discriminator Loss: 0.4189, Generator Loss: 2.2926
D(x): 0.8533, D(G(z)): 0.1597
Epoch: [13/20], Batch Num: [473/600]
Discriminator Loss: 0.5175, Generator Loss: 2.2587
D(x): 0.8119, D(G(z)): 0.1814
Epoch: [13/20], Batch Num: [474/600]
Discriminator Loss: 0.5861, Generator Loss: 2.3838
D(x): 0.7938, D(G(z)): 0.1791
Epoch: [13/20], Batch Num: [475/600]
Discriminator Loss: 0.4523, Generator Loss: 2.2439
D(x): 0.8471, D(G(z)): 0.1645
Epoch: [13/20], Batch Num: [476/600]
Discriminator Loss: 0.4853, Generator Loss: 2.3612
D(x): 0.8713, D(G(z)): 0.2055
Epoch: [13/20], Batch Num: [477/600]
Discriminator Loss: 0.5640, Generator Loss: 2.2407
D(x): 0.8082, D(G(z)): 0.1785
Epoch: [13/20], Batch Num: [478/600]
Discriminator Loss: 0.4792, Generator Loss: 2.3741
D(x): 0.8432, D(G(z)): 0.1940
Epoch: [13/20], Batch Num: [479/600]
Discriminator Loss: 0.4884, Generator Loss: 2.5185
D(x): 0.8288, D(G(z)): 0.1726
Epoch: [13/20], Batch Num: [480/600]
Discriminator Loss: 0.5712, Generator Loss: 2.4101
D(x): 0.8380, D(G(z)): 0.1960
Epoch: [13/20], Batch Num: [481/600]
Discriminator Loss: 0.4387, Generator Loss: 2.3466
D(x): 0.8330, D(G(z)): 0.1565
Epoch: [13/20], Batch Num: [482/600]
Discriminator Loss: 0.5143, Generator Loss: 2.4557
D(x): 0.8435, D(G(z)): 0.1866
Epoch: [13/20], Batch Num: [483/600]
Discriminator Loss: 0.5324, Generator Loss: 2.5925
D(x): 0.8220, D(G(z)): 0.1955
Epoch: [13/20], Batch Num: [484/600]
Discriminator Loss: 0.8621, Generator Loss: 2.6034
D(x): 0.7477, D(G(z)): 0.2291
Epoch: [13/20], Batch Num: [485/600]
Discriminator Loss: 0.6960, Generator Loss: 2.4401
D(x): 0.7826, D(G(z)): 0.2057
Epoch: [13/20], Batch Num: [486/600]
Discriminator Loss: 0.6100, Generator Loss: 2.0266
D(x): 0.8127, D(G(z)): 0.2223
Epoch: [13/20], Batch Num: [487/600]
Discriminator Loss: 0.6597, Generator Loss: 2.0190
D(x): 0.8131, D(G(z)): 0.2122
Epoch: [13/20], Batch Num: [488/600]
Discriminator Loss: 0.8498, Generator Loss: 2.0634
D(x): 0.7883, D(G(z)): 0.2943
Epoch: [13/20], Batch Num: [489/600]
Discriminator Loss: 0.9354, Generator Loss: 2.2667
D(x): 0.7829, D(G(z)): 0.2908
Epoch: [13/20], Batch Num: [490/600]
Discriminator Loss: 0.9281, Generator Loss: 2.4103
D(x): 0.7096, D(G(z)): 0.2326
Epoch: [13/20], Batch Num: [491/600]
Discriminator Loss: 1.2917, Generator Loss: 1.9582
D(x): 0.5885, D(G(z)): 0.2601
Epoch: [13/20], Batch Num: [492/600]
Discriminator Loss: 1.0752, Generator Loss: 1.6532
D(x): 0.6721, D(G(z)): 0.2996
Epoch: [13/20], Batch Num: [493/600]
Discriminator Loss: 1.1710, Generator Loss: 1.4515
D(x): 0.7069, D(G(z)): 0.3427
Epoch: [13/20], Batch Num: [494/600]
Discriminator Loss: 1.2877, Generator Loss: 1.4613
D(x): 0.6603, D(G(z)): 0.3536
Epoch: [13/20], Batch Num: [495/600]
Discriminator Loss: 1.4855, Generator Loss: 1.5987
D(x): 0.6332, D(G(z)): 0.4021
Epoch: [13/20], Batch Num: [496/600]
Discriminator Loss: 1.2400, Generator Loss: 1.7762
D(x): 0.6502, D(G(z)): 0.3428
Epoch: [13/20], Batch Num: [497/600]
Discriminator Loss: 1.3761, Generator Loss: 1.5795
D(x): 0.5443, D(G(z)): 0.2617
Epoch: [13/20], Batch Num: [498/600]
Discriminator Loss: 1.3563, Generator Loss: 1.4696
D(x): 0.6268, D(G(z)): 0.3517
Epoch: [13/20], Batch Num: [499/600]
Discriminator Loss: 1.4625, Generator Loss: 1.3488
D(x): 0.5470, D(G(z)): 0.3276
Epoch: 13, Batch Num: [500/600]

Epoch: [13/20], Batch Num: [500/600]
Discriminator Loss: 1.4140, Generator Loss: 1.1911
D(x): 0.6111, D(G(z)): 0.4081
Epoch: [13/20], Batch Num: [501/600]
Discriminator Loss: 1.2924, Generator Loss: 1.2708
D(x): 0.6451, D(G(z)): 0.4054
Epoch: [13/20], Batch Num: [502/600]
Discriminator Loss: 1.1841, Generator Loss: 1.2185
D(x): 0.6507, D(G(z)): 0.3787
Epoch: [13/20], Batch Num: [503/600]
Discriminator Loss: 1.1860, Generator Loss: 1.3655
D(x): 0.6613, D(G(z)): 0.3906
Epoch: [13/20], Batch Num: [504/600]
Discriminator Loss: 1.0643, Generator Loss: 1.4207
D(x): 0.6294, D(G(z)): 0.3021
Epoch: [13/20], Batch Num: [505/600]
Discriminator Loss: 1.0579, Generator Loss: 1.4361
D(x): 0.6510, D(G(z)): 0.3396
Epoch: [13/20], Batch Num: [506/600]
Discriminator Loss: 0.8963, Generator Loss: 1.4572
D(x): 0.6981, D(G(z)): 0.3005
Epoch: [13/20], Batch Num: [507/600]
Discriminator Loss: 0.9449, Generator Loss: 1.6563
D(x): 0.6583, D(G(z)): 0.3024
Epoch: [13/20], Batch Num: [508/600]
Discriminator Loss: 0.7487, Generator Loss: 1.5352
D(x): 0.7184, D(G(z)): 0.2580
Epoch: [13/20], Batch Num: [509/600]
Discriminator Loss: 0.9137, Generator Loss: 1.4679
D(x): 0.7056, D(G(z)): 0.3246
Epoch: [13/20], Batch Num: [510/600]
Discriminator Loss: 0.7590, Generator Loss: 1.3855
D(x): 0.7451, D(G(z)): 0.2784
Epoch: [13/20], Batch Num: [511/600]
Discriminator Loss: 0.8458, Generator Loss: 1.4205
D(x): 0.7317, D(G(z)): 0.3185
Epoch: [13/20], Batch Num: [512/600]
Discriminator Loss: 0.6860, Generator Loss: 1.5543
D(x): 0.7691, D(G(z)): 0.2633
Epoch: [13/20], Batch Num: [513/600]
Discriminator Loss: 0.6859, Generator Loss: 1.4847
D(x): 0.7554, D(G(z)): 0.2663
Epoch: [13/20], Batch Num: [514/600]
Discriminator Loss: 0.7174, Generator Loss: 1.6742
D(x): 0.7723, D(G(z)): 0.2957
Epoch: [13/20], Batch Num: [515/600]
Discriminator Loss: 0.5987, Generator Loss: 1.7440
D(x): 0.8263, D(G(z)): 0.2764
Epoch: [13/20], Batch Num: [516/600]
Discriminator Loss: 0.6564, Generator Loss: 1.7572
D(x): 0.7533, D(G(z)): 0.2381
Epoch: [13/20], Batch Num: [517/600]
Discriminator Loss: 0.6072, Generator Loss: 1.7386
D(x): 0.7959, D(G(z)): 0.2463
Epoch: [13/20], Batch Num: [518/600]
Discriminator Loss: 0.6750, Generator Loss: 1.8419
D(x): 0.7956, D(G(z)): 0.2858
Epoch: [13/20], Batch Num: [519/600]
Discriminator Loss: 0.5136, Generator Loss: 2.0295
D(x): 0.8047, D(G(z)): 0.1902
Epoch: [13/20], Batch Num: [520/600]
Discriminator Loss: 0.5446, Generator Loss: 2.0331
D(x): 0.8059, D(G(z)): 0.2055
Epoch: [13/20], Batch Num: [521/600]
Discriminator Loss: 0.5304, Generator Loss: 2.0657
D(x): 0.8145, D(G(z)): 0.2180
Epoch: [13/20], Batch Num: [522/600]
Discriminator Loss: 0.5992, Generator Loss: 2.3966
D(x): 0.8074, D(G(z)): 0.2280
Epoch: [13/20], Batch Num: [523/600]
Discriminator Loss: 0.4603, Generator Loss: 2.1662
D(x): 0.8173, D(G(z)): 0.1673
Epoch: [13/20], Batch Num: [524/600]
Discriminator Loss: 0.6080, Generator Loss: 2.2478
D(x): 0.7530, D(G(z)): 0.1866
Epoch: [13/20], Batch Num: [525/600]
Discriminator Loss: 0.4142, Generator Loss: 2.3097
D(x): 0.8893, D(G(z)): 0.2097
Epoch: [13/20], Batch Num: [526/600]
Discriminator Loss: 0.5927, Generator Loss: 2.5563
D(x): 0.8242, D(G(z)): 0.2246
Epoch: [13/20], Batch Num: [527/600]
Discriminator Loss: 0.5329, Generator Loss: 2.4938
D(x): 0.8602, D(G(z)): 0.2153
Epoch: [13/20], Batch Num: [528/600]
Discriminator Loss: 0.5229, Generator Loss: 2.5082
D(x): 0.8333, D(G(z)): 0.2127
Epoch: [13/20], Batch Num: [529/600]
Discriminator Loss: 0.6281, Generator Loss: 2.6261
D(x): 0.7577, D(G(z)): 0.1768
Epoch: [13/20], Batch Num: [530/600]
Discriminator Loss: 0.6399, Generator Loss: 2.8219
D(x): 0.7655, D(G(z)): 0.1667
Epoch: [13/20], Batch Num: [531/600]
Discriminator Loss: 0.7011, Generator Loss: 2.2086
D(x): 0.7304, D(G(z)): 0.1665
Epoch: [13/20], Batch Num: [532/600]
Discriminator Loss: 0.5875, Generator Loss: 1.8362
D(x): 0.8112, D(G(z)): 0.2124
Epoch: [13/20], Batch Num: [533/600]
Discriminator Loss: 0.6002, Generator Loss: 1.9341
D(x): 0.8624, D(G(z)): 0.2676
Epoch: [13/20], Batch Num: [534/600]
Discriminator Loss: 0.7402, Generator Loss: 2.3742
D(x): 0.8403, D(G(z)): 0.2837
Epoch: [13/20], Batch Num: [535/600]
Discriminator Loss: 0.8277, Generator Loss: 2.3972
D(x): 0.7215, D(G(z)): 0.2192
Epoch: [13/20], Batch Num: [536/600]
Discriminator Loss: 0.9277, Generator Loss: 2.4148
D(x): 0.6762, D(G(z)): 0.2106
Epoch: [13/20], Batch Num: [537/600]
Discriminator Loss: 0.8771, Generator Loss: 2.2019
D(x): 0.7130, D(G(z)): 0.2716
Epoch: [13/20], Batch Num: [538/600]
Discriminator Loss: 0.9547, Generator Loss: 1.8889
D(x): 0.7217, D(G(z)): 0.2630
Epoch: [13/20], Batch Num: [539/600]
Discriminator Loss: 1.1951, Generator Loss: 1.9252
D(x): 0.7189, D(G(z)): 0.3533
Epoch: [13/20], Batch Num: [540/600]
Discriminator Loss: 1.0920, Generator Loss: 1.6206
D(x): 0.6777, D(G(z)): 0.3130
Epoch: [13/20], Batch Num: [541/600]
Discriminator Loss: 1.2745, Generator Loss: 1.4903
D(x): 0.6444, D(G(z)): 0.3559
Epoch: [13/20], Batch Num: [542/600]
Discriminator Loss: 1.1007, Generator Loss: 1.5584
D(x): 0.6970, D(G(z)): 0.3403
Epoch: [13/20], Batch Num: [543/600]
Discriminator Loss: 1.1208, Generator Loss: 1.5783
D(x): 0.7167, D(G(z)): 0.3823
Epoch: [13/20], Batch Num: [544/600]
Discriminator Loss: 1.2226, Generator Loss: 1.4854
D(x): 0.6268, D(G(z)): 0.3432
Epoch: [13/20], Batch Num: [545/600]
Discriminator Loss: 1.1057, Generator Loss: 1.4885
D(x): 0.6467, D(G(z)): 0.3300
Epoch: [13/20], Batch Num: [546/600]
Discriminator Loss: 1.3338, Generator Loss: 1.4210
D(x): 0.5929, D(G(z)): 0.3589
Epoch: [13/20], Batch Num: [547/600]
Discriminator Loss: 1.2276, Generator Loss: 1.2539
D(x): 0.6541, D(G(z)): 0.3699
Epoch: [13/20], Batch Num: [548/600]
Discriminator Loss: 1.2854, Generator Loss: 1.2166
D(x): 0.5989, D(G(z)): 0.3724
Epoch: [13/20], Batch Num: [549/600]
Discriminator Loss: 1.1989, Generator Loss: 1.2077
D(x): 0.6264, D(G(z)): 0.3770
Epoch: [13/20], Batch Num: [550/600]
Discriminator Loss: 1.0951, Generator Loss: 1.2908
D(x): 0.6841, D(G(z)): 0.3856
Epoch: [13/20], Batch Num: [551/600]
Discriminator Loss: 1.1654, Generator Loss: 1.3402
D(x): 0.6284, D(G(z)): 0.3744
Epoch: [13/20], Batch Num: [552/600]
Discriminator Loss: 1.0917, Generator Loss: 1.3840
D(x): 0.6205, D(G(z)): 0.3083
Epoch: [13/20], Batch Num: [553/600]
Discriminator Loss: 0.9867, Generator Loss: 1.3080
D(x): 0.6604, D(G(z)): 0.3391
Epoch: [13/20], Batch Num: [554/600]
Discriminator Loss: 0.9620, Generator Loss: 1.2200
D(x): 0.6685, D(G(z)): 0.3289
Epoch: [13/20], Batch Num: [555/600]
Discriminator Loss: 0.8967, Generator Loss: 1.3679
D(x): 0.6918, D(G(z)): 0.3334
Epoch: [13/20], Batch Num: [556/600]
Discriminator Loss: 0.7354, Generator Loss: 1.4143
D(x): 0.7421, D(G(z)): 0.2784
Epoch: [13/20], Batch Num: [557/600]
Discriminator Loss: 0.7828, Generator Loss: 1.5731
D(x): 0.7633, D(G(z)): 0.3022
Epoch: [13/20], Batch Num: [558/600]
Discriminator Loss: 0.7011, Generator Loss: 1.6482
D(x): 0.7709, D(G(z)): 0.2774
Epoch: [13/20], Batch Num: [559/600]
Discriminator Loss: 0.6882, Generator Loss: 1.7000
D(x): 0.7243, D(G(z)): 0.2380
Epoch: [13/20], Batch Num: [560/600]
Discriminator Loss: 0.8129, Generator Loss: 1.7345
D(x): 0.7131, D(G(z)): 0.2742
Epoch: [13/20], Batch Num: [561/600]
Discriminator Loss: 0.6707, Generator Loss: 1.4940
D(x): 0.7608, D(G(z)): 0.2627
Epoch: [13/20], Batch Num: [562/600]
Discriminator Loss: 0.6473, Generator Loss: 1.8662
D(x): 0.7801, D(G(z)): 0.2630
Epoch: [13/20], Batch Num: [563/600]
Discriminator Loss: 0.6702, Generator Loss: 1.8020
D(x): 0.7883, D(G(z)): 0.2491
Epoch: [13/20], Batch Num: [564/600]
Discriminator Loss: 0.6283, Generator Loss: 2.0236
D(x): 0.7924, D(G(z)): 0.2411
Epoch: [13/20], Batch Num: [565/600]
Discriminator Loss: 0.6846, Generator Loss: 1.9870
D(x): 0.7572, D(G(z)): 0.2259
Epoch: [13/20], Batch Num: [566/600]
Discriminator Loss: 0.5779, Generator Loss: 2.1001
D(x): 0.7940, D(G(z)): 0.2102
Epoch: [13/20], Batch Num: [567/600]
Discriminator Loss: 0.6307, Generator Loss: 2.0530
D(x): 0.7794, D(G(z)): 0.2406
Epoch: [13/20], Batch Num: [568/600]
Discriminator Loss: 0.5656, Generator Loss: 2.1848
D(x): 0.7990, D(G(z)): 0.2093
Epoch: [13/20], Batch Num: [569/600]
Discriminator Loss: 0.5865, Generator Loss: 1.9984
D(x): 0.8164, D(G(z)): 0.2330
Epoch: [13/20], Batch Num: [570/600]
Discriminator Loss: 0.5332, Generator Loss: 2.1316
D(x): 0.7965, D(G(z)): 0.1877
Epoch: [13/20], Batch Num: [571/600]
Discriminator Loss: 0.6260, Generator Loss: 2.1387
D(x): 0.7838, D(G(z)): 0.2193
Epoch: [13/20], Batch Num: [572/600]
Discriminator Loss: 0.4641, Generator Loss: 2.3049
D(x): 0.8688, D(G(z)): 0.2197
Epoch: [13/20], Batch Num: [573/600]
Discriminator Loss: 0.5994, Generator Loss: 2.3408
D(x): 0.7979, D(G(z)): 0.2083
Epoch: [13/20], Batch Num: [574/600]
Discriminator Loss: 0.5612, Generator Loss: 2.3078
D(x): 0.8075, D(G(z)): 0.1833
Epoch: [13/20], Batch Num: [575/600]
Discriminator Loss: 0.5032, Generator Loss: 2.2435
D(x): 0.7819, D(G(z)): 0.1414
Epoch: [13/20], Batch Num: [576/600]
Discriminator Loss: 0.4596, Generator Loss: 2.1078
D(x): 0.8370, D(G(z)): 0.1848
Epoch: [13/20], Batch Num: [577/600]
Discriminator Loss: 0.4882, Generator Loss: 1.9871
D(x): 0.8539, D(G(z)): 0.1972
Epoch: [13/20], Batch Num: [578/600]
Discriminator Loss: 0.5586, Generator Loss: 2.1638
D(x): 0.8487, D(G(z)): 0.2276
Epoch: [13/20], Batch Num: [579/600]
Discriminator Loss: 0.5522, Generator Loss: 2.4506
D(x): 0.8418, D(G(z)): 0.2029
Epoch: [13/20], Batch Num: [580/600]
Discriminator Loss: 0.6406, Generator Loss: 2.7751
D(x): 0.7915, D(G(z)): 0.2099
Epoch: [13/20], Batch Num: [581/600]
Discriminator Loss: 0.5447, Generator Loss: 2.4084
D(x): 0.7856, D(G(z)): 0.1589
Epoch: [13/20], Batch Num: [582/600]
Discriminator Loss: 0.6574, Generator Loss: 2.3518
D(x): 0.7598, D(G(z)): 0.1744
Epoch: [13/20], Batch Num: [583/600]
Discriminator Loss: 0.6924, Generator Loss: 2.1934
D(x): 0.7405, D(G(z)): 0.2039
Epoch: [13/20], Batch Num: [584/600]
Discriminator Loss: 0.5566, Generator Loss: 2.0762
D(x): 0.8744, D(G(z)): 0.2546
Epoch: [13/20], Batch Num: [585/600]
Discriminator Loss: 0.7066, Generator Loss: 2.4603
D(x): 0.8070, D(G(z)): 0.2546
Epoch: [13/20], Batch Num: [586/600]
Discriminator Loss: 0.8905, Generator Loss: 2.3495
D(x): 0.7087, D(G(z)): 0.2025
Epoch: [13/20], Batch Num: [587/600]
Discriminator Loss: 0.8538, Generator Loss: 2.2545
D(x): 0.7214, D(G(z)): 0.2511
Epoch: [13/20], Batch Num: [588/600]
Discriminator Loss: 0.8660, Generator Loss: 2.0503
D(x): 0.7658, D(G(z)): 0.2737
Epoch: [13/20], Batch Num: [589/600]
Discriminator Loss: 0.8691, Generator Loss: 1.8693
D(x): 0.7001, D(G(z)): 0.2499
Epoch: [13/20], Batch Num: [590/600]
Discriminator Loss: 0.9923, Generator Loss: 1.7592
D(x): 0.7034, D(G(z)): 0.2912
Epoch: [13/20], Batch Num: [591/600]
Discriminator Loss: 0.9557, Generator Loss: 2.1698
D(x): 0.7612, D(G(z)): 0.3296
Epoch: [13/20], Batch Num: [592/600]
Discriminator Loss: 0.8130, Generator Loss: 2.1601
D(x): 0.7184, D(G(z)): 0.2165
Epoch: [13/20], Batch Num: [593/600]
Discriminator Loss: 0.9687, Generator Loss: 1.7618
D(x): 0.6729, D(G(z)): 0.2165
Epoch: [13/20], Batch Num: [594/600]
Discriminator Loss: 1.0910, Generator Loss: 1.4380
D(x): 0.6644, D(G(z)): 0.3085
Epoch: [13/20], Batch Num: [595/600]
Discriminator Loss: 1.2155, Generator Loss: 1.3782
D(x): 0.6814, D(G(z)): 0.3881
Epoch: [13/20], Batch Num: [596/600]
Discriminator Loss: 1.0526, Generator Loss: 1.5508
D(x): 0.7419, D(G(z)): 0.3768
Epoch: [13/20], Batch Num: [597/600]
Discriminator Loss: 1.2022, Generator Loss: 1.6740
D(x): 0.6293, D(G(z)): 0.3186
Epoch: [13/20], Batch Num: [598/600]
Discriminator Loss: 1.1980, Generator Loss: 1.5590
D(x): 0.6209, D(G(z)): 0.2946
Epoch: [13/20], Batch Num: [599/600]
Discriminator Loss: 1.0672, Generator Loss: 1.2423
D(x): 0.6296, D(G(z)): 0.2704
Epoch: 14, Batch Num: [0/600]

Epoch: [14/20], Batch Num: [0/600]
Discriminator Loss: 1.0477, Generator Loss: 1.4197
D(x): 0.7352, D(G(z)): 0.3643
Epoch: [14/20], Batch Num: [1/600]
Discriminator Loss: 1.0544, Generator Loss: 1.2611
D(x): 0.7109, D(G(z)): 0.3549
Epoch: [14/20], Batch Num: [2/600]
Discriminator Loss: 0.9002, Generator Loss: 1.7149
D(x): 0.7399, D(G(z)): 0.3190
Epoch: [14/20], Batch Num: [3/600]
Discriminator Loss: 1.0458, Generator Loss: 1.5883
D(x): 0.6427, D(G(z)): 0.2660
Epoch: [14/20], Batch Num: [4/600]
Discriminator Loss: 0.9988, Generator Loss: 1.4784
D(x): 0.6572, D(G(z)): 0.2733
Epoch: [14/20], Batch Num: [5/600]
Discriminator Loss: 0.9335, Generator Loss: 1.3975
D(x): 0.7226, D(G(z)): 0.3063
Epoch: [14/20], Batch Num: [6/600]
Discriminator Loss: 0.8766, Generator Loss: 1.5327
D(x): 0.7817, D(G(z)): 0.3378
Epoch: [14/20], Batch Num: [7/600]
Discriminator Loss: 0.9039, Generator Loss: 1.6265
D(x): 0.7348, D(G(z)): 0.3214
Epoch: [14/20], Batch Num: [8/600]
Discriminator Loss: 0.7511, Generator Loss: 1.9110
D(x): 0.7468, D(G(z)): 0.2707
Epoch: [14/20], Batch Num: [9/600]
Discriminator Loss: 0.6745, Generator Loss: 1.7537
D(x): 0.7580, D(G(z)): 0.2347
Epoch: [14/20], Batch Num: [10/600]
Discriminator Loss: 0.7786, Generator Loss: 1.7312
D(x): 0.6688, D(G(z)): 0.2011
Epoch: [14/20], Batch Num: [11/600]
Discriminator Loss: 0.6575, Generator Loss: 1.6458
D(x): 0.7855, D(G(z)): 0.2567
Epoch: [14/20], Batch Num: [12/600]
Discriminator Loss: 0.7457, Generator Loss: 1.5152
D(x): 0.7648, D(G(z)): 0.2704
Epoch: [14/20], Batch Num: [13/600]
Discriminator Loss: 0.6561, Generator Loss: 1.4950
D(x): 0.8132, D(G(z)): 0.2712
Epoch: [14/20], Batch Num: [14/600]
Discriminator Loss: 0.6538, Generator Loss: 1.6864
D(x): 0.8058, D(G(z)): 0.2629
Epoch: [14/20], Batch Num: [15/600]
Discriminator Loss: 0.6616, Generator Loss: 1.7267
D(x): 0.7762, D(G(z)): 0.2671
Epoch: [14/20], Batch Num: [16/600]
Discriminator Loss: 0.6074, Generator Loss: 2.0603
D(x): 0.8248, D(G(z)): 0.2568
Epoch: [14/20], Batch Num: [17/600]
Discriminator Loss: 0.5931, Generator Loss: 1.9612
D(x): 0.7748, D(G(z)): 0.1976
Epoch: [14/20], Batch Num: [18/600]
Discriminator Loss: 0.6679, Generator Loss: 2.0442
D(x): 0.7303, D(G(z)): 0.1918
Epoch: [14/20], Batch Num: [19/600]
Discriminator Loss: 0.6508, Generator Loss: 1.8076
D(x): 0.7600, D(G(z)): 0.1989
Epoch: [14/20], Batch Num: [20/600]
Discriminator Loss: 0.5725, Generator Loss: 1.6203
D(x): 0.8358, D(G(z)): 0.2339
Epoch: [14/20], Batch Num: [21/600]
Discriminator Loss: 0.6249, Generator Loss: 1.8199
D(x): 0.8258, D(G(z)): 0.2654
Epoch: [14/20], Batch Num: [22/600]
Discriminator Loss: 0.5772, Generator Loss: 2.0060
D(x): 0.8252, D(G(z)): 0.2452
Epoch: [14/20], Batch Num: [23/600]
Discriminator Loss: 0.6358, Generator Loss: 2.0827
D(x): 0.8067, D(G(z)): 0.2297
Epoch: [14/20], Batch Num: [24/600]
Discriminator Loss: 0.5178, Generator Loss: 2.4682
D(x): 0.8382, D(G(z)): 0.2168
Epoch: [14/20], Batch Num: [25/600]
Discriminator Loss: 0.5974, Generator Loss: 2.2827
D(x): 0.7567, D(G(z)): 0.1590
Epoch: [14/20], Batch Num: [26/600]
Discriminator Loss: 0.7034, Generator Loss: 2.0599
D(x): 0.7388, D(G(z)): 0.1980
Epoch: [14/20], Batch Num: [27/600]
Discriminator Loss: 0.6225, Generator Loss: 1.9666
D(x): 0.7976, D(G(z)): 0.2423
Epoch: [14/20], Batch Num: [28/600]
Discriminator Loss: 0.6139, Generator Loss: 1.7834
D(x): 0.8160, D(G(z)): 0.2425
Epoch: [14/20], Batch Num: [29/600]
Discriminator Loss: 0.7546, Generator Loss: 1.6963
D(x): 0.7750, D(G(z)): 0.2613
Epoch: [14/20], Batch Num: [30/600]
Discriminator Loss: 0.7693, Generator Loss: 1.8507
D(x): 0.7939, D(G(z)): 0.2729
Epoch: [14/20], Batch Num: [31/600]
Discriminator Loss: 0.7214, Generator Loss: 2.1207
D(x): 0.7797, D(G(z)): 0.2690
Epoch: [14/20], Batch Num: [32/600]
Discriminator Loss: 0.7624, Generator Loss: 2.0920
D(x): 0.7407, D(G(z)): 0.2109
Epoch: [14/20], Batch Num: [33/600]
Discriminator Loss: 0.6466, Generator Loss: 1.8141
D(x): 0.7605, D(G(z)): 0.1674
Epoch: [14/20], Batch Num: [34/600]
Discriminator Loss: 0.7606, Generator Loss: 1.7978
D(x): 0.7768, D(G(z)): 0.2522
Epoch: [14/20], Batch Num: [35/600]
Discriminator Loss: 0.8888, Generator Loss: 1.8080
D(x): 0.8187, D(G(z)): 0.3295
Epoch: [14/20], Batch Num: [36/600]
Discriminator Loss: 0.9958, Generator Loss: 2.1431
D(x): 0.6510, D(G(z)): 0.2389
Epoch: [14/20], Batch Num: [37/600]
Discriminator Loss: 0.8261, Generator Loss: 2.1786
D(x): 0.7779, D(G(z)): 0.2769
Epoch: [14/20], Batch Num: [38/600]
Discriminator Loss: 0.9201, Generator Loss: 1.7989
D(x): 0.6649, D(G(z)): 0.2119
Epoch: [14/20], Batch Num: [39/600]
Discriminator Loss: 0.8721, Generator Loss: 1.5046
D(x): 0.7157, D(G(z)): 0.2509
Epoch: [14/20], Batch Num: [40/600]
Discriminator Loss: 0.8694, Generator Loss: 1.6340
D(x): 0.8216, D(G(z)): 0.3320
Epoch: [14/20], Batch Num: [41/600]
Discriminator Loss: 0.9029, Generator Loss: 2.1144
D(x): 0.7853, D(G(z)): 0.3305
Epoch: [14/20], Batch Num: [42/600]
Discriminator Loss: 1.0463, Generator Loss: 2.0101
D(x): 0.6691, D(G(z)): 0.2593
Epoch: [14/20], Batch Num: [43/600]
Discriminator Loss: 1.1735, Generator Loss: 1.9822
D(x): 0.6047, D(G(z)): 0.2546
Epoch: [14/20], Batch Num: [44/600]
Discriminator Loss: 0.9656, Generator Loss: 1.5584
D(x): 0.6776, D(G(z)): 0.2494
Epoch: [14/20], Batch Num: [45/600]
Discriminator Loss: 0.9731, Generator Loss: 1.4985
D(x): 0.7610, D(G(z)): 0.3177
Epoch: [14/20], Batch Num: [46/600]
Discriminator Loss: 1.0182, Generator Loss: 1.5337
D(x): 0.7750, D(G(z)): 0.3800
Epoch: [14/20], Batch Num: [47/600]
Discriminator Loss: 0.8306, Generator Loss: 1.9155
D(x): 0.7568, D(G(z)): 0.2901
Epoch: [14/20], Batch Num: [48/600]
Discriminator Loss: 0.9474, Generator Loss: 1.9031
D(x): 0.6755, D(G(z)): 0.2743
Epoch: [14/20], Batch Num: [49/600]
Discriminator Loss: 0.9355, Generator Loss: 1.9871
D(x): 0.6766, D(G(z)): 0.2766
Epoch: [14/20], Batch Num: [50/600]
Discriminator Loss: 1.0086, Generator Loss: 1.8388
D(x): 0.6508, D(G(z)): 0.2424
Epoch: [14/20], Batch Num: [51/600]
Discriminator Loss: 0.7589, Generator Loss: 1.5953
D(x): 0.7266, D(G(z)): 0.2438
Epoch: [14/20], Batch Num: [52/600]
Discriminator Loss: 0.9672, Generator Loss: 1.4222
D(x): 0.7011, D(G(z)): 0.3117
Epoch: [14/20], Batch Num: [53/600]
Discriminator Loss: 0.9433, Generator Loss: 1.5093
D(x): 0.7311, D(G(z)): 0.3316
Epoch: [14/20], Batch Num: [54/600]
Discriminator Loss: 0.8620, Generator Loss: 1.5932
D(x): 0.7801, D(G(z)): 0.3209
Epoch: [14/20], Batch Num: [55/600]
Discriminator Loss: 0.9129, Generator Loss: 1.7536
D(x): 0.7189, D(G(z)): 0.3159
Epoch: [14/20], Batch Num: [56/600]
Discriminator Loss: 0.9348, Generator Loss: 1.7404
D(x): 0.6661, D(G(z)): 0.2717
Epoch: [14/20], Batch Num: [57/600]
Discriminator Loss: 0.9649, Generator Loss: 1.6775
D(x): 0.6675, D(G(z)): 0.2738
Epoch: [14/20], Batch Num: [58/600]
Discriminator Loss: 0.8300, Generator Loss: 1.5582
D(x): 0.7211, D(G(z)): 0.2798
Epoch: [14/20], Batch Num: [59/600]
Discriminator Loss: 0.9056, Generator Loss: 1.4996
D(x): 0.7009, D(G(z)): 0.3079
Epoch: [14/20], Batch Num: [60/600]
Discriminator Loss: 0.9265, Generator Loss: 1.3558
D(x): 0.6980, D(G(z)): 0.2962
Epoch: [14/20], Batch Num: [61/600]
Discriminator Loss: 0.9370, Generator Loss: 1.4764
D(x): 0.7218, D(G(z)): 0.3321
Epoch: [14/20], Batch Num: [62/600]
Discriminator Loss: 1.0051, Generator Loss: 1.5483
D(x): 0.6805, D(G(z)): 0.3308
Epoch: [14/20], Batch Num: [63/600]
Discriminator Loss: 0.8945, Generator Loss: 1.6651
D(x): 0.7256, D(G(z)): 0.2989
Epoch: [14/20], Batch Num: [64/600]
Discriminator Loss: 0.8257, Generator Loss: 1.6095
D(x): 0.7177, D(G(z)): 0.2731
Epoch: [14/20], Batch Num: [65/600]
Discriminator Loss: 0.8555, Generator Loss: 1.6446
D(x): 0.6892, D(G(z)): 0.2506
Epoch: [14/20], Batch Num: [66/600]
Discriminator Loss: 0.8086, Generator Loss: 1.6378
D(x): 0.7378, D(G(z)): 0.2759
Epoch: [14/20], Batch Num: [67/600]
Discriminator Loss: 0.8272, Generator Loss: 1.5630
D(x): 0.7539, D(G(z)): 0.3031
Epoch: [14/20], Batch Num: [68/600]
Discriminator Loss: 0.8027, Generator Loss: 1.7175
D(x): 0.7547, D(G(z)): 0.2803
Epoch: [14/20], Batch Num: [69/600]
Discriminator Loss: 0.8909, Generator Loss: 1.5645
D(x): 0.7122, D(G(z)): 0.2891
Epoch: [14/20], Batch Num: [70/600]
Discriminator Loss: 0.7068, Generator Loss: 1.7479
D(x): 0.7618, D(G(z)): 0.2416
Epoch: [14/20], Batch Num: [71/600]
Discriminator Loss: 0.8339, Generator Loss: 1.5352
D(x): 0.7334, D(G(z)): 0.2772
Epoch: [14/20], Batch Num: [72/600]
Discriminator Loss: 0.8478, Generator Loss: 1.5039
D(x): 0.7275, D(G(z)): 0.3005
Epoch: [14/20], Batch Num: [73/600]
Discriminator Loss: 0.7700, Generator Loss: 1.7690
D(x): 0.7479, D(G(z)): 0.2662
Epoch: [14/20], Batch Num: [74/600]
Discriminator Loss: 0.8251, Generator Loss: 1.8482
D(x): 0.7257, D(G(z)): 0.2832
Epoch: [14/20], Batch Num: [75/600]
Discriminator Loss: 0.6913, Generator Loss: 1.7332
D(x): 0.7757, D(G(z)): 0.2585
Epoch: [14/20], Batch Num: [76/600]
Discriminator Loss: 0.6795, Generator Loss: 1.9845
D(x): 0.7615, D(G(z)): 0.2388
Epoch: [14/20], Batch Num: [77/600]
Discriminator Loss: 0.6169, Generator Loss: 1.9422
D(x): 0.7481, D(G(z)): 0.1835
Epoch: [14/20], Batch Num: [78/600]
Discriminator Loss: 0.7089, Generator Loss: 2.0775
D(x): 0.7900, D(G(z)): 0.2534
Epoch: [14/20], Batch Num: [79/600]
Discriminator Loss: 0.6720, Generator Loss: 1.7955
D(x): 0.7690, D(G(z)): 0.2254
Epoch: [14/20], Batch Num: [80/600]
Discriminator Loss: 0.7183, Generator Loss: 1.9626
D(x): 0.7832, D(G(z)): 0.2797
Epoch: [14/20], Batch Num: [81/600]
Discriminator Loss: 0.7020, Generator Loss: 1.9251
D(x): 0.7719, D(G(z)): 0.2561
Epoch: [14/20], Batch Num: [82/600]
Discriminator Loss: 0.7524, Generator Loss: 2.1538
D(x): 0.7541, D(G(z)): 0.2592
Epoch: [14/20], Batch Num: [83/600]
Discriminator Loss: 0.7490, Generator Loss: 1.9347
D(x): 0.7556, D(G(z)): 0.2501
Epoch: [14/20], Batch Num: [84/600]
Discriminator Loss: 0.7179, Generator Loss: 2.0325
D(x): 0.7219, D(G(z)): 0.2083
Epoch: [14/20], Batch Num: [85/600]
Discriminator Loss: 0.5632, Generator Loss: 2.0585
D(x): 0.7944, D(G(z)): 0.1957
Epoch: [14/20], Batch Num: [86/600]
Discriminator Loss: 0.6437, Generator Loss: 1.7030
D(x): 0.7530, D(G(z)): 0.1990
Epoch: [14/20], Batch Num: [87/600]
Discriminator Loss: 0.8145, Generator Loss: 1.9597
D(x): 0.8215, D(G(z)): 0.2893
Epoch: [14/20], Batch Num: [88/600]
Discriminator Loss: 0.7265, Generator Loss: 2.1355
D(x): 0.8143, D(G(z)): 0.2590
Epoch: [14/20], Batch Num: [89/600]
Discriminator Loss: 0.7380, Generator Loss: 2.3880
D(x): 0.7560, D(G(z)): 0.2426
Epoch: [14/20], Batch Num: [90/600]
Discriminator Loss: 0.7134, Generator Loss: 2.3496
D(x): 0.7205, D(G(z)): 0.1870
Epoch: [14/20], Batch Num: [91/600]
Discriminator Loss: 0.6932, Generator Loss: 2.1713
D(x): 0.7536, D(G(z)): 0.1878
Epoch: [14/20], Batch Num: [92/600]
Discriminator Loss: 0.7607, Generator Loss: 1.7455
D(x): 0.7433, D(G(z)): 0.2131
Epoch: [14/20], Batch Num: [93/600]
Discriminator Loss: 0.8067, Generator Loss: 1.9878
D(x): 0.8046, D(G(z)): 0.3032
Epoch: [14/20], Batch Num: [94/600]
Discriminator Loss: 0.8966, Generator Loss: 1.9465
D(x): 0.7623, D(G(z)): 0.3036
Epoch: [14/20], Batch Num: [95/600]
Discriminator Loss: 0.7750, Generator Loss: 2.0821
D(x): 0.7353, D(G(z)): 0.2483
Epoch: [14/20], Batch Num: [96/600]
Discriminator Loss: 0.8726, Generator Loss: 1.9705
D(x): 0.7163, D(G(z)): 0.2307
Epoch: [14/20], Batch Num: [97/600]
Discriminator Loss: 0.8708, Generator Loss: 2.0448
D(x): 0.7183, D(G(z)): 0.2307
Epoch: [14/20], Batch Num: [98/600]
Discriminator Loss: 1.0486, Generator Loss: 1.7552
D(x): 0.6580, D(G(z)): 0.2609
Epoch: [14/20], Batch Num: [99/600]
Discriminator Loss: 0.9617, Generator Loss: 1.6223
D(x): 0.7463, D(G(z)): 0.3085
Epoch: 14, Batch Num: [100/600]

Epoch: [14/20], Batch Num: [100/600]
Discriminator Loss: 0.9329, Generator Loss: 1.7882
D(x): 0.7722, D(G(z)): 0.3403
Epoch: [14/20], Batch Num: [101/600]
Discriminator Loss: 0.9069, Generator Loss: 2.0320
D(x): 0.7415, D(G(z)): 0.2974
Epoch: [14/20], Batch Num: [102/600]
Discriminator Loss: 0.9692, Generator Loss: 2.2689
D(x): 0.6736, D(G(z)): 0.2560
Epoch: [14/20], Batch Num: [103/600]
Discriminator Loss: 1.2378, Generator Loss: 1.8782
D(x): 0.5664, D(G(z)): 0.1903
Epoch: [14/20], Batch Num: [104/600]
Discriminator Loss: 0.9198, Generator Loss: 1.5121
D(x): 0.7134, D(G(z)): 0.2744
Epoch: [14/20], Batch Num: [105/600]
Discriminator Loss: 0.9366, Generator Loss: 1.2634
D(x): 0.7356, D(G(z)): 0.3349
Epoch: [14/20], Batch Num: [106/600]
Discriminator Loss: 0.9412, Generator Loss: 1.5707
D(x): 0.7996, D(G(z)): 0.3712
Epoch: [14/20], Batch Num: [107/600]
Discriminator Loss: 0.9206, Generator Loss: 1.7849
D(x): 0.7604, D(G(z)): 0.3107
Epoch: [14/20], Batch Num: [108/600]
Discriminator Loss: 1.1840, Generator Loss: 2.1226
D(x): 0.6788, D(G(z)): 0.3228
Epoch: [14/20], Batch Num: [109/600]
Discriminator Loss: 1.0717, Generator Loss: 1.9884
D(x): 0.6083, D(G(z)): 0.2344
Epoch: [14/20], Batch Num: [110/600]
Discriminator Loss: 0.8143, Generator Loss: 1.9123
D(x): 0.6643, D(G(z)): 0.2039
Epoch: [14/20], Batch Num: [111/600]
Discriminator Loss: 1.1839, Generator Loss: 1.5274
D(x): 0.6230, D(G(z)): 0.2667
Epoch: [14/20], Batch Num: [112/600]
Discriminator Loss: 0.8922, Generator Loss: 1.1188
D(x): 0.7185, D(G(z)): 0.3106
Epoch: [14/20], Batch Num: [113/600]
Discriminator Loss: 0.9274, Generator Loss: 1.1744
D(x): 0.7730, D(G(z)): 0.3707
Epoch: [14/20], Batch Num: [114/600]
Discriminator Loss: 1.2584, Generator Loss: 1.4151
D(x): 0.7412, D(G(z)): 0.4571
Epoch: [14/20], Batch Num: [115/600]
Discriminator Loss: 0.9437, Generator Loss: 1.7066
D(x): 0.7316, D(G(z)): 0.3288
Epoch: [14/20], Batch Num: [116/600]
Discriminator Loss: 0.6553, Generator Loss: 2.0075
D(x): 0.7788, D(G(z)): 0.2383
Epoch: [14/20], Batch Num: [117/600]
Discriminator Loss: 1.0083, Generator Loss: 2.0046
D(x): 0.5760, D(G(z)): 0.1929
Epoch: [14/20], Batch Num: [118/600]
Discriminator Loss: 0.8296, Generator Loss: 1.7820
D(x): 0.6662, D(G(z)): 0.2149
Epoch: [14/20], Batch Num: [119/600]
Discriminator Loss: 0.8128, Generator Loss: 1.5431
D(x): 0.6648, D(G(z)): 0.2149
Epoch: [14/20], Batch Num: [120/600]
Discriminator Loss: 0.6920, Generator Loss: 1.3208
D(x): 0.8029, D(G(z)): 0.2995
Epoch: [14/20], Batch Num: [121/600]
Discriminator Loss: 0.7942, Generator Loss: 1.3298
D(x): 0.8092, D(G(z)): 0.3472
Epoch: [14/20], Batch Num: [122/600]
Discriminator Loss: 0.7711, Generator Loss: 1.5342
D(x): 0.8324, D(G(z)): 0.3551
Epoch: [14/20], Batch Num: [123/600]
Discriminator Loss: 0.6691, Generator Loss: 1.9273
D(x): 0.8198, D(G(z)): 0.2946
Epoch: [14/20], Batch Num: [124/600]
Discriminator Loss: 0.7329, Generator Loss: 2.1281
D(x): 0.7474, D(G(z)): 0.2401
Epoch: [14/20], Batch Num: [125/600]
Discriminator Loss: 0.7345, Generator Loss: 2.1877
D(x): 0.6775, D(G(z)): 0.1667
Epoch: [14/20], Batch Num: [126/600]
Discriminator Loss: 0.7199, Generator Loss: 2.1490
D(x): 0.7171, D(G(z)): 0.1973
Epoch: [14/20], Batch Num: [127/600]
Discriminator Loss: 0.6029, Generator Loss: 1.8908
D(x): 0.7728, D(G(z)): 0.1915
Epoch: [14/20], Batch Num: [128/600]
Discriminator Loss: 0.5333, Generator Loss: 1.9126
D(x): 0.7940, D(G(z)): 0.1876
Epoch: [14/20], Batch Num: [129/600]
Discriminator Loss: 0.6164, Generator Loss: 1.8944
D(x): 0.8197, D(G(z)): 0.2373
Epoch: [14/20], Batch Num: [130/600]
Discriminator Loss: 0.6308, Generator Loss: 1.7470
D(x): 0.7885, D(G(z)): 0.2356
Epoch: [14/20], Batch Num: [131/600]
Discriminator Loss: 0.7004, Generator Loss: 1.9554
D(x): 0.8054, D(G(z)): 0.2682
Epoch: [14/20], Batch Num: [132/600]
Discriminator Loss: 0.5141, Generator Loss: 1.9130
D(x): 0.8279, D(G(z)): 0.2040
Epoch: [14/20], Batch Num: [133/600]
Discriminator Loss: 0.6959, Generator Loss: 2.1025
D(x): 0.7710, D(G(z)): 0.2217
Epoch: [14/20], Batch Num: [134/600]
Discriminator Loss: 0.5341, Generator Loss: 2.0921
D(x): 0.8065, D(G(z)): 0.1945
Epoch: [14/20], Batch Num: [135/600]
Discriminator Loss: 0.6514, Generator Loss: 2.0808
D(x): 0.7490, D(G(z)): 0.1654
Epoch: [14/20], Batch Num: [136/600]
Discriminator Loss: 0.5612, Generator Loss: 2.1272
D(x): 0.8422, D(G(z)): 0.2093
Epoch: [14/20], Batch Num: [137/600]
Discriminator Loss: 0.5334, Generator Loss: 2.1339
D(x): 0.8000, D(G(z)): 0.1700
Epoch: [14/20], Batch Num: [138/600]
Discriminator Loss: 0.4829, Generator Loss: 2.1669
D(x): 0.8310, D(G(z)): 0.1776
Epoch: [14/20], Batch Num: [139/600]
Discriminator Loss: 0.6004, Generator Loss: 2.0256
D(x): 0.8504, D(G(z)): 0.2364
Epoch: [14/20], Batch Num: [140/600]
Discriminator Loss: 0.4942, Generator Loss: 2.1901
D(x): 0.8250, D(G(z)): 0.1875
Epoch: [14/20], Batch Num: [141/600]
Discriminator Loss: 0.5686, Generator Loss: 2.2886
D(x): 0.8200, D(G(z)): 0.2080
Epoch: [14/20], Batch Num: [142/600]
Discriminator Loss: 0.5291, Generator Loss: 2.0767
D(x): 0.8184, D(G(z)): 0.1800
Epoch: [14/20], Batch Num: [143/600]
Discriminator Loss: 0.4600, Generator Loss: 2.3703
D(x): 0.8587, D(G(z)): 0.1873
Epoch: [14/20], Batch Num: [144/600]
Discriminator Loss: 0.5854, Generator Loss: 2.0118
D(x): 0.8255, D(G(z)): 0.2110
Epoch: [14/20], Batch Num: [145/600]
Discriminator Loss: 0.6180, Generator Loss: 2.6600
D(x): 0.8292, D(G(z)): 0.2186
Epoch: [14/20], Batch Num: [146/600]
Discriminator Loss: 0.6578, Generator Loss: 2.6651
D(x): 0.8178, D(G(z)): 0.2068
Epoch: [14/20], Batch Num: [147/600]
Discriminator Loss: 0.6260, Generator Loss: 2.8452
D(x): 0.7711, D(G(z)): 0.1788
Epoch: [14/20], Batch Num: [148/600]
Discriminator Loss: 0.5341, Generator Loss: 2.4352
D(x): 0.7814, D(G(z)): 0.1364
Epoch: [14/20], Batch Num: [149/600]
Discriminator Loss: 0.6375, Generator Loss: 2.1264
D(x): 0.7987, D(G(z)): 0.1956
Epoch: [14/20], Batch Num: [150/600]
Discriminator Loss: 0.7096, Generator Loss: 1.9147
D(x): 0.8180, D(G(z)): 0.2517
Epoch: [14/20], Batch Num: [151/600]
Discriminator Loss: 0.7372, Generator Loss: 2.0467
D(x): 0.8357, D(G(z)): 0.2760
Epoch: [14/20], Batch Num: [152/600]
Discriminator Loss: 0.7290, Generator Loss: 2.5335
D(x): 0.7968, D(G(z)): 0.2612
Epoch: [14/20], Batch Num: [153/600]
Discriminator Loss: 0.7954, Generator Loss: 2.4703
D(x): 0.7723, D(G(z)): 0.2015
Epoch: [14/20], Batch Num: [154/600]
Discriminator Loss: 0.7300, Generator Loss: 2.5090
D(x): 0.7331, D(G(z)): 0.1794
Epoch: [14/20], Batch Num: [155/600]
Discriminator Loss: 0.8123, Generator Loss: 2.2828
D(x): 0.6958, D(G(z)): 0.1831
Epoch: [14/20], Batch Num: [156/600]
Discriminator Loss: 0.8411, Generator Loss: 1.7217
D(x): 0.7665, D(G(z)): 0.2654
Epoch: [14/20], Batch Num: [157/600]
Discriminator Loss: 0.7960, Generator Loss: 1.7900
D(x): 0.8251, D(G(z)): 0.3052
Epoch: [14/20], Batch Num: [158/600]
Discriminator Loss: 1.0135, Generator Loss: 2.0363
D(x): 0.7521, D(G(z)): 0.3390
Epoch: [14/20], Batch Num: [159/600]
Discriminator Loss: 0.8288, Generator Loss: 2.2772
D(x): 0.7729, D(G(z)): 0.2572
Epoch: [14/20], Batch Num: [160/600]
Discriminator Loss: 1.0595, Generator Loss: 2.2106
D(x): 0.6264, D(G(z)): 0.1957
Epoch: [14/20], Batch Num: [161/600]
Discriminator Loss: 0.9372, Generator Loss: 1.7691
D(x): 0.6469, D(G(z)): 0.1965
Epoch: [14/20], Batch Num: [162/600]
Discriminator Loss: 0.9820, Generator Loss: 1.3362
D(x): 0.6971, D(G(z)): 0.2815
Epoch: [14/20], Batch Num: [163/600]
Discriminator Loss: 1.1169, Generator Loss: 1.2020
D(x): 0.7542, D(G(z)): 0.3880
Epoch: [14/20], Batch Num: [164/600]
Discriminator Loss: 1.2132, Generator Loss: 1.7739
D(x): 0.7602, D(G(z)): 0.4263
Epoch: [14/20], Batch Num: [165/600]
Discriminator Loss: 1.1441, Generator Loss: 1.9785
D(x): 0.7526, D(G(z)): 0.3808
Epoch: [14/20], Batch Num: [166/600]
Discriminator Loss: 1.0023, Generator Loss: 2.1361
D(x): 0.6036, D(G(z)): 0.2050
Epoch: [14/20], Batch Num: [167/600]
Discriminator Loss: 1.1513, Generator Loss: 2.0292
D(x): 0.6030, D(G(z)): 0.2288
Epoch: [14/20], Batch Num: [168/600]
Discriminator Loss: 0.9999, Generator Loss: 1.7355
D(x): 0.6724, D(G(z)): 0.2663
Epoch: [14/20], Batch Num: [169/600]
Discriminator Loss: 1.1285, Generator Loss: 1.5816
D(x): 0.6707, D(G(z)): 0.3034
Epoch: [14/20], Batch Num: [170/600]
Discriminator Loss: 1.0790, Generator Loss: 1.3692
D(x): 0.7353, D(G(z)): 0.4061
Epoch: [14/20], Batch Num: [171/600]
Discriminator Loss: 0.9004, Generator Loss: 1.4657
D(x): 0.7507, D(G(z)): 0.3333
Epoch: [14/20], Batch Num: [172/600]
Discriminator Loss: 1.0790, Generator Loss: 1.4105
D(x): 0.6991, D(G(z)): 0.3606
Epoch: [14/20], Batch Num: [173/600]
Discriminator Loss: 1.1503, Generator Loss: 1.8001
D(x): 0.6713, D(G(z)): 0.3481
Epoch: [14/20], Batch Num: [174/600]
Discriminator Loss: 1.0093, Generator Loss: 1.5883
D(x): 0.6728, D(G(z)): 0.3117
Epoch: [14/20], Batch Num: [175/600]
Discriminator Loss: 1.0732, Generator Loss: 1.5010
D(x): 0.6580, D(G(z)): 0.3239
Epoch: [14/20], Batch Num: [176/600]
Discriminator Loss: 1.0148, Generator Loss: 1.5492
D(x): 0.6587, D(G(z)): 0.3214
Epoch: [14/20], Batch Num: [177/600]
Discriminator Loss: 1.0289, Generator Loss: 1.4541
D(x): 0.6430, D(G(z)): 0.2948
Epoch: [14/20], Batch Num: [178/600]
Discriminator Loss: 0.9553, Generator Loss: 1.3039
D(x): 0.6835, D(G(z)): 0.3138
Epoch: [14/20], Batch Num: [179/600]
Discriminator Loss: 1.0479, Generator Loss: 1.4954
D(x): 0.6928, D(G(z)): 0.3527
Epoch: [14/20], Batch Num: [180/600]
Discriminator Loss: 1.0402, Generator Loss: 1.4502
D(x): 0.6781, D(G(z)): 0.3542
Epoch: [14/20], Batch Num: [181/600]
Discriminator Loss: 0.8130, Generator Loss: 1.6061
D(x): 0.7441, D(G(z)): 0.2974
Epoch: [14/20], Batch Num: [182/600]
Discriminator Loss: 0.9001, Generator Loss: 1.6762
D(x): 0.7562, D(G(z)): 0.3526
Epoch: [14/20], Batch Num: [183/600]
Discriminator Loss: 0.8640, Generator Loss: 1.7906
D(x): 0.6602, D(G(z)): 0.2428
Epoch: [14/20], Batch Num: [184/600]
Discriminator Loss: 0.9011, Generator Loss: 1.8254
D(x): 0.6527, D(G(z)): 0.2617
Epoch: [14/20], Batch Num: [185/600]
Discriminator Loss: 0.9020, Generator Loss: 1.7708
D(x): 0.6949, D(G(z)): 0.2949
Epoch: [14/20], Batch Num: [186/600]
Discriminator Loss: 0.8814, Generator Loss: 1.4891
D(x): 0.6853, D(G(z)): 0.2833
Epoch: [14/20], Batch Num: [187/600]
Discriminator Loss: 0.8364, Generator Loss: 1.4322
D(x): 0.7533, D(G(z)): 0.3332
Epoch: [14/20], Batch Num: [188/600]
Discriminator Loss: 0.7436, Generator Loss: 1.4998
D(x): 0.7298, D(G(z)): 0.2730
Epoch: [14/20], Batch Num: [189/600]
Discriminator Loss: 0.8244, Generator Loss: 1.4113
D(x): 0.7334, D(G(z)): 0.3115
Epoch: [14/20], Batch Num: [190/600]
Discriminator Loss: 0.9182, Generator Loss: 1.2387
D(x): 0.6947, D(G(z)): 0.3113
Epoch: [14/20], Batch Num: [191/600]
Discriminator Loss: 0.8726, Generator Loss: 1.4127
D(x): 0.7317, D(G(z)): 0.3399
Epoch: [14/20], Batch Num: [192/600]
Discriminator Loss: 0.7255, Generator Loss: 1.4095
D(x): 0.7583, D(G(z)): 0.2891
Epoch: [14/20], Batch Num: [193/600]
Discriminator Loss: 0.7970, Generator Loss: 1.6829
D(x): 0.7206, D(G(z)): 0.2841
Epoch: [14/20], Batch Num: [194/600]
Discriminator Loss: 0.7682, Generator Loss: 1.7056
D(x): 0.7290, D(G(z)): 0.2794
Epoch: [14/20], Batch Num: [195/600]
Discriminator Loss: 0.7756, Generator Loss: 1.5861
D(x): 0.7329, D(G(z)): 0.2707
Epoch: [14/20], Batch Num: [196/600]
Discriminator Loss: 0.8830, Generator Loss: 1.5959
D(x): 0.6640, D(G(z)): 0.2544
Epoch: [14/20], Batch Num: [197/600]
Discriminator Loss: 0.8841, Generator Loss: 1.6451
D(x): 0.7017, D(G(z)): 0.2971
Epoch: [14/20], Batch Num: [198/600]
Discriminator Loss: 0.8033, Generator Loss: 1.5085
D(x): 0.7741, D(G(z)): 0.3240
Epoch: [14/20], Batch Num: [199/600]
Discriminator Loss: 0.7905, Generator Loss: 1.7491
D(x): 0.7788, D(G(z)): 0.3205
Epoch: 14, Batch Num: [200/600]

Epoch: [14/20], Batch Num: [200/600]
Discriminator Loss: 0.7217, Generator Loss: 1.9534
D(x): 0.7766, D(G(z)): 0.2823
Epoch: [14/20], Batch Num: [201/600]
Discriminator Loss: 0.7277, Generator Loss: 2.0834
D(x): 0.7354, D(G(z)): 0.2380
Epoch: [14/20], Batch Num: [202/600]
Discriminator Loss: 0.6655, Generator Loss: 1.9150
D(x): 0.7466, D(G(z)): 0.2179
Epoch: [14/20], Batch Num: [203/600]
Discriminator Loss: 0.6839, Generator Loss: 1.9558
D(x): 0.7349, D(G(z)): 0.2255
Epoch: [14/20], Batch Num: [204/600]
Discriminator Loss: 0.7285, Generator Loss: 1.8962
D(x): 0.7418, D(G(z)): 0.2386
Epoch: [14/20], Batch Num: [205/600]
Discriminator Loss: 0.7290, Generator Loss: 1.7641
D(x): 0.7441, D(G(z)): 0.2437
Epoch: [14/20], Batch Num: [206/600]
Discriminator Loss: 0.7172, Generator Loss: 1.7775
D(x): 0.7825, D(G(z)): 0.2736
Epoch: [14/20], Batch Num: [207/600]
Discriminator Loss: 0.7396, Generator Loss: 1.7781
D(x): 0.7652, D(G(z)): 0.2744
Epoch: [14/20], Batch Num: [208/600]
Discriminator Loss: 0.6797, Generator Loss: 1.6107
D(x): 0.7859, D(G(z)): 0.2463
Epoch: [14/20], Batch Num: [209/600]
Discriminator Loss: 0.7762, Generator Loss: 2.1286
D(x): 0.7835, D(G(z)): 0.3018
Epoch: [14/20], Batch Num: [210/600]
Discriminator Loss: 0.8202, Generator Loss: 2.0436
D(x): 0.7613, D(G(z)): 0.2676
Epoch: [14/20], Batch Num: [211/600]
Discriminator Loss: 0.7168, Generator Loss: 2.1093
D(x): 0.7266, D(G(z)): 0.1905
Epoch: [14/20], Batch Num: [212/600]
Discriminator Loss: 0.6877, Generator Loss: 1.9729
D(x): 0.7442, D(G(z)): 0.1996
Epoch: [14/20], Batch Num: [213/600]
Discriminator Loss: 0.8143, Generator Loss: 1.7915
D(x): 0.7321, D(G(z)): 0.2505
Epoch: [14/20], Batch Num: [214/600]
Discriminator Loss: 0.7696, Generator Loss: 1.4897
D(x): 0.7446, D(G(z)): 0.2593
Epoch: [14/20], Batch Num: [215/600]
Discriminator Loss: 1.0024, Generator Loss: 1.8178
D(x): 0.7595, D(G(z)): 0.3581
Epoch: [14/20], Batch Num: [216/600]
Discriminator Loss: 0.8947, Generator Loss: 2.1818
D(x): 0.7813, D(G(z)): 0.3377
Epoch: [14/20], Batch Num: [217/600]
Discriminator Loss: 0.9737, Generator Loss: 2.2766
D(x): 0.6712, D(G(z)): 0.2472
Epoch: [14/20], Batch Num: [218/600]
Discriminator Loss: 1.0208, Generator Loss: 2.0330
D(x): 0.6487, D(G(z)): 0.2256
Epoch: [14/20], Batch Num: [219/600]
Discriminator Loss: 0.9717, Generator Loss: 1.6206
D(x): 0.6162, D(G(z)): 0.1930
Epoch: [14/20], Batch Num: [220/600]
Discriminator Loss: 0.9516, Generator Loss: 1.3668
D(x): 0.7405, D(G(z)): 0.3148
Epoch: [14/20], Batch Num: [221/600]
Discriminator Loss: 1.0589, Generator Loss: 1.4399
D(x): 0.7762, D(G(z)): 0.4330
Epoch: [14/20], Batch Num: [222/600]
Discriminator Loss: 0.9498, Generator Loss: 1.6308
D(x): 0.7346, D(G(z)): 0.3502
Epoch: [14/20], Batch Num: [223/600]
Discriminator Loss: 0.9277, Generator Loss: 1.7457
D(x): 0.7236, D(G(z)): 0.3168
Epoch: [14/20], Batch Num: [224/600]
Discriminator Loss: 1.2317, Generator Loss: 1.8189
D(x): 0.6091, D(G(z)): 0.2953
Epoch: [14/20], Batch Num: [225/600]
Discriminator Loss: 1.1020, Generator Loss: 1.5640
D(x): 0.6113, D(G(z)): 0.2675
Epoch: [14/20], Batch Num: [226/600]
Discriminator Loss: 1.1864, Generator Loss: 1.3920
D(x): 0.6400, D(G(z)): 0.3348
Epoch: [14/20], Batch Num: [227/600]
Discriminator Loss: 1.1319, Generator Loss: 1.2463
D(x): 0.6808, D(G(z)): 0.3445
Epoch: [14/20], Batch Num: [228/600]
Discriminator Loss: 1.1051, Generator Loss: 1.3207
D(x): 0.7537, D(G(z)): 0.4420
Epoch: [14/20], Batch Num: [229/600]
Discriminator Loss: 1.0997, Generator Loss: 1.3005
D(x): 0.7003, D(G(z)): 0.4053
Epoch: [14/20], Batch Num: [230/600]
Discriminator Loss: 1.0484, Generator Loss: 1.4254
D(x): 0.6721, D(G(z)): 0.3274
Epoch: [14/20], Batch Num: [231/600]
Discriminator Loss: 0.9193, Generator Loss: 1.5907
D(x): 0.6280, D(G(z)): 0.2351
Epoch: [14/20], Batch Num: [232/600]
Discriminator Loss: 0.9580, Generator Loss: 1.5500
D(x): 0.6311, D(G(z)): 0.2402
Epoch: [14/20], Batch Num: [233/600]
Discriminator Loss: 0.7920, Generator Loss: 1.3765
D(x): 0.7066, D(G(z)): 0.2642
Epoch: [14/20], Batch Num: [234/600]
Discriminator Loss: 0.8383, Generator Loss: 1.3150
D(x): 0.7312, D(G(z)): 0.3190
Epoch: [14/20], Batch Num: [235/600]
Discriminator Loss: 0.8868, Generator Loss: 1.3270
D(x): 0.7476, D(G(z)): 0.3499
Epoch: [14/20], Batch Num: [236/600]
Discriminator Loss: 1.1154, Generator Loss: 1.4225
D(x): 0.6963, D(G(z)): 0.4091
Epoch: [14/20], Batch Num: [237/600]
Discriminator Loss: 0.8460, Generator Loss: 1.4627
D(x): 0.7560, D(G(z)): 0.3469
Epoch: [14/20], Batch Num: [238/600]
Discriminator Loss: 0.9760, Generator Loss: 1.5979
D(x): 0.6651, D(G(z)): 0.3097
Epoch: [14/20], Batch Num: [239/600]
Discriminator Loss: 0.8396, Generator Loss: 1.7035
D(x): 0.6614, D(G(z)): 0.2418
Epoch: [14/20], Batch Num: [240/600]
Discriminator Loss: 0.8219, Generator Loss: 1.5162
D(x): 0.6778, D(G(z)): 0.2380
Epoch: [14/20], Batch Num: [241/600]
Discriminator Loss: 0.7203, Generator Loss: 1.7267
D(x): 0.7276, D(G(z)): 0.2490
Epoch: [14/20], Batch Num: [242/600]
Discriminator Loss: 0.6877, Generator Loss: 1.4873
D(x): 0.7921, D(G(z)): 0.2971
Epoch: [14/20], Batch Num: [243/600]
Discriminator Loss: 0.6097, Generator Loss: 1.4523
D(x): 0.7802, D(G(z)): 0.2439
Epoch: [14/20], Batch Num: [244/600]
Discriminator Loss: 0.7061, Generator Loss: 1.5150
D(x): 0.7910, D(G(z)): 0.2840
Epoch: [14/20], Batch Num: [245/600]
Discriminator Loss: 0.6153, Generator Loss: 1.7112
D(x): 0.8257, D(G(z)): 0.2747
Epoch: [14/20], Batch Num: [246/600]
Discriminator Loss: 0.5480, Generator Loss: 1.9165
D(x): 0.8169, D(G(z)): 0.2218
Epoch: [14/20], Batch Num: [247/600]
Discriminator Loss: 0.5129, Generator Loss: 2.2595
D(x): 0.8096, D(G(z)): 0.1945
Epoch: [14/20], Batch Num: [248/600]
Discriminator Loss: 0.6320, Generator Loss: 2.2563
D(x): 0.7750, D(G(z)): 0.2035
Epoch: [14/20], Batch Num: [249/600]
Discriminator Loss: 0.5969, Generator Loss: 2.2833
D(x): 0.7415, D(G(z)): 0.1411
Epoch: [14/20], Batch Num: [250/600]
Discriminator Loss: 0.4556, Generator Loss: 1.9482
D(x): 0.8429, D(G(z)): 0.1810
Epoch: [14/20], Batch Num: [251/600]
Discriminator Loss: 0.5302, Generator Loss: 2.0565
D(x): 0.8592, D(G(z)): 0.2129
Epoch: [14/20], Batch Num: [252/600]
Discriminator Loss: 0.4448, Generator Loss: 2.0643
D(x): 0.8731, D(G(z)): 0.2041
Epoch: [14/20], Batch Num: [253/600]
Discriminator Loss: 0.4493, Generator Loss: 2.3635
D(x): 0.8590, D(G(z)): 0.1906
Epoch: [14/20], Batch Num: [254/600]
Discriminator Loss: 0.5778, Generator Loss: 2.5146
D(x): 0.8239, D(G(z)): 0.2128
Epoch: [14/20], Batch Num: [255/600]
Discriminator Loss: 0.4668, Generator Loss: 2.2990
D(x): 0.8258, D(G(z)): 0.1622
Epoch: [14/20], Batch Num: [256/600]
Discriminator Loss: 0.5203, Generator Loss: 2.4652
D(x): 0.7946, D(G(z)): 0.1480
Epoch: [14/20], Batch Num: [257/600]
Discriminator Loss: 0.3892, Generator Loss: 2.4219
D(x): 0.8469, D(G(z)): 0.1439
Epoch: [14/20], Batch Num: [258/600]
Discriminator Loss: 0.4395, Generator Loss: 2.0825
D(x): 0.8699, D(G(z)): 0.1942
Epoch: [14/20], Batch Num: [259/600]
Discriminator Loss: 0.5930, Generator Loss: 2.1497
D(x): 0.8229, D(G(z)): 0.2247
Epoch: [14/20], Batch Num: [260/600]
Discriminator Loss: 0.4361, Generator Loss: 2.3611
D(x): 0.8669, D(G(z)): 0.1807
Epoch: [14/20], Batch Num: [261/600]
Discriminator Loss: 0.4850, Generator Loss: 2.5074
D(x): 0.8645, D(G(z)): 0.1965
Epoch: [14/20], Batch Num: [262/600]
Discriminator Loss: 0.5678, Generator Loss: 2.5584
D(x): 0.8315, D(G(z)): 0.1826
Epoch: [14/20], Batch Num: [263/600]
Discriminator Loss: 0.5606, Generator Loss: 2.9621
D(x): 0.8211, D(G(z)): 0.1591
Epoch: [14/20], Batch Num: [264/600]
Discriminator Loss: 0.6522, Generator Loss: 2.7311
D(x): 0.7628, D(G(z)): 0.1363
Epoch: [14/20], Batch Num: [265/600]
Discriminator Loss: 0.5235, Generator Loss: 2.5464
D(x): 0.8395, D(G(z)): 0.1889
Epoch: [14/20], Batch Num: [266/600]
Discriminator Loss: 0.5802, Generator Loss: 2.1961
D(x): 0.8285, D(G(z)): 0.2060
Epoch: [14/20], Batch Num: [267/600]
Discriminator Loss: 0.6698, Generator Loss: 2.5688
D(x): 0.8012, D(G(z)): 0.2187
Epoch: [14/20], Batch Num: [268/600]
Discriminator Loss: 0.7816, Generator Loss: 2.0028
D(x): 0.7461, D(G(z)): 0.2196
Epoch: [14/20], Batch Num: [269/600]
Discriminator Loss: 0.7039, Generator Loss: 2.3720
D(x): 0.8011, D(G(z)): 0.2517
Epoch: [14/20], Batch Num: [270/600]
Discriminator Loss: 0.8493, Generator Loss: 2.0733
D(x): 0.7487, D(G(z)): 0.2223
Epoch: [14/20], Batch Num: [271/600]
Discriminator Loss: 1.0464, Generator Loss: 1.6615
D(x): 0.6577, D(G(z)): 0.2203
Epoch: [14/20], Batch Num: [272/600]
Discriminator Loss: 1.0659, Generator Loss: 1.8422
D(x): 0.8100, D(G(z)): 0.3855
Epoch: [14/20], Batch Num: [273/600]
Discriminator Loss: 1.0649, Generator Loss: 2.1662
D(x): 0.7171, D(G(z)): 0.3169
Epoch: [14/20], Batch Num: [274/600]
Discriminator Loss: 1.2286, Generator Loss: 2.2830
D(x): 0.6349, D(G(z)): 0.2769
Epoch: [14/20], Batch Num: [275/600]
Discriminator Loss: 1.0098, Generator Loss: 1.8853
D(x): 0.6552, D(G(z)): 0.2077
Epoch: [14/20], Batch Num: [276/600]
Discriminator Loss: 0.8834, Generator Loss: 1.4427
D(x): 0.7404, D(G(z)): 0.2920
Epoch: [14/20], Batch Num: [277/600]
Discriminator Loss: 1.0124, Generator Loss: 1.5501
D(x): 0.7150, D(G(z)): 0.3261
Epoch: [14/20], Batch Num: [278/600]
Discriminator Loss: 0.9807, Generator Loss: 1.7076
D(x): 0.7719, D(G(z)): 0.3490
Epoch: [14/20], Batch Num: [279/600]
Discriminator Loss: 0.9975, Generator Loss: 1.6901
D(x): 0.6799, D(G(z)): 0.2829
Epoch: [14/20], Batch Num: [280/600]
Discriminator Loss: 1.1087, Generator Loss: 1.8946
D(x): 0.6774, D(G(z)): 0.2932
Epoch: [14/20], Batch Num: [281/600]
Discriminator Loss: 0.9289, Generator Loss: 1.7922
D(x): 0.6882, D(G(z)): 0.2501
Epoch: [14/20], Batch Num: [282/600]
Discriminator Loss: 0.8068, Generator Loss: 1.6537
D(x): 0.7346, D(G(z)): 0.2404
Epoch: [14/20], Batch Num: [283/600]
Discriminator Loss: 1.0440, Generator Loss: 1.7995
D(x): 0.6949, D(G(z)): 0.2841
Epoch: [14/20], Batch Num: [284/600]
Discriminator Loss: 0.7627, Generator Loss: 1.7195
D(x): 0.7595, D(G(z)): 0.2414
Epoch: [14/20], Batch Num: [285/600]
Discriminator Loss: 0.9018, Generator Loss: 1.6616
D(x): 0.7281, D(G(z)): 0.2691
Epoch: [14/20], Batch Num: [286/600]
Discriminator Loss: 0.7849, Generator Loss: 1.8916
D(x): 0.7897, D(G(z)): 0.3052
Epoch: [14/20], Batch Num: [287/600]
Discriminator Loss: 0.8662, Generator Loss: 1.9720
D(x): 0.7054, D(G(z)): 0.2493
Epoch: [14/20], Batch Num: [288/600]
Discriminator Loss: 0.7766, Generator Loss: 1.9190
D(x): 0.7544, D(G(z)): 0.2574
Epoch: [14/20], Batch Num: [289/600]
Discriminator Loss: 0.7894, Generator Loss: 1.9442
D(x): 0.7323, D(G(z)): 0.2454
Epoch: [14/20], Batch Num: [290/600]
Discriminator Loss: 0.8774, Generator Loss: 1.8116
D(x): 0.6652, D(G(z)): 0.1905
Epoch: [14/20], Batch Num: [291/600]
Discriminator Loss: 0.7481, Generator Loss: 1.6547
D(x): 0.7865, D(G(z)): 0.2812
Epoch: [14/20], Batch Num: [292/600]
Discriminator Loss: 0.8738, Generator Loss: 1.7302
D(x): 0.7900, D(G(z)): 0.3247
Epoch: [14/20], Batch Num: [293/600]
Discriminator Loss: 0.6733, Generator Loss: 2.0847
D(x): 0.8492, D(G(z)): 0.3099
Epoch: [14/20], Batch Num: [294/600]
Discriminator Loss: 0.6669, Generator Loss: 2.0813
D(x): 0.7704, D(G(z)): 0.2101
Epoch: [14/20], Batch Num: [295/600]
Discriminator Loss: 0.7321, Generator Loss: 2.1898
D(x): 0.7453, D(G(z)): 0.1977
Epoch: [14/20], Batch Num: [296/600]
Discriminator Loss: 0.6673, Generator Loss: 2.1227
D(x): 0.7479, D(G(z)): 0.1822
Epoch: [14/20], Batch Num: [297/600]
Discriminator Loss: 0.6078, Generator Loss: 1.8223
D(x): 0.7935, D(G(z)): 0.1855
Epoch: [14/20], Batch Num: [298/600]
Discriminator Loss: 0.6205, Generator Loss: 2.0846
D(x): 0.7835, D(G(z)): 0.2018
Epoch: [14/20], Batch Num: [299/600]
Discriminator Loss: 0.7143, Generator Loss: 1.8956
D(x): 0.7956, D(G(z)): 0.2843
Epoch: 14, Batch Num: [300/600]

Epoch: [14/20], Batch Num: [300/600]
Discriminator Loss: 0.5741, Generator Loss: 1.9782
D(x): 0.8171, D(G(z)): 0.2224
Epoch: [14/20], Batch Num: [301/600]
Discriminator Loss: 0.5960, Generator Loss: 2.0287
D(x): 0.8335, D(G(z)): 0.2384
Epoch: [14/20], Batch Num: [302/600]
Discriminator Loss: 0.5724, Generator Loss: 1.9927
D(x): 0.8150, D(G(z)): 0.2157
Epoch: [14/20], Batch Num: [303/600]
Discriminator Loss: 0.5965, Generator Loss: 2.1248
D(x): 0.8007, D(G(z)): 0.1855
Epoch: [14/20], Batch Num: [304/600]
Discriminator Loss: 0.5371, Generator Loss: 2.1580
D(x): 0.8061, D(G(z)): 0.1790
Epoch: [14/20], Batch Num: [305/600]
Discriminator Loss: 0.4994, Generator Loss: 1.9741
D(x): 0.8343, D(G(z)): 0.1917
Epoch: [14/20], Batch Num: [306/600]
Discriminator Loss: 0.4000, Generator Loss: 1.9789
D(x): 0.8531, D(G(z)): 0.1646
Epoch: [14/20], Batch Num: [307/600]
Discriminator Loss: 0.6381, Generator Loss: 2.0311
D(x): 0.8164, D(G(z)): 0.2266
Epoch: [14/20], Batch Num: [308/600]
Discriminator Loss: 0.5338, Generator Loss: 2.1895
D(x): 0.8596, D(G(z)): 0.2294
Epoch: [14/20], Batch Num: [309/600]
Discriminator Loss: 0.5236, Generator Loss: 2.2690
D(x): 0.8323, D(G(z)): 0.1925
Epoch: [14/20], Batch Num: [310/600]
Discriminator Loss: 0.5136, Generator Loss: 2.5208
D(x): 0.8199, D(G(z)): 0.1839
Epoch: [14/20], Batch Num: [311/600]
Discriminator Loss: 0.5702, Generator Loss: 2.4224
D(x): 0.7752, D(G(z)): 0.1657
Epoch: [14/20], Batch Num: [312/600]
Discriminator Loss: 0.5398, Generator Loss: 2.1529
D(x): 0.8015, D(G(z)): 0.1593
Epoch: [14/20], Batch Num: [313/600]
Discriminator Loss: 0.6727, Generator Loss: 1.7645
D(x): 0.7957, D(G(z)): 0.2156
Epoch: [14/20], Batch Num: [314/600]
Discriminator Loss: 0.8080, Generator Loss: 1.7559
D(x): 0.8358, D(G(z)): 0.3152
Epoch: [14/20], Batch Num: [315/600]
Discriminator Loss: 0.6440, Generator Loss: 2.0826
D(x): 0.8546, D(G(z)): 0.2706
Epoch: [14/20], Batch Num: [316/600]
Discriminator Loss: 0.6451, Generator Loss: 2.4901
D(x): 0.7969, D(G(z)): 0.2166
Epoch: [14/20], Batch Num: [317/600]
Discriminator Loss: 0.8146, Generator Loss: 2.3087
D(x): 0.6571, D(G(z)): 0.1432
Epoch: [14/20], Batch Num: [318/600]
Discriminator Loss: 0.9431, Generator Loss: 1.9189
D(x): 0.7031, D(G(z)): 0.2505
Epoch: [14/20], Batch Num: [319/600]
Discriminator Loss: 0.8028, Generator Loss: 1.6010
D(x): 0.7899, D(G(z)): 0.2747
Epoch: [14/20], Batch Num: [320/600]
Discriminator Loss: 0.7164, Generator Loss: 1.9000
D(x): 0.8080, D(G(z)): 0.2819
Epoch: [14/20], Batch Num: [321/600]
Discriminator Loss: 0.8062, Generator Loss: 1.9049
D(x): 0.8277, D(G(z)): 0.3118
Epoch: [14/20], Batch Num: [322/600]
Discriminator Loss: 0.9051, Generator Loss: 2.0036
D(x): 0.7210, D(G(z)): 0.2278
Epoch: [14/20], Batch Num: [323/600]
Discriminator Loss: 0.9328, Generator Loss: 1.8494
D(x): 0.6626, D(G(z)): 0.1990
Epoch: [14/20], Batch Num: [324/600]
Discriminator Loss: 0.8180, Generator Loss: 1.7479
D(x): 0.7530, D(G(z)): 0.2696
Epoch: [14/20], Batch Num: [325/600]
Discriminator Loss: 1.0378, Generator Loss: 1.5977
D(x): 0.6671, D(G(z)): 0.2817
Epoch: [14/20], Batch Num: [326/600]
Discriminator Loss: 0.8376, Generator Loss: 1.4376
D(x): 0.7577, D(G(z)): 0.2724
Epoch: [14/20], Batch Num: [327/600]
Discriminator Loss: 1.0563, Generator Loss: 1.7192
D(x): 0.7451, D(G(z)): 0.3692
Epoch: [14/20], Batch Num: [328/600]
Discriminator Loss: 0.9964, Generator Loss: 1.9225
D(x): 0.7171, D(G(z)): 0.3044
Epoch: [14/20], Batch Num: [329/600]
Discriminator Loss: 0.9421, Generator Loss: 1.9544
D(x): 0.6892, D(G(z)): 0.2560
Epoch: [14/20], Batch Num: [330/600]
Discriminator Loss: 0.9185, Generator Loss: 1.8712
D(x): 0.6914, D(G(z)): 0.2278
Epoch: [14/20], Batch Num: [331/600]
Discriminator Loss: 1.0144, Generator Loss: 1.5283
D(x): 0.6321, D(G(z)): 0.2441
Epoch: [14/20], Batch Num: [332/600]
Discriminator Loss: 1.0175, Generator Loss: 1.3862
D(x): 0.7003, D(G(z)): 0.2937
Epoch: [14/20], Batch Num: [333/600]
Discriminator Loss: 0.9222, Generator Loss: 1.3439
D(x): 0.7749, D(G(z)): 0.3286
Epoch: [14/20], Batch Num: [334/600]
Discriminator Loss: 1.0537, Generator Loss: 1.5836
D(x): 0.7807, D(G(z)): 0.3730
Epoch: [14/20], Batch Num: [335/600]
Discriminator Loss: 0.9015, Generator Loss: 1.7036
D(x): 0.7323, D(G(z)): 0.2917
Epoch: [14/20], Batch Num: [336/600]
Discriminator Loss: 0.8095, Generator Loss: 1.7107
D(x): 0.7584, D(G(z)): 0.2714
Epoch: [14/20], Batch Num: [337/600]
Discriminator Loss: 0.8860, Generator Loss: 1.8665
D(x): 0.6506, D(G(z)): 0.1949
Epoch: [14/20], Batch Num: [338/600]
Discriminator Loss: 0.8218, Generator Loss: 1.8317
D(x): 0.7254, D(G(z)): 0.2484
Epoch: [14/20], Batch Num: [339/600]
Discriminator Loss: 0.8617, Generator Loss: 1.8049
D(x): 0.7262, D(G(z)): 0.2841
Epoch: [14/20], Batch Num: [340/600]
Discriminator Loss: 0.8896, Generator Loss: 1.8525
D(x): 0.7406, D(G(z)): 0.2935
Epoch: [14/20], Batch Num: [341/600]
Discriminator Loss: 0.7368, Generator Loss: 1.6844
D(x): 0.7460, D(G(z)): 0.2294
Epoch: [14/20], Batch Num: [342/600]
Discriminator Loss: 0.7758, Generator Loss: 1.7958
D(x): 0.7662, D(G(z)): 0.2524
Epoch: [14/20], Batch Num: [343/600]
Discriminator Loss: 0.7750, Generator Loss: 1.7552
D(x): 0.7402, D(G(z)): 0.2513
Epoch: [14/20], Batch Num: [344/600]
Discriminator Loss: 0.8462, Generator Loss: 1.6806
D(x): 0.7647, D(G(z)): 0.2879
Epoch: [14/20], Batch Num: [345/600]
Discriminator Loss: 0.7209, Generator Loss: 1.6504
D(x): 0.7760, D(G(z)): 0.2659
Epoch: [14/20], Batch Num: [346/600]
Discriminator Loss: 0.7224, Generator Loss: 1.7166
D(x): 0.7861, D(G(z)): 0.2733
Epoch: [14/20], Batch Num: [347/600]
Discriminator Loss: 0.6439, Generator Loss: 2.0363
D(x): 0.7984, D(G(z)): 0.2466
Epoch: [14/20], Batch Num: [348/600]
Discriminator Loss: 0.7460, Generator Loss: 2.1167
D(x): 0.7267, D(G(z)): 0.2285
Epoch: [14/20], Batch Num: [349/600]
Discriminator Loss: 0.8051, Generator Loss: 1.9654
D(x): 0.7218, D(G(z)): 0.2380
Epoch: [14/20], Batch Num: [350/600]
Discriminator Loss: 0.6655, Generator Loss: 1.8253
D(x): 0.7328, D(G(z)): 0.1903
Epoch: [14/20], Batch Num: [351/600]
Discriminator Loss: 0.5502, Generator Loss: 1.8715
D(x): 0.8070, D(G(z)): 0.2148
Epoch: [14/20], Batch Num: [352/600]
Discriminator Loss: 0.5709, Generator Loss: 1.6921
D(x): 0.8358, D(G(z)): 0.2317
Epoch: [14/20], Batch Num: [353/600]
Discriminator Loss: 0.6639, Generator Loss: 1.7324
D(x): 0.8072, D(G(z)): 0.2510
Epoch: [14/20], Batch Num: [354/600]
Discriminator Loss: 0.5976, Generator Loss: 1.8282
D(x): 0.8156, D(G(z)): 0.2378
Epoch: [14/20], Batch Num: [355/600]
Discriminator Loss: 0.6748, Generator Loss: 1.7561
D(x): 0.7560, D(G(z)): 0.2358
Epoch: [14/20], Batch Num: [356/600]
Discriminator Loss: 0.6628, Generator Loss: 1.7760
D(x): 0.8198, D(G(z)): 0.2668
Epoch: [14/20], Batch Num: [357/600]
Discriminator Loss: 0.5684, Generator Loss: 2.0267
D(x): 0.8121, D(G(z)): 0.2205
Epoch: [14/20], Batch Num: [358/600]
Discriminator Loss: 0.5665, Generator Loss: 1.9096
D(x): 0.8173, D(G(z)): 0.2031
Epoch: [14/20], Batch Num: [359/600]
Discriminator Loss: 0.6231, Generator Loss: 2.1184
D(x): 0.7826, D(G(z)): 0.2165
Epoch: [14/20], Batch Num: [360/600]
Discriminator Loss: 0.7612, Generator Loss: 1.9474
D(x): 0.7316, D(G(z)): 0.2271
Epoch: [14/20], Batch Num: [361/600]
Discriminator Loss: 0.7701, Generator Loss: 2.1973
D(x): 0.7504, D(G(z)): 0.2386
Epoch: [14/20], Batch Num: [362/600]
Discriminator Loss: 0.6119, Generator Loss: 2.0725
D(x): 0.8052, D(G(z)): 0.2099
Epoch: [14/20], Batch Num: [363/600]
Discriminator Loss: 0.8389, Generator Loss: 1.7912
D(x): 0.7265, D(G(z)): 0.2394
Epoch: [14/20], Batch Num: [364/600]
Discriminator Loss: 0.6373, Generator Loss: 1.7454
D(x): 0.8157, D(G(z)): 0.2569
Epoch: [14/20], Batch Num: [365/600]
Discriminator Loss: 0.6666, Generator Loss: 1.7541
D(x): 0.8306, D(G(z)): 0.2593
Epoch: [14/20], Batch Num: [366/600]
Discriminator Loss: 0.8325, Generator Loss: 2.1734
D(x): 0.7999, D(G(z)): 0.3115
Epoch: [14/20], Batch Num: [367/600]
Discriminator Loss: 0.7067, Generator Loss: 2.4289
D(x): 0.7659, D(G(z)): 0.2258
Epoch: [14/20], Batch Num: [368/600]
Discriminator Loss: 0.7153, Generator Loss: 2.2200
D(x): 0.7116, D(G(z)): 0.1762
Epoch: [14/20], Batch Num: [369/600]
Discriminator Loss: 0.7329, Generator Loss: 2.0594
D(x): 0.7289, D(G(z)): 0.2116
Epoch: [14/20], Batch Num: [370/600]
Discriminator Loss: 0.6674, Generator Loss: 1.6604
D(x): 0.7828, D(G(z)): 0.2369
Epoch: [14/20], Batch Num: [371/600]
Discriminator Loss: 0.7970, Generator Loss: 1.6458
D(x): 0.8035, D(G(z)): 0.3308
Epoch: [14/20], Batch Num: [372/600]
Discriminator Loss: 0.6886, Generator Loss: 1.7727
D(x): 0.8169, D(G(z)): 0.2875
Epoch: [14/20], Batch Num: [373/600]
Discriminator Loss: 0.8101, Generator Loss: 2.1045
D(x): 0.7747, D(G(z)): 0.2835
Epoch: [14/20], Batch Num: [374/600]
Discriminator Loss: 0.8582, Generator Loss: 2.1479
D(x): 0.6953, D(G(z)): 0.2240
Epoch: [14/20], Batch Num: [375/600]
Discriminator Loss: 0.8474, Generator Loss: 1.9941
D(x): 0.7001, D(G(z)): 0.2162
Epoch: [14/20], Batch Num: [376/600]
Discriminator Loss: 0.8117, Generator Loss: 1.6445
D(x): 0.7241, D(G(z)): 0.2495
Epoch: [14/20], Batch Num: [377/600]
Discriminator Loss: 0.8714, Generator Loss: 1.3878
D(x): 0.7894, D(G(z)): 0.3341
Epoch: [14/20], Batch Num: [378/600]
Discriminator Loss: 0.8400, Generator Loss: 1.5739
D(x): 0.7543, D(G(z)): 0.3110
Epoch: [14/20], Batch Num: [379/600]
Discriminator Loss: 1.0145, Generator Loss: 1.5474
D(x): 0.7317, D(G(z)): 0.3396
Epoch: [14/20], Batch Num: [380/600]
Discriminator Loss: 0.7721, Generator Loss: 1.8069
D(x): 0.7889, D(G(z)): 0.3115
Epoch: [14/20], Batch Num: [381/600]
Discriminator Loss: 0.7913, Generator Loss: 2.1006
D(x): 0.7591, D(G(z)): 0.2665
Epoch: [14/20], Batch Num: [382/600]
Discriminator Loss: 0.8855, Generator Loss: 2.0357
D(x): 0.6542, D(G(z)): 0.2237
Epoch: [14/20], Batch Num: [383/600]
Discriminator Loss: 0.8938, Generator Loss: 1.7094
D(x): 0.6632, D(G(z)): 0.2165
Epoch: [14/20], Batch Num: [384/600]
Discriminator Loss: 0.7375, Generator Loss: 1.7226
D(x): 0.7734, D(G(z)): 0.2549
Epoch: [14/20], Batch Num: [385/600]
Discriminator Loss: 0.7925, Generator Loss: 1.7272
D(x): 0.7834, D(G(z)): 0.3076
Epoch: [14/20], Batch Num: [386/600]
Discriminator Loss: 0.7449, Generator Loss: 1.8074
D(x): 0.8076, D(G(z)): 0.2996
Epoch: [14/20], Batch Num: [387/600]
Discriminator Loss: 0.5928, Generator Loss: 1.8534
D(x): 0.8107, D(G(z)): 0.2475
Epoch: [14/20], Batch Num: [388/600]
Discriminator Loss: 0.7900, Generator Loss: 2.1919
D(x): 0.7362, D(G(z)): 0.2475
Epoch: [14/20], Batch Num: [389/600]
Discriminator Loss: 0.7306, Generator Loss: 1.9010
D(x): 0.6929, D(G(z)): 0.1754
Epoch: [14/20], Batch Num: [390/600]
Discriminator Loss: 0.6571, Generator Loss: 1.8405
D(x): 0.7734, D(G(z)): 0.2176
Epoch: [14/20], Batch Num: [391/600]
Discriminator Loss: 0.6679, Generator Loss: 1.9233
D(x): 0.7739, D(G(z)): 0.2394
Epoch: [14/20], Batch Num: [392/600]
Discriminator Loss: 0.6903, Generator Loss: 1.5957
D(x): 0.7799, D(G(z)): 0.2424
Epoch: [14/20], Batch Num: [393/600]
Discriminator Loss: 0.5818, Generator Loss: 1.7542
D(x): 0.8301, D(G(z)): 0.2398
Epoch: [14/20], Batch Num: [394/600]
Discriminator Loss: 0.6224, Generator Loss: 1.8636
D(x): 0.8063, D(G(z)): 0.2499
Epoch: [14/20], Batch Num: [395/600]
Discriminator Loss: 0.6451, Generator Loss: 1.9890
D(x): 0.7886, D(G(z)): 0.2276
Epoch: [14/20], Batch Num: [396/600]
Discriminator Loss: 0.7404, Generator Loss: 1.9419
D(x): 0.7879, D(G(z)): 0.2611
Epoch: [14/20], Batch Num: [397/600]
Discriminator Loss: 0.6491, Generator Loss: 2.2911
D(x): 0.8059, D(G(z)): 0.2338
Epoch: [14/20], Batch Num: [398/600]
Discriminator Loss: 0.6373, Generator Loss: 2.4305
D(x): 0.8142, D(G(z)): 0.2382
Epoch: [14/20], Batch Num: [399/600]
Discriminator Loss: 0.5324, Generator Loss: 2.5230
D(x): 0.8013, D(G(z)): 0.1602
Epoch: 14, Batch Num: [400/600]

Epoch: [14/20], Batch Num: [400/600]
Discriminator Loss: 0.7623, Generator Loss: 2.3437
D(x): 0.6891, D(G(z)): 0.1690
Epoch: [14/20], Batch Num: [401/600]
Discriminator Loss: 0.7487, Generator Loss: 1.7988
D(x): 0.7266, D(G(z)): 0.1968
Epoch: [14/20], Batch Num: [402/600]
Discriminator Loss: 0.6654, Generator Loss: 1.7618
D(x): 0.8016, D(G(z)): 0.2543
Epoch: [14/20], Batch Num: [403/600]
Discriminator Loss: 0.7854, Generator Loss: 2.0390
D(x): 0.8337, D(G(z)): 0.3198
Epoch: [14/20], Batch Num: [404/600]
Discriminator Loss: 0.6746, Generator Loss: 1.8902
D(x): 0.8316, D(G(z)): 0.2747
Epoch: [14/20], Batch Num: [405/600]
Discriminator Loss: 0.7351, Generator Loss: 2.5328
D(x): 0.7784, D(G(z)): 0.2244
Epoch: [14/20], Batch Num: [406/600]
Discriminator Loss: 0.9009, Generator Loss: 2.4721
D(x): 0.6788, D(G(z)): 0.1797
Epoch: [14/20], Batch Num: [407/600]
Discriminator Loss: 0.6200, Generator Loss: 2.2199
D(x): 0.7672, D(G(z)): 0.1609
Epoch: [14/20], Batch Num: [408/600]
Discriminator Loss: 0.6935, Generator Loss: 1.9229
D(x): 0.7680, D(G(z)): 0.2228
Epoch: [14/20], Batch Num: [409/600]
Discriminator Loss: 0.7894, Generator Loss: 1.8800
D(x): 0.7713, D(G(z)): 0.2588
Epoch: [14/20], Batch Num: [410/600]
Discriminator Loss: 0.7032, Generator Loss: 2.1401
D(x): 0.8226, D(G(z)): 0.2822
Epoch: [14/20], Batch Num: [411/600]
Discriminator Loss: 0.8200, Generator Loss: 2.1390
D(x): 0.7574, D(G(z)): 0.2663
Epoch: [14/20], Batch Num: [412/600]
Discriminator Loss: 0.7902, Generator Loss: 2.1621
D(x): 0.7070, D(G(z)): 0.2105
Epoch: [14/20], Batch Num: [413/600]
Discriminator Loss: 0.8247, Generator Loss: 1.7630
D(x): 0.7022, D(G(z)): 0.2197
Epoch: [14/20], Batch Num: [414/600]
Discriminator Loss: 0.9554, Generator Loss: 1.4863
D(x): 0.7408, D(G(z)): 0.3070
Epoch: [14/20], Batch Num: [415/600]
Discriminator Loss: 0.9230, Generator Loss: 1.7608
D(x): 0.7938, D(G(z)): 0.3537
Epoch: [14/20], Batch Num: [416/600]
Discriminator Loss: 0.8133, Generator Loss: 2.0381
D(x): 0.7456, D(G(z)): 0.2658
Epoch: [14/20], Batch Num: [417/600]
Discriminator Loss: 0.8808, Generator Loss: 1.8889
D(x): 0.6820, D(G(z)): 0.2223
Epoch: [14/20], Batch Num: [418/600]
Discriminator Loss: 0.9333, Generator Loss: 1.6637
D(x): 0.6961, D(G(z)): 0.2702
Epoch: [14/20], Batch Num: [419/600]
Discriminator Loss: 0.8445, Generator Loss: 1.6770
D(x): 0.7447, D(G(z)): 0.2767
Epoch: [14/20], Batch Num: [420/600]
Discriminator Loss: 0.9273, Generator Loss: 1.6243
D(x): 0.7743, D(G(z)): 0.3421
Epoch: [14/20], Batch Num: [421/600]
Discriminator Loss: 0.7815, Generator Loss: 1.7691
D(x): 0.7393, D(G(z)): 0.2720
Epoch: [14/20], Batch Num: [422/600]
Discriminator Loss: 0.8232, Generator Loss: 1.8492
D(x): 0.7231, D(G(z)): 0.2665
Epoch: [14/20], Batch Num: [423/600]
Discriminator Loss: 1.0204, Generator Loss: 1.6175
D(x): 0.6619, D(G(z)): 0.2646
Epoch: [14/20], Batch Num: [424/600]
Discriminator Loss: 1.0522, Generator Loss: 1.4667
D(x): 0.7042, D(G(z)): 0.3270
Epoch: [14/20], Batch Num: [425/600]
Discriminator Loss: 1.0182, Generator Loss: 1.8063
D(x): 0.7556, D(G(z)): 0.3652
Epoch: [14/20], Batch Num: [426/600]
Discriminator Loss: 0.8533, Generator Loss: 2.1847
D(x): 0.7031, D(G(z)): 0.2505
Epoch: [14/20], Batch Num: [427/600]
Discriminator Loss: 0.7827, Generator Loss: 2.0883
D(x): 0.7353, D(G(z)): 0.2312
Epoch: [14/20], Batch Num: [428/600]
Discriminator Loss: 0.8435, Generator Loss: 1.8584
D(x): 0.7077, D(G(z)): 0.2270
Epoch: [14/20], Batch Num: [429/600]
Discriminator Loss: 0.7209, Generator Loss: 1.6759
D(x): 0.7541, D(G(z)): 0.2132
Epoch: [14/20], Batch Num: [430/600]
Discriminator Loss: 0.7618, Generator Loss: 1.6203
D(x): 0.7886, D(G(z)): 0.2610
Epoch: [14/20], Batch Num: [431/600]
Discriminator Loss: 0.7803, Generator Loss: 1.7332
D(x): 0.7815, D(G(z)): 0.2766
Epoch: [14/20], Batch Num: [432/600]
Discriminator Loss: 0.6317, Generator Loss: 1.8111
D(x): 0.8332, D(G(z)): 0.2660
Epoch: [14/20], Batch Num: [433/600]
Discriminator Loss: 0.7254, Generator Loss: 2.2083
D(x): 0.7502, D(G(z)): 0.2241
Epoch: [14/20], Batch Num: [434/600]
Discriminator Loss: 0.6346, Generator Loss: 2.0845
D(x): 0.7622, D(G(z)): 0.1990
Epoch: [14/20], Batch Num: [435/600]
Discriminator Loss: 0.7526, Generator Loss: 2.3791
D(x): 0.7381, D(G(z)): 0.2034
Epoch: [14/20], Batch Num: [436/600]
Discriminator Loss: 0.7494, Generator Loss: 1.9733
D(x): 0.7385, D(G(z)): 0.1992
Epoch: [14/20], Batch Num: [437/600]
Discriminator Loss: 0.7280, Generator Loss: 2.0827
D(x): 0.7734, D(G(z)): 0.2332
Epoch: [14/20], Batch Num: [438/600]
Discriminator Loss: 0.5634, Generator Loss: 2.1340
D(x): 0.8438, D(G(z)): 0.2354
Epoch: [14/20], Batch Num: [439/600]
Discriminator Loss: 0.6561, Generator Loss: 2.1385
D(x): 0.7824, D(G(z)): 0.2035
Epoch: [14/20], Batch Num: [440/600]
Discriminator Loss: 0.6577, Generator Loss: 2.2249
D(x): 0.8310, D(G(z)): 0.2572
Epoch: [14/20], Batch Num: [441/600]
Discriminator Loss: 0.7168, Generator Loss: 2.3472
D(x): 0.7603, D(G(z)): 0.1904
Epoch: [14/20], Batch Num: [442/600]
Discriminator Loss: 0.8124, Generator Loss: 2.1488
D(x): 0.7674, D(G(z)): 0.2317
Epoch: [14/20], Batch Num: [443/600]
Discriminator Loss: 0.5938, Generator Loss: 2.1575
D(x): 0.8061, D(G(z)): 0.2254
Epoch: [14/20], Batch Num: [444/600]
Discriminator Loss: 0.5807, Generator Loss: 2.3662
D(x): 0.8124, D(G(z)): 0.1937
Epoch: [14/20], Batch Num: [445/600]
Discriminator Loss: 0.7507, Generator Loss: 2.3389
D(x): 0.7597, D(G(z)): 0.2252
Epoch: [14/20], Batch Num: [446/600]
Discriminator Loss: 0.7003, Generator Loss: 2.2173
D(x): 0.7390, D(G(z)): 0.1899
Epoch: [14/20], Batch Num: [447/600]
Discriminator Loss: 0.6891, Generator Loss: 2.0564
D(x): 0.8122, D(G(z)): 0.2488
Epoch: [14/20], Batch Num: [448/600]
Discriminator Loss: 0.6118, Generator Loss: 2.0209
D(x): 0.8467, D(G(z)): 0.2456
Epoch: [14/20], Batch Num: [449/600]
Discriminator Loss: 0.6420, Generator Loss: 2.2534
D(x): 0.8255, D(G(z)): 0.2337
Epoch: [14/20], Batch Num: [450/600]
Discriminator Loss: 0.7517, Generator Loss: 2.2888
D(x): 0.7710, D(G(z)): 0.2090
Epoch: [14/20], Batch Num: [451/600]
Discriminator Loss: 0.6702, Generator Loss: 2.3303
D(x): 0.7774, D(G(z)): 0.1856
Epoch: [14/20], Batch Num: [452/600]
Discriminator Loss: 0.7474, Generator Loss: 2.0522
D(x): 0.7203, D(G(z)): 0.1708
Epoch: [14/20], Batch Num: [453/600]
Discriminator Loss: 0.7162, Generator Loss: 1.8803
D(x): 0.8199, D(G(z)): 0.2663
Epoch: [14/20], Batch Num: [454/600]
Discriminator Loss: 0.8537, Generator Loss: 1.9333
D(x): 0.7506, D(G(z)): 0.2654
Epoch: [14/20], Batch Num: [455/600]
Discriminator Loss: 0.7703, Generator Loss: 1.9363
D(x): 0.8041, D(G(z)): 0.2847
Epoch: [14/20], Batch Num: [456/600]
Discriminator Loss: 0.7591, Generator Loss: 2.0719
D(x): 0.7382, D(G(z)): 0.2323
Epoch: [14/20], Batch Num: [457/600]
Discriminator Loss: 0.9329, Generator Loss: 1.8536
D(x): 0.6885, D(G(z)): 0.2646
Epoch: [14/20], Batch Num: [458/600]
Discriminator Loss: 0.8721, Generator Loss: 1.7963
D(x): 0.7442, D(G(z)): 0.2748
Epoch: [14/20], Batch Num: [459/600]
Discriminator Loss: 0.8174, Generator Loss: 1.6080
D(x): 0.7515, D(G(z)): 0.2746
Epoch: [14/20], Batch Num: [460/600]
Discriminator Loss: 0.9056, Generator Loss: 1.8945
D(x): 0.7306, D(G(z)): 0.2908
Epoch: [14/20], Batch Num: [461/600]
Discriminator Loss: 0.9045, Generator Loss: 1.6800
D(x): 0.7221, D(G(z)): 0.2680
Epoch: [14/20], Batch Num: [462/600]
Discriminator Loss: 0.8829, Generator Loss: 1.7364
D(x): 0.7262, D(G(z)): 0.2625
Epoch: [14/20], Batch Num: [463/600]
Discriminator Loss: 0.6935, Generator Loss: 1.6050
D(x): 0.7777, D(G(z)): 0.2564
Epoch: [14/20], Batch Num: [464/600]
Discriminator Loss: 0.9027, Generator Loss: 1.5257
D(x): 0.7378, D(G(z)): 0.3028
Epoch: [14/20], Batch Num: [465/600]
Discriminator Loss: 1.0356, Generator Loss: 1.4915
D(x): 0.6957, D(G(z)): 0.3314
Epoch: [14/20], Batch Num: [466/600]
Discriminator Loss: 0.9491, Generator Loss: 1.7942
D(x): 0.7542, D(G(z)): 0.3510
Epoch: [14/20], Batch Num: [467/600]
Discriminator Loss: 0.9045, Generator Loss: 1.8992
D(x): 0.7213, D(G(z)): 0.2913
Epoch: [14/20], Batch Num: [468/600]
Discriminator Loss: 1.0291, Generator Loss: 1.6986
D(x): 0.6156, D(G(z)): 0.2186
Epoch: [14/20], Batch Num: [469/600]
Discriminator Loss: 1.0272, Generator Loss: 1.4502
D(x): 0.6415, D(G(z)): 0.2629
Epoch: [14/20], Batch Num: [470/600]
Discriminator Loss: 0.7684, Generator Loss: 1.2354
D(x): 0.7244, D(G(z)): 0.2553
Epoch: [14/20], Batch Num: [471/600]
Discriminator Loss: 1.0067, Generator Loss: 1.2823
D(x): 0.7454, D(G(z)): 0.3579
Epoch: [14/20], Batch Num: [472/600]
Discriminator Loss: 0.8977, Generator Loss: 1.3234
D(x): 0.7916, D(G(z)): 0.3746
Epoch: [14/20], Batch Num: [473/600]
Discriminator Loss: 0.7472, Generator Loss: 1.6373
D(x): 0.7992, D(G(z)): 0.3153
Epoch: [14/20], Batch Num: [474/600]
Discriminator Loss: 0.8381, Generator Loss: 1.9791
D(x): 0.7331, D(G(z)): 0.2833
Epoch: [14/20], Batch Num: [475/600]
Discriminator Loss: 0.7835, Generator Loss: 1.9628
D(x): 0.7080, D(G(z)): 0.2298
Epoch: [14/20], Batch Num: [476/600]
Discriminator Loss: 0.7295, Generator Loss: 2.0326
D(x): 0.7222, D(G(z)): 0.2050
Epoch: [14/20], Batch Num: [477/600]
Discriminator Loss: 0.9177, Generator Loss: 1.8196
D(x): 0.6491, D(G(z)): 0.1834
Epoch: [14/20], Batch Num: [478/600]
Discriminator Loss: 0.6914, Generator Loss: 1.4757
D(x): 0.7550, D(G(z)): 0.2246
Epoch: [14/20], Batch Num: [479/600]
Discriminator Loss: 0.6351, Generator Loss: 1.3467
D(x): 0.8224, D(G(z)): 0.2791
Epoch: [14/20], Batch Num: [480/600]
Discriminator Loss: 0.7647, Generator Loss: 1.4387
D(x): 0.8363, D(G(z)): 0.3171
Epoch: [14/20], Batch Num: [481/600]
Discriminator Loss: 0.7155, Generator Loss: 1.7418
D(x): 0.8387, D(G(z)): 0.3001
Epoch: [14/20], Batch Num: [482/600]
Discriminator Loss: 0.5741, Generator Loss: 1.9530
D(x): 0.8295, D(G(z)): 0.2401
Epoch: [14/20], Batch Num: [483/600]
Discriminator Loss: 0.6189, Generator Loss: 2.2043
D(x): 0.7662, D(G(z)): 0.1898
Epoch: [14/20], Batch Num: [484/600]
Discriminator Loss: 0.6650, Generator Loss: 2.0520
D(x): 0.7506, D(G(z)): 0.1952
Epoch: [14/20], Batch Num: [485/600]
Discriminator Loss: 0.6246, Generator Loss: 2.1178
D(x): 0.7509, D(G(z)): 0.1893
Epoch: [14/20], Batch Num: [486/600]
Discriminator Loss: 0.6296, Generator Loss: 1.9929
D(x): 0.7814, D(G(z)): 0.1804
Epoch: [14/20], Batch Num: [487/600]
Discriminator Loss: 0.5966, Generator Loss: 1.9395
D(x): 0.7779, D(G(z)): 0.2045
Epoch: [14/20], Batch Num: [488/600]
Discriminator Loss: 0.5183, Generator Loss: 1.5480
D(x): 0.8395, D(G(z)): 0.2247
Epoch: [14/20], Batch Num: [489/600]
Discriminator Loss: 0.6163, Generator Loss: 1.7930
D(x): 0.8414, D(G(z)): 0.2793
Epoch: [14/20], Batch Num: [490/600]
Discriminator Loss: 0.5109, Generator Loss: 2.0188
D(x): 0.8930, D(G(z)): 0.2570
Epoch: [14/20], Batch Num: [491/600]
Discriminator Loss: 0.4313, Generator Loss: 2.5026
D(x): 0.8636, D(G(z)): 0.1867
Epoch: [14/20], Batch Num: [492/600]
Discriminator Loss: 0.5479, Generator Loss: 2.3584
D(x): 0.7697, D(G(z)): 0.1471
Epoch: [14/20], Batch Num: [493/600]
Discriminator Loss: 0.6336, Generator Loss: 2.7237
D(x): 0.7526, D(G(z)): 0.1459
Epoch: [14/20], Batch Num: [494/600]
Discriminator Loss: 0.6225, Generator Loss: 2.1376
D(x): 0.7677, D(G(z)): 0.1677
Epoch: [14/20], Batch Num: [495/600]
Discriminator Loss: 0.5104, Generator Loss: 1.7948
D(x): 0.8219, D(G(z)): 0.1891
Epoch: [14/20], Batch Num: [496/600]
Discriminator Loss: 0.5313, Generator Loss: 1.7949
D(x): 0.8598, D(G(z)): 0.2338
Epoch: [14/20], Batch Num: [497/600]
Discriminator Loss: 0.5867, Generator Loss: 1.8511
D(x): 0.8445, D(G(z)): 0.2327
Epoch: [14/20], Batch Num: [498/600]
Discriminator Loss: 0.7299, Generator Loss: 2.2131
D(x): 0.8519, D(G(z)): 0.2857
Epoch: [14/20], Batch Num: [499/600]
Discriminator Loss: 0.6776, Generator Loss: 2.5319
D(x): 0.7938, D(G(z)): 0.2292
Epoch: 14, Batch Num: [500/600]

Epoch: [14/20], Batch Num: [500/600]
Discriminator Loss: 0.7024, Generator Loss: 2.5820
D(x): 0.7280, D(G(z)): 0.1643
Epoch: [14/20], Batch Num: [501/600]
Discriminator Loss: 0.7898, Generator Loss: 2.2614
D(x): 0.6881, D(G(z)): 0.1413
Epoch: [14/20], Batch Num: [502/600]
Discriminator Loss: 0.8141, Generator Loss: 1.8886
D(x): 0.7283, D(G(z)): 0.2079
Epoch: [14/20], Batch Num: [503/600]
Discriminator Loss: 0.6798, Generator Loss: 1.5480
D(x): 0.8260, D(G(z)): 0.2770
Epoch: [14/20], Batch Num: [504/600]
Discriminator Loss: 0.6465, Generator Loss: 1.7093
D(x): 0.8633, D(G(z)): 0.2746
Epoch: [14/20], Batch Num: [505/600]
Discriminator Loss: 0.8984, Generator Loss: 2.0313
D(x): 0.8144, D(G(z)): 0.3194
Epoch: [14/20], Batch Num: [506/600]
Discriminator Loss: 0.6704, Generator Loss: 2.2454
D(x): 0.7838, D(G(z)): 0.2209
Epoch: [14/20], Batch Num: [507/600]
Discriminator Loss: 0.8256, Generator Loss: 2.1673
D(x): 0.7045, D(G(z)): 0.1897
Epoch: [14/20], Batch Num: [508/600]
Discriminator Loss: 0.8783, Generator Loss: 2.3782
D(x): 0.7448, D(G(z)): 0.2709
Epoch: [14/20], Batch Num: [509/600]
Discriminator Loss: 0.9570, Generator Loss: 1.9532
D(x): 0.6646, D(G(z)): 0.1989
Epoch: [14/20], Batch Num: [510/600]
Discriminator Loss: 0.7784, Generator Loss: 1.6724
D(x): 0.7469, D(G(z)): 0.2228
Epoch: [14/20], Batch Num: [511/600]
Discriminator Loss: 0.8898, Generator Loss: 1.6850
D(x): 0.7250, D(G(z)): 0.2664
Epoch: [14/20], Batch Num: [512/600]
Discriminator Loss: 0.8615, Generator Loss: 1.4788
D(x): 0.7815, D(G(z)): 0.3312
Epoch: [14/20], Batch Num: [513/600]
Discriminator Loss: 0.9784, Generator Loss: 1.8125
D(x): 0.7802, D(G(z)): 0.3708
Epoch: [14/20], Batch Num: [514/600]
Discriminator Loss: 0.9791, Generator Loss: 1.6723
D(x): 0.7206, D(G(z)): 0.2866
Epoch: [14/20], Batch Num: [515/600]
Discriminator Loss: 0.9351, Generator Loss: 1.8519
D(x): 0.6974, D(G(z)): 0.2601
Epoch: [14/20], Batch Num: [516/600]
Discriminator Loss: 1.0165, Generator Loss: 1.6235
D(x): 0.6219, D(G(z)): 0.2327
Epoch: [14/20], Batch Num: [517/600]
Discriminator Loss: 0.9175, Generator Loss: 1.4917
D(x): 0.7143, D(G(z)): 0.2908
Epoch: [14/20], Batch Num: [518/600]
Discriminator Loss: 0.8638, Generator Loss: 1.4967
D(x): 0.7761, D(G(z)): 0.3319
Epoch: [14/20], Batch Num: [519/600]
Discriminator Loss: 1.0879, Generator Loss: 1.6528
D(x): 0.7287, D(G(z)): 0.3641
Epoch: [14/20], Batch Num: [520/600]
Discriminator Loss: 0.9667, Generator Loss: 1.7498
D(x): 0.7276, D(G(z)): 0.2914
Epoch: [14/20], Batch Num: [521/600]
Discriminator Loss: 0.7976, Generator Loss: 2.0194
D(x): 0.6983, D(G(z)): 0.2262
Epoch: [14/20], Batch Num: [522/600]
Discriminator Loss: 0.8447, Generator Loss: 2.0583
D(x): 0.6857, D(G(z)): 0.2323
Epoch: [14/20], Batch Num: [523/600]
Discriminator Loss: 0.8397, Generator Loss: 1.6733
D(x): 0.6888, D(G(z)): 0.2358
Epoch: [14/20], Batch Num: [524/600]
Discriminator Loss: 0.6572, Generator Loss: 1.5862
D(x): 0.7918, D(G(z)): 0.2331
Epoch: [14/20], Batch Num: [525/600]
Discriminator Loss: 0.8250, Generator Loss: 1.6346
D(x): 0.7998, D(G(z)): 0.3377
Epoch: [14/20], Batch Num: [526/600]
Discriminator Loss: 0.7214, Generator Loss: 1.6372
D(x): 0.7963, D(G(z)): 0.2868
Epoch: [14/20], Batch Num: [527/600]
Discriminator Loss: 0.7885, Generator Loss: 2.0339
D(x): 0.7235, D(G(z)): 0.2655
Epoch: [14/20], Batch Num: [528/600]
Discriminator Loss: 0.8320, Generator Loss: 2.0272
D(x): 0.7437, D(G(z)): 0.2759
Epoch: [14/20], Batch Num: [529/600]
Discriminator Loss: 0.8646, Generator Loss: 2.1767
D(x): 0.6737, D(G(z)): 0.2287
Epoch: [14/20], Batch Num: [530/600]
Discriminator Loss: 0.7641, Generator Loss: 2.0050
D(x): 0.7723, D(G(z)): 0.2758
Epoch: [14/20], Batch Num: [531/600]
Discriminator Loss: 0.7130, Generator Loss: 2.0831
D(x): 0.7555, D(G(z)): 0.2192
Epoch: [14/20], Batch Num: [532/600]
Discriminator Loss: 0.6672, Generator Loss: 2.0682
D(x): 0.7722, D(G(z)): 0.2255
Epoch: [14/20], Batch Num: [533/600]
Discriminator Loss: 0.6671, Generator Loss: 1.9861
D(x): 0.7519, D(G(z)): 0.1994
Epoch: [14/20], Batch Num: [534/600]
Discriminator Loss: 0.7265, Generator Loss: 1.7579
D(x): 0.7699, D(G(z)): 0.2402
Epoch: [14/20], Batch Num: [535/600]
Discriminator Loss: 0.5229, Generator Loss: 1.7541
D(x): 0.8542, D(G(z)): 0.2224
Epoch: [14/20], Batch Num: [536/600]
Discriminator Loss: 0.6806, Generator Loss: 2.0380
D(x): 0.8037, D(G(z)): 0.2485
Epoch: [14/20], Batch Num: [537/600]
Discriminator Loss: 0.6406, Generator Loss: 2.2128
D(x): 0.7992, D(G(z)): 0.2206
Epoch: [14/20], Batch Num: [538/600]
Discriminator Loss: 0.6083, Generator Loss: 2.5698
D(x): 0.7962, D(G(z)): 0.2072
Epoch: [14/20], Batch Num: [539/600]
Discriminator Loss: 0.6990, Generator Loss: 2.2426
D(x): 0.7207, D(G(z)): 0.1597
Epoch: [14/20], Batch Num: [540/600]
Discriminator Loss: 0.6583, Generator Loss: 2.2061
D(x): 0.7774, D(G(z)): 0.2093
Epoch: [14/20], Batch Num: [541/600]
Discriminator Loss: 0.6989, Generator Loss: 2.1521
D(x): 0.7935, D(G(z)): 0.2438
Epoch: [14/20], Batch Num: [542/600]
Discriminator Loss: 0.6741, Generator Loss: 2.1119
D(x): 0.7855, D(G(z)): 0.2357
Epoch: [14/20], Batch Num: [543/600]
Discriminator Loss: 0.5811, Generator Loss: 2.4366
D(x): 0.8629, D(G(z)): 0.2343
Epoch: [14/20], Batch Num: [544/600]
Discriminator Loss: 0.6348, Generator Loss: 2.6229
D(x): 0.8267, D(G(z)): 0.2190
Epoch: [14/20], Batch Num: [545/600]
Discriminator Loss: 0.6467, Generator Loss: 2.5722
D(x): 0.7436, D(G(z)): 0.1361
Epoch: [14/20], Batch Num: [546/600]
Discriminator Loss: 0.6595, Generator Loss: 2.1828
D(x): 0.7769, D(G(z)): 0.1685
Epoch: [14/20], Batch Num: [547/600]
Discriminator Loss: 0.6065, Generator Loss: 2.0194
D(x): 0.8197, D(G(z)): 0.2178
Epoch: [14/20], Batch Num: [548/600]
Discriminator Loss: 0.7977, Generator Loss: 2.0404
D(x): 0.7192, D(G(z)): 0.2006
Epoch: [14/20], Batch Num: [549/600]
Discriminator Loss: 0.6860, Generator Loss: 1.7191
D(x): 0.8106, D(G(z)): 0.2324
Epoch: [14/20], Batch Num: [550/600]
Discriminator Loss: 0.9346, Generator Loss: 1.8450
D(x): 0.7862, D(G(z)): 0.3272
Epoch: [14/20], Batch Num: [551/600]
Discriminator Loss: 0.8096, Generator Loss: 2.4403
D(x): 0.8493, D(G(z)): 0.3237
Epoch: [14/20], Batch Num: [552/600]
Discriminator Loss: 0.9401, Generator Loss: 2.5407
D(x): 0.6744, D(G(z)): 0.2059
Epoch: [14/20], Batch Num: [553/600]
Discriminator Loss: 0.9610, Generator Loss: 2.1439
D(x): 0.6793, D(G(z)): 0.2221
Epoch: [14/20], Batch Num: [554/600]
Discriminator Loss: 0.9958, Generator Loss: 1.7180
D(x): 0.6930, D(G(z)): 0.2620
Epoch: [14/20], Batch Num: [555/600]
Discriminator Loss: 1.0624, Generator Loss: 1.5450
D(x): 0.7024, D(G(z)): 0.3166
Epoch: [14/20], Batch Num: [556/600]
Discriminator Loss: 1.0546, Generator Loss: 1.7071
D(x): 0.7511, D(G(z)): 0.3339
Epoch: [14/20], Batch Num: [557/600]
Discriminator Loss: 1.1153, Generator Loss: 1.8059
D(x): 0.7169, D(G(z)): 0.3154
Epoch: [14/20], Batch Num: [558/600]
Discriminator Loss: 1.0780, Generator Loss: 1.5646
D(x): 0.6875, D(G(z)): 0.3174
Epoch: [14/20], Batch Num: [559/600]
Discriminator Loss: 1.0813, Generator Loss: 1.9121
D(x): 0.6781, D(G(z)): 0.3125
Epoch: [14/20], Batch Num: [560/600]
Discriminator Loss: 1.1498, Generator Loss: 1.8711
D(x): 0.6016, D(G(z)): 0.2524
Epoch: [14/20], Batch Num: [561/600]
Discriminator Loss: 1.1017, Generator Loss: 1.4739
D(x): 0.6721, D(G(z)): 0.3345
Epoch: [14/20], Batch Num: [562/600]
Discriminator Loss: 1.1265, Generator Loss: 1.7143
D(x): 0.7171, D(G(z)): 0.3586
Epoch: [14/20], Batch Num: [563/600]
Discriminator Loss: 1.3219, Generator Loss: 1.6186
D(x): 0.6714, D(G(z)): 0.3774
Epoch: [14/20], Batch Num: [564/600]
Discriminator Loss: 1.1468, Generator Loss: 1.6680
D(x): 0.6464, D(G(z)): 0.3215
Epoch: [14/20], Batch Num: [565/600]
Discriminator Loss: 0.9862, Generator Loss: 1.8074
D(x): 0.6588, D(G(z)): 0.2532
Epoch: [14/20], Batch Num: [566/600]
Discriminator Loss: 1.1366, Generator Loss: 1.5630
D(x): 0.6142, D(G(z)): 0.2918
Epoch: [14/20], Batch Num: [567/600]
Discriminator Loss: 0.9257, Generator Loss: 1.4107
D(x): 0.6664, D(G(z)): 0.2742
Epoch: [14/20], Batch Num: [568/600]
Discriminator Loss: 0.9823, Generator Loss: 1.3692
D(x): 0.6800, D(G(z)): 0.3002
Epoch: [14/20], Batch Num: [569/600]
Discriminator Loss: 0.8227, Generator Loss: 1.2630
D(x): 0.7713, D(G(z)): 0.3390
Epoch: [14/20], Batch Num: [570/600]
Discriminator Loss: 0.9760, Generator Loss: 1.2960
D(x): 0.7068, D(G(z)): 0.3487
Epoch: [14/20], Batch Num: [571/600]
Discriminator Loss: 0.8469, Generator Loss: 1.4473
D(x): 0.7640, D(G(z)): 0.3404
Epoch: [14/20], Batch Num: [572/600]
Discriminator Loss: 0.7679, Generator Loss: 1.6403
D(x): 0.7623, D(G(z)): 0.3101
Epoch: [14/20], Batch Num: [573/600]
Discriminator Loss: 0.8054, Generator Loss: 1.7409
D(x): 0.7292, D(G(z)): 0.2767
Epoch: [14/20], Batch Num: [574/600]
Discriminator Loss: 0.7728, Generator Loss: 1.8880
D(x): 0.6630, D(G(z)): 0.2004
Epoch: [14/20], Batch Num: [575/600]
Discriminator Loss: 0.7415, Generator Loss: 1.8964
D(x): 0.7043, D(G(z)): 0.2050
Epoch: [14/20], Batch Num: [576/600]
Discriminator Loss: 0.7452, Generator Loss: 1.6864
D(x): 0.7087, D(G(z)): 0.2297
Epoch: [14/20], Batch Num: [577/600]
Discriminator Loss: 0.6414, Generator Loss: 1.4357
D(x): 0.8147, D(G(z)): 0.2718
Epoch: [14/20], Batch Num: [578/600]
Discriminator Loss: 0.6294, Generator Loss: 1.4372
D(x): 0.7919, D(G(z)): 0.2608
Epoch: [14/20], Batch Num: [579/600]
Discriminator Loss: 0.5257, Generator Loss: 1.6788
D(x): 0.8405, D(G(z)): 0.2377
Epoch: [14/20], Batch Num: [580/600]
Discriminator Loss: 0.6329, Generator Loss: 1.7163
D(x): 0.7978, D(G(z)): 0.2602
Epoch: [14/20], Batch Num: [581/600]
Discriminator Loss: 0.6379, Generator Loss: 1.7577
D(x): 0.7857, D(G(z)): 0.2336
Epoch: [14/20], Batch Num: [582/600]
Discriminator Loss: 0.5864, Generator Loss: 1.9107
D(x): 0.8244, D(G(z)): 0.2443
Epoch: [14/20], Batch Num: [583/600]
Discriminator Loss: 0.5486, Generator Loss: 2.2453
D(x): 0.8395, D(G(z)): 0.2192
Epoch: [14/20], Batch Num: [584/600]
Discriminator Loss: 0.6546, Generator Loss: 2.4684
D(x): 0.7435, D(G(z)): 0.1524
Epoch: [14/20], Batch Num: [585/600]
Discriminator Loss: 0.5702, Generator Loss: 2.2021
D(x): 0.7511, D(G(z)): 0.1451
Epoch: [14/20], Batch Num: [586/600]
Discriminator Loss: 0.5902, Generator Loss: 2.0028
D(x): 0.7679, D(G(z)): 0.1818
Epoch: [14/20], Batch Num: [587/600]
Discriminator Loss: 0.5369, Generator Loss: 1.9798
D(x): 0.8612, D(G(z)): 0.2363
Epoch: [14/20], Batch Num: [588/600]
Discriminator Loss: 0.4698, Generator Loss: 1.7134
D(x): 0.8799, D(G(z)): 0.2230
Epoch: [14/20], Batch Num: [589/600]
Discriminator Loss: 0.5502, Generator Loss: 2.2550
D(x): 0.8712, D(G(z)): 0.2606
Epoch: [14/20], Batch Num: [590/600]
Discriminator Loss: 0.5731, Generator Loss: 2.2387
D(x): 0.8311, D(G(z)): 0.2214
Epoch: [14/20], Batch Num: [591/600]
Discriminator Loss: 0.5399, Generator Loss: 2.3218
D(x): 0.8179, D(G(z)): 0.1732
Epoch: [14/20], Batch Num: [592/600]
Discriminator Loss: 0.6515, Generator Loss: 2.2942
D(x): 0.7180, D(G(z)): 0.1371
Epoch: [14/20], Batch Num: [593/600]
Discriminator Loss: 0.5543, Generator Loss: 2.0999
D(x): 0.7908, D(G(z)): 0.1719
Epoch: [14/20], Batch Num: [594/600]
Discriminator Loss: 0.6845, Generator Loss: 2.1924
D(x): 0.8266, D(G(z)): 0.2690
Epoch: [14/20], Batch Num: [595/600]
Discriminator Loss: 0.6986, Generator Loss: 2.0071
D(x): 0.8002, D(G(z)): 0.2483
Epoch: [14/20], Batch Num: [596/600]
Discriminator Loss: 0.9275, Generator Loss: 1.9666
D(x): 0.7599, D(G(z)): 0.2913
Epoch: [14/20], Batch Num: [597/600]
Discriminator Loss: 0.6157, Generator Loss: 2.1350
D(x): 0.8438, D(G(z)): 0.2366
Epoch: [14/20], Batch Num: [598/600]
Discriminator Loss: 0.7597, Generator Loss: 2.1376
D(x): 0.7729, D(G(z)): 0.2495
Epoch: [14/20], Batch Num: [599/600]
Discriminator Loss: 1.0847, Generator Loss: 1.8674
D(x): 0.6512, D(G(z)): 0.2396
Epoch: 15, Batch Num: [0/600]

Epoch: [15/20], Batch Num: [0/600]
Discriminator Loss: 0.9004, Generator Loss: 1.7915
D(x): 0.7237, D(G(z)): 0.2685
Epoch: [15/20], Batch Num: [1/600]
Discriminator Loss: 0.9596, Generator Loss: 1.6654
D(x): 0.7328, D(G(z)): 0.3016
Epoch: [15/20], Batch Num: [2/600]
Discriminator Loss: 0.8489, Generator Loss: 1.5395
D(x): 0.7982, D(G(z)): 0.3215
Epoch: [15/20], Batch Num: [3/600]
Discriminator Loss: 0.9896, Generator Loss: 1.6905
D(x): 0.7258, D(G(z)): 0.3224
Epoch: [15/20], Batch Num: [4/600]
Discriminator Loss: 1.0421, Generator Loss: 1.7298
D(x): 0.7064, D(G(z)): 0.3273
Epoch: [15/20], Batch Num: [5/600]
Discriminator Loss: 0.9477, Generator Loss: 1.7205
D(x): 0.6799, D(G(z)): 0.2593
Epoch: [15/20], Batch Num: [6/600]
Discriminator Loss: 1.0149, Generator Loss: 1.6661
D(x): 0.7387, D(G(z)): 0.3311
Epoch: [15/20], Batch Num: [7/600]
Discriminator Loss: 0.8976, Generator Loss: 1.6479
D(x): 0.6967, D(G(z)): 0.2692
Epoch: [15/20], Batch Num: [8/600]
Discriminator Loss: 1.1361, Generator Loss: 1.4835
D(x): 0.6600, D(G(z)): 0.3300
Epoch: [15/20], Batch Num: [9/600]
Discriminator Loss: 1.0698, Generator Loss: 1.5564
D(x): 0.7101, D(G(z)): 0.3480
Epoch: [15/20], Batch Num: [10/600]
Discriminator Loss: 0.8683, Generator Loss: 1.4375
D(x): 0.7221, D(G(z)): 0.2901
Epoch: [15/20], Batch Num: [11/600]
Discriminator Loss: 1.1553, Generator Loss: 1.5465
D(x): 0.6721, D(G(z)): 0.3554
Epoch: [15/20], Batch Num: [12/600]
Discriminator Loss: 0.9901, Generator Loss: 1.4774
D(x): 0.7054, D(G(z)): 0.3146
Epoch: [15/20], Batch Num: [13/600]
Discriminator Loss: 0.9365, Generator Loss: 1.8692
D(x): 0.7193, D(G(z)): 0.3264
Epoch: [15/20], Batch Num: [14/600]
Discriminator Loss: 0.9962, Generator Loss: 1.6575
D(x): 0.6476, D(G(z)): 0.2844
Epoch: [15/20], Batch Num: [15/600]
Discriminator Loss: 0.8118, Generator Loss: 1.5284
D(x): 0.6901, D(G(z)): 0.2625
Epoch: [15/20], Batch Num: [16/600]
Discriminator Loss: 0.9138, Generator Loss: 1.4181
D(x): 0.7288, D(G(z)): 0.3045
Epoch: [15/20], Batch Num: [17/600]
Discriminator Loss: 0.8926, Generator Loss: 1.6130
D(x): 0.7524, D(G(z)): 0.3412
Epoch: [15/20], Batch Num: [18/600]
Discriminator Loss: 0.6511, Generator Loss: 1.5704
D(x): 0.8159, D(G(z)): 0.2901
Epoch: [15/20], Batch Num: [19/600]
Discriminator Loss: 0.7485, Generator Loss: 1.7463
D(x): 0.7466, D(G(z)): 0.2794
Epoch: [15/20], Batch Num: [20/600]
Discriminator Loss: 0.7397, Generator Loss: 1.8582
D(x): 0.7564, D(G(z)): 0.2633
Epoch: [15/20], Batch Num: [21/600]
Discriminator Loss: 0.7045, Generator Loss: 1.8748
D(x): 0.7324, D(G(z)): 0.2093
Epoch: [15/20], Batch Num: [22/600]
Discriminator Loss: 0.7476, Generator Loss: 1.8936
D(x): 0.7242, D(G(z)): 0.2406
Epoch: [15/20], Batch Num: [23/600]
Discriminator Loss: 0.8394, Generator Loss: 1.6532
D(x): 0.7086, D(G(z)): 0.2335
Epoch: [15/20], Batch Num: [24/600]
Discriminator Loss: 0.5753, Generator Loss: 1.8543
D(x): 0.8300, D(G(z)): 0.2405
Epoch: [15/20], Batch Num: [25/600]
Discriminator Loss: 0.6611, Generator Loss: 1.9341
D(x): 0.7895, D(G(z)): 0.2352
Epoch: [15/20], Batch Num: [26/600]
Discriminator Loss: 0.7193, Generator Loss: 1.8223
D(x): 0.7934, D(G(z)): 0.2740
Epoch: [15/20], Batch Num: [27/600]
Discriminator Loss: 0.6173, Generator Loss: 1.8755
D(x): 0.7656, D(G(z)): 0.1964
Epoch: [15/20], Batch Num: [28/600]
Discriminator Loss: 0.5931, Generator Loss: 2.0412
D(x): 0.8488, D(G(z)): 0.2496
Epoch: [15/20], Batch Num: [29/600]
Discriminator Loss: 0.5409, Generator Loss: 2.0848
D(x): 0.8372, D(G(z)): 0.2131
Epoch: [15/20], Batch Num: [30/600]
Discriminator Loss: 0.6173, Generator Loss: 2.2060
D(x): 0.8054, D(G(z)): 0.2134
Epoch: [15/20], Batch Num: [31/600]
Discriminator Loss: 0.6211, Generator Loss: 2.2067
D(x): 0.7339, D(G(z)): 0.1634
Epoch: [15/20], Batch Num: [32/600]
Discriminator Loss: 0.4580, Generator Loss: 2.1079
D(x): 0.8104, D(G(z)): 0.1591
Epoch: [15/20], Batch Num: [33/600]
Discriminator Loss: 0.5993, Generator Loss: 2.0564
D(x): 0.8171, D(G(z)): 0.2205
Epoch: [15/20], Batch Num: [34/600]
Discriminator Loss: 0.5757, Generator Loss: 2.0599
D(x): 0.8272, D(G(z)): 0.2255
Epoch: [15/20], Batch Num: [35/600]
Discriminator Loss: 0.6361, Generator Loss: 1.9946
D(x): 0.7843, D(G(z)): 0.2236
Epoch: [15/20], Batch Num: [36/600]
Discriminator Loss: 0.6224, Generator Loss: 2.1203
D(x): 0.8399, D(G(z)): 0.2597
Epoch: [15/20], Batch Num: [37/600]
Discriminator Loss: 0.5858, Generator Loss: 2.4670
D(x): 0.8024, D(G(z)): 0.2133
Epoch: [15/20], Batch Num: [38/600]
Discriminator Loss: 0.7256, Generator Loss: 2.4617
D(x): 0.7481, D(G(z)): 0.2143
Epoch: [15/20], Batch Num: [39/600]
Discriminator Loss: 0.6543, Generator Loss: 2.1659
D(x): 0.7699, D(G(z)): 0.1759
Epoch: [15/20], Batch Num: [40/600]
Discriminator Loss: 0.5565, Generator Loss: 1.9927
D(x): 0.7823, D(G(z)): 0.1617
Epoch: [15/20], Batch Num: [41/600]
Discriminator Loss: 0.9196, Generator Loss: 1.7649
D(x): 0.7244, D(G(z)): 0.2625
Epoch: [15/20], Batch Num: [42/600]
Discriminator Loss: 0.7477, Generator Loss: 1.6757
D(x): 0.7839, D(G(z)): 0.2667
Epoch: [15/20], Batch Num: [43/600]
Discriminator Loss: 0.9890, Generator Loss: 1.7233
D(x): 0.7795, D(G(z)): 0.3293
Epoch: [15/20], Batch Num: [44/600]
Discriminator Loss: 0.7436, Generator Loss: 1.8476
D(x): 0.7376, D(G(z)): 0.2253
Epoch: [15/20], Batch Num: [45/600]
Discriminator Loss: 0.6068, Generator Loss: 2.1244
D(x): 0.8298, D(G(z)): 0.2391
Epoch: [15/20], Batch Num: [46/600]
Discriminator Loss: 0.8445, Generator Loss: 2.2077
D(x): 0.7294, D(G(z)): 0.2361
Epoch: [15/20], Batch Num: [47/600]
Discriminator Loss: 0.8683, Generator Loss: 1.8806
D(x): 0.6877, D(G(z)): 0.2043
Epoch: [15/20], Batch Num: [48/600]
Discriminator Loss: 0.9871, Generator Loss: 1.5344
D(x): 0.6990, D(G(z)): 0.2717
Epoch: [15/20], Batch Num: [49/600]
Discriminator Loss: 0.8955, Generator Loss: 1.6119
D(x): 0.7477, D(G(z)): 0.2743
Epoch: [15/20], Batch Num: [50/600]
Discriminator Loss: 1.0137, Generator Loss: 1.7630
D(x): 0.8013, D(G(z)): 0.3932
Epoch: [15/20], Batch Num: [51/600]
Discriminator Loss: 0.9083, Generator Loss: 1.9946
D(x): 0.6867, D(G(z)): 0.2498
Epoch: [15/20], Batch Num: [52/600]
Discriminator Loss: 1.0207, Generator Loss: 2.2012
D(x): 0.6918, D(G(z)): 0.2601
Epoch: [15/20], Batch Num: [53/600]
Discriminator Loss: 0.8747, Generator Loss: 1.9297
D(x): 0.6975, D(G(z)): 0.2415
Epoch: [15/20], Batch Num: [54/600]
Discriminator Loss: 0.9765, Generator Loss: 1.7891
D(x): 0.6930, D(G(z)): 0.2511
Epoch: [15/20], Batch Num: [55/600]
Discriminator Loss: 0.8883, Generator Loss: 1.6153
D(x): 0.7215, D(G(z)): 0.2534
Epoch: [15/20], Batch Num: [56/600]
Discriminator Loss: 1.1058, Generator Loss: 1.5241
D(x): 0.6976, D(G(z)): 0.3260
Epoch: [15/20], Batch Num: [57/600]
Discriminator Loss: 1.0896, Generator Loss: 1.6038
D(x): 0.7064, D(G(z)): 0.3619
Epoch: [15/20], Batch Num: [58/600]
Discriminator Loss: 0.9044, Generator Loss: 1.9556
D(x): 0.7437, D(G(z)): 0.2850
Epoch: [15/20], Batch Num: [59/600]
Discriminator Loss: 0.9085, Generator Loss: 1.7743
D(x): 0.7228, D(G(z)): 0.2620
Epoch: [15/20], Batch Num: [60/600]
Discriminator Loss: 0.8779, Generator Loss: 1.7978
D(x): 0.7028, D(G(z)): 0.2183
Epoch: [15/20], Batch Num: [61/600]
Discriminator Loss: 0.8613, Generator Loss: 1.6660
D(x): 0.6855, D(G(z)): 0.2119
Epoch: [15/20], Batch Num: [62/600]
Discriminator Loss: 0.7746, Generator Loss: 1.5732
D(x): 0.7683, D(G(z)): 0.2478
Epoch: [15/20], Batch Num: [63/600]
Discriminator Loss: 0.8867, Generator Loss: 1.3910
D(x): 0.7487, D(G(z)): 0.3131
Epoch: [15/20], Batch Num: [64/600]
Discriminator Loss: 0.7518, Generator Loss: 1.5872
D(x): 0.7524, D(G(z)): 0.2777
Epoch: [15/20], Batch Num: [65/600]
Discriminator Loss: 0.7664, Generator Loss: 1.5707
D(x): 0.8102, D(G(z)): 0.3232
Epoch: [15/20], Batch Num: [66/600]
Discriminator Loss: 0.7077, Generator Loss: 1.9567
D(x): 0.7651, D(G(z)): 0.2537
Epoch: [15/20], Batch Num: [67/600]
Discriminator Loss: 0.8356, Generator Loss: 1.9812
D(x): 0.6908, D(G(z)): 0.2151
Epoch: [15/20], Batch Num: [68/600]
Discriminator Loss: 0.6744, Generator Loss: 2.1911
D(x): 0.7510, D(G(z)): 0.2047
Epoch: [15/20], Batch Num: [69/600]
Discriminator Loss: 0.7238, Generator Loss: 1.9592
D(x): 0.7189, D(G(z)): 0.2138
Epoch: [15/20], Batch Num: [70/600]
Discriminator Loss: 0.6295, Generator Loss: 1.8372
D(x): 0.7649, D(G(z)): 0.1928
Epoch: [15/20], Batch Num: [71/600]
Discriminator Loss: 0.6775, Generator Loss: 1.8042
D(x): 0.8152, D(G(z)): 0.2876
Epoch: [15/20], Batch Num: [72/600]
Discriminator Loss: 0.6768, Generator Loss: 1.7058
D(x): 0.8031, D(G(z)): 0.2634
Epoch: [15/20], Batch Num: [73/600]
Discriminator Loss: 0.5566, Generator Loss: 1.8820
D(x): 0.8299, D(G(z)): 0.2271
Epoch: [15/20], Batch Num: [74/600]
Discriminator Loss: 0.6594, Generator Loss: 1.9572
D(x): 0.8180, D(G(z)): 0.2619
Epoch: [15/20], Batch Num: [75/600]
Discriminator Loss: 0.7080, Generator Loss: 2.0565
D(x): 0.7314, D(G(z)): 0.1912
Epoch: [15/20], Batch Num: [76/600]
Discriminator Loss: 0.6225, Generator Loss: 2.2087
D(x): 0.7650, D(G(z)): 0.1862
Epoch: [15/20], Batch Num: [77/600]
Discriminator Loss: 0.6133, Generator Loss: 2.1658
D(x): 0.8095, D(G(z)): 0.1987
Epoch: [15/20], Batch Num: [78/600]
Discriminator Loss: 0.6791, Generator Loss: 1.8923
D(x): 0.7661, D(G(z)): 0.1984
Epoch: [15/20], Batch Num: [79/600]
Discriminator Loss: 0.6295, Generator Loss: 1.8349
D(x): 0.8183, D(G(z)): 0.2424
Epoch: [15/20], Batch Num: [80/600]
Discriminator Loss: 0.6396, Generator Loss: 2.0714
D(x): 0.8194, D(G(z)): 0.2388
Epoch: [15/20], Batch Num: [81/600]
Discriminator Loss: 0.6951, Generator Loss: 2.0304
D(x): 0.7951, D(G(z)): 0.2504
Epoch: [15/20], Batch Num: [82/600]
Discriminator Loss: 0.7133, Generator Loss: 1.8313
D(x): 0.8122, D(G(z)): 0.2605
Epoch: [15/20], Batch Num: [83/600]
Discriminator Loss: 0.6536, Generator Loss: 2.1136
D(x): 0.8019, D(G(z)): 0.2354
Epoch: [15/20], Batch Num: [84/600]
Discriminator Loss: 0.5119, Generator Loss: 2.0559
D(x): 0.8110, D(G(z)): 0.1695
Epoch: [15/20], Batch Num: [85/600]
Discriminator Loss: 0.8013, Generator Loss: 2.1306
D(x): 0.7386, D(G(z)): 0.2277
Epoch: [15/20], Batch Num: [86/600]
Discriminator Loss: 0.6932, Generator Loss: 1.9784
D(x): 0.8046, D(G(z)): 0.2652
Epoch: [15/20], Batch Num: [87/600]
Discriminator Loss: 0.9233, Generator Loss: 2.0645
D(x): 0.7209, D(G(z)): 0.2717
Epoch: [15/20], Batch Num: [88/600]
Discriminator Loss: 0.7486, Generator Loss: 2.0501
D(x): 0.7766, D(G(z)): 0.2467
Epoch: [15/20], Batch Num: [89/600]
Discriminator Loss: 0.8676, Generator Loss: 1.5924
D(x): 0.7402, D(G(z)): 0.2667
Epoch: [15/20], Batch Num: [90/600]
Discriminator Loss: 0.8762, Generator Loss: 1.6898
D(x): 0.7637, D(G(z)): 0.3141
Epoch: [15/20], Batch Num: [91/600]
Discriminator Loss: 0.7011, Generator Loss: 1.7593
D(x): 0.8000, D(G(z)): 0.2802
Epoch: [15/20], Batch Num: [92/600]
Discriminator Loss: 0.7336, Generator Loss: 2.1757
D(x): 0.8107, D(G(z)): 0.2823
Epoch: [15/20], Batch Num: [93/600]
Discriminator Loss: 0.8073, Generator Loss: 1.9478
D(x): 0.7113, D(G(z)): 0.2223
Epoch: [15/20], Batch Num: [94/600]
Discriminator Loss: 0.8458, Generator Loss: 1.9220
D(x): 0.7072, D(G(z)): 0.2097
Epoch: [15/20], Batch Num: [95/600]
Discriminator Loss: 1.0321, Generator Loss: 1.4849
D(x): 0.6634, D(G(z)): 0.2831
Epoch: [15/20], Batch Num: [96/600]
Discriminator Loss: 0.8258, Generator Loss: 1.4991
D(x): 0.8066, D(G(z)): 0.3459
Epoch: [15/20], Batch Num: [97/600]
Discriminator Loss: 0.9995, Generator Loss: 1.8535
D(x): 0.7686, D(G(z)): 0.3566
Epoch: [15/20], Batch Num: [98/600]
Discriminator Loss: 1.0252, Generator Loss: 1.9203
D(x): 0.6710, D(G(z)): 0.2821
Epoch: [15/20], Batch Num: [99/600]
Discriminator Loss: 1.0293, Generator Loss: 1.9547
D(x): 0.6130, D(G(z)): 0.2149
Epoch: 15, Batch Num: [100/600]

Epoch: [15/20], Batch Num: [100/600]
Discriminator Loss: 1.1865, Generator Loss: 1.5860
D(x): 0.5892, D(G(z)): 0.2823
Epoch: [15/20], Batch Num: [101/600]
Discriminator Loss: 0.9909, Generator Loss: 1.2298
D(x): 0.7276, D(G(z)): 0.3301
Epoch: [15/20], Batch Num: [102/600]
Discriminator Loss: 0.9993, Generator Loss: 1.2544
D(x): 0.7654, D(G(z)): 0.3909
Epoch: [15/20], Batch Num: [103/600]
Discriminator Loss: 1.0242, Generator Loss: 1.5271
D(x): 0.7968, D(G(z)): 0.4252
Epoch: [15/20], Batch Num: [104/600]
Discriminator Loss: 1.0179, Generator Loss: 1.9455
D(x): 0.6899, D(G(z)): 0.3297
Epoch: [15/20], Batch Num: [105/600]
Discriminator Loss: 0.9921, Generator Loss: 1.9520
D(x): 0.6440, D(G(z)): 0.2470
Epoch: [15/20], Batch Num: [106/600]
Discriminator Loss: 0.9486, Generator Loss: 1.8620
D(x): 0.6514, D(G(z)): 0.2498
Epoch: [15/20], Batch Num: [107/600]
Discriminator Loss: 1.0425, Generator Loss: 1.6063
D(x): 0.6104, D(G(z)): 0.2603
Epoch: [15/20], Batch Num: [108/600]
Discriminator Loss: 0.9601, Generator Loss: 1.4522
D(x): 0.6748, D(G(z)): 0.2686
Epoch: [15/20], Batch Num: [109/600]
Discriminator Loss: 0.9620, Generator Loss: 1.3562
D(x): 0.7246, D(G(z)): 0.3508
Epoch: [15/20], Batch Num: [110/600]
Discriminator Loss: 0.9046, Generator Loss: 1.3244
D(x): 0.7358, D(G(z)): 0.3277
Epoch: [15/20], Batch Num: [111/600]
Discriminator Loss: 0.8772, Generator Loss: 1.4172
D(x): 0.7798, D(G(z)): 0.3763
Epoch: [15/20], Batch Num: [112/600]
Discriminator Loss: 0.9169, Generator Loss: 1.5193
D(x): 0.7189, D(G(z)): 0.3164
Epoch: [15/20], Batch Num: [113/600]
Discriminator Loss: 0.7704, Generator Loss: 1.7119
D(x): 0.7052, D(G(z)): 0.2387
Epoch: [15/20], Batch Num: [114/600]
Discriminator Loss: 0.8824, Generator Loss: 1.8086
D(x): 0.6842, D(G(z)): 0.2597
Epoch: [15/20], Batch Num: [115/600]
Discriminator Loss: 0.8993, Generator Loss: 1.6191
D(x): 0.6716, D(G(z)): 0.2506
Epoch: [15/20], Batch Num: [116/600]
Discriminator Loss: 0.7562, Generator Loss: 1.5150
D(x): 0.7317, D(G(z)): 0.2615
Epoch: [15/20], Batch Num: [117/600]
Discriminator Loss: 0.8254, Generator Loss: 1.5731
D(x): 0.7532, D(G(z)): 0.3147
Epoch: [15/20], Batch Num: [118/600]
Discriminator Loss: 0.7126, Generator Loss: 1.5691
D(x): 0.7770, D(G(z)): 0.2746
Epoch: [15/20], Batch Num: [119/600]
Discriminator Loss: 0.7405, Generator Loss: 1.6302
D(x): 0.7466, D(G(z)): 0.2577
Epoch: [15/20], Batch Num: [120/600]
Discriminator Loss: 0.6555, Generator Loss: 1.9648
D(x): 0.8137, D(G(z)): 0.2843
Epoch: [15/20], Batch Num: [121/600]
Discriminator Loss: 0.7395, Generator Loss: 1.7944
D(x): 0.7302, D(G(z)): 0.2380
Epoch: [15/20], Batch Num: [122/600]
Discriminator Loss: 0.6803, Generator Loss: 1.9206
D(x): 0.7055, D(G(z)): 0.1775
Epoch: [15/20], Batch Num: [123/600]
Discriminator Loss: 0.6471, Generator Loss: 1.7883
D(x): 0.7625, D(G(z)): 0.2022
Epoch: [15/20], Batch Num: [124/600]
Discriminator Loss: 0.7121, Generator Loss: 1.6564
D(x): 0.7665, D(G(z)): 0.2632
Epoch: [15/20], Batch Num: [125/600]
Discriminator Loss: 0.6039, Generator Loss: 1.6631
D(x): 0.8103, D(G(z)): 0.2500
Epoch: [15/20], Batch Num: [126/600]
Discriminator Loss: 0.6599, Generator Loss: 1.8234
D(x): 0.7954, D(G(z)): 0.2467
Epoch: [15/20], Batch Num: [127/600]
Discriminator Loss: 0.7351, Generator Loss: 2.0999
D(x): 0.8020, D(G(z)): 0.2699
Epoch: [15/20], Batch Num: [128/600]
Discriminator Loss: 0.4164, Generator Loss: 2.0893
D(x): 0.8471, D(G(z)): 0.1621
Epoch: [15/20], Batch Num: [129/600]
Discriminator Loss: 0.6385, Generator Loss: 2.3049
D(x): 0.7791, D(G(z)): 0.2150
Epoch: [15/20], Batch Num: [130/600]
Discriminator Loss: 0.5807, Generator Loss: 2.0614
D(x): 0.7471, D(G(z)): 0.1634
Epoch: [15/20], Batch Num: [131/600]
Discriminator Loss: 0.7281, Generator Loss: 1.9905
D(x): 0.7451, D(G(z)): 0.1997
Epoch: [15/20], Batch Num: [132/600]
Discriminator Loss: 0.6377, Generator Loss: 2.1628
D(x): 0.8168, D(G(z)): 0.2460
Epoch: [15/20], Batch Num: [133/600]
Discriminator Loss: 0.7048, Generator Loss: 2.1112
D(x): 0.8018, D(G(z)): 0.2708
Epoch: [15/20], Batch Num: [134/600]
Discriminator Loss: 0.6729, Generator Loss: 2.2504
D(x): 0.7853, D(G(z)): 0.2299
Epoch: [15/20], Batch Num: [135/600]
Discriminator Loss: 0.6410, Generator Loss: 2.3990
D(x): 0.8174, D(G(z)): 0.2422
Epoch: [15/20], Batch Num: [136/600]
Discriminator Loss: 0.7409, Generator Loss: 2.4693
D(x): 0.7187, D(G(z)): 0.1888
Epoch: [15/20], Batch Num: [137/600]
Discriminator Loss: 0.6841, Generator Loss: 1.9935
D(x): 0.7535, D(G(z)): 0.1779
Epoch: [15/20], Batch Num: [138/600]
Discriminator Loss: 0.9044, Generator Loss: 1.8371
D(x): 0.7279, D(G(z)): 0.2526
Epoch: [15/20], Batch Num: [139/600]
Discriminator Loss: 0.9614, Generator Loss: 1.7436
D(x): 0.7670, D(G(z)): 0.3293
Epoch: [15/20], Batch Num: [140/600]
Discriminator Loss: 0.8173, Generator Loss: 1.8670
D(x): 0.8009, D(G(z)): 0.3251
Epoch: [15/20], Batch Num: [141/600]
Discriminator Loss: 0.9644, Generator Loss: 2.2782
D(x): 0.7254, D(G(z)): 0.2741
Epoch: [15/20], Batch Num: [142/600]
Discriminator Loss: 1.0051, Generator Loss: 2.2507
D(x): 0.6511, D(G(z)): 0.2090
Epoch: [15/20], Batch Num: [143/600]
Discriminator Loss: 1.0967, Generator Loss: 1.7033
D(x): 0.6377, D(G(z)): 0.2368
Epoch: [15/20], Batch Num: [144/600]
Discriminator Loss: 0.9761, Generator Loss: 1.5570
D(x): 0.7493, D(G(z)): 0.3311
Epoch: [15/20], Batch Num: [145/600]
Discriminator Loss: 0.9352, Generator Loss: 1.5277
D(x): 0.7938, D(G(z)): 0.3443
Epoch: [15/20], Batch Num: [146/600]
Discriminator Loss: 0.9671, Generator Loss: 1.6548
D(x): 0.7257, D(G(z)): 0.3250
Epoch: [15/20], Batch Num: [147/600]
Discriminator Loss: 1.1645, Generator Loss: 1.8241
D(x): 0.6926, D(G(z)): 0.3364
Epoch: [15/20], Batch Num: [148/600]
Discriminator Loss: 0.9612, Generator Loss: 1.6866
D(x): 0.6469, D(G(z)): 0.2435
Epoch: [15/20], Batch Num: [149/600]
Discriminator Loss: 1.1037, Generator Loss: 1.3946
D(x): 0.6134, D(G(z)): 0.2703
Epoch: [15/20], Batch Num: [150/600]
Discriminator Loss: 0.9212, Generator Loss: 1.3376
D(x): 0.7149, D(G(z)): 0.3334
Epoch: [15/20], Batch Num: [151/600]
Discriminator Loss: 0.9617, Generator Loss: 1.1481
D(x): 0.7054, D(G(z)): 0.3288
Epoch: [15/20], Batch Num: [152/600]
Discriminator Loss: 0.9975, Generator Loss: 1.4624
D(x): 0.7577, D(G(z)): 0.3776
Epoch: [15/20], Batch Num: [153/600]
Discriminator Loss: 0.9145, Generator Loss: 1.7160
D(x): 0.7194, D(G(z)): 0.3081
Epoch: [15/20], Batch Num: [154/600]
Discriminator Loss: 0.9690, Generator Loss: 1.7118
D(x): 0.6590, D(G(z)): 0.2708
Epoch: [15/20], Batch Num: [155/600]
Discriminator Loss: 0.9870, Generator Loss: 1.8657
D(x): 0.6761, D(G(z)): 0.3113
Epoch: [15/20], Batch Num: [156/600]
Discriminator Loss: 0.9621, Generator Loss: 1.6711
D(x): 0.6281, D(G(z)): 0.2081
Epoch: [15/20], Batch Num: [157/600]
Discriminator Loss: 0.7687, Generator Loss: 1.3983
D(x): 0.7053, D(G(z)): 0.2301
Epoch: [15/20], Batch Num: [158/600]
Discriminator Loss: 0.8088, Generator Loss: 1.3553
D(x): 0.7510, D(G(z)): 0.2880
Epoch: [15/20], Batch Num: [159/600]
Discriminator Loss: 0.9379, Generator Loss: 1.3719
D(x): 0.7310, D(G(z)): 0.3390
Epoch: [15/20], Batch Num: [160/600]
Discriminator Loss: 0.8544, Generator Loss: 1.4777
D(x): 0.8162, D(G(z)): 0.3672
Epoch: [15/20], Batch Num: [161/600]
Discriminator Loss: 0.7284, Generator Loss: 1.6918
D(x): 0.7815, D(G(z)): 0.3051
Epoch: [15/20], Batch Num: [162/600]
Discriminator Loss: 0.6855, Generator Loss: 1.7906
D(x): 0.7643, D(G(z)): 0.2601
Epoch: [15/20], Batch Num: [163/600]
Discriminator Loss: 0.8880, Generator Loss: 1.9464
D(x): 0.6436, D(G(z)): 0.1956
Epoch: [15/20], Batch Num: [164/600]
Discriminator Loss: 0.6911, Generator Loss: 1.9104
D(x): 0.7341, D(G(z)): 0.2098
Epoch: [15/20], Batch Num: [165/600]
Discriminator Loss: 0.6901, Generator Loss: 1.6454
D(x): 0.7382, D(G(z)): 0.2210
Epoch: [15/20], Batch Num: [166/600]
Discriminator Loss: 0.7165, Generator Loss: 1.5623
D(x): 0.7512, D(G(z)): 0.2412
Epoch: [15/20], Batch Num: [167/600]
Discriminator Loss: 0.7027, Generator Loss: 1.4598
D(x): 0.7907, D(G(z)): 0.2751
Epoch: [15/20], Batch Num: [168/600]
Discriminator Loss: 0.5556, Generator Loss: 1.8096
D(x): 0.8690, D(G(z)): 0.2746
Epoch: [15/20], Batch Num: [169/600]
Discriminator Loss: 0.7800, Generator Loss: 1.8703
D(x): 0.8032, D(G(z)): 0.3111
Epoch: [15/20], Batch Num: [170/600]
Discriminator Loss: 0.6601, Generator Loss: 2.0573
D(x): 0.7819, D(G(z)): 0.2457
Epoch: [15/20], Batch Num: [171/600]
Discriminator Loss: 0.5721, Generator Loss: 2.1974
D(x): 0.7867, D(G(z)): 0.1890
Epoch: [15/20], Batch Num: [172/600]
Discriminator Loss: 0.6663, Generator Loss: 2.1260
D(x): 0.7165, D(G(z)): 0.1753
Epoch: [15/20], Batch Num: [173/600]
Discriminator Loss: 0.6297, Generator Loss: 2.1972
D(x): 0.7621, D(G(z)): 0.1894
Epoch: [15/20], Batch Num: [174/600]
Discriminator Loss: 0.6672, Generator Loss: 2.1174
D(x): 0.7602, D(G(z)): 0.2038
Epoch: [15/20], Batch Num: [175/600]
Discriminator Loss: 0.6216, Generator Loss: 1.7270
D(x): 0.7997, D(G(z)): 0.2295
Epoch: [15/20], Batch Num: [176/600]
Discriminator Loss: 0.7028, Generator Loss: 1.8642
D(x): 0.8264, D(G(z)): 0.2721
Epoch: [15/20], Batch Num: [177/600]
Discriminator Loss: 0.5580, Generator Loss: 1.8959
D(x): 0.8343, D(G(z)): 0.2305
Epoch: [15/20], Batch Num: [178/600]
Discriminator Loss: 0.7027, Generator Loss: 2.1197
D(x): 0.7708, D(G(z)): 0.2253
Epoch: [15/20], Batch Num: [179/600]
Discriminator Loss: 0.6146, Generator Loss: 2.2554
D(x): 0.7870, D(G(z)): 0.1862
Epoch: [15/20], Batch Num: [180/600]
Discriminator Loss: 0.6682, Generator Loss: 2.0310
D(x): 0.7819, D(G(z)): 0.2056
Epoch: [15/20], Batch Num: [181/600]
Discriminator Loss: 0.6808, Generator Loss: 2.0066
D(x): 0.7595, D(G(z)): 0.1951
Epoch: [15/20], Batch Num: [182/600]
Discriminator Loss: 0.7797, Generator Loss: 1.9755
D(x): 0.7687, D(G(z)): 0.2565
Epoch: [15/20], Batch Num: [183/600]
Discriminator Loss: 0.8477, Generator Loss: 2.0323
D(x): 0.7831, D(G(z)): 0.2843
Epoch: [15/20], Batch Num: [184/600]
Discriminator Loss: 0.6754, Generator Loss: 2.2255
D(x): 0.7854, D(G(z)): 0.2424
Epoch: [15/20], Batch Num: [185/600]
Discriminator Loss: 0.6503, Generator Loss: 2.1243
D(x): 0.7867, D(G(z)): 0.2096
Epoch: [15/20], Batch Num: [186/600]
Discriminator Loss: 0.7419, Generator Loss: 1.9803
D(x): 0.7577, D(G(z)): 0.2110
Epoch: [15/20], Batch Num: [187/600]
Discriminator Loss: 0.9472, Generator Loss: 1.7631
D(x): 0.7181, D(G(z)): 0.2763
Epoch: [15/20], Batch Num: [188/600]
Discriminator Loss: 0.8780, Generator Loss: 1.5063
D(x): 0.7416, D(G(z)): 0.2580
Epoch: [15/20], Batch Num: [189/600]
Discriminator Loss: 0.9752, Generator Loss: 1.4989
D(x): 0.7074, D(G(z)): 0.2821
Epoch: [15/20], Batch Num: [190/600]
Discriminator Loss: 0.7858, Generator Loss: 1.6895
D(x): 0.7955, D(G(z)): 0.3076
Epoch: [15/20], Batch Num: [191/600]
Discriminator Loss: 1.1580, Generator Loss: 1.7815
D(x): 0.6693, D(G(z)): 0.3231
Epoch: [15/20], Batch Num: [192/600]
Discriminator Loss: 0.9292, Generator Loss: 1.7716
D(x): 0.7145, D(G(z)): 0.2759
Epoch: [15/20], Batch Num: [193/600]
Discriminator Loss: 1.0777, Generator Loss: 1.7885
D(x): 0.6559, D(G(z)): 0.2724
Epoch: [15/20], Batch Num: [194/600]
Discriminator Loss: 0.9894, Generator Loss: 1.4489
D(x): 0.6859, D(G(z)): 0.2902
Epoch: [15/20], Batch Num: [195/600]
Discriminator Loss: 1.0682, Generator Loss: 1.3117
D(x): 0.6678, D(G(z)): 0.3184
Epoch: [15/20], Batch Num: [196/600]
Discriminator Loss: 1.0472, Generator Loss: 1.2762
D(x): 0.7082, D(G(z)): 0.3492
Epoch: [15/20], Batch Num: [197/600]
Discriminator Loss: 0.9030, Generator Loss: 1.4257
D(x): 0.7378, D(G(z)): 0.3479
Epoch: [15/20], Batch Num: [198/600]
Discriminator Loss: 1.0792, Generator Loss: 1.4572
D(x): 0.6719, D(G(z)): 0.3544
Epoch: [15/20], Batch Num: [199/600]
Discriminator Loss: 1.0978, Generator Loss: 1.5625
D(x): 0.6973, D(G(z)): 0.3461
Epoch: 15, Batch Num: [200/600]

Epoch: [15/20], Batch Num: [200/600]
Discriminator Loss: 0.9210, Generator Loss: 1.7866
D(x): 0.6593, D(G(z)): 0.2619
Epoch: [15/20], Batch Num: [201/600]
Discriminator Loss: 0.9937, Generator Loss: 1.5098
D(x): 0.6417, D(G(z)): 0.2721
Epoch: [15/20], Batch Num: [202/600]
Discriminator Loss: 0.9475, Generator Loss: 1.4887
D(x): 0.6668, D(G(z)): 0.2871
Epoch: [15/20], Batch Num: [203/600]
Discriminator Loss: 0.8352, Generator Loss: 1.3432
D(x): 0.7060, D(G(z)): 0.2764
Epoch: [15/20], Batch Num: [204/600]
Discriminator Loss: 0.9786, Generator Loss: 1.4667
D(x): 0.7904, D(G(z)): 0.3876
Epoch: [15/20], Batch Num: [205/600]
Discriminator Loss: 0.8481, Generator Loss: 1.6542
D(x): 0.7844, D(G(z)): 0.3470
Epoch: [15/20], Batch Num: [206/600]
Discriminator Loss: 0.7046, Generator Loss: 1.8696
D(x): 0.7777, D(G(z)): 0.2725
Epoch: [15/20], Batch Num: [207/600]
Discriminator Loss: 0.8470, Generator Loss: 1.9701
D(x): 0.6617, D(G(z)): 0.2091
Epoch: [15/20], Batch Num: [208/600]
Discriminator Loss: 0.7288, Generator Loss: 1.9531
D(x): 0.7178, D(G(z)): 0.2097
Epoch: [15/20], Batch Num: [209/600]
Discriminator Loss: 0.8113, Generator Loss: 1.7121
D(x): 0.7038, D(G(z)): 0.2308
Epoch: [15/20], Batch Num: [210/600]
Discriminator Loss: 0.8530, Generator Loss: 1.7072
D(x): 0.6930, D(G(z)): 0.2645
Epoch: [15/20], Batch Num: [211/600]
Discriminator Loss: 0.7991, Generator Loss: 1.5805
D(x): 0.7236, D(G(z)): 0.2741
Epoch: [15/20], Batch Num: [212/600]
Discriminator Loss: 0.7520, Generator Loss: 1.5367
D(x): 0.7650, D(G(z)): 0.2709
Epoch: [15/20], Batch Num: [213/600]
Discriminator Loss: 0.8266, Generator Loss: 1.5765
D(x): 0.7661, D(G(z)): 0.3260
Epoch: [15/20], Batch Num: [214/600]
Discriminator Loss: 0.7724, Generator Loss: 1.8354
D(x): 0.7961, D(G(z)): 0.3304
Epoch: [15/20], Batch Num: [215/600]
Discriminator Loss: 0.6544, Generator Loss: 2.1445
D(x): 0.7950, D(G(z)): 0.2723
Epoch: [15/20], Batch Num: [216/600]
Discriminator Loss: 0.8079, Generator Loss: 1.9112
D(x): 0.7031, D(G(z)): 0.2232
Epoch: [15/20], Batch Num: [217/600]
Discriminator Loss: 0.7976, Generator Loss: 2.0802
D(x): 0.7004, D(G(z)): 0.2067
Epoch: [15/20], Batch Num: [218/600]
Discriminator Loss: 0.8496, Generator Loss: 1.8504
D(x): 0.6657, D(G(z)): 0.2146
Epoch: [15/20], Batch Num: [219/600]
Discriminator Loss: 0.7731, Generator Loss: 1.4411
D(x): 0.7362, D(G(z)): 0.2500
Epoch: [15/20], Batch Num: [220/600]
Discriminator Loss: 0.7509, Generator Loss: 1.5640
D(x): 0.7910, D(G(z)): 0.3090
Epoch: [15/20], Batch Num: [221/600]
Discriminator Loss: 0.8732, Generator Loss: 1.7715
D(x): 0.7819, D(G(z)): 0.3211
Epoch: [15/20], Batch Num: [222/600]
Discriminator Loss: 0.8226, Generator Loss: 2.0492
D(x): 0.7590, D(G(z)): 0.2875
Epoch: [15/20], Batch Num: [223/600]
Discriminator Loss: 1.0329, Generator Loss: 1.7896
D(x): 0.6653, D(G(z)): 0.2881
Epoch: [15/20], Batch Num: [224/600]
Discriminator Loss: 0.8657, Generator Loss: 2.0939
D(x): 0.6725, D(G(z)): 0.2165
Epoch: [15/20], Batch Num: [225/600]
Discriminator Loss: 1.0613, Generator Loss: 1.7318
D(x): 0.6540, D(G(z)): 0.3004
Epoch: [15/20], Batch Num: [226/600]
Discriminator Loss: 0.9928, Generator Loss: 1.5783
D(x): 0.6641, D(G(z)): 0.2842
Epoch: [15/20], Batch Num: [227/600]
Discriminator Loss: 0.9421, Generator Loss: 1.3431
D(x): 0.7223, D(G(z)): 0.3266
Epoch: [15/20], Batch Num: [228/600]
Discriminator Loss: 0.9135, Generator Loss: 1.4221
D(x): 0.7451, D(G(z)): 0.3179
Epoch: [15/20], Batch Num: [229/600]
Discriminator Loss: 0.9313, Generator Loss: 1.7831
D(x): 0.7415, D(G(z)): 0.3432
Epoch: [15/20], Batch Num: [230/600]
Discriminator Loss: 1.2019, Generator Loss: 2.0871
D(x): 0.6464, D(G(z)): 0.3467
Epoch: [15/20], Batch Num: [231/600]
Discriminator Loss: 1.1688, Generator Loss: 1.8448
D(x): 0.6177, D(G(z)): 0.2900
Epoch: [15/20], Batch Num: [232/600]
Discriminator Loss: 1.0781, Generator Loss: 1.4553
D(x): 0.6265, D(G(z)): 0.2237
Epoch: [15/20], Batch Num: [233/600]
Discriminator Loss: 1.0051, Generator Loss: 1.3184
D(x): 0.6839, D(G(z)): 0.3300
Epoch: [15/20], Batch Num: [234/600]
Discriminator Loss: 1.1063, Generator Loss: 1.0648
D(x): 0.7070, D(G(z)): 0.3893
Epoch: [15/20], Batch Num: [235/600]
Discriminator Loss: 1.2039, Generator Loss: 1.4295
D(x): 0.7412, D(G(z)): 0.4517
Epoch: [15/20], Batch Num: [236/600]
Discriminator Loss: 1.1763, Generator Loss: 1.4806
D(x): 0.6323, D(G(z)): 0.3539
Epoch: [15/20], Batch Num: [237/600]
Discriminator Loss: 1.0675, Generator Loss: 1.6916
D(x): 0.6359, D(G(z)): 0.2888
Epoch: [15/20], Batch Num: [238/600]
Discriminator Loss: 1.0385, Generator Loss: 1.4774
D(x): 0.6274, D(G(z)): 0.2640
Epoch: [15/20], Batch Num: [239/600]
Discriminator Loss: 1.0286, Generator Loss: 1.3837
D(x): 0.6334, D(G(z)): 0.2950
Epoch: [15/20], Batch Num: [240/600]
Discriminator Loss: 1.0150, Generator Loss: 1.2582
D(x): 0.6270, D(G(z)): 0.3034
Epoch: [15/20], Batch Num: [241/600]
Discriminator Loss: 1.0009, Generator Loss: 1.0864
D(x): 0.7077, D(G(z)): 0.3588
Epoch: [15/20], Batch Num: [242/600]
Discriminator Loss: 1.1417, Generator Loss: 1.2818
D(x): 0.7150, D(G(z)): 0.4407
Epoch: [15/20], Batch Num: [243/600]
Discriminator Loss: 1.0419, Generator Loss: 1.2959
D(x): 0.7182, D(G(z)): 0.3814
Epoch: [15/20], Batch Num: [244/600]
Discriminator Loss: 0.9598, Generator Loss: 1.5093
D(x): 0.6608, D(G(z)): 0.2994
Epoch: [15/20], Batch Num: [245/600]
Discriminator Loss: 0.8545, Generator Loss: 1.5638
D(x): 0.6737, D(G(z)): 0.2538
Epoch: [15/20], Batch Num: [246/600]
Discriminator Loss: 0.8033, Generator Loss: 1.6397
D(x): 0.6908, D(G(z)): 0.2707
Epoch: [15/20], Batch Num: [247/600]
Discriminator Loss: 0.7723, Generator Loss: 1.4756
D(x): 0.6976, D(G(z)): 0.2276
Epoch: [15/20], Batch Num: [248/600]
Discriminator Loss: 0.7315, Generator Loss: 1.5173
D(x): 0.7393, D(G(z)): 0.2751
Epoch: [15/20], Batch Num: [249/600]
Discriminator Loss: 0.9076, Generator Loss: 1.4386
D(x): 0.7061, D(G(z)): 0.3316
Epoch: [15/20], Batch Num: [250/600]
Discriminator Loss: 0.7491, Generator Loss: 1.4515
D(x): 0.7882, D(G(z)): 0.3081
Epoch: [15/20], Batch Num: [251/600]
Discriminator Loss: 0.7629, Generator Loss: 1.7166
D(x): 0.7534, D(G(z)): 0.3108
Epoch: [15/20], Batch Num: [252/600]
Discriminator Loss: 0.7447, Generator Loss: 1.6332
D(x): 0.7526, D(G(z)): 0.2774
Epoch: [15/20], Batch Num: [253/600]
Discriminator Loss: 0.6323, Generator Loss: 1.8927
D(x): 0.7951, D(G(z)): 0.2595
Epoch: [15/20], Batch Num: [254/600]
Discriminator Loss: 0.6856, Generator Loss: 1.8965
D(x): 0.7694, D(G(z)): 0.2429
Epoch: [15/20], Batch Num: [255/600]
Discriminator Loss: 0.7099, Generator Loss: 1.7882
D(x): 0.7456, D(G(z)): 0.2279
Epoch: [15/20], Batch Num: [256/600]
Discriminator Loss: 0.6502, Generator Loss: 1.9563
D(x): 0.7462, D(G(z)): 0.2029
Epoch: [15/20], Batch Num: [257/600]
Discriminator Loss: 0.6967, Generator Loss: 2.0556
D(x): 0.7513, D(G(z)): 0.2188
Epoch: [15/20], Batch Num: [258/600]
Discriminator Loss: 0.5942, Generator Loss: 1.7659
D(x): 0.7752, D(G(z)): 0.2108
Epoch: [15/20], Batch Num: [259/600]
Discriminator Loss: 0.5822, Generator Loss: 1.9522
D(x): 0.8294, D(G(z)): 0.2396
Epoch: [15/20], Batch Num: [260/600]
Discriminator Loss: 0.5545, Generator Loss: 1.9385
D(x): 0.8737, D(G(z)): 0.2595
Epoch: [15/20], Batch Num: [261/600]
Discriminator Loss: 0.5373, Generator Loss: 2.3546
D(x): 0.8451, D(G(z)): 0.2266
Epoch: [15/20], Batch Num: [262/600]
Discriminator Loss: 0.4778, Generator Loss: 2.4547
D(x): 0.8169, D(G(z)): 0.1592
Epoch: [15/20], Batch Num: [263/600]
Discriminator Loss: 0.5975, Generator Loss: 2.5254
D(x): 0.7523, D(G(z)): 0.1545
Epoch: [15/20], Batch Num: [264/600]
Discriminator Loss: 0.4961, Generator Loss: 2.4535
D(x): 0.7891, D(G(z)): 0.1389
Epoch: [15/20], Batch Num: [265/600]
Discriminator Loss: 0.6038, Generator Loss: 2.3363
D(x): 0.7915, D(G(z)): 0.1688
Epoch: [15/20], Batch Num: [266/600]
Discriminator Loss: 0.6520, Generator Loss: 1.8290
D(x): 0.7969, D(G(z)): 0.2023
Epoch: [15/20], Batch Num: [267/600]
Discriminator Loss: 0.5152, Generator Loss: 2.3087
D(x): 0.8943, D(G(z)): 0.2539
Epoch: [15/20], Batch Num: [268/600]
Discriminator Loss: 0.6596, Generator Loss: 2.6565
D(x): 0.8397, D(G(z)): 0.2343
Epoch: [15/20], Batch Num: [269/600]
Discriminator Loss: 0.5911, Generator Loss: 2.8943
D(x): 0.8120, D(G(z)): 0.2040
Epoch: [15/20], Batch Num: [270/600]
Discriminator Loss: 0.6476, Generator Loss: 2.7717
D(x): 0.7478, D(G(z)): 0.1508
Epoch: [15/20], Batch Num: [271/600]
Discriminator Loss: 0.7162, Generator Loss: 2.4728
D(x): 0.7103, D(G(z)): 0.1333
Epoch: [15/20], Batch Num: [272/600]
Discriminator Loss: 0.6210, Generator Loss: 2.2290
D(x): 0.7776, D(G(z)): 0.1885
Epoch: [15/20], Batch Num: [273/600]
Discriminator Loss: 0.5721, Generator Loss: 2.0225
D(x): 0.8405, D(G(z)): 0.2183
Epoch: [15/20], Batch Num: [274/600]
Discriminator Loss: 0.6833, Generator Loss: 2.0229
D(x): 0.8807, D(G(z)): 0.3007
Epoch: [15/20], Batch Num: [275/600]
Discriminator Loss: 0.7257, Generator Loss: 2.7634
D(x): 0.8538, D(G(z)): 0.2802
Epoch: [15/20], Batch Num: [276/600]
Discriminator Loss: 0.7686, Generator Loss: 2.6403
D(x): 0.7458, D(G(z)): 0.1791
Epoch: [15/20], Batch Num: [277/600]
Discriminator Loss: 0.8400, Generator Loss: 2.7408
D(x): 0.7156, D(G(z)): 0.1945
Epoch: [15/20], Batch Num: [278/600]
Discriminator Loss: 0.8660, Generator Loss: 2.3831
D(x): 0.7167, D(G(z)): 0.1860
Epoch: [15/20], Batch Num: [279/600]
Discriminator Loss: 0.7343, Generator Loss: 1.6009
D(x): 0.7028, D(G(z)): 0.1458
Epoch: [15/20], Batch Num: [280/600]
Discriminator Loss: 0.9119, Generator Loss: 1.4186
D(x): 0.7399, D(G(z)): 0.2813
Epoch: [15/20], Batch Num: [281/600]
Discriminator Loss: 0.9820, Generator Loss: 1.7398
D(x): 0.8427, D(G(z)): 0.4085
Epoch: [15/20], Batch Num: [282/600]
Discriminator Loss: 0.9335, Generator Loss: 2.0351
D(x): 0.7981, D(G(z)): 0.3590
Epoch: [15/20], Batch Num: [283/600]
Discriminator Loss: 0.9605, Generator Loss: 2.3786
D(x): 0.6618, D(G(z)): 0.2012
Epoch: [15/20], Batch Num: [284/600]
Discriminator Loss: 1.2510, Generator Loss: 2.2362
D(x): 0.5567, D(G(z)): 0.1906
Epoch: [15/20], Batch Num: [285/600]
Discriminator Loss: 1.0655, Generator Loss: 1.7731
D(x): 0.6543, D(G(z)): 0.2488
Epoch: [15/20], Batch Num: [286/600]
Discriminator Loss: 0.8954, Generator Loss: 1.3846
D(x): 0.7361, D(G(z)): 0.2881
Epoch: [15/20], Batch Num: [287/600]
Discriminator Loss: 1.1111, Generator Loss: 1.6121
D(x): 0.7488, D(G(z)): 0.4040
Epoch: [15/20], Batch Num: [288/600]
Discriminator Loss: 1.1189, Generator Loss: 1.7407
D(x): 0.7474, D(G(z)): 0.3725
Epoch: [15/20], Batch Num: [289/600]
Discriminator Loss: 1.0119, Generator Loss: 1.6039
D(x): 0.6618, D(G(z)): 0.2867
Epoch: [15/20], Batch Num: [290/600]
Discriminator Loss: 1.1308, Generator Loss: 1.9367
D(x): 0.6494, D(G(z)): 0.3084
Epoch: [15/20], Batch Num: [291/600]
Discriminator Loss: 0.9788, Generator Loss: 1.8396
D(x): 0.6460, D(G(z)): 0.2600
Epoch: [15/20], Batch Num: [292/600]
Discriminator Loss: 1.2374, Generator Loss: 1.3805
D(x): 0.5817, D(G(z)): 0.3006
Epoch: [15/20], Batch Num: [293/600]
Discriminator Loss: 1.2229, Generator Loss: 1.2476
D(x): 0.6548, D(G(z)): 0.3412
Epoch: [15/20], Batch Num: [294/600]
Discriminator Loss: 1.0491, Generator Loss: 1.0849
D(x): 0.7117, D(G(z)): 0.3499
Epoch: [15/20], Batch Num: [295/600]
Discriminator Loss: 1.1014, Generator Loss: 1.2546
D(x): 0.7575, D(G(z)): 0.4298
Epoch: [15/20], Batch Num: [296/600]
Discriminator Loss: 1.1671, Generator Loss: 1.2996
D(x): 0.6823, D(G(z)): 0.3809
Epoch: [15/20], Batch Num: [297/600]
Discriminator Loss: 0.9395, Generator Loss: 1.5209
D(x): 0.6801, D(G(z)): 0.3005
Epoch: [15/20], Batch Num: [298/600]
Discriminator Loss: 1.0673, Generator Loss: 1.5694
D(x): 0.6343, D(G(z)): 0.3078
Epoch: [15/20], Batch Num: [299/600]
Discriminator Loss: 1.1092, Generator Loss: 1.5302
D(x): 0.6066, D(G(z)): 0.3050
Epoch: 15, Batch Num: [300/600]

Epoch: [15/20], Batch Num: [300/600]
Discriminator Loss: 1.0951, Generator Loss: 1.4532
D(x): 0.6351, D(G(z)): 0.2924
Epoch: [15/20], Batch Num: [301/600]
Discriminator Loss: 0.9565, Generator Loss: 1.4771
D(x): 0.6964, D(G(z)): 0.3250
Epoch: [15/20], Batch Num: [302/600]
Discriminator Loss: 0.9988, Generator Loss: 1.4316
D(x): 0.6965, D(G(z)): 0.3316
Epoch: [15/20], Batch Num: [303/600]
Discriminator Loss: 1.0086, Generator Loss: 1.4014
D(x): 0.7018, D(G(z)): 0.3672
Epoch: [15/20], Batch Num: [304/600]
Discriminator Loss: 1.0202, Generator Loss: 1.2887
D(x): 0.7064, D(G(z)): 0.3605
Epoch: [15/20], Batch Num: [305/600]
Discriminator Loss: 0.8416, Generator Loss: 1.4316
D(x): 0.7087, D(G(z)): 0.3069
Epoch: [15/20], Batch Num: [306/600]
Discriminator Loss: 0.8408, Generator Loss: 1.3909
D(x): 0.7380, D(G(z)): 0.3100
Epoch: [15/20], Batch Num: [307/600]
Discriminator Loss: 0.8305, Generator Loss: 1.4143
D(x): 0.7370, D(G(z)): 0.3137
Epoch: [15/20], Batch Num: [308/600]
Discriminator Loss: 0.7343, Generator Loss: 1.4731
D(x): 0.7571, D(G(z)): 0.2817
Epoch: [15/20], Batch Num: [309/600]
Discriminator Loss: 0.8554, Generator Loss: 1.4734
D(x): 0.7053, D(G(z)): 0.3062
Epoch: [15/20], Batch Num: [310/600]
Discriminator Loss: 0.7762, Generator Loss: 1.5495
D(x): 0.7214, D(G(z)): 0.2756
Epoch: [15/20], Batch Num: [311/600]
Discriminator Loss: 0.7762, Generator Loss: 1.4312
D(x): 0.7190, D(G(z)): 0.2732
Epoch: [15/20], Batch Num: [312/600]
Discriminator Loss: 0.7042, Generator Loss: 1.6035
D(x): 0.7412, D(G(z)): 0.2471
Epoch: [15/20], Batch Num: [313/600]
Discriminator Loss: 0.8036, Generator Loss: 1.4714
D(x): 0.7387, D(G(z)): 0.2987
Epoch: [15/20], Batch Num: [314/600]
Discriminator Loss: 0.8038, Generator Loss: 1.3909
D(x): 0.7651, D(G(z)): 0.3265
Epoch: [15/20], Batch Num: [315/600]
Discriminator Loss: 0.6557, Generator Loss: 1.5232
D(x): 0.8010, D(G(z)): 0.2814
Epoch: [15/20], Batch Num: [316/600]
Discriminator Loss: 0.7906, Generator Loss: 1.4416
D(x): 0.7547, D(G(z)): 0.2913
Epoch: [15/20], Batch Num: [317/600]
Discriminator Loss: 0.7380, Generator Loss: 1.6443
D(x): 0.7725, D(G(z)): 0.3033
Epoch: [15/20], Batch Num: [318/600]
Discriminator Loss: 0.7047, Generator Loss: 1.5917
D(x): 0.7605, D(G(z)): 0.2720
Epoch: [15/20], Batch Num: [319/600]
Discriminator Loss: 0.6269, Generator Loss: 1.7683
D(x): 0.7628, D(G(z)): 0.2284
Epoch: [15/20], Batch Num: [320/600]
Discriminator Loss: 0.7646, Generator Loss: 1.5679
D(x): 0.6972, D(G(z)): 0.2447
Epoch: [15/20], Batch Num: [321/600]
Discriminator Loss: 0.6934, Generator Loss: 1.7643
D(x): 0.7777, D(G(z)): 0.2544
Epoch: [15/20], Batch Num: [322/600]
Discriminator Loss: 0.7625, Generator Loss: 1.4691
D(x): 0.7623, D(G(z)): 0.2876
Epoch: [15/20], Batch Num: [323/600]
Discriminator Loss: 0.7303, Generator Loss: 1.5008
D(x): 0.7989, D(G(z)): 0.3060
Epoch: [15/20], Batch Num: [324/600]
Discriminator Loss: 0.7813, Generator Loss: 1.7724
D(x): 0.7990, D(G(z)): 0.3190
Epoch: [15/20], Batch Num: [325/600]
Discriminator Loss: 0.7892, Generator Loss: 1.8582
D(x): 0.7808, D(G(z)): 0.3118
Epoch: [15/20], Batch Num: [326/600]
Discriminator Loss: 0.7203, Generator Loss: 2.0320
D(x): 0.7718, D(G(z)): 0.2631
Epoch: [15/20], Batch Num: [327/600]
Discriminator Loss: 0.7635, Generator Loss: 2.1571
D(x): 0.7121, D(G(z)): 0.2203
Epoch: [15/20], Batch Num: [328/600]
Discriminator Loss: 0.5806, Generator Loss: 1.9752
D(x): 0.7668, D(G(z)): 0.1743
Epoch: [15/20], Batch Num: [329/600]
Discriminator Loss: 0.6564, Generator Loss: 1.9609
D(x): 0.7666, D(G(z)): 0.2177
Epoch: [15/20], Batch Num: [330/600]
Discriminator Loss: 0.7132, Generator Loss: 1.7421
D(x): 0.7185, D(G(z)): 0.2119
Epoch: [15/20], Batch Num: [331/600]
Discriminator Loss: 0.7898, Generator Loss: 1.5082
D(x): 0.7733, D(G(z)): 0.2974
Epoch: [15/20], Batch Num: [332/600]
Discriminator Loss: 0.6688, Generator Loss: 1.5924
D(x): 0.7945, D(G(z)): 0.2610
Epoch: [15/20], Batch Num: [333/600]
Discriminator Loss: 0.7307, Generator Loss: 1.9667
D(x): 0.8116, D(G(z)): 0.3219
Epoch: [15/20], Batch Num: [334/600]
Discriminator Loss: 1.0030, Generator Loss: 2.1211
D(x): 0.7514, D(G(z)): 0.3176
Epoch: [15/20], Batch Num: [335/600]
Discriminator Loss: 0.8095, Generator Loss: 2.2621
D(x): 0.7231, D(G(z)): 0.2391
Epoch: [15/20], Batch Num: [336/600]
Discriminator Loss: 1.1030, Generator Loss: 1.9874
D(x): 0.5773, D(G(z)): 0.2163
Epoch: [15/20], Batch Num: [337/600]
Discriminator Loss: 0.8088, Generator Loss: 1.8236
D(x): 0.6856, D(G(z)): 0.2237
Epoch: [15/20], Batch Num: [338/600]
Discriminator Loss: 1.0968, Generator Loss: 1.4926
D(x): 0.7694, D(G(z)): 0.3797
Epoch: [15/20], Batch Num: [339/600]
Discriminator Loss: 1.0370, Generator Loss: 1.6664
D(x): 0.7388, D(G(z)): 0.3628
Epoch: [15/20], Batch Num: [340/600]
Discriminator Loss: 0.9094, Generator Loss: 2.0307
D(x): 0.7694, D(G(z)): 0.3373
Epoch: [15/20], Batch Num: [341/600]
Discriminator Loss: 1.0347, Generator Loss: 2.1393
D(x): 0.7091, D(G(z)): 0.2903
Epoch: [15/20], Batch Num: [342/600]
Discriminator Loss: 0.9010, Generator Loss: 2.0902
D(x): 0.6289, D(G(z)): 0.1763
Epoch: [15/20], Batch Num: [343/600]
Discriminator Loss: 0.9915, Generator Loss: 1.7846
D(x): 0.6390, D(G(z)): 0.2370
Epoch: [15/20], Batch Num: [344/600]
Discriminator Loss: 1.0853, Generator Loss: 1.3616
D(x): 0.6236, D(G(z)): 0.2581
Epoch: [15/20], Batch Num: [345/600]
Discriminator Loss: 0.9943, Generator Loss: 1.2146
D(x): 0.7605, D(G(z)): 0.3655
Epoch: [15/20], Batch Num: [346/600]
Discriminator Loss: 0.9966, Generator Loss: 1.3065
D(x): 0.8273, D(G(z)): 0.4252
Epoch: [15/20], Batch Num: [347/600]
Discriminator Loss: 1.0635, Generator Loss: 1.6418
D(x): 0.7590, D(G(z)): 0.3796
Epoch: [15/20], Batch Num: [348/600]
Discriminator Loss: 0.8744, Generator Loss: 1.9097
D(x): 0.6929, D(G(z)): 0.2433
Epoch: [15/20], Batch Num: [349/600]
Discriminator Loss: 1.1123, Generator Loss: 1.8364
D(x): 0.5722, D(G(z)): 0.2328
Epoch: [15/20], Batch Num: [350/600]
Discriminator Loss: 1.0763, Generator Loss: 1.5722
D(x): 0.6046, D(G(z)): 0.2617
Epoch: [15/20], Batch Num: [351/600]
Discriminator Loss: 0.9981, Generator Loss: 1.3716
D(x): 0.6470, D(G(z)): 0.2949
Epoch: [15/20], Batch Num: [352/600]
Discriminator Loss: 1.1068, Generator Loss: 1.2420
D(x): 0.6884, D(G(z)): 0.3507
Epoch: [15/20], Batch Num: [353/600]
Discriminator Loss: 0.9591, Generator Loss: 1.3338
D(x): 0.7538, D(G(z)): 0.3741
Epoch: [15/20], Batch Num: [354/600]
Discriminator Loss: 0.8779, Generator Loss: 1.3092
D(x): 0.7430, D(G(z)): 0.3441
Epoch: [15/20], Batch Num: [355/600]
Discriminator Loss: 0.8663, Generator Loss: 1.4163
D(x): 0.7102, D(G(z)): 0.2981
Epoch: [15/20], Batch Num: [356/600]
Discriminator Loss: 0.9040, Generator Loss: 1.5413
D(x): 0.7140, D(G(z)): 0.3132
Epoch: [15/20], Batch Num: [357/600]
Discriminator Loss: 0.8734, Generator Loss: 1.5049
D(x): 0.7081, D(G(z)): 0.3092
Epoch: [15/20], Batch Num: [358/600]
Discriminator Loss: 0.9562, Generator Loss: 1.5718
D(x): 0.6524, D(G(z)): 0.2950
Epoch: [15/20], Batch Num: [359/600]
Discriminator Loss: 0.9090, Generator Loss: 1.4509
D(x): 0.6938, D(G(z)): 0.2919
Epoch: [15/20], Batch Num: [360/600]
Discriminator Loss: 1.0053, Generator Loss: 1.4607
D(x): 0.6459, D(G(z)): 0.3036
Epoch: [15/20], Batch Num: [361/600]
Discriminator Loss: 0.9296, Generator Loss: 1.3673
D(x): 0.6549, D(G(z)): 0.2624
Epoch: [15/20], Batch Num: [362/600]
Discriminator Loss: 0.7601, Generator Loss: 1.3866
D(x): 0.7525, D(G(z)): 0.2944
Epoch: [15/20], Batch Num: [363/600]
Discriminator Loss: 0.9097, Generator Loss: 1.4042
D(x): 0.7130, D(G(z)): 0.3325
Epoch: [15/20], Batch Num: [364/600]
Discriminator Loss: 0.9167, Generator Loss: 1.4171
D(x): 0.7087, D(G(z)): 0.3337
Epoch: [15/20], Batch Num: [365/600]
Discriminator Loss: 0.7271, Generator Loss: 1.4522
D(x): 0.7793, D(G(z)): 0.3101
Epoch: [15/20], Batch Num: [366/600]
Discriminator Loss: 0.7294, Generator Loss: 1.8015
D(x): 0.7489, D(G(z)): 0.2656
Epoch: [15/20], Batch Num: [367/600]
Discriminator Loss: 0.8736, Generator Loss: 1.7809
D(x): 0.6591, D(G(z)): 0.2497
Epoch: [15/20], Batch Num: [368/600]
Discriminator Loss: 0.7437, Generator Loss: 1.5567
D(x): 0.7104, D(G(z)): 0.2327
Epoch: [15/20], Batch Num: [369/600]
Discriminator Loss: 0.7267, Generator Loss: 1.6529
D(x): 0.7707, D(G(z)): 0.2873
Epoch: [15/20], Batch Num: [370/600]
Discriminator Loss: 0.7716, Generator Loss: 1.6828
D(x): 0.7218, D(G(z)): 0.2652
Epoch: [15/20], Batch Num: [371/600]
Discriminator Loss: 0.8181, Generator Loss: 1.5135
D(x): 0.7426, D(G(z)): 0.2983
Epoch: [15/20], Batch Num: [372/600]
Discriminator Loss: 0.5864, Generator Loss: 1.6161
D(x): 0.8259, D(G(z)): 0.2677
Epoch: [15/20], Batch Num: [373/600]
Discriminator Loss: 0.6734, Generator Loss: 1.9255
D(x): 0.7900, D(G(z)): 0.2493
Epoch: [15/20], Batch Num: [374/600]
Discriminator Loss: 0.7669, Generator Loss: 2.2580
D(x): 0.7663, D(G(z)): 0.2775
Epoch: [15/20], Batch Num: [375/600]
Discriminator Loss: 0.6924, Generator Loss: 2.0716
D(x): 0.7007, D(G(z)): 0.1846
Epoch: [15/20], Batch Num: [376/600]
Discriminator Loss: 0.5879, Generator Loss: 1.9847
D(x): 0.7600, D(G(z)): 0.1847
Epoch: [15/20], Batch Num: [377/600]
Discriminator Loss: 0.6837, Generator Loss: 1.9372
D(x): 0.7969, D(G(z)): 0.2479
Epoch: [15/20], Batch Num: [378/600]
Discriminator Loss: 0.7631, Generator Loss: 1.9447
D(x): 0.7756, D(G(z)): 0.2769
Epoch: [15/20], Batch Num: [379/600]
Discriminator Loss: 0.6473, Generator Loss: 2.0043
D(x): 0.7623, D(G(z)): 0.2221
Epoch: [15/20], Batch Num: [380/600]
Discriminator Loss: 0.5689, Generator Loss: 1.9682
D(x): 0.8240, D(G(z)): 0.2345
Epoch: [15/20], Batch Num: [381/600]
Discriminator Loss: 0.6383, Generator Loss: 1.9880
D(x): 0.8014, D(G(z)): 0.2348
Epoch: [15/20], Batch Num: [382/600]
Discriminator Loss: 0.7012, Generator Loss: 2.1215
D(x): 0.7493, D(G(z)): 0.2248
Epoch: [15/20], Batch Num: [383/600]
Discriminator Loss: 0.7595, Generator Loss: 1.7370
D(x): 0.6946, D(G(z)): 0.1733
Epoch: [15/20], Batch Num: [384/600]
Discriminator Loss: 0.7281, Generator Loss: 1.8382
D(x): 0.7659, D(G(z)): 0.2573
Epoch: [15/20], Batch Num: [385/600]
Discriminator Loss: 0.7323, Generator Loss: 1.6481
D(x): 0.7913, D(G(z)): 0.2950
Epoch: [15/20], Batch Num: [386/600]
Discriminator Loss: 0.7720, Generator Loss: 2.0332
D(x): 0.8078, D(G(z)): 0.3002
Epoch: [15/20], Batch Num: [387/600]
Discriminator Loss: 0.7060, Generator Loss: 2.1143
D(x): 0.7778, D(G(z)): 0.2441
Epoch: [15/20], Batch Num: [388/600]
Discriminator Loss: 0.7705, Generator Loss: 1.9631
D(x): 0.7256, D(G(z)): 0.2138
Epoch: [15/20], Batch Num: [389/600]
Discriminator Loss: 0.7713, Generator Loss: 2.0120
D(x): 0.7292, D(G(z)): 0.1962
Epoch: [15/20], Batch Num: [390/600]
Discriminator Loss: 0.8765, Generator Loss: 1.8868
D(x): 0.6989, D(G(z)): 0.2535
Epoch: [15/20], Batch Num: [391/600]
Discriminator Loss: 0.7765, Generator Loss: 1.7836
D(x): 0.7928, D(G(z)): 0.2630
Epoch: [15/20], Batch Num: [392/600]
Discriminator Loss: 0.8600, Generator Loss: 1.5789
D(x): 0.7549, D(G(z)): 0.2857
Epoch: [15/20], Batch Num: [393/600]
Discriminator Loss: 0.8441, Generator Loss: 1.7552
D(x): 0.7601, D(G(z)): 0.2651
Epoch: [15/20], Batch Num: [394/600]
Discriminator Loss: 0.9191, Generator Loss: 1.8343
D(x): 0.7113, D(G(z)): 0.2484
Epoch: [15/20], Batch Num: [395/600]
Discriminator Loss: 1.0830, Generator Loss: 1.7063
D(x): 0.6583, D(G(z)): 0.2927
Epoch: [15/20], Batch Num: [396/600]
Discriminator Loss: 1.0965, Generator Loss: 1.4902
D(x): 0.6858, D(G(z)): 0.3099
Epoch: [15/20], Batch Num: [397/600]
Discriminator Loss: 1.0893, Generator Loss: 1.7671
D(x): 0.6862, D(G(z)): 0.3331
Epoch: [15/20], Batch Num: [398/600]
Discriminator Loss: 0.9601, Generator Loss: 1.9444
D(x): 0.7044, D(G(z)): 0.2905
Epoch: [15/20], Batch Num: [399/600]
Discriminator Loss: 1.1622, Generator Loss: 1.7272
D(x): 0.6109, D(G(z)): 0.2485
Epoch: 15, Batch Num: [400/600]

Epoch: [15/20], Batch Num: [400/600]
Discriminator Loss: 1.0236, Generator Loss: 1.6671
D(x): 0.7187, D(G(z)): 0.3200
Epoch: [15/20], Batch Num: [401/600]
Discriminator Loss: 1.0208, Generator Loss: 1.3930
D(x): 0.6648, D(G(z)): 0.2816
Epoch: [15/20], Batch Num: [402/600]
Discriminator Loss: 1.1876, Generator Loss: 1.4406
D(x): 0.6830, D(G(z)): 0.3843
Epoch: [15/20], Batch Num: [403/600]
Discriminator Loss: 1.2642, Generator Loss: 1.3840
D(x): 0.6229, D(G(z)): 0.3650
Epoch: [15/20], Batch Num: [404/600]
Discriminator Loss: 1.1212, Generator Loss: 1.2861
D(x): 0.6694, D(G(z)): 0.3482
Epoch: [15/20], Batch Num: [405/600]
Discriminator Loss: 1.0714, Generator Loss: 1.5512
D(x): 0.6787, D(G(z)): 0.3429
Epoch: [15/20], Batch Num: [406/600]
Discriminator Loss: 1.0537, Generator Loss: 1.6829
D(x): 0.6562, D(G(z)): 0.2916
Epoch: [15/20], Batch Num: [407/600]
Discriminator Loss: 1.1323, Generator Loss: 1.5983
D(x): 0.5907, D(G(z)): 0.2921
Epoch: [15/20], Batch Num: [408/600]
Discriminator Loss: 1.1094, Generator Loss: 1.5069
D(x): 0.6125, D(G(z)): 0.2901
Epoch: [15/20], Batch Num: [409/600]
Discriminator Loss: 0.9624, Generator Loss: 1.5501
D(x): 0.6907, D(G(z)): 0.3188
Epoch: [15/20], Batch Num: [410/600]
Discriminator Loss: 0.9702, Generator Loss: 1.4932
D(x): 0.7542, D(G(z)): 0.3733
Epoch: [15/20], Batch Num: [411/600]
Discriminator Loss: 0.9846, Generator Loss: 1.5751
D(x): 0.7063, D(G(z)): 0.3333
Epoch: [15/20], Batch Num: [412/600]
Discriminator Loss: 0.7932, Generator Loss: 1.5766
D(x): 0.7568, D(G(z)): 0.2884
Epoch: [15/20], Batch Num: [413/600]
Discriminator Loss: 0.8559, Generator Loss: 1.7481
D(x): 0.7125, D(G(z)): 0.2776
Epoch: [15/20], Batch Num: [414/600]
Discriminator Loss: 0.8107, Generator Loss: 1.6847
D(x): 0.6832, D(G(z)): 0.2223
Epoch: [15/20], Batch Num: [415/600]
Discriminator Loss: 0.7590, Generator Loss: 1.7177
D(x): 0.7346, D(G(z)): 0.2493
Epoch: [15/20], Batch Num: [416/600]
Discriminator Loss: 0.8364, Generator Loss: 1.6877
D(x): 0.7323, D(G(z)): 0.3117
Epoch: [15/20], Batch Num: [417/600]
Discriminator Loss: 0.6049, Generator Loss: 1.5787
D(x): 0.7694, D(G(z)): 0.2116
Epoch: [15/20], Batch Num: [418/600]
Discriminator Loss: 0.6703, Generator Loss: 1.5992
D(x): 0.8181, D(G(z)): 0.2899
Epoch: [15/20], Batch Num: [419/600]
Discriminator Loss: 0.6299, Generator Loss: 1.7733
D(x): 0.8290, D(G(z)): 0.2663
Epoch: [15/20], Batch Num: [420/600]
Discriminator Loss: 0.5896, Generator Loss: 1.9116
D(x): 0.8305, D(G(z)): 0.2664
Epoch: [15/20], Batch Num: [421/600]
Discriminator Loss: 0.6768, Generator Loss: 2.1435
D(x): 0.7737, D(G(z)): 0.2420
Epoch: [15/20], Batch Num: [422/600]
Discriminator Loss: 0.5926, Generator Loss: 2.3957
D(x): 0.7777, D(G(z)): 0.1971
Epoch: [15/20], Batch Num: [423/600]
Discriminator Loss: 0.4617, Generator Loss: 2.3643
D(x): 0.8179, D(G(z)): 0.1477
Epoch: [15/20], Batch Num: [424/600]
Discriminator Loss: 0.5350, Generator Loss: 2.5152
D(x): 0.7770, D(G(z)): 0.1549
Epoch: [15/20], Batch Num: [425/600]
Discriminator Loss: 0.5330, Generator Loss: 2.2823
D(x): 0.7927, D(G(z)): 0.1735
Epoch: [15/20], Batch Num: [426/600]
Discriminator Loss: 0.4301, Generator Loss: 2.0937
D(x): 0.8269, D(G(z)): 0.1349
Epoch: [15/20], Batch Num: [427/600]
Discriminator Loss: 0.5602, Generator Loss: 2.1533
D(x): 0.8236, D(G(z)): 0.2227
Epoch: [15/20], Batch Num: [428/600]
Discriminator Loss: 0.5542, Generator Loss: 1.9375
D(x): 0.8689, D(G(z)): 0.2340
Epoch: [15/20], Batch Num: [429/600]
Discriminator Loss: 0.5210, Generator Loss: 2.1688
D(x): 0.8648, D(G(z)): 0.2324
Epoch: [15/20], Batch Num: [430/600]
Discriminator Loss: 0.4180, Generator Loss: 2.3245
D(x): 0.8699, D(G(z)): 0.1851
Epoch: [15/20], Batch Num: [431/600]
Discriminator Loss: 0.4484, Generator Loss: 2.4053
D(x): 0.8765, D(G(z)): 0.1734
Epoch: [15/20], Batch Num: [432/600]
Discriminator Loss: 0.4536, Generator Loss: 2.8455
D(x): 0.8464, D(G(z)): 0.1663
Epoch: [15/20], Batch Num: [433/600]
Discriminator Loss: 0.4619, Generator Loss: 2.8553
D(x): 0.8602, D(G(z)): 0.1595
Epoch: [15/20], Batch Num: [434/600]
Discriminator Loss: 0.4822, Generator Loss: 2.6183
D(x): 0.7970, D(G(z)): 0.1212
Epoch: [15/20], Batch Num: [435/600]
Discriminator Loss: 0.5659, Generator Loss: 2.2550
D(x): 0.7835, D(G(z)): 0.1650
Epoch: [15/20], Batch Num: [436/600]
Discriminator Loss: 0.4706, Generator Loss: 2.1810
D(x): 0.8379, D(G(z)): 0.1801
Epoch: [15/20], Batch Num: [437/600]
Discriminator Loss: 0.5249, Generator Loss: 2.1625
D(x): 0.8725, D(G(z)): 0.2180
Epoch: [15/20], Batch Num: [438/600]
Discriminator Loss: 0.7932, Generator Loss: 2.1646
D(x): 0.8450, D(G(z)): 0.2950
Epoch: [15/20], Batch Num: [439/600]
Discriminator Loss: 0.6837, Generator Loss: 2.3580
D(x): 0.8452, D(G(z)): 0.2547
Epoch: [15/20], Batch Num: [440/600]
Discriminator Loss: 0.7806, Generator Loss: 2.5366
D(x): 0.7775, D(G(z)): 0.2250
Epoch: [15/20], Batch Num: [441/600]
Discriminator Loss: 0.7540, Generator Loss: 2.3317
D(x): 0.7159, D(G(z)): 0.1597
Epoch: [15/20], Batch Num: [442/600]
Discriminator Loss: 0.8054, Generator Loss: 2.2902
D(x): 0.7574, D(G(z)): 0.2247
Epoch: [15/20], Batch Num: [443/600]
Discriminator Loss: 0.8626, Generator Loss: 1.9948
D(x): 0.7373, D(G(z)): 0.2590
Epoch: [15/20], Batch Num: [444/600]
Discriminator Loss: 0.8120, Generator Loss: 1.9153
D(x): 0.7977, D(G(z)): 0.3042
Epoch: [15/20], Batch Num: [445/600]
Discriminator Loss: 0.9961, Generator Loss: 2.0372
D(x): 0.7900, D(G(z)): 0.3126
Epoch: [15/20], Batch Num: [446/600]
Discriminator Loss: 0.9120, Generator Loss: 1.9457
D(x): 0.7321, D(G(z)): 0.2630
Epoch: [15/20], Batch Num: [447/600]
Discriminator Loss: 1.0338, Generator Loss: 2.0525
D(x): 0.7020, D(G(z)): 0.2825
Epoch: [15/20], Batch Num: [448/600]
Discriminator Loss: 0.7515, Generator Loss: 1.9907
D(x): 0.7459, D(G(z)): 0.2175
Epoch: [15/20], Batch Num: [449/600]
Discriminator Loss: 1.1724, Generator Loss: 1.9659
D(x): 0.6769, D(G(z)): 0.3162
Epoch: [15/20], Batch Num: [450/600]
Discriminator Loss: 1.1257, Generator Loss: 1.6473
D(x): 0.6390, D(G(z)): 0.2769
Epoch: [15/20], Batch Num: [451/600]
Discriminator Loss: 1.0954, Generator Loss: 1.3474
D(x): 0.7280, D(G(z)): 0.3655
Epoch: [15/20], Batch Num: [452/600]
Discriminator Loss: 1.0352, Generator Loss: 1.4931
D(x): 0.7672, D(G(z)): 0.3677
Epoch: [15/20], Batch Num: [453/600]
Discriminator Loss: 0.9952, Generator Loss: 1.6789
D(x): 0.7107, D(G(z)): 0.3223
Epoch: [15/20], Batch Num: [454/600]
Discriminator Loss: 1.1144, Generator Loss: 1.8669
D(x): 0.6485, D(G(z)): 0.3265
Epoch: [15/20], Batch Num: [455/600]
Discriminator Loss: 1.0359, Generator Loss: 1.7267
D(x): 0.6932, D(G(z)): 0.2927
Epoch: [15/20], Batch Num: [456/600]
Discriminator Loss: 0.9184, Generator Loss: 1.4306
D(x): 0.6907, D(G(z)): 0.2648
Epoch: [15/20], Batch Num: [457/600]
Discriminator Loss: 1.0641, Generator Loss: 1.4160
D(x): 0.6607, D(G(z)): 0.3152
Epoch: [15/20], Batch Num: [458/600]
Discriminator Loss: 0.7564, Generator Loss: 1.5073
D(x): 0.7716, D(G(z)): 0.3086
Epoch: [15/20], Batch Num: [459/600]
Discriminator Loss: 0.7645, Generator Loss: 1.5523
D(x): 0.7624, D(G(z)): 0.2862
Epoch: [15/20], Batch Num: [460/600]
Discriminator Loss: 0.8917, Generator Loss: 1.6604
D(x): 0.7340, D(G(z)): 0.3209
Epoch: [15/20], Batch Num: [461/600]
Discriminator Loss: 0.8158, Generator Loss: 1.8066
D(x): 0.7343, D(G(z)): 0.2709
Epoch: [15/20], Batch Num: [462/600]
Discriminator Loss: 0.6681, Generator Loss: 1.8851
D(x): 0.7771, D(G(z)): 0.2614
Epoch: [15/20], Batch Num: [463/600]
Discriminator Loss: 0.8039, Generator Loss: 1.9224
D(x): 0.7280, D(G(z)): 0.2753
Epoch: [15/20], Batch Num: [464/600]
Discriminator Loss: 0.7762, Generator Loss: 2.1334
D(x): 0.7261, D(G(z)): 0.2466
Epoch: [15/20], Batch Num: [465/600]
Discriminator Loss: 0.7459, Generator Loss: 1.9453
D(x): 0.7034, D(G(z)): 0.2051
Epoch: [15/20], Batch Num: [466/600]
Discriminator Loss: 0.7670, Generator Loss: 1.9470
D(x): 0.7020, D(G(z)): 0.2009
Epoch: [15/20], Batch Num: [467/600]
Discriminator Loss: 0.6759, Generator Loss: 1.7913
D(x): 0.7643, D(G(z)): 0.2308
Epoch: [15/20], Batch Num: [468/600]
Discriminator Loss: 0.7003, Generator Loss: 1.8103
D(x): 0.7787, D(G(z)): 0.2434
Epoch: [15/20], Batch Num: [469/600]
Discriminator Loss: 0.6728, Generator Loss: 1.9566
D(x): 0.7692, D(G(z)): 0.2477
Epoch: [15/20], Batch Num: [470/600]
Discriminator Loss: 0.6119, Generator Loss: 1.9262
D(x): 0.8095, D(G(z)): 0.2476
Epoch: [15/20], Batch Num: [471/600]
Discriminator Loss: 0.5754, Generator Loss: 1.8936
D(x): 0.8310, D(G(z)): 0.2458
Epoch: [15/20], Batch Num: [472/600]
Discriminator Loss: 0.6885, Generator Loss: 2.1969
D(x): 0.8097, D(G(z)): 0.2716
Epoch: [15/20], Batch Num: [473/600]
Discriminator Loss: 0.7267, Generator Loss: 2.5107
D(x): 0.7432, D(G(z)): 0.2333
Epoch: [15/20], Batch Num: [474/600]
Discriminator Loss: 0.6070, Generator Loss: 2.6331
D(x): 0.7583, D(G(z)): 0.1649
Epoch: [15/20], Batch Num: [475/600]
Discriminator Loss: 0.6900, Generator Loss: 2.3240
D(x): 0.7497, D(G(z)): 0.1946
Epoch: [15/20], Batch Num: [476/600]
Discriminator Loss: 0.7065, Generator Loss: 2.0922
D(x): 0.7197, D(G(z)): 0.1733
Epoch: [15/20], Batch Num: [477/600]
Discriminator Loss: 0.7378, Generator Loss: 1.6941
D(x): 0.7693, D(G(z)): 0.2572
Epoch: [15/20], Batch Num: [478/600]
Discriminator Loss: 0.7287, Generator Loss: 1.7758
D(x): 0.8063, D(G(z)): 0.2623
Epoch: [15/20], Batch Num: [479/600]
Discriminator Loss: 0.7617, Generator Loss: 1.8694
D(x): 0.8185, D(G(z)): 0.2868
Epoch: [15/20], Batch Num: [480/600]
Discriminator Loss: 0.8460, Generator Loss: 2.0710
D(x): 0.7400, D(G(z)): 0.2607
Epoch: [15/20], Batch Num: [481/600]
Discriminator Loss: 0.7714, Generator Loss: 2.1160
D(x): 0.7311, D(G(z)): 0.2048
Epoch: [15/20], Batch Num: [482/600]
Discriminator Loss: 0.6215, Generator Loss: 2.0448
D(x): 0.7746, D(G(z)): 0.2157
Epoch: [15/20], Batch Num: [483/600]
Discriminator Loss: 0.9088, Generator Loss: 1.9164
D(x): 0.7125, D(G(z)): 0.2622
Epoch: [15/20], Batch Num: [484/600]
Discriminator Loss: 0.7498, Generator Loss: 1.6501
D(x): 0.7540, D(G(z)): 0.2571
Epoch: [15/20], Batch Num: [485/600]
Discriminator Loss: 0.7840, Generator Loss: 1.7833
D(x): 0.7688, D(G(z)): 0.2598
Epoch: [15/20], Batch Num: [486/600]
Discriminator Loss: 0.9449, Generator Loss: 1.7361
D(x): 0.7054, D(G(z)): 0.2792
Epoch: [15/20], Batch Num: [487/600]
Discriminator Loss: 0.9879, Generator Loss: 1.9127
D(x): 0.7335, D(G(z)): 0.3246
Epoch: [15/20], Batch Num: [488/600]
Discriminator Loss: 1.0290, Generator Loss: 1.8074
D(x): 0.6761, D(G(z)): 0.2621
Epoch: [15/20], Batch Num: [489/600]
Discriminator Loss: 0.9156, Generator Loss: 1.7835
D(x): 0.7224, D(G(z)): 0.2749
Epoch: [15/20], Batch Num: [490/600]
Discriminator Loss: 0.9211, Generator Loss: 1.7998
D(x): 0.7265, D(G(z)): 0.2614
Epoch: [15/20], Batch Num: [491/600]
Discriminator Loss: 0.9657, Generator Loss: 1.5052
D(x): 0.6891, D(G(z)): 0.2802
Epoch: [15/20], Batch Num: [492/600]
Discriminator Loss: 0.9185, Generator Loss: 1.6293
D(x): 0.7384, D(G(z)): 0.3264
Epoch: [15/20], Batch Num: [493/600]
Discriminator Loss: 1.1418, Generator Loss: 1.5283
D(x): 0.6650, D(G(z)): 0.3288
Epoch: [15/20], Batch Num: [494/600]
Discriminator Loss: 1.0549, Generator Loss: 1.4948
D(x): 0.6607, D(G(z)): 0.2936
Epoch: [15/20], Batch Num: [495/600]
Discriminator Loss: 1.0900, Generator Loss: 1.5174
D(x): 0.6874, D(G(z)): 0.3527
Epoch: [15/20], Batch Num: [496/600]
Discriminator Loss: 1.0891, Generator Loss: 1.5134
D(x): 0.6883, D(G(z)): 0.3392
Epoch: [15/20], Batch Num: [497/600]
Discriminator Loss: 0.9255, Generator Loss: 1.5428
D(x): 0.7315, D(G(z)): 0.3170
Epoch: [15/20], Batch Num: [498/600]
Discriminator Loss: 1.1326, Generator Loss: 1.5696
D(x): 0.6627, D(G(z)): 0.3568
Epoch: [15/20], Batch Num: [499/600]
Discriminator Loss: 1.0284, Generator Loss: 1.5757
D(x): 0.6471, D(G(z)): 0.2760
Epoch: 15, Batch Num: [500/600]

Epoch: [15/20], Batch Num: [500/600]
Discriminator Loss: 1.0734, Generator Loss: 1.4677
D(x): 0.6642, D(G(z)): 0.3260
Epoch: [15/20], Batch Num: [501/600]
Discriminator Loss: 1.2118, Generator Loss: 1.5425
D(x): 0.5877, D(G(z)): 0.3257
Epoch: [15/20], Batch Num: [502/600]
Discriminator Loss: 1.1192, Generator Loss: 1.3554
D(x): 0.5988, D(G(z)): 0.2940
Epoch: [15/20], Batch Num: [503/600]
Discriminator Loss: 1.1406, Generator Loss: 1.3245
D(x): 0.6730, D(G(z)): 0.3690
Epoch: [15/20], Batch Num: [504/600]
Discriminator Loss: 1.1435, Generator Loss: 1.2960
D(x): 0.7284, D(G(z)): 0.4526
Epoch: [15/20], Batch Num: [505/600]
Discriminator Loss: 0.9028, Generator Loss: 1.4149
D(x): 0.7310, D(G(z)): 0.3327
Epoch: [15/20], Batch Num: [506/600]
Discriminator Loss: 0.9461, Generator Loss: 1.6353
D(x): 0.6764, D(G(z)): 0.3050
Epoch: [15/20], Batch Num: [507/600]
Discriminator Loss: 0.9675, Generator Loss: 1.6724
D(x): 0.6448, D(G(z)): 0.2601
Epoch: [15/20], Batch Num: [508/600]
Discriminator Loss: 0.8662, Generator Loss: 1.6181
D(x): 0.6542, D(G(z)): 0.2487
Epoch: [15/20], Batch Num: [509/600]
Discriminator Loss: 0.8354, Generator Loss: 1.5631
D(x): 0.6793, D(G(z)): 0.2402
Epoch: [15/20], Batch Num: [510/600]
Discriminator Loss: 0.8924, Generator Loss: 1.2911
D(x): 0.6761, D(G(z)): 0.2891
Epoch: [15/20], Batch Num: [511/600]
Discriminator Loss: 0.9124, Generator Loss: 1.1128
D(x): 0.7209, D(G(z)): 0.3392
Epoch: [15/20], Batch Num: [512/600]
Discriminator Loss: 0.8909, Generator Loss: 1.2085
D(x): 0.7483, D(G(z)): 0.3525
Epoch: [15/20], Batch Num: [513/600]
Discriminator Loss: 0.9634, Generator Loss: 1.3271
D(x): 0.7334, D(G(z)): 0.3765
Epoch: [15/20], Batch Num: [514/600]
Discriminator Loss: 0.8969, Generator Loss: 1.5358
D(x): 0.7223, D(G(z)): 0.3193
Epoch: [15/20], Batch Num: [515/600]
Discriminator Loss: 0.8458, Generator Loss: 1.8458
D(x): 0.7223, D(G(z)): 0.2928
Epoch: [15/20], Batch Num: [516/600]
Discriminator Loss: 0.9501, Generator Loss: 1.9772
D(x): 0.6573, D(G(z)): 0.2546
Epoch: [15/20], Batch Num: [517/600]
Discriminator Loss: 0.8118, Generator Loss: 1.8484
D(x): 0.6690, D(G(z)): 0.2073
Epoch: [15/20], Batch Num: [518/600]
Discriminator Loss: 0.8228, Generator Loss: 1.8344
D(x): 0.6731, D(G(z)): 0.2096
Epoch: [15/20], Batch Num: [519/600]
Discriminator Loss: 0.8519, Generator Loss: 1.5539
D(x): 0.6683, D(G(z)): 0.2274
Epoch: [15/20], Batch Num: [520/600]
Discriminator Loss: 0.8040, Generator Loss: 1.4840
D(x): 0.7383, D(G(z)): 0.2965
Epoch: [15/20], Batch Num: [521/600]
Discriminator Loss: 0.8484, Generator Loss: 1.3448
D(x): 0.7507, D(G(z)): 0.3235
Epoch: [15/20], Batch Num: [522/600]
Discriminator Loss: 0.8247, Generator Loss: 1.3876
D(x): 0.7804, D(G(z)): 0.3352
Epoch: [15/20], Batch Num: [523/600]
Discriminator Loss: 0.6595, Generator Loss: 1.6667
D(x): 0.8159, D(G(z)): 0.2933
Epoch: [15/20], Batch Num: [524/600]
Discriminator Loss: 0.7451, Generator Loss: 1.7196
D(x): 0.7810, D(G(z)): 0.2768
Epoch: [15/20], Batch Num: [525/600]
Discriminator Loss: 0.8681, Generator Loss: 2.0663
D(x): 0.6967, D(G(z)): 0.2723
Epoch: [15/20], Batch Num: [526/600]
Discriminator Loss: 0.7108, Generator Loss: 1.9447
D(x): 0.7033, D(G(z)): 0.1955
Epoch: [15/20], Batch Num: [527/600]
Discriminator Loss: 0.7750, Generator Loss: 1.9436
D(x): 0.7026, D(G(z)): 0.2337
Epoch: [15/20], Batch Num: [528/600]
Discriminator Loss: 0.6236, Generator Loss: 1.7981
D(x): 0.7765, D(G(z)): 0.2289
Epoch: [15/20], Batch Num: [529/600]
Discriminator Loss: 0.6380, Generator Loss: 1.8183
D(x): 0.7946, D(G(z)): 0.2501
Epoch: [15/20], Batch Num: [530/600]
Discriminator Loss: 0.8288, Generator Loss: 1.6657
D(x): 0.7575, D(G(z)): 0.2947
Epoch: [15/20], Batch Num: [531/600]
Discriminator Loss: 0.6406, Generator Loss: 1.8013
D(x): 0.7816, D(G(z)): 0.2338
Epoch: [15/20], Batch Num: [532/600]
Discriminator Loss: 0.6256, Generator Loss: 1.7395
D(x): 0.8062, D(G(z)): 0.2349
Epoch: [15/20], Batch Num: [533/600]
Discriminator Loss: 0.8480, Generator Loss: 1.9768
D(x): 0.6916, D(G(z)): 0.2361
Epoch: [15/20], Batch Num: [534/600]
Discriminator Loss: 0.6584, Generator Loss: 1.8322
D(x): 0.7823, D(G(z)): 0.2382
Epoch: [15/20], Batch Num: [535/600]
Discriminator Loss: 0.8054, Generator Loss: 1.9998
D(x): 0.7651, D(G(z)): 0.3002
Epoch: [15/20], Batch Num: [536/600]
Discriminator Loss: 0.7509, Generator Loss: 2.0646
D(x): 0.7665, D(G(z)): 0.2457
Epoch: [15/20], Batch Num: [537/600]
Discriminator Loss: 0.7478, Generator Loss: 1.9291
D(x): 0.7259, D(G(z)): 0.2135
Epoch: [15/20], Batch Num: [538/600]
Discriminator Loss: 0.7783, Generator Loss: 1.8410
D(x): 0.7479, D(G(z)): 0.2351
Epoch: [15/20], Batch Num: [539/600]
Discriminator Loss: 0.9175, Generator Loss: 1.6170
D(x): 0.7242, D(G(z)): 0.2724
Epoch: [15/20], Batch Num: [540/600]
Discriminator Loss: 0.8664, Generator Loss: 1.7671
D(x): 0.7529, D(G(z)): 0.3007
Epoch: [15/20], Batch Num: [541/600]
Discriminator Loss: 0.9283, Generator Loss: 1.7874
D(x): 0.7193, D(G(z)): 0.2806
Epoch: [15/20], Batch Num: [542/600]
Discriminator Loss: 0.8834, Generator Loss: 1.6126
D(x): 0.7020, D(G(z)): 0.2639
Epoch: [15/20], Batch Num: [543/600]
Discriminator Loss: 0.8426, Generator Loss: 1.5479
D(x): 0.7443, D(G(z)): 0.2763
Epoch: [15/20], Batch Num: [544/600]
Discriminator Loss: 0.9867, Generator Loss: 1.5587
D(x): 0.7181, D(G(z)): 0.3433
Epoch: [15/20], Batch Num: [545/600]
Discriminator Loss: 0.9789, Generator Loss: 1.5968
D(x): 0.7101, D(G(z)): 0.3380
Epoch: [15/20], Batch Num: [546/600]
Discriminator Loss: 0.9061, Generator Loss: 1.4732
D(x): 0.7126, D(G(z)): 0.3134
Epoch: [15/20], Batch Num: [547/600]
Discriminator Loss: 0.8961, Generator Loss: 1.5391
D(x): 0.7227, D(G(z)): 0.3041
Epoch: [15/20], Batch Num: [548/600]
Discriminator Loss: 1.0295, Generator Loss: 1.6215
D(x): 0.6737, D(G(z)): 0.3219
Epoch: [15/20], Batch Num: [549/600]
Discriminator Loss: 1.0434, Generator Loss: 1.4793
D(x): 0.7071, D(G(z)): 0.3333
Epoch: [15/20], Batch Num: [550/600]
Discriminator Loss: 1.1526, Generator Loss: 1.7043
D(x): 0.6566, D(G(z)): 0.3048
Epoch: [15/20], Batch Num: [551/600]
Discriminator Loss: 1.2544, Generator Loss: 1.3070
D(x): 0.6042, D(G(z)): 0.3316
Epoch: [15/20], Batch Num: [552/600]
Discriminator Loss: 0.9697, Generator Loss: 1.3684
D(x): 0.7529, D(G(z)): 0.3442
Epoch: [15/20], Batch Num: [553/600]
Discriminator Loss: 0.9702, Generator Loss: 1.6374
D(x): 0.7551, D(G(z)): 0.3946
Epoch: [15/20], Batch Num: [554/600]
Discriminator Loss: 0.9690, Generator Loss: 1.8710
D(x): 0.6753, D(G(z)): 0.2915
Epoch: [15/20], Batch Num: [555/600]
Discriminator Loss: 1.0436, Generator Loss: 1.7412
D(x): 0.6438, D(G(z)): 0.3044
Epoch: [15/20], Batch Num: [556/600]
Discriminator Loss: 0.9687, Generator Loss: 1.4567
D(x): 0.6843, D(G(z)): 0.3068
Epoch: [15/20], Batch Num: [557/600]
Discriminator Loss: 1.0422, Generator Loss: 1.5472
D(x): 0.6226, D(G(z)): 0.2818
Epoch: [15/20], Batch Num: [558/600]
Discriminator Loss: 0.9837, Generator Loss: 1.4180
D(x): 0.6993, D(G(z)): 0.3332
Epoch: [15/20], Batch Num: [559/600]
Discriminator Loss: 0.8091, Generator Loss: 1.4677
D(x): 0.7408, D(G(z)): 0.3057
Epoch: [15/20], Batch Num: [560/600]
Discriminator Loss: 0.8726, Generator Loss: 1.5884
D(x): 0.7391, D(G(z)): 0.3267
Epoch: [15/20], Batch Num: [561/600]
Discriminator Loss: 0.8067, Generator Loss: 1.8695
D(x): 0.7359, D(G(z)): 0.3016
Epoch: [15/20], Batch Num: [562/600]
Discriminator Loss: 0.6904, Generator Loss: 1.6938
D(x): 0.7376, D(G(z)): 0.2361
Epoch: [15/20], Batch Num: [563/600]
Discriminator Loss: 0.8158, Generator Loss: 1.7298
D(x): 0.7427, D(G(z)): 0.2950
Epoch: [15/20], Batch Num: [564/600]
Discriminator Loss: 0.7543, Generator Loss: 1.7489
D(x): 0.7113, D(G(z)): 0.2215
Epoch: [15/20], Batch Num: [565/600]
Discriminator Loss: 0.6664, Generator Loss: 1.8573
D(x): 0.7619, D(G(z)): 0.2368
Epoch: [15/20], Batch Num: [566/600]
Discriminator Loss: 0.5914, Generator Loss: 1.9491
D(x): 0.7976, D(G(z)): 0.2190
Epoch: [15/20], Batch Num: [567/600]
Discriminator Loss: 0.7631, Generator Loss: 1.8666
D(x): 0.7339, D(G(z)): 0.2517
Epoch: [15/20], Batch Num: [568/600]
Discriminator Loss: 0.5801, Generator Loss: 2.0095
D(x): 0.7846, D(G(z)): 0.2006
Epoch: [15/20], Batch Num: [569/600]
Discriminator Loss: 0.6941, Generator Loss: 1.9060
D(x): 0.7630, D(G(z)): 0.2547
Epoch: [15/20], Batch Num: [570/600]
Discriminator Loss: 0.5263, Generator Loss: 2.0226
D(x): 0.8175, D(G(z)): 0.2142
Epoch: [15/20], Batch Num: [571/600]
Discriminator Loss: 0.5911, Generator Loss: 2.1240
D(x): 0.8163, D(G(z)): 0.2296
Epoch: [15/20], Batch Num: [572/600]
Discriminator Loss: 0.5955, Generator Loss: 2.3257
D(x): 0.7975, D(G(z)): 0.2159
Epoch: [15/20], Batch Num: [573/600]
Discriminator Loss: 0.5738, Generator Loss: 2.4085
D(x): 0.7826, D(G(z)): 0.1746
Epoch: [15/20], Batch Num: [574/600]
Discriminator Loss: 0.4987, Generator Loss: 2.3601
D(x): 0.8229, D(G(z)): 0.1891
Epoch: [15/20], Batch Num: [575/600]
Discriminator Loss: 0.5759, Generator Loss: 2.3876
D(x): 0.7897, D(G(z)): 0.1823
Epoch: [15/20], Batch Num: [576/600]
Discriminator Loss: 0.5546, Generator Loss: 2.4063
D(x): 0.8215, D(G(z)): 0.1868
Epoch: [15/20], Batch Num: [577/600]
Discriminator Loss: 0.5058, Generator Loss: 2.3080
D(x): 0.8391, D(G(z)): 0.2137
Epoch: [15/20], Batch Num: [578/600]
Discriminator Loss: 0.5020, Generator Loss: 2.4492
D(x): 0.8137, D(G(z)): 0.1748
Epoch: [15/20], Batch Num: [579/600]
Discriminator Loss: 0.5862, Generator Loss: 2.4417
D(x): 0.8094, D(G(z)): 0.2019
Epoch: [15/20], Batch Num: [580/600]
Discriminator Loss: 0.5340, Generator Loss: 2.6853
D(x): 0.8643, D(G(z)): 0.1891
Epoch: [15/20], Batch Num: [581/600]
Discriminator Loss: 0.5376, Generator Loss: 2.7773
D(x): 0.7978, D(G(z)): 0.1614
Epoch: [15/20], Batch Num: [582/600]
Discriminator Loss: 0.6313, Generator Loss: 2.6468
D(x): 0.7756, D(G(z)): 0.1791
Epoch: [15/20], Batch Num: [583/600]
Discriminator Loss: 0.6307, Generator Loss: 2.4112
D(x): 0.7695, D(G(z)): 0.1939
Epoch: [15/20], Batch Num: [584/600]
Discriminator Loss: 0.6278, Generator Loss: 1.9664
D(x): 0.8097, D(G(z)): 0.2117
Epoch: [15/20], Batch Num: [585/600]
Discriminator Loss: 0.7226, Generator Loss: 2.1087
D(x): 0.8172, D(G(z)): 0.2536
Epoch: [15/20], Batch Num: [586/600]
Discriminator Loss: 0.7930, Generator Loss: 2.2525
D(x): 0.7639, D(G(z)): 0.2476
Epoch: [15/20], Batch Num: [587/600]
Discriminator Loss: 0.7426, Generator Loss: 2.2693
D(x): 0.7709, D(G(z)): 0.2084
Epoch: [15/20], Batch Num: [588/600]
Discriminator Loss: 0.8189, Generator Loss: 2.3661
D(x): 0.7461, D(G(z)): 0.2139
Epoch: [15/20], Batch Num: [589/600]
Discriminator Loss: 0.9192, Generator Loss: 1.9702
D(x): 0.7246, D(G(z)): 0.2462
Epoch: [15/20], Batch Num: [590/600]
Discriminator Loss: 0.8527, Generator Loss: 1.8840
D(x): 0.7263, D(G(z)): 0.2386
Epoch: [15/20], Batch Num: [591/600]
Discriminator Loss: 1.1613, Generator Loss: 1.5961
D(x): 0.7328, D(G(z)): 0.3760
Epoch: [15/20], Batch Num: [592/600]
Discriminator Loss: 1.2954, Generator Loss: 2.1453
D(x): 0.7037, D(G(z)): 0.3868
Epoch: [15/20], Batch Num: [593/600]
Discriminator Loss: 1.2850, Generator Loss: 1.7994
D(x): 0.6301, D(G(z)): 0.2945
Epoch: [15/20], Batch Num: [594/600]
Discriminator Loss: 1.1263, Generator Loss: 1.5836
D(x): 0.6527, D(G(z)): 0.2657
Epoch: [15/20], Batch Num: [595/600]
Discriminator Loss: 1.1519, Generator Loss: 1.2904
D(x): 0.6537, D(G(z)): 0.3159
Epoch: [15/20], Batch Num: [596/600]
Discriminator Loss: 1.2844, Generator Loss: 1.2757
D(x): 0.7043, D(G(z)): 0.3722
Epoch: [15/20], Batch Num: [597/600]
Discriminator Loss: 1.4917, Generator Loss: 1.3184
D(x): 0.6355, D(G(z)): 0.4322
Epoch: [15/20], Batch Num: [598/600]
Discriminator Loss: 1.3096, Generator Loss: 1.4058
D(x): 0.6626, D(G(z)): 0.3973
Epoch: [15/20], Batch Num: [599/600]
Discriminator Loss: 1.2052, Generator Loss: 1.4323
D(x): 0.6903, D(G(z)): 0.3988
Epoch: 16, Batch Num: [0/600]

Epoch: [16/20], Batch Num: [0/600]
Discriminator Loss: 1.0507, Generator Loss: 1.6809
D(x): 0.6374, D(G(z)): 0.2923
Epoch: [16/20], Batch Num: [1/600]
Discriminator Loss: 1.2109, Generator Loss: 1.6784
D(x): 0.5746, D(G(z)): 0.2949
Epoch: [16/20], Batch Num: [2/600]
Discriminator Loss: 1.2743, Generator Loss: 1.6692
D(x): 0.6092, D(G(z)): 0.3640
Epoch: [16/20], Batch Num: [3/600]
Discriminator Loss: 1.2621, Generator Loss: 1.2603
D(x): 0.6679, D(G(z)): 0.4101
Epoch: [16/20], Batch Num: [4/600]
Discriminator Loss: 1.0997, Generator Loss: 1.3030
D(x): 0.6715, D(G(z)): 0.3521
Epoch: [16/20], Batch Num: [5/600]
Discriminator Loss: 1.1297, Generator Loss: 1.3889
D(x): 0.6825, D(G(z)): 0.4079
Epoch: [16/20], Batch Num: [6/600]
Discriminator Loss: 1.0092, Generator Loss: 1.4833
D(x): 0.7151, D(G(z)): 0.3646
Epoch: [16/20], Batch Num: [7/600]
Discriminator Loss: 1.0845, Generator Loss: 1.7108
D(x): 0.6246, D(G(z)): 0.3187
Epoch: [16/20], Batch Num: [8/600]
Discriminator Loss: 0.8313, Generator Loss: 1.7378
D(x): 0.6940, D(G(z)): 0.2631
Epoch: [16/20], Batch Num: [9/600]
Discriminator Loss: 0.8619, Generator Loss: 1.5494
D(x): 0.6986, D(G(z)): 0.2889
Epoch: [16/20], Batch Num: [10/600]
Discriminator Loss: 0.9533, Generator Loss: 1.5829
D(x): 0.6347, D(G(z)): 0.2640
Epoch: [16/20], Batch Num: [11/600]
Discriminator Loss: 0.9028, Generator Loss: 1.3392
D(x): 0.6564, D(G(z)): 0.2807
Epoch: [16/20], Batch Num: [12/600]
Discriminator Loss: 0.7802, Generator Loss: 1.3085
D(x): 0.7351, D(G(z)): 0.2843
Epoch: [16/20], Batch Num: [13/600]
Discriminator Loss: 0.8093, Generator Loss: 1.5068
D(x): 0.7789, D(G(z)): 0.3470
Epoch: [16/20], Batch Num: [14/600]
Discriminator Loss: 0.6819, Generator Loss: 1.5550
D(x): 0.7749, D(G(z)): 0.2877
Epoch: [16/20], Batch Num: [15/600]
Discriminator Loss: 0.7292, Generator Loss: 1.6486
D(x): 0.7577, D(G(z)): 0.2728
Epoch: [16/20], Batch Num: [16/600]
Discriminator Loss: 0.7433, Generator Loss: 1.8061
D(x): 0.7299, D(G(z)): 0.2645
Epoch: [16/20], Batch Num: [17/600]
Discriminator Loss: 0.8036, Generator Loss: 1.7578
D(x): 0.7248, D(G(z)): 0.2727
Epoch: [16/20], Batch Num: [18/600]
Discriminator Loss: 0.5708, Generator Loss: 1.9400
D(x): 0.7965, D(G(z)): 0.2072
Epoch: [16/20], Batch Num: [19/600]
Discriminator Loss: 0.6119, Generator Loss: 1.9151
D(x): 0.7523, D(G(z)): 0.2003
Epoch: [16/20], Batch Num: [20/600]
Discriminator Loss: 0.6247, Generator Loss: 2.0327
D(x): 0.7597, D(G(z)): 0.2162
Epoch: [16/20], Batch Num: [21/600]
Discriminator Loss: 0.6685, Generator Loss: 1.6837
D(x): 0.7495, D(G(z)): 0.2172
Epoch: [16/20], Batch Num: [22/600]
Discriminator Loss: 0.5623, Generator Loss: 1.8353
D(x): 0.8147, D(G(z)): 0.2337
Epoch: [16/20], Batch Num: [23/600]
Discriminator Loss: 0.5479, Generator Loss: 1.8482
D(x): 0.8168, D(G(z)): 0.2337
Epoch: [16/20], Batch Num: [24/600]
Discriminator Loss: 0.6461, Generator Loss: 1.8011
D(x): 0.8144, D(G(z)): 0.2717
Epoch: [16/20], Batch Num: [25/600]
Discriminator Loss: 0.5139, Generator Loss: 2.0347
D(x): 0.8282, D(G(z)): 0.2186
Epoch: [16/20], Batch Num: [26/600]
Discriminator Loss: 0.6583, Generator Loss: 2.0840
D(x): 0.7868, D(G(z)): 0.2263
Epoch: [16/20], Batch Num: [27/600]
Discriminator Loss: 0.7522, Generator Loss: 2.0403
D(x): 0.7728, D(G(z)): 0.2397
Epoch: [16/20], Batch Num: [28/600]
Discriminator Loss: 0.6464, Generator Loss: 2.1861
D(x): 0.7911, D(G(z)): 0.2178
Epoch: [16/20], Batch Num: [29/600]
Discriminator Loss: 0.7212, Generator Loss: 2.2017
D(x): 0.7288, D(G(z)): 0.2204
Epoch: [16/20], Batch Num: [30/600]
Discriminator Loss: 0.6577, Generator Loss: 2.0883
D(x): 0.7867, D(G(z)): 0.2188
Epoch: [16/20], Batch Num: [31/600]
Discriminator Loss: 0.7820, Generator Loss: 1.9089
D(x): 0.7811, D(G(z)): 0.2729
Epoch: [16/20], Batch Num: [32/600]
Discriminator Loss: 0.8817, Generator Loss: 1.9609
D(x): 0.7114, D(G(z)): 0.2402
Epoch: [16/20], Batch Num: [33/600]
Discriminator Loss: 0.8913, Generator Loss: 1.7937
D(x): 0.7662, D(G(z)): 0.3068
Epoch: [16/20], Batch Num: [34/600]
Discriminator Loss: 0.8234, Generator Loss: 2.0633
D(x): 0.7744, D(G(z)): 0.2778
Epoch: [16/20], Batch Num: [35/600]
Discriminator Loss: 0.8617, Generator Loss: 1.8632
D(x): 0.7194, D(G(z)): 0.2529
Epoch: [16/20], Batch Num: [36/600]
Discriminator Loss: 0.9474, Generator Loss: 1.7413
D(x): 0.7166, D(G(z)): 0.2887
Epoch: [16/20], Batch Num: [37/600]
Discriminator Loss: 1.0135, Generator Loss: 1.8502
D(x): 0.7109, D(G(z)): 0.3232
Epoch: [16/20], Batch Num: [38/600]
Discriminator Loss: 1.0944, Generator Loss: 1.4281
D(x): 0.6685, D(G(z)): 0.2866
Epoch: [16/20], Batch Num: [39/600]
Discriminator Loss: 1.2761, Generator Loss: 1.5269
D(x): 0.6817, D(G(z)): 0.3505
Epoch: [16/20], Batch Num: [40/600]
Discriminator Loss: 1.1980, Generator Loss: 1.4286
D(x): 0.6660, D(G(z)): 0.3679
Epoch: [16/20], Batch Num: [41/600]
Discriminator Loss: 1.1828, Generator Loss: 1.4861
D(x): 0.6344, D(G(z)): 0.3400
Epoch: [16/20], Batch Num: [42/600]
Discriminator Loss: 1.1569, Generator Loss: 1.3476
D(x): 0.6018, D(G(z)): 0.2851
Epoch: [16/20], Batch Num: [43/600]
Discriminator Loss: 1.4764, Generator Loss: 1.1570
D(x): 0.5721, D(G(z)): 0.3831
Epoch: [16/20], Batch Num: [44/600]
Discriminator Loss: 1.2624, Generator Loss: 1.1676
D(x): 0.7105, D(G(z)): 0.4365
Epoch: [16/20], Batch Num: [45/600]
Discriminator Loss: 1.2633, Generator Loss: 1.3461
D(x): 0.6657, D(G(z)): 0.4055
Epoch: [16/20], Batch Num: [46/600]
Discriminator Loss: 1.2269, Generator Loss: 1.4425
D(x): 0.6647, D(G(z)): 0.3602
Epoch: [16/20], Batch Num: [47/600]
Discriminator Loss: 1.1057, Generator Loss: 1.3641
D(x): 0.5936, D(G(z)): 0.2719
Epoch: [16/20], Batch Num: [48/600]
Discriminator Loss: 1.3882, Generator Loss: 1.3668
D(x): 0.5202, D(G(z)): 0.3339
Epoch: [16/20], Batch Num: [49/600]
Discriminator Loss: 1.0612, Generator Loss: 1.1361
D(x): 0.6563, D(G(z)): 0.3459
Epoch: [16/20], Batch Num: [50/600]
Discriminator Loss: 1.0907, Generator Loss: 1.1232
D(x): 0.6880, D(G(z)): 0.3780
Epoch: [16/20], Batch Num: [51/600]
Discriminator Loss: 1.1103, Generator Loss: 1.0688
D(x): 0.7286, D(G(z)): 0.4428
Epoch: [16/20], Batch Num: [52/600]
Discriminator Loss: 1.0253, Generator Loss: 1.1489
D(x): 0.7139, D(G(z)): 0.4142
Epoch: [16/20], Batch Num: [53/600]
Discriminator Loss: 0.9135, Generator Loss: 1.3008
D(x): 0.7029, D(G(z)): 0.3538
Epoch: [16/20], Batch Num: [54/600]
Discriminator Loss: 0.9923, Generator Loss: 1.3303
D(x): 0.6456, D(G(z)): 0.3169
Epoch: [16/20], Batch Num: [55/600]
Discriminator Loss: 0.9801, Generator Loss: 1.4224
D(x): 0.6450, D(G(z)): 0.3150
Epoch: [16/20], Batch Num: [56/600]
Discriminator Loss: 0.9876, Generator Loss: 1.4056
D(x): 0.6530, D(G(z)): 0.3117
Epoch: [16/20], Batch Num: [57/600]
Discriminator Loss: 0.9699, Generator Loss: 1.3038
D(x): 0.6458, D(G(z)): 0.3181
Epoch: [16/20], Batch Num: [58/600]
Discriminator Loss: 0.8757, Generator Loss: 1.2931
D(x): 0.6856, D(G(z)): 0.3043
Epoch: [16/20], Batch Num: [59/600]
Discriminator Loss: 0.8775, Generator Loss: 1.3170
D(x): 0.7177, D(G(z)): 0.3344
Epoch: [16/20], Batch Num: [60/600]
Discriminator Loss: 0.7493, Generator Loss: 1.1845
D(x): 0.7580, D(G(z)): 0.3146
Epoch: [16/20], Batch Num: [61/600]
Discriminator Loss: 0.7726, Generator Loss: 1.2647
D(x): 0.7453, D(G(z)): 0.3217
Epoch: [16/20], Batch Num: [62/600]
Discriminator Loss: 0.8266, Generator Loss: 1.4155
D(x): 0.7443, D(G(z)): 0.3254
Epoch: [16/20], Batch Num: [63/600]
Discriminator Loss: 0.7571, Generator Loss: 1.4885
D(x): 0.7328, D(G(z)): 0.2956
Epoch: [16/20], Batch Num: [64/600]
Discriminator Loss: 0.7251, Generator Loss: 1.5318
D(x): 0.7325, D(G(z)): 0.2738
Epoch: [16/20], Batch Num: [65/600]
Discriminator Loss: 0.6951, Generator Loss: 1.6655
D(x): 0.7425, D(G(z)): 0.2524
Epoch: [16/20], Batch Num: [66/600]
Discriminator Loss: 0.6994, Generator Loss: 1.6369
D(x): 0.7305, D(G(z)): 0.2598
Epoch: [16/20], Batch Num: [67/600]
Discriminator Loss: 0.7429, Generator Loss: 1.5781
D(x): 0.7295, D(G(z)): 0.2707
Epoch: [16/20], Batch Num: [68/600]
Discriminator Loss: 0.6539, Generator Loss: 1.5965
D(x): 0.7625, D(G(z)): 0.2584
Epoch: [16/20], Batch Num: [69/600]
Discriminator Loss: 0.6298, Generator Loss: 1.6361
D(x): 0.7869, D(G(z)): 0.2553
Epoch: [16/20], Batch Num: [70/600]
Discriminator Loss: 0.6240, Generator Loss: 1.7020
D(x): 0.8081, D(G(z)): 0.2750
Epoch: [16/20], Batch Num: [71/600]
Discriminator Loss: 0.6119, Generator Loss: 1.7184
D(x): 0.7849, D(G(z)): 0.2415
Epoch: [16/20], Batch Num: [72/600]
Discriminator Loss: 0.5482, Generator Loss: 1.8625
D(x): 0.8319, D(G(z)): 0.2476
Epoch: [16/20], Batch Num: [73/600]
Discriminator Loss: 0.6196, Generator Loss: 1.8361
D(x): 0.7599, D(G(z)): 0.2223
Epoch: [16/20], Batch Num: [74/600]
Discriminator Loss: 0.5391, Generator Loss: 1.9669
D(x): 0.8075, D(G(z)): 0.2178
Epoch: [16/20], Batch Num: [75/600]
Discriminator Loss: 0.5392, Generator Loss: 2.0058
D(x): 0.8132, D(G(z)): 0.2165
Epoch: [16/20], Batch Num: [76/600]
Discriminator Loss: 0.6773, Generator Loss: 1.9522
D(x): 0.7553, D(G(z)): 0.2256
Epoch: [16/20], Batch Num: [77/600]
Discriminator Loss: 0.6167, Generator Loss: 1.9398
D(x): 0.8012, D(G(z)): 0.2406
Epoch: [16/20], Batch Num: [78/600]
Discriminator Loss: 0.5547, Generator Loss: 1.9669
D(x): 0.7930, D(G(z)): 0.1968
Epoch: [16/20], Batch Num: [79/600]
Discriminator Loss: 0.5430, Generator Loss: 2.1695
D(x): 0.8249, D(G(z)): 0.2214
Epoch: [16/20], Batch Num: [80/600]
Discriminator Loss: 0.4010, Generator Loss: 2.1173
D(x): 0.8631, D(G(z)): 0.1780
Epoch: [16/20], Batch Num: [81/600]
Discriminator Loss: 0.5304, Generator Loss: 2.1721
D(x): 0.8025, D(G(z)): 0.1754
Epoch: [16/20], Batch Num: [82/600]
Discriminator Loss: 0.6037, Generator Loss: 1.9293
D(x): 0.8256, D(G(z)): 0.2075
Epoch: [16/20], Batch Num: [83/600]
Discriminator Loss: 0.6463, Generator Loss: 2.0656
D(x): 0.8081, D(G(z)): 0.2430
Epoch: [16/20], Batch Num: [84/600]
Discriminator Loss: 0.5582, Generator Loss: 1.9967
D(x): 0.8414, D(G(z)): 0.2258
Epoch: [16/20], Batch Num: [85/600]
Discriminator Loss: 0.6348, Generator Loss: 2.4293
D(x): 0.8286, D(G(z)): 0.2490
Epoch: [16/20], Batch Num: [86/600]
Discriminator Loss: 0.6396, Generator Loss: 2.4116
D(x): 0.7760, D(G(z)): 0.1822
Epoch: [16/20], Batch Num: [87/600]
Discriminator Loss: 0.8157, Generator Loss: 1.8929
D(x): 0.7129, D(G(z)): 0.1954
Epoch: [16/20], Batch Num: [88/600]
Discriminator Loss: 0.8155, Generator Loss: 1.8405
D(x): 0.7904, D(G(z)): 0.2832
Epoch: [16/20], Batch Num: [89/600]
Discriminator Loss: 0.9328, Generator Loss: 1.8527
D(x): 0.7196, D(G(z)): 0.2726
Epoch: [16/20], Batch Num: [90/600]
Discriminator Loss: 0.8109, Generator Loss: 1.8043
D(x): 0.7918, D(G(z)): 0.2914
Epoch: [16/20], Batch Num: [91/600]
Discriminator Loss: 0.9135, Generator Loss: 2.0333
D(x): 0.7685, D(G(z)): 0.3264
Epoch: [16/20], Batch Num: [92/600]
Discriminator Loss: 0.8670, Generator Loss: 2.1956
D(x): 0.7286, D(G(z)): 0.2579
Epoch: [16/20], Batch Num: [93/600]
Discriminator Loss: 0.9094, Generator Loss: 1.7951
D(x): 0.7117, D(G(z)): 0.2290
Epoch: [16/20], Batch Num: [94/600]
Discriminator Loss: 1.1808, Generator Loss: 1.6174
D(x): 0.6556, D(G(z)): 0.2810
Epoch: [16/20], Batch Num: [95/600]
Discriminator Loss: 0.9059, Generator Loss: 1.6927
D(x): 0.7416, D(G(z)): 0.3063
Epoch: [16/20], Batch Num: [96/600]
Discriminator Loss: 1.0190, Generator Loss: 1.4222
D(x): 0.7073, D(G(z)): 0.3327
Epoch: [16/20], Batch Num: [97/600]
Discriminator Loss: 1.0933, Generator Loss: 1.5483
D(x): 0.6957, D(G(z)): 0.3226
Epoch: [16/20], Batch Num: [98/600]
Discriminator Loss: 0.9079, Generator Loss: 1.5069
D(x): 0.7097, D(G(z)): 0.2980
Epoch: [16/20], Batch Num: [99/600]
Discriminator Loss: 1.1363, Generator Loss: 1.6321
D(x): 0.6407, D(G(z)): 0.2900
Epoch: 16, Batch Num: [100/600]

Epoch: [16/20], Batch Num: [100/600]
Discriminator Loss: 1.0777, Generator Loss: 1.4753
D(x): 0.6853, D(G(z)): 0.3330
Epoch: [16/20], Batch Num: [101/600]
Discriminator Loss: 1.0227, Generator Loss: 1.5338
D(x): 0.7095, D(G(z)): 0.3362
Epoch: [16/20], Batch Num: [102/600]
Discriminator Loss: 0.9935, Generator Loss: 1.5409
D(x): 0.7359, D(G(z)): 0.3420
Epoch: [16/20], Batch Num: [103/600]
Discriminator Loss: 0.8951, Generator Loss: 1.5535
D(x): 0.7334, D(G(z)): 0.3085
Epoch: [16/20], Batch Num: [104/600]
Discriminator Loss: 1.0182, Generator Loss: 1.7004
D(x): 0.6418, D(G(z)): 0.2682
Epoch: [16/20], Batch Num: [105/600]
Discriminator Loss: 0.7990, Generator Loss: 1.7446
D(x): 0.7562, D(G(z)): 0.2962
Epoch: [16/20], Batch Num: [106/600]
Discriminator Loss: 0.8438, Generator Loss: 1.5888
D(x): 0.6730, D(G(z)): 0.2338
Epoch: [16/20], Batch Num: [107/600]
Discriminator Loss: 0.8152, Generator Loss: 1.6851
D(x): 0.7364, D(G(z)): 0.2805
Epoch: [16/20], Batch Num: [108/600]
Discriminator Loss: 0.8684, Generator Loss: 1.5561
D(x): 0.7157, D(G(z)): 0.2816
Epoch: [16/20], Batch Num: [109/600]
Discriminator Loss: 0.8080, Generator Loss: 1.6323
D(x): 0.7216, D(G(z)): 0.2506
Epoch: [16/20], Batch Num: [110/600]
Discriminator Loss: 0.9011, Generator Loss: 1.7034
D(x): 0.7812, D(G(z)): 0.3249
Epoch: [16/20], Batch Num: [111/600]
Discriminator Loss: 0.7958, Generator Loss: 1.7191
D(x): 0.7513, D(G(z)): 0.2783
Epoch: [16/20], Batch Num: [112/600]
Discriminator Loss: 0.6252, Generator Loss: 2.0501
D(x): 0.7801, D(G(z)): 0.2265
Epoch: [16/20], Batch Num: [113/600]
Discriminator Loss: 0.8312, Generator Loss: 2.0612
D(x): 0.6939, D(G(z)): 0.2136
Epoch: [16/20], Batch Num: [114/600]
Discriminator Loss: 0.7927, Generator Loss: 1.9428
D(x): 0.7288, D(G(z)): 0.2196
Epoch: [16/20], Batch Num: [115/600]
Discriminator Loss: 0.5225, Generator Loss: 1.5862
D(x): 0.8135, D(G(z)): 0.1884
Epoch: [16/20], Batch Num: [116/600]
Discriminator Loss: 0.5423, Generator Loss: 1.7338
D(x): 0.8292, D(G(z)): 0.2290
Epoch: [16/20], Batch Num: [117/600]
Discriminator Loss: 0.6951, Generator Loss: 1.6533
D(x): 0.8139, D(G(z)): 0.2584
Epoch: [16/20], Batch Num: [118/600]
Discriminator Loss: 0.6054, Generator Loss: 1.9307
D(x): 0.8501, D(G(z)): 0.2775
Epoch: [16/20], Batch Num: [119/600]
Discriminator Loss: 0.7165, Generator Loss: 2.1044
D(x): 0.8022, D(G(z)): 0.2546
Epoch: [16/20], Batch Num: [120/600]
Discriminator Loss: 0.6937, Generator Loss: 2.1089
D(x): 0.7732, D(G(z)): 0.2038
Epoch: [16/20], Batch Num: [121/600]
Discriminator Loss: 0.6548, Generator Loss: 2.1794
D(x): 0.7599, D(G(z)): 0.1814
Epoch: [16/20], Batch Num: [122/600]
Discriminator Loss: 0.5802, Generator Loss: 2.0546
D(x): 0.8059, D(G(z)): 0.1880
Epoch: [16/20], Batch Num: [123/600]
Discriminator Loss: 0.6323, Generator Loss: 1.7993
D(x): 0.7787, D(G(z)): 0.1939
Epoch: [16/20], Batch Num: [124/600]
Discriminator Loss: 0.5725, Generator Loss: 1.7817
D(x): 0.7948, D(G(z)): 0.1877
Epoch: [16/20], Batch Num: [125/600]
Discriminator Loss: 0.5429, Generator Loss: 1.7321
D(x): 0.8785, D(G(z)): 0.2648
Epoch: [16/20], Batch Num: [126/600]
Discriminator Loss: 0.7750, Generator Loss: 1.9515
D(x): 0.8365, D(G(z)): 0.2987
Epoch: [16/20], Batch Num: [127/600]
Discriminator Loss: 0.5544, Generator Loss: 2.2080
D(x): 0.8252, D(G(z)): 0.2023
Epoch: [16/20], Batch Num: [128/600]
Discriminator Loss: 0.5547, Generator Loss: 2.3012
D(x): 0.7895, D(G(z)): 0.1661
Epoch: [16/20], Batch Num: [129/600]
Discriminator Loss: 0.6514, Generator Loss: 2.4423
D(x): 0.7559, D(G(z)): 0.1719
Epoch: [16/20], Batch Num: [130/600]
Discriminator Loss: 0.8832, Generator Loss: 2.1407
D(x): 0.7143, D(G(z)): 0.2072
Epoch: [16/20], Batch Num: [131/600]
Discriminator Loss: 0.7938, Generator Loss: 2.1140
D(x): 0.7557, D(G(z)): 0.2377
Epoch: [16/20], Batch Num: [132/600]
Discriminator Loss: 0.9931, Generator Loss: 1.8591
D(x): 0.7397, D(G(z)): 0.2800
Epoch: [16/20], Batch Num: [133/600]
Discriminator Loss: 0.7898, Generator Loss: 2.0334
D(x): 0.7912, D(G(z)): 0.2741
Epoch: [16/20], Batch Num: [134/600]
Discriminator Loss: 0.7109, Generator Loss: 1.9903
D(x): 0.7654, D(G(z)): 0.2234
Epoch: [16/20], Batch Num: [135/600]
Discriminator Loss: 0.8386, Generator Loss: 1.7510
D(x): 0.7735, D(G(z)): 0.2815
Epoch: [16/20], Batch Num: [136/600]
Discriminator Loss: 0.9005, Generator Loss: 1.8729
D(x): 0.7652, D(G(z)): 0.3069
Epoch: [16/20], Batch Num: [137/600]
Discriminator Loss: 0.7334, Generator Loss: 2.0340
D(x): 0.7756, D(G(z)): 0.2552
Epoch: [16/20], Batch Num: [138/600]
Discriminator Loss: 1.0934, Generator Loss: 2.1255
D(x): 0.7109, D(G(z)): 0.2687
Epoch: [16/20], Batch Num: [139/600]
Discriminator Loss: 1.1202, Generator Loss: 2.0707
D(x): 0.6308, D(G(z)): 0.2638
Epoch: [16/20], Batch Num: [140/600]
Discriminator Loss: 0.9895, Generator Loss: 1.6911
D(x): 0.6840, D(G(z)): 0.2613
Epoch: [16/20], Batch Num: [141/600]
Discriminator Loss: 1.2515, Generator Loss: 1.6921
D(x): 0.6766, D(G(z)): 0.3422
Epoch: [16/20], Batch Num: [142/600]
Discriminator Loss: 1.1331, Generator Loss: 1.6980
D(x): 0.7708, D(G(z)): 0.3819
Epoch: [16/20], Batch Num: [143/600]
Discriminator Loss: 0.9545, Generator Loss: 1.6290
D(x): 0.7043, D(G(z)): 0.2630
Epoch: [16/20], Batch Num: [144/600]
Discriminator Loss: 0.9185, Generator Loss: 1.6819
D(x): 0.6803, D(G(z)): 0.2445
Epoch: [16/20], Batch Num: [145/600]
Discriminator Loss: 1.0493, Generator Loss: 1.5903
D(x): 0.6612, D(G(z)): 0.2835
Epoch: [16/20], Batch Num: [146/600]
Discriminator Loss: 0.8448, Generator Loss: 1.5218
D(x): 0.7418, D(G(z)): 0.2921
Epoch: [16/20], Batch Num: [147/600]
Discriminator Loss: 0.8267, Generator Loss: 1.5832
D(x): 0.7261, D(G(z)): 0.2735
Epoch: [16/20], Batch Num: [148/600]
Discriminator Loss: 1.0199, Generator Loss: 1.5478
D(x): 0.7046, D(G(z)): 0.3289
Epoch: [16/20], Batch Num: [149/600]
Discriminator Loss: 1.0820, Generator Loss: 1.4895
D(x): 0.7020, D(G(z)): 0.3500
Epoch: [16/20], Batch Num: [150/600]
Discriminator Loss: 1.0488, Generator Loss: 1.5087
D(x): 0.6717, D(G(z)): 0.3138
Epoch: [16/20], Batch Num: [151/600]
Discriminator Loss: 0.9848, Generator Loss: 1.6101
D(x): 0.6547, D(G(z)): 0.2846
Epoch: [16/20], Batch Num: [152/600]
Discriminator Loss: 1.0155, Generator Loss: 1.5921
D(x): 0.7206, D(G(z)): 0.3430
Epoch: [16/20], Batch Num: [153/600]
Discriminator Loss: 1.0433, Generator Loss: 1.7200
D(x): 0.6173, D(G(z)): 0.2652
Epoch: [16/20], Batch Num: [154/600]
Discriminator Loss: 0.9473, Generator Loss: 1.5316
D(x): 0.6477, D(G(z)): 0.2420
Epoch: [16/20], Batch Num: [155/600]
Discriminator Loss: 0.9480, Generator Loss: 1.4312
D(x): 0.7136, D(G(z)): 0.3338
Epoch: [16/20], Batch Num: [156/600]
Discriminator Loss: 1.0516, Generator Loss: 1.5246
D(x): 0.7757, D(G(z)): 0.3838
Epoch: [16/20], Batch Num: [157/600]
Discriminator Loss: 0.8448, Generator Loss: 1.4875
D(x): 0.7534, D(G(z)): 0.3150
Epoch: [16/20], Batch Num: [158/600]
Discriminator Loss: 0.8926, Generator Loss: 1.6704
D(x): 0.7115, D(G(z)): 0.2889
Epoch: [16/20], Batch Num: [159/600]
Discriminator Loss: 0.9489, Generator Loss: 1.7845
D(x): 0.6349, D(G(z)): 0.2468
Epoch: [16/20], Batch Num: [160/600]
Discriminator Loss: 0.9110, Generator Loss: 1.6578
D(x): 0.7003, D(G(z)): 0.2995
Epoch: [16/20], Batch Num: [161/600]
Discriminator Loss: 0.8926, Generator Loss: 1.6757
D(x): 0.6799, D(G(z)): 0.2541
Epoch: [16/20], Batch Num: [162/600]
Discriminator Loss: 0.8562, Generator Loss: 1.7542
D(x): 0.6905, D(G(z)): 0.2877
Epoch: [16/20], Batch Num: [163/600]
Discriminator Loss: 0.8845, Generator Loss: 1.5758
D(x): 0.6850, D(G(z)): 0.2752
Epoch: [16/20], Batch Num: [164/600]
Discriminator Loss: 0.8562, Generator Loss: 1.3434
D(x): 0.7137, D(G(z)): 0.3078
Epoch: [16/20], Batch Num: [165/600]
Discriminator Loss: 0.8990, Generator Loss: 1.5473
D(x): 0.7605, D(G(z)): 0.3515
Epoch: [16/20], Batch Num: [166/600]
Discriminator Loss: 0.7737, Generator Loss: 1.6068
D(x): 0.7557, D(G(z)): 0.2997
Epoch: [16/20], Batch Num: [167/600]
Discriminator Loss: 0.8875, Generator Loss: 1.7130
D(x): 0.6841, D(G(z)): 0.2792
Epoch: [16/20], Batch Num: [168/600]
Discriminator Loss: 0.8310, Generator Loss: 1.6657
D(x): 0.7381, D(G(z)): 0.3004
Epoch: [16/20], Batch Num: [169/600]
Discriminator Loss: 0.6881, Generator Loss: 1.5065
D(x): 0.7439, D(G(z)): 0.2461
Epoch: [16/20], Batch Num: [170/600]
Discriminator Loss: 0.8074, Generator Loss: 1.5453
D(x): 0.7290, D(G(z)): 0.2637
Epoch: [16/20], Batch Num: [171/600]
Discriminator Loss: 0.8948, Generator Loss: 1.5049
D(x): 0.6723, D(G(z)): 0.2777
Epoch: [16/20], Batch Num: [172/600]
Discriminator Loss: 0.7850, Generator Loss: 1.6431
D(x): 0.7418, D(G(z)): 0.2842
Epoch: [16/20], Batch Num: [173/600]
Discriminator Loss: 0.8449, Generator Loss: 1.4640
D(x): 0.7568, D(G(z)): 0.3292
Epoch: [16/20], Batch Num: [174/600]
Discriminator Loss: 0.7704, Generator Loss: 1.4861
D(x): 0.7728, D(G(z)): 0.3064
Epoch: [16/20], Batch Num: [175/600]
Discriminator Loss: 0.8318, Generator Loss: 1.4970
D(x): 0.7193, D(G(z)): 0.2989
Epoch: [16/20], Batch Num: [176/600]
Discriminator Loss: 0.7728, Generator Loss: 1.8052
D(x): 0.7261, D(G(z)): 0.2596
Epoch: [16/20], Batch Num: [177/600]
Discriminator Loss: 0.7863, Generator Loss: 1.6466
D(x): 0.7278, D(G(z)): 0.2653
Epoch: [16/20], Batch Num: [178/600]
Discriminator Loss: 0.9013, Generator Loss: 1.7412
D(x): 0.6864, D(G(z)): 0.2725
Epoch: [16/20], Batch Num: [179/600]
Discriminator Loss: 0.9020, Generator Loss: 1.7330
D(x): 0.6761, D(G(z)): 0.2451
Epoch: [16/20], Batch Num: [180/600]
Discriminator Loss: 0.7788, Generator Loss: 1.5159
D(x): 0.7343, D(G(z)): 0.2650
Epoch: [16/20], Batch Num: [181/600]
Discriminator Loss: 0.7298, Generator Loss: 1.5402
D(x): 0.7713, D(G(z)): 0.2800
Epoch: [16/20], Batch Num: [182/600]
Discriminator Loss: 0.7180, Generator Loss: 1.5123
D(x): 0.7552, D(G(z)): 0.2613
Epoch: [16/20], Batch Num: [183/600]
Discriminator Loss: 0.7793, Generator Loss: 1.4559
D(x): 0.7704, D(G(z)): 0.3078
Epoch: [16/20], Batch Num: [184/600]
Discriminator Loss: 0.7206, Generator Loss: 1.4655
D(x): 0.7615, D(G(z)): 0.2770
Epoch: [16/20], Batch Num: [185/600]
Discriminator Loss: 0.7823, Generator Loss: 1.5933
D(x): 0.7370, D(G(z)): 0.2666
Epoch: [16/20], Batch Num: [186/600]
Discriminator Loss: 0.7940, Generator Loss: 1.6626
D(x): 0.7623, D(G(z)): 0.3047
Epoch: [16/20], Batch Num: [187/600]
Discriminator Loss: 0.6644, Generator Loss: 1.6397
D(x): 0.7863, D(G(z)): 0.2586
Epoch: [16/20], Batch Num: [188/600]
Discriminator Loss: 0.7648, Generator Loss: 1.6283
D(x): 0.7012, D(G(z)): 0.2145
Epoch: [16/20], Batch Num: [189/600]
Discriminator Loss: 0.8093, Generator Loss: 1.5551
D(x): 0.6968, D(G(z)): 0.2448
Epoch: [16/20], Batch Num: [190/600]
Discriminator Loss: 0.7650, Generator Loss: 1.5532
D(x): 0.7494, D(G(z)): 0.2568
Epoch: [16/20], Batch Num: [191/600]
Discriminator Loss: 0.7485, Generator Loss: 1.5181
D(x): 0.7998, D(G(z)): 0.3079
Epoch: [16/20], Batch Num: [192/600]
Discriminator Loss: 0.7381, Generator Loss: 1.4909
D(x): 0.7961, D(G(z)): 0.2963
Epoch: [16/20], Batch Num: [193/600]
Discriminator Loss: 0.6088, Generator Loss: 1.8678
D(x): 0.8221, D(G(z)): 0.2551
Epoch: [16/20], Batch Num: [194/600]
Discriminator Loss: 0.6719, Generator Loss: 1.7714
D(x): 0.7515, D(G(z)): 0.2002
Epoch: [16/20], Batch Num: [195/600]
Discriminator Loss: 0.7621, Generator Loss: 1.8360
D(x): 0.7171, D(G(z)): 0.2144
Epoch: [16/20], Batch Num: [196/600]
Discriminator Loss: 0.5879, Generator Loss: 1.8640
D(x): 0.8184, D(G(z)): 0.2342
Epoch: [16/20], Batch Num: [197/600]
Discriminator Loss: 0.7873, Generator Loss: 1.8200
D(x): 0.7607, D(G(z)): 0.2483
Epoch: [16/20], Batch Num: [198/600]
Discriminator Loss: 0.6377, Generator Loss: 1.8771
D(x): 0.7795, D(G(z)): 0.2162
Epoch: [16/20], Batch Num: [199/600]
Discriminator Loss: 0.7150, Generator Loss: 1.7825
D(x): 0.7557, D(G(z)): 0.2177
Epoch: 16, Batch Num: [200/600]

Epoch: [16/20], Batch Num: [200/600]
Discriminator Loss: 0.6083, Generator Loss: 1.8126
D(x): 0.8103, D(G(z)): 0.2367
Epoch: [16/20], Batch Num: [201/600]
Discriminator Loss: 0.7572, Generator Loss: 1.8006
D(x): 0.7577, D(G(z)): 0.2422
Epoch: [16/20], Batch Num: [202/600]
Discriminator Loss: 0.7366, Generator Loss: 2.0569
D(x): 0.7855, D(G(z)): 0.2666
Epoch: [16/20], Batch Num: [203/600]
Discriminator Loss: 0.8372, Generator Loss: 1.9481
D(x): 0.7282, D(G(z)): 0.2275
Epoch: [16/20], Batch Num: [204/600]
Discriminator Loss: 0.8354, Generator Loss: 1.8869
D(x): 0.7250, D(G(z)): 0.2309
Epoch: [16/20], Batch Num: [205/600]
Discriminator Loss: 0.7471, Generator Loss: 1.8677
D(x): 0.7495, D(G(z)): 0.2447
Epoch: [16/20], Batch Num: [206/600]
Discriminator Loss: 0.6798, Generator Loss: 1.7732
D(x): 0.7566, D(G(z)): 0.2183
Epoch: [16/20], Batch Num: [207/600]
Discriminator Loss: 0.7275, Generator Loss: 1.7398
D(x): 0.7837, D(G(z)): 0.2685
Epoch: [16/20], Batch Num: [208/600]
Discriminator Loss: 0.8749, Generator Loss: 1.9606
D(x): 0.7905, D(G(z)): 0.3088
Epoch: [16/20], Batch Num: [209/600]
Discriminator Loss: 0.5906, Generator Loss: 2.1104
D(x): 0.7915, D(G(z)): 0.1864
Epoch: [16/20], Batch Num: [210/600]
Discriminator Loss: 0.7872, Generator Loss: 1.9403
D(x): 0.7056, D(G(z)): 0.2084
Epoch: [16/20], Batch Num: [211/600]
Discriminator Loss: 0.7891, Generator Loss: 1.7563
D(x): 0.7228, D(G(z)): 0.2143
Epoch: [16/20], Batch Num: [212/600]
Discriminator Loss: 0.8766, Generator Loss: 1.7125
D(x): 0.7044, D(G(z)): 0.2239
Epoch: [16/20], Batch Num: [213/600]
Discriminator Loss: 0.8985, Generator Loss: 1.6607
D(x): 0.7967, D(G(z)): 0.3355
Epoch: [16/20], Batch Num: [214/600]
Discriminator Loss: 0.7750, Generator Loss: 1.7785
D(x): 0.7680, D(G(z)): 0.2606
Epoch: [16/20], Batch Num: [215/600]
Discriminator Loss: 0.6787, Generator Loss: 2.1460
D(x): 0.8484, D(G(z)): 0.3041
Epoch: [16/20], Batch Num: [216/600]
Discriminator Loss: 0.8750, Generator Loss: 2.1215
D(x): 0.6553, D(G(z)): 0.1814
Epoch: [16/20], Batch Num: [217/600]
Discriminator Loss: 0.7105, Generator Loss: 1.7422
D(x): 0.7118, D(G(z)): 0.1816
Epoch: [16/20], Batch Num: [218/600]
Discriminator Loss: 0.8804, Generator Loss: 1.7328
D(x): 0.7005, D(G(z)): 0.2558
Epoch: [16/20], Batch Num: [219/600]
Discriminator Loss: 0.9086, Generator Loss: 1.6272
D(x): 0.7204, D(G(z)): 0.2757
Epoch: [16/20], Batch Num: [220/600]
Discriminator Loss: 0.7431, Generator Loss: 1.5896
D(x): 0.7888, D(G(z)): 0.2897
Epoch: [16/20], Batch Num: [221/600]
Discriminator Loss: 0.9824, Generator Loss: 1.5662
D(x): 0.8098, D(G(z)): 0.3616
Epoch: [16/20], Batch Num: [222/600]
Discriminator Loss: 0.8606, Generator Loss: 2.1438
D(x): 0.7666, D(G(z)): 0.2803
Epoch: [16/20], Batch Num: [223/600]
Discriminator Loss: 0.8621, Generator Loss: 2.2701
D(x): 0.7007, D(G(z)): 0.2081
Epoch: [16/20], Batch Num: [224/600]
Discriminator Loss: 1.0325, Generator Loss: 2.3130
D(x): 0.6382, D(G(z)): 0.2074
Epoch: [16/20], Batch Num: [225/600]
Discriminator Loss: 0.7822, Generator Loss: 2.0300
D(x): 0.7025, D(G(z)): 0.1812
Epoch: [16/20], Batch Num: [226/600]
Discriminator Loss: 1.1468, Generator Loss: 1.3921
D(x): 0.6203, D(G(z)): 0.2601
Epoch: [16/20], Batch Num: [227/600]
Discriminator Loss: 0.9122, Generator Loss: 1.0676
D(x): 0.7498, D(G(z)): 0.2932
Epoch: [16/20], Batch Num: [228/600]
Discriminator Loss: 0.9744, Generator Loss: 1.3250
D(x): 0.8131, D(G(z)): 0.3949
Epoch: [16/20], Batch Num: [229/600]
Discriminator Loss: 1.0237, Generator Loss: 1.6113
D(x): 0.7714, D(G(z)): 0.3680
Epoch: [16/20], Batch Num: [230/600]
Discriminator Loss: 1.0388, Generator Loss: 1.7946
D(x): 0.6729, D(G(z)): 0.2913
Epoch: [16/20], Batch Num: [231/600]
Discriminator Loss: 1.0956, Generator Loss: 2.1405
D(x): 0.6225, D(G(z)): 0.2368
Epoch: [16/20], Batch Num: [232/600]
Discriminator Loss: 1.3029, Generator Loss: 1.5769
D(x): 0.5451, D(G(z)): 0.2544
Epoch: [16/20], Batch Num: [233/600]
Discriminator Loss: 1.1097, Generator Loss: 1.4047
D(x): 0.6729, D(G(z)): 0.3272
Epoch: [16/20], Batch Num: [234/600]
Discriminator Loss: 1.1869, Generator Loss: 1.4438
D(x): 0.6811, D(G(z)): 0.3696
Epoch: [16/20], Batch Num: [235/600]
Discriminator Loss: 0.9572, Generator Loss: 1.2915
D(x): 0.7180, D(G(z)): 0.3254
Epoch: [16/20], Batch Num: [236/600]
Discriminator Loss: 1.1202, Generator Loss: 1.1828
D(x): 0.6495, D(G(z)): 0.3386
Epoch: [16/20], Batch Num: [237/600]
Discriminator Loss: 1.1238, Generator Loss: 1.4356
D(x): 0.7102, D(G(z)): 0.3819
Epoch: [16/20], Batch Num: [238/600]
Discriminator Loss: 1.0054, Generator Loss: 1.6572
D(x): 0.6934, D(G(z)): 0.3188
Epoch: [16/20], Batch Num: [239/600]
Discriminator Loss: 1.3100, Generator Loss: 1.8190
D(x): 0.5845, D(G(z)): 0.3265
Epoch: [16/20], Batch Num: [240/600]
Discriminator Loss: 1.1115, Generator Loss: 1.5334
D(x): 0.5973, D(G(z)): 0.2788
Epoch: [16/20], Batch Num: [241/600]
Discriminator Loss: 1.1498, Generator Loss: 1.3343
D(x): 0.6372, D(G(z)): 0.3161
Epoch: [16/20], Batch Num: [242/600]
Discriminator Loss: 1.0501, Generator Loss: 1.2080
D(x): 0.6580, D(G(z)): 0.2966
Epoch: [16/20], Batch Num: [243/600]
Discriminator Loss: 0.9634, Generator Loss: 1.1083
D(x): 0.7163, D(G(z)): 0.3485
Epoch: [16/20], Batch Num: [244/600]
Discriminator Loss: 1.0285, Generator Loss: 1.2872
D(x): 0.6974, D(G(z)): 0.3774
Epoch: [16/20], Batch Num: [245/600]
Discriminator Loss: 0.9972, Generator Loss: 1.2854
D(x): 0.6975, D(G(z)): 0.3484
Epoch: [16/20], Batch Num: [246/600]
Discriminator Loss: 1.0672, Generator Loss: 1.3195
D(x): 0.6956, D(G(z)): 0.3701
Epoch: [16/20], Batch Num: [247/600]
Discriminator Loss: 1.0789, Generator Loss: 1.3871
D(x): 0.6531, D(G(z)): 0.3552
Epoch: [16/20], Batch Num: [248/600]
Discriminator Loss: 1.0727, Generator Loss: 1.4552
D(x): 0.6314, D(G(z)): 0.3296
Epoch: [16/20], Batch Num: [249/600]
Discriminator Loss: 1.0942, Generator Loss: 1.3485
D(x): 0.5784, D(G(z)): 0.2929
Epoch: [16/20], Batch Num: [250/600]
Discriminator Loss: 1.0665, Generator Loss: 1.4041
D(x): 0.6575, D(G(z)): 0.3520
Epoch: [16/20], Batch Num: [251/600]
Discriminator Loss: 0.9444, Generator Loss: 1.2944
D(x): 0.7080, D(G(z)): 0.3325
Epoch: [16/20], Batch Num: [252/600]
Discriminator Loss: 0.9621, Generator Loss: 1.3831
D(x): 0.7419, D(G(z)): 0.3812
Epoch: [16/20], Batch Num: [253/600]
Discriminator Loss: 1.0161, Generator Loss: 1.3837
D(x): 0.7042, D(G(z)): 0.3685
Epoch: [16/20], Batch Num: [254/600]
Discriminator Loss: 0.8788, Generator Loss: 1.4637
D(x): 0.7154, D(G(z)): 0.3135
Epoch: [16/20], Batch Num: [255/600]
Discriminator Loss: 1.0094, Generator Loss: 1.6062
D(x): 0.6163, D(G(z)): 0.2877
Epoch: [16/20], Batch Num: [256/600]
Discriminator Loss: 0.9161, Generator Loss: 1.4488
D(x): 0.6757, D(G(z)): 0.3125
Epoch: [16/20], Batch Num: [257/600]
Discriminator Loss: 0.9971, Generator Loss: 1.5764
D(x): 0.6576, D(G(z)): 0.3273
Epoch: [16/20], Batch Num: [258/600]
Discriminator Loss: 0.8110, Generator Loss: 1.6001
D(x): 0.6990, D(G(z)): 0.2855
Epoch: [16/20], Batch Num: [259/600]
Discriminator Loss: 0.9804, Generator Loss: 1.4491
D(x): 0.6307, D(G(z)): 0.2874
Epoch: [16/20], Batch Num: [260/600]
Discriminator Loss: 0.8188, Generator Loss: 1.1825
D(x): 0.6879, D(G(z)): 0.2752
Epoch: [16/20], Batch Num: [261/600]
Discriminator Loss: 0.8957, Generator Loss: 1.2464
D(x): 0.7147, D(G(z)): 0.3186
Epoch: [16/20], Batch Num: [262/600]
Discriminator Loss: 0.8979, Generator Loss: 1.3810
D(x): 0.7440, D(G(z)): 0.3702
Epoch: [16/20], Batch Num: [263/600]
Discriminator Loss: 0.9654, Generator Loss: 1.4287
D(x): 0.7565, D(G(z)): 0.4003
Epoch: [16/20], Batch Num: [264/600]
Discriminator Loss: 0.9050, Generator Loss: 1.6019
D(x): 0.7257, D(G(z)): 0.3405
Epoch: [16/20], Batch Num: [265/600]
Discriminator Loss: 0.9291, Generator Loss: 1.8208
D(x): 0.6813, D(G(z)): 0.3068
Epoch: [16/20], Batch Num: [266/600]
Discriminator Loss: 0.8842, Generator Loss: 1.7478
D(x): 0.6315, D(G(z)): 0.2335
Epoch: [16/20], Batch Num: [267/600]
Discriminator Loss: 0.8600, Generator Loss: 1.7359
D(x): 0.6693, D(G(z)): 0.2485
Epoch: [16/20], Batch Num: [268/600]
Discriminator Loss: 0.8279, Generator Loss: 1.6899
D(x): 0.7192, D(G(z)): 0.2912
Epoch: [16/20], Batch Num: [269/600]
Discriminator Loss: 0.8331, Generator Loss: 1.5702
D(x): 0.7294, D(G(z)): 0.3003
Epoch: [16/20], Batch Num: [270/600]
Discriminator Loss: 0.7940, Generator Loss: 1.4695
D(x): 0.7168, D(G(z)): 0.2618
Epoch: [16/20], Batch Num: [271/600]
Discriminator Loss: 0.8208, Generator Loss: 1.5858
D(x): 0.7374, D(G(z)): 0.3019
Epoch: [16/20], Batch Num: [272/600]
Discriminator Loss: 0.8945, Generator Loss: 1.4711
D(x): 0.7250, D(G(z)): 0.3317
Epoch: [16/20], Batch Num: [273/600]
Discriminator Loss: 0.9762, Generator Loss: 1.5585
D(x): 0.7215, D(G(z)): 0.3634
Epoch: [16/20], Batch Num: [274/600]
Discriminator Loss: 0.9781, Generator Loss: 1.7139
D(x): 0.6769, D(G(z)): 0.3284
Epoch: [16/20], Batch Num: [275/600]
Discriminator Loss: 1.0402, Generator Loss: 1.5901
D(x): 0.6151, D(G(z)): 0.2929
Epoch: [16/20], Batch Num: [276/600]
Discriminator Loss: 0.9863, Generator Loss: 1.4423
D(x): 0.6589, D(G(z)): 0.2951
Epoch: [16/20], Batch Num: [277/600]
Discriminator Loss: 0.9194, Generator Loss: 1.4117
D(x): 0.6912, D(G(z)): 0.3066
Epoch: [16/20], Batch Num: [278/600]
Discriminator Loss: 0.9187, Generator Loss: 1.4100
D(x): 0.6731, D(G(z)): 0.2933
Epoch: [16/20], Batch Num: [279/600]
Discriminator Loss: 0.9345, Generator Loss: 1.4968
D(x): 0.7296, D(G(z)): 0.3612
Epoch: [16/20], Batch Num: [280/600]
Discriminator Loss: 0.9151, Generator Loss: 1.4724
D(x): 0.7549, D(G(z)): 0.3645
Epoch: [16/20], Batch Num: [281/600]
Discriminator Loss: 1.0622, Generator Loss: 1.5586
D(x): 0.6253, D(G(z)): 0.3029
Epoch: [16/20], Batch Num: [282/600]
Discriminator Loss: 0.9711, Generator Loss: 1.7076
D(x): 0.6779, D(G(z)): 0.3170
Epoch: [16/20], Batch Num: [283/600]
Discriminator Loss: 1.1511, Generator Loss: 1.5223
D(x): 0.6360, D(G(z)): 0.3440
Epoch: [16/20], Batch Num: [284/600]
Discriminator Loss: 1.0310, Generator Loss: 1.4986
D(x): 0.6893, D(G(z)): 0.3333
Epoch: [16/20], Batch Num: [285/600]
Discriminator Loss: 1.0131, Generator Loss: 1.5831
D(x): 0.6570, D(G(z)): 0.3076
Epoch: [16/20], Batch Num: [286/600]
Discriminator Loss: 1.1123, Generator Loss: 1.3736
D(x): 0.6576, D(G(z)): 0.3498
Epoch: [16/20], Batch Num: [287/600]
Discriminator Loss: 1.0388, Generator Loss: 1.3299
D(x): 0.7212, D(G(z)): 0.3679
Epoch: [16/20], Batch Num: [288/600]
Discriminator Loss: 1.0481, Generator Loss: 1.5864
D(x): 0.6968, D(G(z)): 0.3420
Epoch: [16/20], Batch Num: [289/600]
Discriminator Loss: 1.0618, Generator Loss: 1.6371
D(x): 0.6668, D(G(z)): 0.3552
Epoch: [16/20], Batch Num: [290/600]
Discriminator Loss: 1.1115, Generator Loss: 1.4981
D(x): 0.6010, D(G(z)): 0.2765
Epoch: [16/20], Batch Num: [291/600]
Discriminator Loss: 0.9648, Generator Loss: 1.4405
D(x): 0.6542, D(G(z)): 0.2796
Epoch: [16/20], Batch Num: [292/600]
Discriminator Loss: 1.0394, Generator Loss: 1.3412
D(x): 0.6857, D(G(z)): 0.3239
Epoch: [16/20], Batch Num: [293/600]
Discriminator Loss: 1.1115, Generator Loss: 1.2875
D(x): 0.7005, D(G(z)): 0.3822
Epoch: [16/20], Batch Num: [294/600]
Discriminator Loss: 0.9572, Generator Loss: 1.2331
D(x): 0.7014, D(G(z)): 0.3309
Epoch: [16/20], Batch Num: [295/600]
Discriminator Loss: 0.9109, Generator Loss: 1.5765
D(x): 0.7091, D(G(z)): 0.3219
Epoch: [16/20], Batch Num: [296/600]
Discriminator Loss: 1.0054, Generator Loss: 1.5116
D(x): 0.6402, D(G(z)): 0.2834
Epoch: [16/20], Batch Num: [297/600]
Discriminator Loss: 0.9886, Generator Loss: 1.5795
D(x): 0.6667, D(G(z)): 0.3259
Epoch: [16/20], Batch Num: [298/600]
Discriminator Loss: 0.9559, Generator Loss: 1.3891
D(x): 0.6349, D(G(z)): 0.2888
Epoch: [16/20], Batch Num: [299/600]
Discriminator Loss: 0.9801, Generator Loss: 1.4472
D(x): 0.6966, D(G(z)): 0.3398
Epoch: 16, Batch Num: [300/600]

Epoch: [16/20], Batch Num: [300/600]
Discriminator Loss: 0.9694, Generator Loss: 1.3654
D(x): 0.6914, D(G(z)): 0.3452
Epoch: [16/20], Batch Num: [301/600]
Discriminator Loss: 0.9932, Generator Loss: 1.3653
D(x): 0.7221, D(G(z)): 0.3778
Epoch: [16/20], Batch Num: [302/600]
Discriminator Loss: 0.8716, Generator Loss: 1.5891
D(x): 0.7314, D(G(z)): 0.3187
Epoch: [16/20], Batch Num: [303/600]
Discriminator Loss: 0.7825, Generator Loss: 1.5344
D(x): 0.7306, D(G(z)): 0.2641
Epoch: [16/20], Batch Num: [304/600]
Discriminator Loss: 0.9524, Generator Loss: 1.7560
D(x): 0.6641, D(G(z)): 0.2801
Epoch: [16/20], Batch Num: [305/600]
Discriminator Loss: 0.8944, Generator Loss: 1.5207
D(x): 0.6588, D(G(z)): 0.2513
Epoch: [16/20], Batch Num: [306/600]
Discriminator Loss: 0.9347, Generator Loss: 1.5207
D(x): 0.7103, D(G(z)): 0.3120
Epoch: [16/20], Batch Num: [307/600]
Discriminator Loss: 0.9236, Generator Loss: 1.4030
D(x): 0.6943, D(G(z)): 0.2910
Epoch: [16/20], Batch Num: [308/600]
Discriminator Loss: 0.8662, Generator Loss: 1.4128
D(x): 0.7420, D(G(z)): 0.3328
Epoch: [16/20], Batch Num: [309/600]
Discriminator Loss: 0.9170, Generator Loss: 1.5103
D(x): 0.7170, D(G(z)): 0.3414
Epoch: [16/20], Batch Num: [310/600]
Discriminator Loss: 0.9282, Generator Loss: 1.5352
D(x): 0.7334, D(G(z)): 0.3434
Epoch: [16/20], Batch Num: [311/600]
Discriminator Loss: 0.8017, Generator Loss: 1.8063
D(x): 0.7110, D(G(z)): 0.2518
Epoch: [16/20], Batch Num: [312/600]
Discriminator Loss: 0.9582, Generator Loss: 1.7502
D(x): 0.6459, D(G(z)): 0.2644
Epoch: [16/20], Batch Num: [313/600]
Discriminator Loss: 0.8218, Generator Loss: 1.6056
D(x): 0.7223, D(G(z)): 0.2625
Epoch: [16/20], Batch Num: [314/600]
Discriminator Loss: 0.8283, Generator Loss: 1.4923
D(x): 0.7024, D(G(z)): 0.2610
Epoch: [16/20], Batch Num: [315/600]
Discriminator Loss: 0.9120, Generator Loss: 1.4927
D(x): 0.7334, D(G(z)): 0.3208
Epoch: [16/20], Batch Num: [316/600]
Discriminator Loss: 0.7957, Generator Loss: 1.4327
D(x): 0.7874, D(G(z)): 0.3113
Epoch: [16/20], Batch Num: [317/600]
Discriminator Loss: 0.9775, Generator Loss: 1.7217
D(x): 0.6961, D(G(z)): 0.3166
Epoch: [16/20], Batch Num: [318/600]
Discriminator Loss: 0.9274, Generator Loss: 1.7191
D(x): 0.6815, D(G(z)): 0.2881
Epoch: [16/20], Batch Num: [319/600]
Discriminator Loss: 0.7758, Generator Loss: 1.7355
D(x): 0.7180, D(G(z)): 0.2480
Epoch: [16/20], Batch Num: [320/600]
Discriminator Loss: 0.8942, Generator Loss: 1.6388
D(x): 0.7029, D(G(z)): 0.2896
Epoch: [16/20], Batch Num: [321/600]
Discriminator Loss: 0.8785, Generator Loss: 1.6380
D(x): 0.7343, D(G(z)): 0.3205
Epoch: [16/20], Batch Num: [322/600]
Discriminator Loss: 0.8296, Generator Loss: 1.5176
D(x): 0.7282, D(G(z)): 0.2957
Epoch: [16/20], Batch Num: [323/600]
Discriminator Loss: 0.9074, Generator Loss: 1.5404
D(x): 0.7188, D(G(z)): 0.3195
Epoch: [16/20], Batch Num: [324/600]
Discriminator Loss: 0.9366, Generator Loss: 1.5314
D(x): 0.7257, D(G(z)): 0.3134
Epoch: [16/20], Batch Num: [325/600]
Discriminator Loss: 0.8105, Generator Loss: 1.7005
D(x): 0.7536, D(G(z)): 0.3081
Epoch: [16/20], Batch Num: [326/600]
Discriminator Loss: 0.9580, Generator Loss: 1.6456
D(x): 0.7079, D(G(z)): 0.3204
Epoch: [16/20], Batch Num: [327/600]
Discriminator Loss: 0.8659, Generator Loss: 1.7774
D(x): 0.7173, D(G(z)): 0.2853
Epoch: [16/20], Batch Num: [328/600]
Discriminator Loss: 0.9871, Generator Loss: 1.5752
D(x): 0.6385, D(G(z)): 0.2531
Epoch: [16/20], Batch Num: [329/600]
Discriminator Loss: 0.9584, Generator Loss: 1.6901
D(x): 0.6773, D(G(z)): 0.2797
Epoch: [16/20], Batch Num: [330/600]
Discriminator Loss: 0.8149, Generator Loss: 1.5003
D(x): 0.7356, D(G(z)): 0.2904
Epoch: [16/20], Batch Num: [331/600]
Discriminator Loss: 0.9247, Generator Loss: 1.4075
D(x): 0.7556, D(G(z)): 0.3448
Epoch: [16/20], Batch Num: [332/600]
Discriminator Loss: 0.9338, Generator Loss: 1.6412
D(x): 0.7644, D(G(z)): 0.3613
Epoch: [16/20], Batch Num: [333/600]
Discriminator Loss: 1.0213, Generator Loss: 1.5567
D(x): 0.6691, D(G(z)): 0.3252
Epoch: [16/20], Batch Num: [334/600]
Discriminator Loss: 0.9962, Generator Loss: 1.7997
D(x): 0.6464, D(G(z)): 0.2786
Epoch: [16/20], Batch Num: [335/600]
Discriminator Loss: 0.9895, Generator Loss: 1.5129
D(x): 0.6310, D(G(z)): 0.2671
Epoch: [16/20], Batch Num: [336/600]
Discriminator Loss: 1.0586, Generator Loss: 1.3984
D(x): 0.6512, D(G(z)): 0.3331
Epoch: [16/20], Batch Num: [337/600]
Discriminator Loss: 0.9590, Generator Loss: 1.3655
D(x): 0.7070, D(G(z)): 0.3345
Epoch: [16/20], Batch Num: [338/600]
Discriminator Loss: 0.9967, Generator Loss: 1.4699
D(x): 0.7601, D(G(z)): 0.3765
Epoch: [16/20], Batch Num: [339/600]
Discriminator Loss: 0.9022, Generator Loss: 1.4252
D(x): 0.6979, D(G(z)): 0.3112
Epoch: [16/20], Batch Num: [340/600]
Discriminator Loss: 0.9223, Generator Loss: 1.5293
D(x): 0.7128, D(G(z)): 0.3042
Epoch: [16/20], Batch Num: [341/600]
Discriminator Loss: 0.9189, Generator Loss: 1.4947
D(x): 0.7193, D(G(z)): 0.3278
Epoch: [16/20], Batch Num: [342/600]
Discriminator Loss: 0.8586, Generator Loss: 1.4925
D(x): 0.6887, D(G(z)): 0.2820
Epoch: [16/20], Batch Num: [343/600]
Discriminator Loss: 0.9267, Generator Loss: 1.6205
D(x): 0.6958, D(G(z)): 0.2904
Epoch: [16/20], Batch Num: [344/600]
Discriminator Loss: 0.9371, Generator Loss: 1.5148
D(x): 0.6897, D(G(z)): 0.2959
Epoch: [16/20], Batch Num: [345/600]
Discriminator Loss: 0.8404, Generator Loss: 1.5456
D(x): 0.7089, D(G(z)): 0.2704
Epoch: [16/20], Batch Num: [346/600]
Discriminator Loss: 0.9253, Generator Loss: 1.5635
D(x): 0.6750, D(G(z)): 0.2954
Epoch: [16/20], Batch Num: [347/600]
Discriminator Loss: 0.8752, Generator Loss: 1.4192
D(x): 0.6989, D(G(z)): 0.2824
Epoch: [16/20], Batch Num: [348/600]
Discriminator Loss: 0.9790, Generator Loss: 1.3313
D(x): 0.7181, D(G(z)): 0.3514
Epoch: [16/20], Batch Num: [349/600]
Discriminator Loss: 0.9797, Generator Loss: 1.5183
D(x): 0.6363, D(G(z)): 0.2706
Epoch: [16/20], Batch Num: [350/600]
Discriminator Loss: 0.7927, Generator Loss: 1.5276
D(x): 0.7426, D(G(z)): 0.2833
Epoch: [16/20], Batch Num: [351/600]
Discriminator Loss: 0.9821, Generator Loss: 1.5191
D(x): 0.7231, D(G(z)): 0.3488
Epoch: [16/20], Batch Num: [352/600]
Discriminator Loss: 1.0280, Generator Loss: 1.4659
D(x): 0.6940, D(G(z)): 0.3424
Epoch: [16/20], Batch Num: [353/600]
Discriminator Loss: 0.9113, Generator Loss: 1.5076
D(x): 0.6702, D(G(z)): 0.2666
Epoch: [16/20], Batch Num: [354/600]
Discriminator Loss: 0.9206, Generator Loss: 1.5720
D(x): 0.6870, D(G(z)): 0.2633
Epoch: [16/20], Batch Num: [355/600]
Discriminator Loss: 0.7919, Generator Loss: 1.5609
D(x): 0.7463, D(G(z)): 0.2807
Epoch: [16/20], Batch Num: [356/600]
Discriminator Loss: 0.9023, Generator Loss: 1.5320
D(x): 0.7389, D(G(z)): 0.3367
Epoch: [16/20], Batch Num: [357/600]
Discriminator Loss: 0.9826, Generator Loss: 1.5419
D(x): 0.6623, D(G(z)): 0.2873
Epoch: [16/20], Batch Num: [358/600]
Discriminator Loss: 0.8910, Generator Loss: 1.6062
D(x): 0.7380, D(G(z)): 0.3055
Epoch: [16/20], Batch Num: [359/600]
Discriminator Loss: 0.9754, Generator Loss: 1.7304
D(x): 0.7012, D(G(z)): 0.3132
Epoch: [16/20], Batch Num: [360/600]
Discriminator Loss: 0.9250, Generator Loss: 1.6862
D(x): 0.7009, D(G(z)): 0.3068
Epoch: [16/20], Batch Num: [361/600]
Discriminator Loss: 0.9069, Generator Loss: 1.8151
D(x): 0.6610, D(G(z)): 0.2484
Epoch: [16/20], Batch Num: [362/600]
Discriminator Loss: 0.9418, Generator Loss: 1.6053
D(x): 0.6885, D(G(z)): 0.2930
Epoch: [16/20], Batch Num: [363/600]
Discriminator Loss: 0.9132, Generator Loss: 1.6481
D(x): 0.6893, D(G(z)): 0.2971
Epoch: [16/20], Batch Num: [364/600]
Discriminator Loss: 0.8039, Generator Loss: 1.6069
D(x): 0.7337, D(G(z)): 0.2772
Epoch: [16/20], Batch Num: [365/600]
Discriminator Loss: 0.8354, Generator Loss: 1.2674
D(x): 0.7387, D(G(z)): 0.3104
Epoch: [16/20], Batch Num: [366/600]
Discriminator Loss: 1.0425, Generator Loss: 1.5525
D(x): 0.6657, D(G(z)): 0.3356
Epoch: [16/20], Batch Num: [367/600]
Discriminator Loss: 0.9751, Generator Loss: 1.5016
D(x): 0.6954, D(G(z)): 0.3390
Epoch: [16/20], Batch Num: [368/600]
Discriminator Loss: 1.0263, Generator Loss: 1.4902
D(x): 0.6911, D(G(z)): 0.3518
Epoch: [16/20], Batch Num: [369/600]
Discriminator Loss: 1.0804, Generator Loss: 1.6069
D(x): 0.6543, D(G(z)): 0.3106
Epoch: [16/20], Batch Num: [370/600]
Discriminator Loss: 0.8588, Generator Loss: 1.4680
D(x): 0.6576, D(G(z)): 0.2452
Epoch: [16/20], Batch Num: [371/600]
Discriminator Loss: 1.0881, Generator Loss: 1.3740
D(x): 0.6064, D(G(z)): 0.2903
Epoch: [16/20], Batch Num: [372/600]
Discriminator Loss: 1.0822, Generator Loss: 1.1117
D(x): 0.6463, D(G(z)): 0.3374
Epoch: [16/20], Batch Num: [373/600]
Discriminator Loss: 1.0062, Generator Loss: 1.1499
D(x): 0.7295, D(G(z)): 0.3631
Epoch: [16/20], Batch Num: [374/600]
Discriminator Loss: 1.1294, Generator Loss: 1.1778
D(x): 0.7056, D(G(z)): 0.4119
Epoch: [16/20], Batch Num: [375/600]
Discriminator Loss: 0.9181, Generator Loss: 1.3495
D(x): 0.7506, D(G(z)): 0.3303
Epoch: [16/20], Batch Num: [376/600]
Discriminator Loss: 1.0675, Generator Loss: 1.5348
D(x): 0.6545, D(G(z)): 0.3505
Epoch: [16/20], Batch Num: [377/600]
Discriminator Loss: 1.0149, Generator Loss: 1.5204
D(x): 0.6272, D(G(z)): 0.2728
Epoch: [16/20], Batch Num: [378/600]
Discriminator Loss: 1.0899, Generator Loss: 1.5202
D(x): 0.6023, D(G(z)): 0.2910
Epoch: [16/20], Batch Num: [379/600]
Discriminator Loss: 1.1489, Generator Loss: 1.2128
D(x): 0.5615, D(G(z)): 0.2831
Epoch: [16/20], Batch Num: [380/600]
Discriminator Loss: 0.9747, Generator Loss: 1.0970
D(x): 0.7052, D(G(z)): 0.3406
Epoch: [16/20], Batch Num: [381/600]
Discriminator Loss: 1.0193, Generator Loss: 1.3237
D(x): 0.7317, D(G(z)): 0.4041
Epoch: [16/20], Batch Num: [382/600]
Discriminator Loss: 1.1101, Generator Loss: 1.3477
D(x): 0.7282, D(G(z)): 0.4391
Epoch: [16/20], Batch Num: [383/600]
Discriminator Loss: 0.8821, Generator Loss: 1.7810
D(x): 0.6890, D(G(z)): 0.3076
Epoch: [16/20], Batch Num: [384/600]
Discriminator Loss: 0.8895, Generator Loss: 1.6834
D(x): 0.6299, D(G(z)): 0.2365
Epoch: [16/20], Batch Num: [385/600]
Discriminator Loss: 0.9280, Generator Loss: 1.4902
D(x): 0.6007, D(G(z)): 0.2309
Epoch: [16/20], Batch Num: [386/600]
Discriminator Loss: 0.8663, Generator Loss: 1.5449
D(x): 0.6858, D(G(z)): 0.2642
Epoch: [16/20], Batch Num: [387/600]
Discriminator Loss: 0.9467, Generator Loss: 1.3653
D(x): 0.6881, D(G(z)): 0.3170
Epoch: [16/20], Batch Num: [388/600]
Discriminator Loss: 0.9011, Generator Loss: 1.4253
D(x): 0.7373, D(G(z)): 0.3415
Epoch: [16/20], Batch Num: [389/600]
Discriminator Loss: 0.8544, Generator Loss: 1.5788
D(x): 0.7387, D(G(z)): 0.3237
Epoch: [16/20], Batch Num: [390/600]
Discriminator Loss: 0.8225, Generator Loss: 1.5577
D(x): 0.7271, D(G(z)): 0.2848
Epoch: [16/20], Batch Num: [391/600]
Discriminator Loss: 0.9323, Generator Loss: 1.9349
D(x): 0.6562, D(G(z)): 0.2854
Epoch: [16/20], Batch Num: [392/600]
Discriminator Loss: 0.8403, Generator Loss: 1.6618
D(x): 0.6958, D(G(z)): 0.2645
Epoch: [16/20], Batch Num: [393/600]
Discriminator Loss: 0.6615, Generator Loss: 1.7195
D(x): 0.7524, D(G(z)): 0.2454
Epoch: [16/20], Batch Num: [394/600]
Discriminator Loss: 0.7559, Generator Loss: 1.7191
D(x): 0.7458, D(G(z)): 0.2737
Epoch: [16/20], Batch Num: [395/600]
Discriminator Loss: 0.7011, Generator Loss: 1.8767
D(x): 0.7998, D(G(z)): 0.3001
Epoch: [16/20], Batch Num: [396/600]
Discriminator Loss: 0.6735, Generator Loss: 2.0024
D(x): 0.7687, D(G(z)): 0.2390
Epoch: [16/20], Batch Num: [397/600]
Discriminator Loss: 0.7484, Generator Loss: 1.9877
D(x): 0.7090, D(G(z)): 0.2085
Epoch: [16/20], Batch Num: [398/600]
Discriminator Loss: 0.8658, Generator Loss: 1.9201
D(x): 0.7230, D(G(z)): 0.2836
Epoch: [16/20], Batch Num: [399/600]
Discriminator Loss: 0.9036, Generator Loss: 2.0249
D(x): 0.7541, D(G(z)): 0.2867
Epoch: 16, Batch Num: [400/600]

Epoch: [16/20], Batch Num: [400/600]
Discriminator Loss: 0.8622, Generator Loss: 1.9714
D(x): 0.7326, D(G(z)): 0.2837
Epoch: [16/20], Batch Num: [401/600]
Discriminator Loss: 0.8968, Generator Loss: 1.9050
D(x): 0.6725, D(G(z)): 0.2445
Epoch: [16/20], Batch Num: [402/600]
Discriminator Loss: 0.9468, Generator Loss: 1.7280
D(x): 0.7336, D(G(z)): 0.2976
Epoch: [16/20], Batch Num: [403/600]
Discriminator Loss: 0.7590, Generator Loss: 1.7747
D(x): 0.7663, D(G(z)): 0.2737
Epoch: [16/20], Batch Num: [404/600]
Discriminator Loss: 0.8033, Generator Loss: 1.8617
D(x): 0.8051, D(G(z)): 0.3152
Epoch: [16/20], Batch Num: [405/600]
Discriminator Loss: 0.9210, Generator Loss: 1.8886
D(x): 0.6899, D(G(z)): 0.2769
Epoch: [16/20], Batch Num: [406/600]
Discriminator Loss: 0.8665, Generator Loss: 2.0087
D(x): 0.7198, D(G(z)): 0.2590
Epoch: [16/20], Batch Num: [407/600]
Discriminator Loss: 0.8877, Generator Loss: 1.7112
D(x): 0.6885, D(G(z)): 0.2604
Epoch: [16/20], Batch Num: [408/600]
Discriminator Loss: 0.9033, Generator Loss: 1.8186
D(x): 0.7275, D(G(z)): 0.3084
Epoch: [16/20], Batch Num: [409/600]
Discriminator Loss: 1.1499, Generator Loss: 1.7110
D(x): 0.6914, D(G(z)): 0.3697
Epoch: [16/20], Batch Num: [410/600]
Discriminator Loss: 1.0892, Generator Loss: 1.5981
D(x): 0.6674, D(G(z)): 0.3148
Epoch: [16/20], Batch Num: [411/600]
Discriminator Loss: 1.1510, Generator Loss: 1.3633
D(x): 0.6260, D(G(z)): 0.3031
Epoch: [16/20], Batch Num: [412/600]
Discriminator Loss: 1.0523, Generator Loss: 1.2803
D(x): 0.7197, D(G(z)): 0.3696
Epoch: [16/20], Batch Num: [413/600]
Discriminator Loss: 1.1733, Generator Loss: 1.2792
D(x): 0.6698, D(G(z)): 0.3701
Epoch: [16/20], Batch Num: [414/600]
Discriminator Loss: 1.1939, Generator Loss: 1.3834
D(x): 0.6525, D(G(z)): 0.3837
Epoch: [16/20], Batch Num: [415/600]
Discriminator Loss: 1.0104, Generator Loss: 1.3057
D(x): 0.6739, D(G(z)): 0.3245
Epoch: [16/20], Batch Num: [416/600]
Discriminator Loss: 0.9983, Generator Loss: 1.2642
D(x): 0.6788, D(G(z)): 0.3421
Epoch: [16/20], Batch Num: [417/600]
Discriminator Loss: 1.2549, Generator Loss: 1.2734
D(x): 0.5984, D(G(z)): 0.3729
Epoch: [16/20], Batch Num: [418/600]
Discriminator Loss: 1.1786, Generator Loss: 1.1837
D(x): 0.6577, D(G(z)): 0.4045
Epoch: [16/20], Batch Num: [419/600]
Discriminator Loss: 1.1674, Generator Loss: 1.1962
D(x): 0.6416, D(G(z)): 0.3790
Epoch: [16/20], Batch Num: [420/600]
Discriminator Loss: 1.2405, Generator Loss: 1.3297
D(x): 0.6321, D(G(z)): 0.4236
Epoch: [16/20], Batch Num: [421/600]
Discriminator Loss: 1.0942, Generator Loss: 1.2003
D(x): 0.6164, D(G(z)): 0.3389
Epoch: [16/20], Batch Num: [422/600]
Discriminator Loss: 0.9624, Generator Loss: 1.2976
D(x): 0.6701, D(G(z)): 0.3459
Epoch: [16/20], Batch Num: [423/600]
Discriminator Loss: 0.9801, Generator Loss: 1.2518
D(x): 0.6695, D(G(z)): 0.3456
Epoch: [16/20], Batch Num: [424/600]
Discriminator Loss: 0.9250, Generator Loss: 1.2063
D(x): 0.6846, D(G(z)): 0.3422
Epoch: [16/20], Batch Num: [425/600]
Discriminator Loss: 1.0568, Generator Loss: 1.1728
D(x): 0.6138, D(G(z)): 0.3291
Epoch: [16/20], Batch Num: [426/600]
Discriminator Loss: 0.9747, Generator Loss: 1.1599
D(x): 0.6662, D(G(z)): 0.3429
Epoch: [16/20], Batch Num: [427/600]
Discriminator Loss: 0.8359, Generator Loss: 1.1875
D(x): 0.7439, D(G(z)): 0.3447
Epoch: [16/20], Batch Num: [428/600]
Discriminator Loss: 1.0605, Generator Loss: 1.2193
D(x): 0.6628, D(G(z)): 0.3864
Epoch: [16/20], Batch Num: [429/600]
Discriminator Loss: 0.9033, Generator Loss: 1.2672
D(x): 0.7124, D(G(z)): 0.3417
Epoch: [16/20], Batch Num: [430/600]
Discriminator Loss: 0.8428, Generator Loss: 1.3631
D(x): 0.7138, D(G(z)): 0.3287
Epoch: [16/20], Batch Num: [431/600]
Discriminator Loss: 0.8643, Generator Loss: 1.4583
D(x): 0.6757, D(G(z)): 0.2857
Epoch: [16/20], Batch Num: [432/600]
Discriminator Loss: 0.8584, Generator Loss: 1.3532
D(x): 0.6615, D(G(z)): 0.2685
Epoch: [16/20], Batch Num: [433/600]
Discriminator Loss: 0.8784, Generator Loss: 1.1968
D(x): 0.6942, D(G(z)): 0.3050
Epoch: [16/20], Batch Num: [434/600]
Discriminator Loss: 0.8769, Generator Loss: 1.3052
D(x): 0.7420, D(G(z)): 0.3414
Epoch: [16/20], Batch Num: [435/600]
Discriminator Loss: 0.9270, Generator Loss: 1.3171
D(x): 0.7356, D(G(z)): 0.3648
Epoch: [16/20], Batch Num: [436/600]
Discriminator Loss: 0.7545, Generator Loss: 1.4861
D(x): 0.7813, D(G(z)): 0.3095
Epoch: [16/20], Batch Num: [437/600]
Discriminator Loss: 0.8024, Generator Loss: 1.6076
D(x): 0.7397, D(G(z)): 0.2884
Epoch: [16/20], Batch Num: [438/600]
Discriminator Loss: 0.7108, Generator Loss: 1.7515
D(x): 0.7098, D(G(z)): 0.2205
Epoch: [16/20], Batch Num: [439/600]
Discriminator Loss: 0.7351, Generator Loss: 1.7902
D(x): 0.7113, D(G(z)): 0.2357
Epoch: [16/20], Batch Num: [440/600]
Discriminator Loss: 0.6473, Generator Loss: 1.6937
D(x): 0.7699, D(G(z)): 0.2340
Epoch: [16/20], Batch Num: [441/600]
Discriminator Loss: 0.6314, Generator Loss: 1.4873
D(x): 0.7443, D(G(z)): 0.2151
Epoch: [16/20], Batch Num: [442/600]
Discriminator Loss: 0.7070, Generator Loss: 1.5724
D(x): 0.7996, D(G(z)): 0.2891
Epoch: [16/20], Batch Num: [443/600]
Discriminator Loss: 0.6620, Generator Loss: 1.6920
D(x): 0.8448, D(G(z)): 0.2845
Epoch: [16/20], Batch Num: [444/600]
Discriminator Loss: 0.7837, Generator Loss: 1.8190
D(x): 0.7393, D(G(z)): 0.2473
Epoch: [16/20], Batch Num: [445/600]
Discriminator Loss: 0.7313, Generator Loss: 1.9439
D(x): 0.7585, D(G(z)): 0.2399
Epoch: [16/20], Batch Num: [446/600]
Discriminator Loss: 0.6553, Generator Loss: 1.8189
D(x): 0.7685, D(G(z)): 0.2082
Epoch: [16/20], Batch Num: [447/600]
Discriminator Loss: 0.6179, Generator Loss: 1.8574
D(x): 0.7600, D(G(z)): 0.1949
Epoch: [16/20], Batch Num: [448/600]
Discriminator Loss: 0.9283, Generator Loss: 1.6670
D(x): 0.7138, D(G(z)): 0.2777
Epoch: [16/20], Batch Num: [449/600]
Discriminator Loss: 0.7105, Generator Loss: 1.7150
D(x): 0.8417, D(G(z)): 0.2976
Epoch: [16/20], Batch Num: [450/600]
Discriminator Loss: 0.7576, Generator Loss: 2.0758
D(x): 0.7567, D(G(z)): 0.2675
Epoch: [16/20], Batch Num: [451/600]
Discriminator Loss: 0.7784, Generator Loss: 2.1386
D(x): 0.7236, D(G(z)): 0.2193
Epoch: [16/20], Batch Num: [452/600]
Discriminator Loss: 0.7657, Generator Loss: 2.0996
D(x): 0.7367, D(G(z)): 0.2311
Epoch: [16/20], Batch Num: [453/600]
Discriminator Loss: 0.9335, Generator Loss: 1.8055
D(x): 0.7350, D(G(z)): 0.2840
Epoch: [16/20], Batch Num: [454/600]
Discriminator Loss: 0.9737, Generator Loss: 1.7382
D(x): 0.6553, D(G(z)): 0.2769
Epoch: [16/20], Batch Num: [455/600]
Discriminator Loss: 0.9054, Generator Loss: 1.5340
D(x): 0.7379, D(G(z)): 0.2878
Epoch: [16/20], Batch Num: [456/600]
Discriminator Loss: 0.8684, Generator Loss: 1.6696
D(x): 0.8137, D(G(z)): 0.3390
Epoch: [16/20], Batch Num: [457/600]
Discriminator Loss: 0.9623, Generator Loss: 1.8689
D(x): 0.7468, D(G(z)): 0.3011
Epoch: [16/20], Batch Num: [458/600]
Discriminator Loss: 0.9305, Generator Loss: 2.0214
D(x): 0.6573, D(G(z)): 0.2109
Epoch: [16/20], Batch Num: [459/600]
Discriminator Loss: 1.1356, Generator Loss: 1.4975
D(x): 0.6019, D(G(z)): 0.2397
Epoch: [16/20], Batch Num: [460/600]
Discriminator Loss: 1.0595, Generator Loss: 1.2769
D(x): 0.6776, D(G(z)): 0.2941
Epoch: [16/20], Batch Num: [461/600]
Discriminator Loss: 1.2541, Generator Loss: 1.3912
D(x): 0.7607, D(G(z)): 0.4386
Epoch: [16/20], Batch Num: [462/600]
Discriminator Loss: 1.2237, Generator Loss: 1.6832
D(x): 0.7216, D(G(z)): 0.4139
Epoch: [16/20], Batch Num: [463/600]
Discriminator Loss: 1.2158, Generator Loss: 1.7720
D(x): 0.6098, D(G(z)): 0.3304
Epoch: [16/20], Batch Num: [464/600]
Discriminator Loss: 1.2993, Generator Loss: 1.8805
D(x): 0.5293, D(G(z)): 0.2765
Epoch: [16/20], Batch Num: [465/600]
Discriminator Loss: 1.2057, Generator Loss: 1.4292
D(x): 0.5481, D(G(z)): 0.2488
Epoch: [16/20], Batch Num: [466/600]
Discriminator Loss: 1.5350, Generator Loss: 1.0275
D(x): 0.5013, D(G(z)): 0.3460
Epoch: [16/20], Batch Num: [467/600]
Discriminator Loss: 1.1654, Generator Loss: 0.8363
D(x): 0.7428, D(G(z)): 0.4357
Epoch: [16/20], Batch Num: [468/600]
Discriminator Loss: 1.2138, Generator Loss: 1.1564
D(x): 0.7660, D(G(z)): 0.4764
Epoch: [16/20], Batch Num: [469/600]
Discriminator Loss: 1.1751, Generator Loss: 1.2784
D(x): 0.6819, D(G(z)): 0.4072
Epoch: [16/20], Batch Num: [470/600]
Discriminator Loss: 1.1687, Generator Loss: 1.4600
D(x): 0.6116, D(G(z)): 0.3531
Epoch: [16/20], Batch Num: [471/600]
Discriminator Loss: 1.2404, Generator Loss: 1.4289
D(x): 0.5106, D(G(z)): 0.2731
Epoch: [16/20], Batch Num: [472/600]
Discriminator Loss: 1.1556, Generator Loss: 1.5139
D(x): 0.5733, D(G(z)): 0.3019
Epoch: [16/20], Batch Num: [473/600]
Discriminator Loss: 1.1665, Generator Loss: 1.2222
D(x): 0.5800, D(G(z)): 0.3194
Epoch: [16/20], Batch Num: [474/600]
Discriminator Loss: 1.0796, Generator Loss: 1.1116
D(x): 0.6519, D(G(z)): 0.3878
Epoch: [16/20], Batch Num: [475/600]
Discriminator Loss: 1.1522, Generator Loss: 0.9845
D(x): 0.6690, D(G(z)): 0.4153
Epoch: [16/20], Batch Num: [476/600]
Discriminator Loss: 1.1259, Generator Loss: 1.0280
D(x): 0.6852, D(G(z)): 0.4339
Epoch: [16/20], Batch Num: [477/600]
Discriminator Loss: 0.9987, Generator Loss: 1.1599
D(x): 0.7192, D(G(z)): 0.4103
Epoch: [16/20], Batch Num: [478/600]
Discriminator Loss: 1.0512, Generator Loss: 1.3047
D(x): 0.6613, D(G(z)): 0.3795
Epoch: [16/20], Batch Num: [479/600]
Discriminator Loss: 0.9824, Generator Loss: 1.3643
D(x): 0.6618, D(G(z)): 0.3438
Epoch: [16/20], Batch Num: [480/600]
Discriminator Loss: 0.9701, Generator Loss: 1.4109
D(x): 0.6181, D(G(z)): 0.2903
Epoch: [16/20], Batch Num: [481/600]
Discriminator Loss: 0.9127, Generator Loss: 1.4628
D(x): 0.6486, D(G(z)): 0.2969
Epoch: [16/20], Batch Num: [482/600]
Discriminator Loss: 0.9704, Generator Loss: 1.5046
D(x): 0.6134, D(G(z)): 0.2846
Epoch: [16/20], Batch Num: [483/600]
Discriminator Loss: 0.8085, Generator Loss: 1.1951
D(x): 0.7057, D(G(z)): 0.2915
Epoch: [16/20], Batch Num: [484/600]
Discriminator Loss: 0.9847, Generator Loss: 1.0946
D(x): 0.6447, D(G(z)): 0.3344
Epoch: [16/20], Batch Num: [485/600]
Discriminator Loss: 0.8600, Generator Loss: 1.1233
D(x): 0.7127, D(G(z)): 0.3380
Epoch: [16/20], Batch Num: [486/600]
Discriminator Loss: 0.9107, Generator Loss: 1.2303
D(x): 0.7413, D(G(z)): 0.3779
Epoch: [16/20], Batch Num: [487/600]
Discriminator Loss: 0.8117, Generator Loss: 1.3126
D(x): 0.7704, D(G(z)): 0.3664
Epoch: [16/20], Batch Num: [488/600]
Discriminator Loss: 0.7832, Generator Loss: 1.3443
D(x): 0.7641, D(G(z)): 0.3300
Epoch: [16/20], Batch Num: [489/600]
Discriminator Loss: 0.8486, Generator Loss: 1.4783
D(x): 0.7221, D(G(z)): 0.3202
Epoch: [16/20], Batch Num: [490/600]
Discriminator Loss: 0.8954, Generator Loss: 1.6905
D(x): 0.6881, D(G(z)): 0.3079
Epoch: [16/20], Batch Num: [491/600]
Discriminator Loss: 0.7513, Generator Loss: 1.6799
D(x): 0.6859, D(G(z)): 0.2284
Epoch: [16/20], Batch Num: [492/600]
Discriminator Loss: 0.8262, Generator Loss: 1.7236
D(x): 0.6619, D(G(z)): 0.2379
Epoch: [16/20], Batch Num: [493/600]
Discriminator Loss: 0.7204, Generator Loss: 1.6312
D(x): 0.7032, D(G(z)): 0.2281
Epoch: [16/20], Batch Num: [494/600]
Discriminator Loss: 0.6707, Generator Loss: 1.6135
D(x): 0.7403, D(G(z)): 0.2464
Epoch: [16/20], Batch Num: [495/600]
Discriminator Loss: 0.7151, Generator Loss: 1.3409
D(x): 0.7848, D(G(z)): 0.2955
Epoch: [16/20], Batch Num: [496/600]
Discriminator Loss: 0.6555, Generator Loss: 1.5908
D(x): 0.8268, D(G(z)): 0.3217
Epoch: [16/20], Batch Num: [497/600]
Discriminator Loss: 0.7470, Generator Loss: 1.7348
D(x): 0.7550, D(G(z)): 0.2813
Epoch: [16/20], Batch Num: [498/600]
Discriminator Loss: 0.6768, Generator Loss: 1.8484
D(x): 0.7593, D(G(z)): 0.2566
Epoch: [16/20], Batch Num: [499/600]
Discriminator Loss: 0.7014, Generator Loss: 1.8929
D(x): 0.7020, D(G(z)): 0.1989
Epoch: 16, Batch Num: [500/600]

Epoch: [16/20], Batch Num: [500/600]
Discriminator Loss: 0.7141, Generator Loss: 1.8101
D(x): 0.7369, D(G(z)): 0.2360
Epoch: [16/20], Batch Num: [501/600]
Discriminator Loss: 0.7102, Generator Loss: 1.6239
D(x): 0.7824, D(G(z)): 0.2798
Epoch: [16/20], Batch Num: [502/600]
Discriminator Loss: 0.6898, Generator Loss: 1.6010
D(x): 0.7900, D(G(z)): 0.2811
Epoch: [16/20], Batch Num: [503/600]
Discriminator Loss: 0.6217, Generator Loss: 1.8197
D(x): 0.8120, D(G(z)): 0.2570
Epoch: [16/20], Batch Num: [504/600]
Discriminator Loss: 0.7623, Generator Loss: 1.9222
D(x): 0.7288, D(G(z)): 0.2438
Epoch: [16/20], Batch Num: [505/600]
Discriminator Loss: 0.7945, Generator Loss: 2.1119
D(x): 0.7229, D(G(z)): 0.2227
Epoch: [16/20], Batch Num: [506/600]
Discriminator Loss: 0.6632, Generator Loss: 1.8866
D(x): 0.7531, D(G(z)): 0.2209
Epoch: [16/20], Batch Num: [507/600]
Discriminator Loss: 0.7751, Generator Loss: 1.6069
D(x): 0.7455, D(G(z)): 0.2758
Epoch: [16/20], Batch Num: [508/600]
Discriminator Loss: 0.7785, Generator Loss: 1.8718
D(x): 0.8184, D(G(z)): 0.3234
Epoch: [16/20], Batch Num: [509/600]
Discriminator Loss: 0.8156, Generator Loss: 1.8726
D(x): 0.7208, D(G(z)): 0.2642
Epoch: [16/20], Batch Num: [510/600]
Discriminator Loss: 0.8788, Generator Loss: 1.7802
D(x): 0.7072, D(G(z)): 0.2613
Epoch: [16/20], Batch Num: [511/600]
Discriminator Loss: 0.9449, Generator Loss: 1.6781
D(x): 0.7188, D(G(z)): 0.2782
Epoch: [16/20], Batch Num: [512/600]
Discriminator Loss: 1.0308, Generator Loss: 1.6089
D(x): 0.6664, D(G(z)): 0.2907
Epoch: [16/20], Batch Num: [513/600]
Discriminator Loss: 0.9937, Generator Loss: 1.6270
D(x): 0.7679, D(G(z)): 0.3516
Epoch: [16/20], Batch Num: [514/600]
Discriminator Loss: 1.0111, Generator Loss: 1.7689
D(x): 0.7209, D(G(z)): 0.3323
Epoch: [16/20], Batch Num: [515/600]
Discriminator Loss: 0.9577, Generator Loss: 1.6420
D(x): 0.6862, D(G(z)): 0.2619
Epoch: [16/20], Batch Num: [516/600]
Discriminator Loss: 0.9098, Generator Loss: 1.5919
D(x): 0.7028, D(G(z)): 0.2862
Epoch: [16/20], Batch Num: [517/600]
Discriminator Loss: 1.0707, Generator Loss: 1.3586
D(x): 0.6831, D(G(z)): 0.3236
Epoch: [16/20], Batch Num: [518/600]
Discriminator Loss: 1.0540, Generator Loss: 1.5316
D(x): 0.7046, D(G(z)): 0.3736
Epoch: [16/20], Batch Num: [519/600]
Discriminator Loss: 1.0281, Generator Loss: 1.4300
D(x): 0.7020, D(G(z)): 0.3303
Epoch: [16/20], Batch Num: [520/600]
Discriminator Loss: 1.0190, Generator Loss: 1.6449
D(x): 0.7042, D(G(z)): 0.3408
Epoch: [16/20], Batch Num: [521/600]
Discriminator Loss: 1.1805, Generator Loss: 1.5524
D(x): 0.6412, D(G(z)): 0.3536
Epoch: [16/20], Batch Num: [522/600]
Discriminator Loss: 0.9701, Generator Loss: 1.3340
D(x): 0.6527, D(G(z)): 0.2859
Epoch: [16/20], Batch Num: [523/600]
Discriminator Loss: 0.8978, Generator Loss: 1.2912
D(x): 0.6820, D(G(z)): 0.2856
Epoch: [16/20], Batch Num: [524/600]
Discriminator Loss: 1.0374, Generator Loss: 1.2742
D(x): 0.6875, D(G(z)): 0.3410
Epoch: [16/20], Batch Num: [525/600]
Discriminator Loss: 0.9472, Generator Loss: 1.1120
D(x): 0.7397, D(G(z)): 0.3653
Epoch: [16/20], Batch Num: [526/600]
Discriminator Loss: 0.9840, Generator Loss: 1.2956
D(x): 0.7941, D(G(z)): 0.4076
Epoch: [16/20], Batch Num: [527/600]
Discriminator Loss: 0.8898, Generator Loss: 1.5195
D(x): 0.7608, D(G(z)): 0.3479
Epoch: [16/20], Batch Num: [528/600]
Discriminator Loss: 0.9088, Generator Loss: 1.8890
D(x): 0.6683, D(G(z)): 0.2637
Epoch: [16/20], Batch Num: [529/600]
Discriminator Loss: 0.8371, Generator Loss: 1.7691
D(x): 0.6486, D(G(z)): 0.2174
Epoch: [16/20], Batch Num: [530/600]
Discriminator Loss: 0.8419, Generator Loss: 1.5588
D(x): 0.6511, D(G(z)): 0.2283
Epoch: [16/20], Batch Num: [531/600]
Discriminator Loss: 0.6985, Generator Loss: 1.3762
D(x): 0.7537, D(G(z)): 0.2594
Epoch: [16/20], Batch Num: [532/600]
Discriminator Loss: 0.7971, Generator Loss: 1.2848
D(x): 0.7806, D(G(z)): 0.3255
Epoch: [16/20], Batch Num: [533/600]
Discriminator Loss: 0.7937, Generator Loss: 1.4283
D(x): 0.8197, D(G(z)): 0.3500
Epoch: [16/20], Batch Num: [534/600]
Discriminator Loss: 0.6431, Generator Loss: 1.5902
D(x): 0.8363, D(G(z)): 0.3042
Epoch: [16/20], Batch Num: [535/600]
Discriminator Loss: 0.7544, Generator Loss: 1.8323
D(x): 0.7311, D(G(z)): 0.2643
Epoch: [16/20], Batch Num: [536/600]
Discriminator Loss: 0.6449, Generator Loss: 1.9462
D(x): 0.7761, D(G(z)): 0.2299
Epoch: [16/20], Batch Num: [537/600]
Discriminator Loss: 0.6696, Generator Loss: 2.0565
D(x): 0.7402, D(G(z)): 0.2096
Epoch: [16/20], Batch Num: [538/600]
Discriminator Loss: 0.6934, Generator Loss: 1.8309
D(x): 0.7462, D(G(z)): 0.2240
Epoch: [16/20], Batch Num: [539/600]
Discriminator Loss: 0.8240, Generator Loss: 1.9177
D(x): 0.6933, D(G(z)): 0.2441
Epoch: [16/20], Batch Num: [540/600]
Discriminator Loss: 0.6874, Generator Loss: 1.7839
D(x): 0.7944, D(G(z)): 0.2548
Epoch: [16/20], Batch Num: [541/600]
Discriminator Loss: 0.6383, Generator Loss: 1.7701
D(x): 0.8032, D(G(z)): 0.2499
Epoch: [16/20], Batch Num: [542/600]
Discriminator Loss: 0.5088, Generator Loss: 1.8257
D(x): 0.8704, D(G(z)): 0.2520
Epoch: [16/20], Batch Num: [543/600]
Discriminator Loss: 0.6399, Generator Loss: 1.8900
D(x): 0.7886, D(G(z)): 0.2454
Epoch: [16/20], Batch Num: [544/600]
Discriminator Loss: 0.7453, Generator Loss: 1.9642
D(x): 0.7377, D(G(z)): 0.2386
Epoch: [16/20], Batch Num: [545/600]
Discriminator Loss: 0.7465, Generator Loss: 1.7960
D(x): 0.7268, D(G(z)): 0.2327
Epoch: [16/20], Batch Num: [546/600]
Discriminator Loss: 0.6353, Generator Loss: 1.8624
D(x): 0.7753, D(G(z)): 0.2239
Epoch: [16/20], Batch Num: [547/600]
Discriminator Loss: 0.6443, Generator Loss: 1.6726
D(x): 0.7907, D(G(z)): 0.2499
Epoch: [16/20], Batch Num: [548/600]
Discriminator Loss: 0.7183, Generator Loss: 1.8991
D(x): 0.8143, D(G(z)): 0.2751
Epoch: [16/20], Batch Num: [549/600]
Discriminator Loss: 0.7862, Generator Loss: 2.0035
D(x): 0.7837, D(G(z)): 0.2714
Epoch: [16/20], Batch Num: [550/600]
Discriminator Loss: 0.7608, Generator Loss: 1.9325
D(x): 0.7603, D(G(z)): 0.2618
Epoch: [16/20], Batch Num: [551/600]
Discriminator Loss: 0.5900, Generator Loss: 1.9666
D(x): 0.8013, D(G(z)): 0.1980
Epoch: [16/20], Batch Num: [552/600]
Discriminator Loss: 0.7119, Generator Loss: 1.8919
D(x): 0.7508, D(G(z)): 0.2300
Epoch: [16/20], Batch Num: [553/600]
Discriminator Loss: 0.8527, Generator Loss: 1.8402
D(x): 0.7222, D(G(z)): 0.2505
Epoch: [16/20], Batch Num: [554/600]
Discriminator Loss: 0.6277, Generator Loss: 1.6952
D(x): 0.8181, D(G(z)): 0.2507
Epoch: [16/20], Batch Num: [555/600]
Discriminator Loss: 0.7657, Generator Loss: 1.8314
D(x): 0.7630, D(G(z)): 0.2642
Epoch: [16/20], Batch Num: [556/600]
Discriminator Loss: 0.8181, Generator Loss: 2.0469
D(x): 0.7736, D(G(z)): 0.2880
Epoch: [16/20], Batch Num: [557/600]
Discriminator Loss: 0.7740, Generator Loss: 2.1681
D(x): 0.7631, D(G(z)): 0.2560
Epoch: [16/20], Batch Num: [558/600]
Discriminator Loss: 0.8753, Generator Loss: 2.1538
D(x): 0.7356, D(G(z)): 0.3002
Epoch: [16/20], Batch Num: [559/600]
Discriminator Loss: 0.8496, Generator Loss: 2.0470
D(x): 0.6758, D(G(z)): 0.1813
Epoch: [16/20], Batch Num: [560/600]
Discriminator Loss: 0.8840, Generator Loss: 1.6314
D(x): 0.6819, D(G(z)): 0.2472
Epoch: [16/20], Batch Num: [561/600]
Discriminator Loss: 0.9244, Generator Loss: 1.4053
D(x): 0.7079, D(G(z)): 0.2992
Epoch: [16/20], Batch Num: [562/600]
Discriminator Loss: 0.9756, Generator Loss: 1.4314
D(x): 0.7875, D(G(z)): 0.3802
Epoch: [16/20], Batch Num: [563/600]
Discriminator Loss: 1.1432, Generator Loss: 1.5648
D(x): 0.6573, D(G(z)): 0.3512
Epoch: [16/20], Batch Num: [564/600]
Discriminator Loss: 1.0401, Generator Loss: 1.5782
D(x): 0.6901, D(G(z)): 0.3233
Epoch: [16/20], Batch Num: [565/600]
Discriminator Loss: 1.2673, Generator Loss: 1.5747
D(x): 0.6043, D(G(z)): 0.3066
Epoch: [16/20], Batch Num: [566/600]
Discriminator Loss: 1.1383, Generator Loss: 1.5542
D(x): 0.6123, D(G(z)): 0.3013
Epoch: [16/20], Batch Num: [567/600]
Discriminator Loss: 1.1795, Generator Loss: 1.3679
D(x): 0.6159, D(G(z)): 0.3218
Epoch: [16/20], Batch Num: [568/600]
Discriminator Loss: 1.2708, Generator Loss: 1.4061
D(x): 0.6721, D(G(z)): 0.4082
Epoch: [16/20], Batch Num: [569/600]
Discriminator Loss: 1.2079, Generator Loss: 1.3149
D(x): 0.6797, D(G(z)): 0.3722
Epoch: [16/20], Batch Num: [570/600]
Discriminator Loss: 1.1879, Generator Loss: 1.6485
D(x): 0.6376, D(G(z)): 0.3737
Epoch: [16/20], Batch Num: [571/600]
Discriminator Loss: 1.1150, Generator Loss: 1.5299
D(x): 0.6128, D(G(z)): 0.2834
Epoch: [16/20], Batch Num: [572/600]
Discriminator Loss: 1.1702, Generator Loss: 1.2578
D(x): 0.5846, D(G(z)): 0.2954
Epoch: [16/20], Batch Num: [573/600]
Discriminator Loss: 1.2327, Generator Loss: 1.2357
D(x): 0.7114, D(G(z)): 0.4200
Epoch: [16/20], Batch Num: [574/600]
Discriminator Loss: 1.0658, Generator Loss: 1.3012
D(x): 0.6529, D(G(z)): 0.3270
Epoch: [16/20], Batch Num: [575/600]
Discriminator Loss: 0.9658, Generator Loss: 1.4775
D(x): 0.6835, D(G(z)): 0.3169
Epoch: [16/20], Batch Num: [576/600]
Discriminator Loss: 0.9096, Generator Loss: 1.4582
D(x): 0.6588, D(G(z)): 0.2740
Epoch: [16/20], Batch Num: [577/600]
Discriminator Loss: 1.0903, Generator Loss: 1.2903
D(x): 0.6263, D(G(z)): 0.3118
Epoch: [16/20], Batch Num: [578/600]
Discriminator Loss: 1.0831, Generator Loss: 1.4265
D(x): 0.6937, D(G(z)): 0.3815
Epoch: [16/20], Batch Num: [579/600]
Discriminator Loss: 1.0286, Generator Loss: 1.4511
D(x): 0.6706, D(G(z)): 0.3386
Epoch: [16/20], Batch Num: [580/600]
Discriminator Loss: 0.9805, Generator Loss: 1.4650
D(x): 0.6693, D(G(z)): 0.3141
Epoch: [16/20], Batch Num: [581/600]
Discriminator Loss: 1.0224, Generator Loss: 1.5431
D(x): 0.6645, D(G(z)): 0.3289
Epoch: [16/20], Batch Num: [582/600]
Discriminator Loss: 0.8976, Generator Loss: 1.5345
D(x): 0.6760, D(G(z)): 0.2947
Epoch: [16/20], Batch Num: [583/600]
Discriminator Loss: 0.7657, Generator Loss: 1.5121
D(x): 0.7248, D(G(z)): 0.2694
Epoch: [16/20], Batch Num: [584/600]
Discriminator Loss: 0.7335, Generator Loss: 1.5870
D(x): 0.7461, D(G(z)): 0.2665
Epoch: [16/20], Batch Num: [585/600]
Discriminator Loss: 0.7901, Generator Loss: 1.6160
D(x): 0.7154, D(G(z)): 0.2677
Epoch: [16/20], Batch Num: [586/600]
Discriminator Loss: 0.8457, Generator Loss: 1.7245
D(x): 0.7289, D(G(z)): 0.3015
Epoch: [16/20], Batch Num: [587/600]
Discriminator Loss: 0.6718, Generator Loss: 1.6269
D(x): 0.7722, D(G(z)): 0.2597
Epoch: [16/20], Batch Num: [588/600]
Discriminator Loss: 0.6837, Generator Loss: 1.7365
D(x): 0.7465, D(G(z)): 0.2516
Epoch: [16/20], Batch Num: [589/600]
Discriminator Loss: 0.7680, Generator Loss: 1.8724
D(x): 0.7803, D(G(z)): 0.3182
Epoch: [16/20], Batch Num: [590/600]
Discriminator Loss: 0.6624, Generator Loss: 1.9389
D(x): 0.7294, D(G(z)): 0.1955
Epoch: [16/20], Batch Num: [591/600]
Discriminator Loss: 0.6709, Generator Loss: 1.8089
D(x): 0.7461, D(G(z)): 0.2157
Epoch: [16/20], Batch Num: [592/600]
Discriminator Loss: 0.5746, Generator Loss: 1.8766
D(x): 0.7982, D(G(z)): 0.2109
Epoch: [16/20], Batch Num: [593/600]
Discriminator Loss: 0.6855, Generator Loss: 1.7962
D(x): 0.7682, D(G(z)): 0.2307
Epoch: [16/20], Batch Num: [594/600]
Discriminator Loss: 0.6177, Generator Loss: 1.8399
D(x): 0.8327, D(G(z)): 0.2572
Epoch: [16/20], Batch Num: [595/600]
Discriminator Loss: 0.6949, Generator Loss: 2.1540
D(x): 0.7975, D(G(z)): 0.2365
Epoch: [16/20], Batch Num: [596/600]
Discriminator Loss: 0.6165, Generator Loss: 2.3116
D(x): 0.7899, D(G(z)): 0.2002
Epoch: [16/20], Batch Num: [597/600]
Discriminator Loss: 0.5818, Generator Loss: 2.4098
D(x): 0.7895, D(G(z)): 0.1998
Epoch: [16/20], Batch Num: [598/600]
Discriminator Loss: 0.7276, Generator Loss: 2.1477
D(x): 0.7011, D(G(z)): 0.2007
Epoch: [16/20], Batch Num: [599/600]
Discriminator Loss: 0.7106, Generator Loss: 1.7475
D(x): 0.7500, D(G(z)): 0.2271
Epoch: 17, Batch Num: [0/600]

Epoch: [17/20], Batch Num: [0/600]
Discriminator Loss: 0.8118, Generator Loss: 2.0130
D(x): 0.7652, D(G(z)): 0.2699
Epoch: [17/20], Batch Num: [1/600]
Discriminator Loss: 0.6515, Generator Loss: 1.7629
D(x): 0.7973, D(G(z)): 0.2337
Epoch: [17/20], Batch Num: [2/600]
Discriminator Loss: 0.9041, Generator Loss: 2.1099
D(x): 0.7796, D(G(z)): 0.3246
Epoch: [17/20], Batch Num: [3/600]
Discriminator Loss: 0.8653, Generator Loss: 2.5065
D(x): 0.7541, D(G(z)): 0.2816
Epoch: [17/20], Batch Num: [4/600]
Discriminator Loss: 0.8554, Generator Loss: 2.3642
D(x): 0.7030, D(G(z)): 0.1969
Epoch: [17/20], Batch Num: [5/600]
Discriminator Loss: 1.0802, Generator Loss: 1.8642
D(x): 0.6202, D(G(z)): 0.1891
Epoch: [17/20], Batch Num: [6/600]
Discriminator Loss: 0.9499, Generator Loss: 1.6421
D(x): 0.7365, D(G(z)): 0.2950
Epoch: [17/20], Batch Num: [7/600]
Discriminator Loss: 1.0419, Generator Loss: 1.6413
D(x): 0.7831, D(G(z)): 0.3874
Epoch: [17/20], Batch Num: [8/600]
Discriminator Loss: 1.0496, Generator Loss: 1.7297
D(x): 0.6967, D(G(z)): 0.2957
Epoch: [17/20], Batch Num: [9/600]
Discriminator Loss: 1.1353, Generator Loss: 1.6055
D(x): 0.6032, D(G(z)): 0.2630
Epoch: [17/20], Batch Num: [10/600]
Discriminator Loss: 1.1781, Generator Loss: 1.3708
D(x): 0.6430, D(G(z)): 0.3167
Epoch: [17/20], Batch Num: [11/600]
Discriminator Loss: 1.0018, Generator Loss: 1.6227
D(x): 0.7546, D(G(z)): 0.3791
Epoch: [17/20], Batch Num: [12/600]
Discriminator Loss: 1.1547, Generator Loss: 1.6069
D(x): 0.6496, D(G(z)): 0.3231
Epoch: [17/20], Batch Num: [13/600]
Discriminator Loss: 1.2395, Generator Loss: 1.4887
D(x): 0.6043, D(G(z)): 0.3038
Epoch: [17/20], Batch Num: [14/600]
Discriminator Loss: 1.1854, Generator Loss: 1.3478
D(x): 0.6375, D(G(z)): 0.3230
Epoch: [17/20], Batch Num: [15/600]
Discriminator Loss: 1.2341, Generator Loss: 1.4916
D(x): 0.6079, D(G(z)): 0.3222
Epoch: [17/20], Batch Num: [16/600]
Discriminator Loss: 1.2100, Generator Loss: 1.2972
D(x): 0.6288, D(G(z)): 0.3626
Epoch: [17/20], Batch Num: [17/600]
Discriminator Loss: 1.1402, Generator Loss: 1.1992
D(x): 0.6658, D(G(z)): 0.3735
Epoch: [17/20], Batch Num: [18/600]
Discriminator Loss: 1.2477, Generator Loss: 1.2177
D(x): 0.6431, D(G(z)): 0.4077
Epoch: [17/20], Batch Num: [19/600]
Discriminator Loss: 1.1313, Generator Loss: 1.2275
D(x): 0.6453, D(G(z)): 0.3560
Epoch: [17/20], Batch Num: [20/600]
Discriminator Loss: 1.0545, Generator Loss: 1.1948
D(x): 0.6431, D(G(z)): 0.3271
Epoch: [17/20], Batch Num: [21/600]
Discriminator Loss: 1.0541, Generator Loss: 1.3958
D(x): 0.6408, D(G(z)): 0.3361
Epoch: [17/20], Batch Num: [22/600]
Discriminator Loss: 1.0841, Generator Loss: 1.2586
D(x): 0.6406, D(G(z)): 0.3560
Epoch: [17/20], Batch Num: [23/600]
Discriminator Loss: 1.0486, Generator Loss: 1.3410
D(x): 0.6216, D(G(z)): 0.3201
Epoch: [17/20], Batch Num: [24/600]
Discriminator Loss: 0.8843, Generator Loss: 1.2764
D(x): 0.6924, D(G(z)): 0.3106
Epoch: [17/20], Batch Num: [25/600]
Discriminator Loss: 0.9740, Generator Loss: 1.2144
D(x): 0.7099, D(G(z)): 0.3639
Epoch: [17/20], Batch Num: [26/600]
Discriminator Loss: 0.9916, Generator Loss: 1.4049
D(x): 0.6810, D(G(z)): 0.3493
Epoch: [17/20], Batch Num: [27/600]
Discriminator Loss: 0.7927, Generator Loss: 1.3039
D(x): 0.7349, D(G(z)): 0.3048
Epoch: [17/20], Batch Num: [28/600]
Discriminator Loss: 0.7252, Generator Loss: 1.3959
D(x): 0.7326, D(G(z)): 0.2773
Epoch: [17/20], Batch Num: [29/600]
Discriminator Loss: 0.7547, Generator Loss: 1.3811
D(x): 0.7176, D(G(z)): 0.2681
Epoch: [17/20], Batch Num: [30/600]
Discriminator Loss: 0.8658, Generator Loss: 1.4425
D(x): 0.6991, D(G(z)): 0.2970
Epoch: [17/20], Batch Num: [31/600]
Discriminator Loss: 0.8018, Generator Loss: 1.5065
D(x): 0.7386, D(G(z)): 0.2955
Epoch: [17/20], Batch Num: [32/600]
Discriminator Loss: 0.7667, Generator Loss: 1.6160
D(x): 0.7708, D(G(z)): 0.3147
Epoch: [17/20], Batch Num: [33/600]
Discriminator Loss: 0.7992, Generator Loss: 1.6465
D(x): 0.7290, D(G(z)): 0.2784
Epoch: [17/20], Batch Num: [34/600]
Discriminator Loss: 0.7046, Generator Loss: 1.6999
D(x): 0.7705, D(G(z)): 0.2707
Epoch: [17/20], Batch Num: [35/600]
Discriminator Loss: 0.6749, Generator Loss: 1.8497
D(x): 0.7561, D(G(z)): 0.2324
Epoch: [17/20], Batch Num: [36/600]
Discriminator Loss: 0.6875, Generator Loss: 1.9607
D(x): 0.7106, D(G(z)): 0.1905
Epoch: [17/20], Batch Num: [37/600]
Discriminator Loss: 0.7515, Generator Loss: 1.7175
D(x): 0.6984, D(G(z)): 0.2003
Epoch: [17/20], Batch Num: [38/600]
Discriminator Loss: 0.7002, Generator Loss: 1.5018
D(x): 0.7766, D(G(z)): 0.2663
Epoch: [17/20], Batch Num: [39/600]
Discriminator Loss: 0.7403, Generator Loss: 1.4886
D(x): 0.7783, D(G(z)): 0.2899
Epoch: [17/20], Batch Num: [40/600]
Discriminator Loss: 0.6301, Generator Loss: 1.4214
D(x): 0.7912, D(G(z)): 0.2530
Epoch: [17/20], Batch Num: [41/600]
Discriminator Loss: 0.7220, Generator Loss: 1.6253
D(x): 0.8109, D(G(z)): 0.3176
Epoch: [17/20], Batch Num: [42/600]
Discriminator Loss: 0.6046, Generator Loss: 1.7417
D(x): 0.8251, D(G(z)): 0.2589
Epoch: [17/20], Batch Num: [43/600]
Discriminator Loss: 0.6954, Generator Loss: 2.1176
D(x): 0.7583, D(G(z)): 0.2265
Epoch: [17/20], Batch Num: [44/600]
Discriminator Loss: 0.7209, Generator Loss: 2.0662
D(x): 0.7509, D(G(z)): 0.2161
Epoch: [17/20], Batch Num: [45/600]
Discriminator Loss: 0.6748, Generator Loss: 2.1097
D(x): 0.7591, D(G(z)): 0.2046
Epoch: [17/20], Batch Num: [46/600]
Discriminator Loss: 0.6166, Generator Loss: 1.9753
D(x): 0.7659, D(G(z)): 0.1785
Epoch: [17/20], Batch Num: [47/600]
Discriminator Loss: 0.6956, Generator Loss: 1.7435
D(x): 0.7636, D(G(z)): 0.2430
Epoch: [17/20], Batch Num: [48/600]
Discriminator Loss: 0.6634, Generator Loss: 1.6663
D(x): 0.7772, D(G(z)): 0.2437
Epoch: [17/20], Batch Num: [49/600]
Discriminator Loss: 0.8207, Generator Loss: 1.6756
D(x): 0.7737, D(G(z)): 0.3001
Epoch: [17/20], Batch Num: [50/600]
Discriminator Loss: 0.7837, Generator Loss: 1.7473
D(x): 0.8078, D(G(z)): 0.3065
Epoch: [17/20], Batch Num: [51/600]
Discriminator Loss: 0.7502, Generator Loss: 2.0284
D(x): 0.7789, D(G(z)): 0.2639
Epoch: [17/20], Batch Num: [52/600]
Discriminator Loss: 0.8726, Generator Loss: 2.0315
D(x): 0.6920, D(G(z)): 0.2546
Epoch: [17/20], Batch Num: [53/600]
Discriminator Loss: 0.7568, Generator Loss: 1.8595
D(x): 0.7352, D(G(z)): 0.2193
Epoch: [17/20], Batch Num: [54/600]
Discriminator Loss: 0.8333, Generator Loss: 1.7825
D(x): 0.7232, D(G(z)): 0.2534
Epoch: [17/20], Batch Num: [55/600]
Discriminator Loss: 0.9423, Generator Loss: 1.6138
D(x): 0.6906, D(G(z)): 0.2608
Epoch: [17/20], Batch Num: [56/600]
Discriminator Loss: 0.9031, Generator Loss: 1.3963
D(x): 0.7532, D(G(z)): 0.3004
Epoch: [17/20], Batch Num: [57/600]
Discriminator Loss: 1.0273, Generator Loss: 1.4374
D(x): 0.7468, D(G(z)): 0.3676
Epoch: [17/20], Batch Num: [58/600]
Discriminator Loss: 1.0867, Generator Loss: 1.6330
D(x): 0.6985, D(G(z)): 0.3138
Epoch: [17/20], Batch Num: [59/600]
Discriminator Loss: 0.9898, Generator Loss: 1.6477
D(x): 0.6751, D(G(z)): 0.2742
Epoch: [17/20], Batch Num: [60/600]
Discriminator Loss: 1.1032, Generator Loss: 1.6118
D(x): 0.6340, D(G(z)): 0.2989
Epoch: [17/20], Batch Num: [61/600]
Discriminator Loss: 1.0331, Generator Loss: 1.3661
D(x): 0.6984, D(G(z)): 0.3428
Epoch: [17/20], Batch Num: [62/600]
Discriminator Loss: 1.0300, Generator Loss: 1.5395
D(x): 0.7220, D(G(z)): 0.3488
Epoch: [17/20], Batch Num: [63/600]
Discriminator Loss: 1.1840, Generator Loss: 1.4245
D(x): 0.6793, D(G(z)): 0.3832
Epoch: [17/20], Batch Num: [64/600]
Discriminator Loss: 1.1456, Generator Loss: 1.6904
D(x): 0.6656, D(G(z)): 0.3563
Epoch: [17/20], Batch Num: [65/600]
Discriminator Loss: 1.2212, Generator Loss: 1.7714
D(x): 0.6465, D(G(z)): 0.3662
Epoch: [17/20], Batch Num: [66/600]
Discriminator Loss: 1.2230, Generator Loss: 1.5274
D(x): 0.5924, D(G(z)): 0.2792
Epoch: [17/20], Batch Num: [67/600]
Discriminator Loss: 1.3046, Generator Loss: 1.5778
D(x): 0.5859, D(G(z)): 0.3270
Epoch: [17/20], Batch Num: [68/600]
Discriminator Loss: 1.1466, Generator Loss: 1.2494
D(x): 0.6302, D(G(z)): 0.3504
Epoch: [17/20], Batch Num: [69/600]
Discriminator Loss: 1.0890, Generator Loss: 1.3594
D(x): 0.7056, D(G(z)): 0.3628
Epoch: [17/20], Batch Num: [70/600]
Discriminator Loss: 1.1880, Generator Loss: 1.2389
D(x): 0.7001, D(G(z)): 0.4216
Epoch: [17/20], Batch Num: [71/600]
Discriminator Loss: 0.9929, Generator Loss: 1.6021
D(x): 0.7199, D(G(z)): 0.3731
Epoch: [17/20], Batch Num: [72/600]
Discriminator Loss: 0.9845, Generator Loss: 1.5344
D(x): 0.6534, D(G(z)): 0.2936
Epoch: [17/20], Batch Num: [73/600]
Discriminator Loss: 1.1268, Generator Loss: 1.4490
D(x): 0.6046, D(G(z)): 0.3207
Epoch: [17/20], Batch Num: [74/600]
Discriminator Loss: 1.0599, Generator Loss: 1.4983
D(x): 0.6485, D(G(z)): 0.3202
Epoch: [17/20], Batch Num: [75/600]
Discriminator Loss: 0.9644, Generator Loss: 1.3592
D(x): 0.6411, D(G(z)): 0.3005
Epoch: [17/20], Batch Num: [76/600]
Discriminator Loss: 0.9538, Generator Loss: 1.3520
D(x): 0.6762, D(G(z)): 0.3274
Epoch: [17/20], Batch Num: [77/600]
Discriminator Loss: 0.9434, Generator Loss: 1.1912
D(x): 0.7324, D(G(z)): 0.3696
Epoch: [17/20], Batch Num: [78/600]
Discriminator Loss: 0.9208, Generator Loss: 1.2252
D(x): 0.7364, D(G(z)): 0.3618
Epoch: [17/20], Batch Num: [79/600]
Discriminator Loss: 0.9066, Generator Loss: 1.3830
D(x): 0.7480, D(G(z)): 0.3527
Epoch: [17/20], Batch Num: [80/600]
Discriminator Loss: 0.9542, Generator Loss: 1.4589
D(x): 0.6802, D(G(z)): 0.3065
Epoch: [17/20], Batch Num: [81/600]
Discriminator Loss: 0.9267, Generator Loss: 1.5285
D(x): 0.6824, D(G(z)): 0.3067
Epoch: [17/20], Batch Num: [82/600]
Discriminator Loss: 0.9420, Generator Loss: 1.6198
D(x): 0.6741, D(G(z)): 0.2930
Epoch: [17/20], Batch Num: [83/600]
Discriminator Loss: 0.8867, Generator Loss: 1.4939
D(x): 0.6247, D(G(z)): 0.2495
Epoch: [17/20], Batch Num: [84/600]
Discriminator Loss: 0.8483, Generator Loss: 1.4171
D(x): 0.6844, D(G(z)): 0.2881
Epoch: [17/20], Batch Num: [85/600]
Discriminator Loss: 0.7361, Generator Loss: 1.3507
D(x): 0.7360, D(G(z)): 0.2747
Epoch: [17/20], Batch Num: [86/600]
Discriminator Loss: 0.8654, Generator Loss: 1.3286
D(x): 0.7320, D(G(z)): 0.3258
Epoch: [17/20], Batch Num: [87/600]
Discriminator Loss: 0.9109, Generator Loss: 1.5056
D(x): 0.8078, D(G(z)): 0.4253
Epoch: [17/20], Batch Num: [88/600]
Discriminator Loss: 0.8277, Generator Loss: 1.6500
D(x): 0.7384, D(G(z)): 0.3161
Epoch: [17/20], Batch Num: [89/600]
Discriminator Loss: 0.8445, Generator Loss: 1.7353
D(x): 0.6845, D(G(z)): 0.2736
Epoch: [17/20], Batch Num: [90/600]
Discriminator Loss: 0.8424, Generator Loss: 1.8094
D(x): 0.6696, D(G(z)): 0.2488
Epoch: [17/20], Batch Num: [91/600]
Discriminator Loss: 0.7940, Generator Loss: 1.6800
D(x): 0.6591, D(G(z)): 0.2164
Epoch: [17/20], Batch Num: [92/600]
Discriminator Loss: 0.7833, Generator Loss: 1.5328
D(x): 0.6876, D(G(z)): 0.2446
Epoch: [17/20], Batch Num: [93/600]
Discriminator Loss: 0.8937, Generator Loss: 1.2799
D(x): 0.7066, D(G(z)): 0.3117
Epoch: [17/20], Batch Num: [94/600]
Discriminator Loss: 0.8451, Generator Loss: 1.2643
D(x): 0.7994, D(G(z)): 0.3616
Epoch: [17/20], Batch Num: [95/600]
Discriminator Loss: 0.7878, Generator Loss: 1.3844
D(x): 0.8076, D(G(z)): 0.3603
Epoch: [17/20], Batch Num: [96/600]
Discriminator Loss: 0.8666, Generator Loss: 1.7203
D(x): 0.7590, D(G(z)): 0.3560
Epoch: [17/20], Batch Num: [97/600]
Discriminator Loss: 0.6958, Generator Loss: 1.9837
D(x): 0.7283, D(G(z)): 0.2252
Epoch: [17/20], Batch Num: [98/600]
Discriminator Loss: 0.9046, Generator Loss: 2.0210
D(x): 0.6452, D(G(z)): 0.2385
Epoch: [17/20], Batch Num: [99/600]
Discriminator Loss: 0.8179, Generator Loss: 1.8230
D(x): 0.6650, D(G(z)): 0.1902
Epoch: 17, Batch Num: [100/600]

Epoch: [17/20], Batch Num: [100/600]
Discriminator Loss: 0.7337, Generator Loss: 1.6181
D(x): 0.7396, D(G(z)): 0.2514
Epoch: [17/20], Batch Num: [101/600]
Discriminator Loss: 0.6826, Generator Loss: 1.5064
D(x): 0.7826, D(G(z)): 0.2686
Epoch: [17/20], Batch Num: [102/600]
Discriminator Loss: 0.7056, Generator Loss: 1.5264
D(x): 0.8142, D(G(z)): 0.3006
Epoch: [17/20], Batch Num: [103/600]
Discriminator Loss: 0.7866, Generator Loss: 1.9484
D(x): 0.7876, D(G(z)): 0.3221
Epoch: [17/20], Batch Num: [104/600]
Discriminator Loss: 0.9080, Generator Loss: 2.0540
D(x): 0.6984, D(G(z)): 0.2832
Epoch: [17/20], Batch Num: [105/600]
Discriminator Loss: 0.8630, Generator Loss: 1.8472
D(x): 0.6674, D(G(z)): 0.2170
Epoch: [17/20], Batch Num: [106/600]
Discriminator Loss: 0.8756, Generator Loss: 1.6732
D(x): 0.6730, D(G(z)): 0.2333
Epoch: [17/20], Batch Num: [107/600]
Discriminator Loss: 1.0662, Generator Loss: 1.4910
D(x): 0.6983, D(G(z)): 0.3212
Epoch: [17/20], Batch Num: [108/600]
Discriminator Loss: 0.9956, Generator Loss: 1.4174
D(x): 0.7659, D(G(z)): 0.3860
Epoch: [17/20], Batch Num: [109/600]
Discriminator Loss: 1.0247, Generator Loss: 1.5369
D(x): 0.6887, D(G(z)): 0.3185
Epoch: [17/20], Batch Num: [110/600]
Discriminator Loss: 0.8823, Generator Loss: 1.6576
D(x): 0.6888, D(G(z)): 0.2942
Epoch: [17/20], Batch Num: [111/600]
Discriminator Loss: 1.0834, Generator Loss: 1.6233
D(x): 0.6546, D(G(z)): 0.3227
Epoch: [17/20], Batch Num: [112/600]
Discriminator Loss: 1.1614, Generator Loss: 1.3822
D(x): 0.6451, D(G(z)): 0.3202
Epoch: [17/20], Batch Num: [113/600]
Discriminator Loss: 0.9764, Generator Loss: 1.3518
D(x): 0.6786, D(G(z)): 0.3106
Epoch: [17/20], Batch Num: [114/600]
Discriminator Loss: 1.0197, Generator Loss: 1.4181
D(x): 0.6869, D(G(z)): 0.3596
Epoch: [17/20], Batch Num: [115/600]
Discriminator Loss: 1.0113, Generator Loss: 1.3279
D(x): 0.6589, D(G(z)): 0.3205
Epoch: [17/20], Batch Num: [116/600]
Discriminator Loss: 1.0169, Generator Loss: 1.2928
D(x): 0.6659, D(G(z)): 0.3334
Epoch: [17/20], Batch Num: [117/600]
Discriminator Loss: 1.2353, Generator Loss: 1.2805
D(x): 0.6407, D(G(z)): 0.3955
Epoch: [17/20], Batch Num: [118/600]
Discriminator Loss: 1.1542, Generator Loss: 1.4737
D(x): 0.6682, D(G(z)): 0.3835
Epoch: [17/20], Batch Num: [119/600]
Discriminator Loss: 1.0832, Generator Loss: 1.4460
D(x): 0.6776, D(G(z)): 0.3439
Epoch: [17/20], Batch Num: [120/600]
Discriminator Loss: 0.8970, Generator Loss: 1.4508
D(x): 0.6701, D(G(z)): 0.2852
Epoch: [17/20], Batch Num: [121/600]
Discriminator Loss: 0.9534, Generator Loss: 1.4182
D(x): 0.6342, D(G(z)): 0.2745
Epoch: [17/20], Batch Num: [122/600]
Discriminator Loss: 0.9854, Generator Loss: 1.3363
D(x): 0.6619, D(G(z)): 0.3219
Epoch: [17/20], Batch Num: [123/600]
Discriminator Loss: 1.0037, Generator Loss: 1.1944
D(x): 0.6859, D(G(z)): 0.3570
Epoch: [17/20], Batch Num: [124/600]
Discriminator Loss: 0.8765, Generator Loss: 1.1589
D(x): 0.7497, D(G(z)): 0.3450
Epoch: [17/20], Batch Num: [125/600]
Discriminator Loss: 0.9872, Generator Loss: 1.3821
D(x): 0.6963, D(G(z)): 0.3516
Epoch: [17/20], Batch Num: [126/600]
Discriminator Loss: 0.8790, Generator Loss: 1.5475
D(x): 0.7316, D(G(z)): 0.3310
Epoch: [17/20], Batch Num: [127/600]
Discriminator Loss: 0.8559, Generator Loss: 1.5484
D(x): 0.6708, D(G(z)): 0.2650
Epoch: [17/20], Batch Num: [128/600]
Discriminator Loss: 0.7801, Generator Loss: 1.3711
D(x): 0.7140, D(G(z)): 0.2752
Epoch: [17/20], Batch Num: [129/600]
Discriminator Loss: 0.8155, Generator Loss: 1.4454
D(x): 0.7245, D(G(z)): 0.2904
Epoch: [17/20], Batch Num: [130/600]
Discriminator Loss: 0.8244, Generator Loss: 1.3995
D(x): 0.7517, D(G(z)): 0.3156
Epoch: [17/20], Batch Num: [131/600]
Discriminator Loss: 0.8254, Generator Loss: 1.4467
D(x): 0.7359, D(G(z)): 0.3156
Epoch: [17/20], Batch Num: [132/600]
Discriminator Loss: 0.8485, Generator Loss: 1.4332
D(x): 0.7088, D(G(z)): 0.2959
Epoch: [17/20], Batch Num: [133/600]
Discriminator Loss: 0.8213, Generator Loss: 1.5093
D(x): 0.7350, D(G(z)): 0.3017
Epoch: [17/20], Batch Num: [134/600]
Discriminator Loss: 0.7672, Generator Loss: 1.6003
D(x): 0.7355, D(G(z)): 0.2706
Epoch: [17/20], Batch Num: [135/600]
Discriminator Loss: 0.8453, Generator Loss: 1.6012
D(x): 0.7094, D(G(z)): 0.2879
Epoch: [17/20], Batch Num: [136/600]
Discriminator Loss: 0.8871, Generator Loss: 1.4748
D(x): 0.7287, D(G(z)): 0.2966
Epoch: [17/20], Batch Num: [137/600]
Discriminator Loss: 0.7913, Generator Loss: 1.4749
D(x): 0.7568, D(G(z)): 0.3013
Epoch: [17/20], Batch Num: [138/600]
Discriminator Loss: 0.7518, Generator Loss: 1.6200
D(x): 0.7553, D(G(z)): 0.2845
Epoch: [17/20], Batch Num: [139/600]
Discriminator Loss: 0.7842, Generator Loss: 1.5453
D(x): 0.7110, D(G(z)): 0.2590
Epoch: [17/20], Batch Num: [140/600]
Discriminator Loss: 0.7466, Generator Loss: 1.5720
D(x): 0.7591, D(G(z)): 0.2908
Epoch: [17/20], Batch Num: [141/600]
Discriminator Loss: 0.8759, Generator Loss: 1.6795
D(x): 0.7329, D(G(z)): 0.3138
Epoch: [17/20], Batch Num: [142/600]
Discriminator Loss: 0.7932, Generator Loss: 1.6951
D(x): 0.7449, D(G(z)): 0.2654
Epoch: [17/20], Batch Num: [143/600]
Discriminator Loss: 0.6225, Generator Loss: 1.8814
D(x): 0.7737, D(G(z)): 0.2165
Epoch: [17/20], Batch Num: [144/600]
Discriminator Loss: 0.8700, Generator Loss: 1.6874
D(x): 0.7009, D(G(z)): 0.2365
Epoch: [17/20], Batch Num: [145/600]
Discriminator Loss: 0.7242, Generator Loss: 1.6711
D(x): 0.7546, D(G(z)): 0.2508
Epoch: [17/20], Batch Num: [146/600]
Discriminator Loss: 0.7493, Generator Loss: 1.3878
D(x): 0.7463, D(G(z)): 0.2538
Epoch: [17/20], Batch Num: [147/600]
Discriminator Loss: 0.8696, Generator Loss: 1.5979
D(x): 0.7712, D(G(z)): 0.3382
Epoch: [17/20], Batch Num: [148/600]
Discriminator Loss: 0.8409, Generator Loss: 1.6224
D(x): 0.7824, D(G(z)): 0.3249
Epoch: [17/20], Batch Num: [149/600]
Discriminator Loss: 0.7113, Generator Loss: 1.9624
D(x): 0.7803, D(G(z)): 0.2552
Epoch: [17/20], Batch Num: [150/600]
Discriminator Loss: 0.8085, Generator Loss: 1.7704
D(x): 0.6963, D(G(z)): 0.2281
Epoch: [17/20], Batch Num: [151/600]
Discriminator Loss: 0.8381, Generator Loss: 1.6357
D(x): 0.7129, D(G(z)): 0.2630
Epoch: [17/20], Batch Num: [152/600]
Discriminator Loss: 0.7451, Generator Loss: 1.5971
D(x): 0.7751, D(G(z)): 0.2786
Epoch: [17/20], Batch Num: [153/600]
Discriminator Loss: 0.7914, Generator Loss: 1.5300
D(x): 0.7331, D(G(z)): 0.2569
Epoch: [17/20], Batch Num: [154/600]
Discriminator Loss: 0.7750, Generator Loss: 1.5308
D(x): 0.7825, D(G(z)): 0.3152
Epoch: [17/20], Batch Num: [155/600]
Discriminator Loss: 0.8225, Generator Loss: 1.6557
D(x): 0.7270, D(G(z)): 0.2735
Epoch: [17/20], Batch Num: [156/600]
Discriminator Loss: 0.8897, Generator Loss: 1.7389
D(x): 0.7063, D(G(z)): 0.3009
Epoch: [17/20], Batch Num: [157/600]
Discriminator Loss: 0.7284, Generator Loss: 1.4850
D(x): 0.7480, D(G(z)): 0.2453
Epoch: [17/20], Batch Num: [158/600]
Discriminator Loss: 0.8101, Generator Loss: 1.6025
D(x): 0.7700, D(G(z)): 0.2931
Epoch: [17/20], Batch Num: [159/600]
Discriminator Loss: 0.8980, Generator Loss: 1.6481
D(x): 0.7348, D(G(z)): 0.3127
Epoch: [17/20], Batch Num: [160/600]
Discriminator Loss: 0.9352, Generator Loss: 1.8073
D(x): 0.6838, D(G(z)): 0.2680
Epoch: [17/20], Batch Num: [161/600]
Discriminator Loss: 0.8310, Generator Loss: 1.6939
D(x): 0.7326, D(G(z)): 0.2737
Epoch: [17/20], Batch Num: [162/600]
Discriminator Loss: 0.8711, Generator Loss: 1.5420
D(x): 0.7081, D(G(z)): 0.2774
Epoch: [17/20], Batch Num: [163/600]
Discriminator Loss: 1.0054, Generator Loss: 1.7044
D(x): 0.6997, D(G(z)): 0.3014
Epoch: [17/20], Batch Num: [164/600]
Discriminator Loss: 0.8799, Generator Loss: 1.6656
D(x): 0.7074, D(G(z)): 0.2709
Epoch: [17/20], Batch Num: [165/600]
Discriminator Loss: 0.8369, Generator Loss: 1.7410
D(x): 0.7505, D(G(z)): 0.3001
Epoch: [17/20], Batch Num: [166/600]
Discriminator Loss: 0.8783, Generator Loss: 1.6488
D(x): 0.7695, D(G(z)): 0.3139
Epoch: [17/20], Batch Num: [167/600]
Discriminator Loss: 1.1093, Generator Loss: 1.9902
D(x): 0.7121, D(G(z)): 0.3474
Epoch: [17/20], Batch Num: [168/600]
Discriminator Loss: 0.9935, Generator Loss: 1.9370
D(x): 0.6427, D(G(z)): 0.2198
Epoch: [17/20], Batch Num: [169/600]
Discriminator Loss: 0.8241, Generator Loss: 1.5965
D(x): 0.6957, D(G(z)): 0.2142
Epoch: [17/20], Batch Num: [170/600]
Discriminator Loss: 0.9939, Generator Loss: 1.4890
D(x): 0.6799, D(G(z)): 0.2660
Epoch: [17/20], Batch Num: [171/600]
Discriminator Loss: 0.9480, Generator Loss: 1.1907
D(x): 0.7775, D(G(z)): 0.3638
Epoch: [17/20], Batch Num: [172/600]
Discriminator Loss: 0.8924, Generator Loss: 1.6042
D(x): 0.7471, D(G(z)): 0.3281
Epoch: [17/20], Batch Num: [173/600]
Discriminator Loss: 1.0405, Generator Loss: 1.4007
D(x): 0.7112, D(G(z)): 0.3534
Epoch: [17/20], Batch Num: [174/600]
Discriminator Loss: 0.8412, Generator Loss: 1.8773
D(x): 0.7536, D(G(z)): 0.2998
Epoch: [17/20], Batch Num: [175/600]
Discriminator Loss: 0.8268, Generator Loss: 1.8019
D(x): 0.6904, D(G(z)): 0.2239
Epoch: [17/20], Batch Num: [176/600]
Discriminator Loss: 0.7979, Generator Loss: 1.5877
D(x): 0.7022, D(G(z)): 0.2415
Epoch: [17/20], Batch Num: [177/600]
Discriminator Loss: 0.9979, Generator Loss: 1.3966
D(x): 0.6737, D(G(z)): 0.2926
Epoch: [17/20], Batch Num: [178/600]
Discriminator Loss: 0.9109, Generator Loss: 1.4350
D(x): 0.7409, D(G(z)): 0.3209
Epoch: [17/20], Batch Num: [179/600]
Discriminator Loss: 1.0188, Generator Loss: 1.5696
D(x): 0.7703, D(G(z)): 0.3814
Epoch: [17/20], Batch Num: [180/600]
Discriminator Loss: 0.9665, Generator Loss: 1.5979
D(x): 0.7134, D(G(z)): 0.3233
Epoch: [17/20], Batch Num: [181/600]
Discriminator Loss: 0.8340, Generator Loss: 1.7975
D(x): 0.6900, D(G(z)): 0.2605
Epoch: [17/20], Batch Num: [182/600]
Discriminator Loss: 0.8854, Generator Loss: 1.6756
D(x): 0.6977, D(G(z)): 0.2737
Epoch: [17/20], Batch Num: [183/600]
Discriminator Loss: 0.9651, Generator Loss: 1.5906
D(x): 0.7121, D(G(z)): 0.2905
Epoch: [17/20], Batch Num: [184/600]
Discriminator Loss: 0.7976, Generator Loss: 1.7110
D(x): 0.7634, D(G(z)): 0.3138
Epoch: [17/20], Batch Num: [185/600]
Discriminator Loss: 0.9408, Generator Loss: 1.6072
D(x): 0.6978, D(G(z)): 0.3115
Epoch: [17/20], Batch Num: [186/600]
Discriminator Loss: 1.0249, Generator Loss: 1.6151
D(x): 0.6522, D(G(z)): 0.2902
Epoch: [17/20], Batch Num: [187/600]
Discriminator Loss: 0.9789, Generator Loss: 1.4724
D(x): 0.6897, D(G(z)): 0.3077
Epoch: [17/20], Batch Num: [188/600]
Discriminator Loss: 0.9420, Generator Loss: 1.7130
D(x): 0.7110, D(G(z)): 0.3177
Epoch: [17/20], Batch Num: [189/600]
Discriminator Loss: 0.7981, Generator Loss: 1.8182
D(x): 0.7338, D(G(z)): 0.2920
Epoch: [17/20], Batch Num: [190/600]
Discriminator Loss: 0.8611, Generator Loss: 1.5880
D(x): 0.7306, D(G(z)): 0.2894
Epoch: [17/20], Batch Num: [191/600]
Discriminator Loss: 0.9849, Generator Loss: 1.6002
D(x): 0.6702, D(G(z)): 0.2884
Epoch: [17/20], Batch Num: [192/600]
Discriminator Loss: 0.9153, Generator Loss: 1.4813
D(x): 0.6982, D(G(z)): 0.3001
Epoch: [17/20], Batch Num: [193/600]
Discriminator Loss: 0.8447, Generator Loss: 1.7124
D(x): 0.7479, D(G(z)): 0.3010
Epoch: [17/20], Batch Num: [194/600]
Discriminator Loss: 0.9207, Generator Loss: 1.7207
D(x): 0.6954, D(G(z)): 0.2698
Epoch: [17/20], Batch Num: [195/600]
Discriminator Loss: 0.8788, Generator Loss: 1.7117
D(x): 0.7035, D(G(z)): 0.2886
Epoch: [17/20], Batch Num: [196/600]
Discriminator Loss: 0.7606, Generator Loss: 1.6158
D(x): 0.7250, D(G(z)): 0.2630
Epoch: [17/20], Batch Num: [197/600]
Discriminator Loss: 0.7469, Generator Loss: 1.6241
D(x): 0.7944, D(G(z)): 0.3159
Epoch: [17/20], Batch Num: [198/600]
Discriminator Loss: 0.9114, Generator Loss: 1.7173
D(x): 0.7076, D(G(z)): 0.3090
Epoch: [17/20], Batch Num: [199/600]
Discriminator Loss: 0.8637, Generator Loss: 1.6305
D(x): 0.7060, D(G(z)): 0.2748
Epoch: 17, Batch Num: [200/600]

Epoch: [17/20], Batch Num: [200/600]
Discriminator Loss: 0.8560, Generator Loss: 1.6610
D(x): 0.6795, D(G(z)): 0.2540
Epoch: [17/20], Batch Num: [201/600]
Discriminator Loss: 0.8203, Generator Loss: 1.8948
D(x): 0.7573, D(G(z)): 0.2961
Epoch: [17/20], Batch Num: [202/600]
Discriminator Loss: 0.9333, Generator Loss: 1.7317
D(x): 0.7021, D(G(z)): 0.3095
Epoch: [17/20], Batch Num: [203/600]
Discriminator Loss: 0.7331, Generator Loss: 1.8168
D(x): 0.7653, D(G(z)): 0.2738
Epoch: [17/20], Batch Num: [204/600]
Discriminator Loss: 0.9020, Generator Loss: 1.9041
D(x): 0.6683, D(G(z)): 0.2402
Epoch: [17/20], Batch Num: [205/600]
Discriminator Loss: 0.7160, Generator Loss: 1.7919
D(x): 0.7626, D(G(z)): 0.2499
Epoch: [17/20], Batch Num: [206/600]
Discriminator Loss: 0.7911, Generator Loss: 1.6945
D(x): 0.7365, D(G(z)): 0.2588
Epoch: [17/20], Batch Num: [207/600]
Discriminator Loss: 0.7946, Generator Loss: 1.7235
D(x): 0.7293, D(G(z)): 0.2509
Epoch: [17/20], Batch Num: [208/600]
Discriminator Loss: 0.9002, Generator Loss: 1.8063
D(x): 0.7304, D(G(z)): 0.3147
Epoch: [17/20], Batch Num: [209/600]
Discriminator Loss: 0.7643, Generator Loss: 1.8273
D(x): 0.7778, D(G(z)): 0.2939
Epoch: [17/20], Batch Num: [210/600]
Discriminator Loss: 0.8514, Generator Loss: 1.9650
D(x): 0.7539, D(G(z)): 0.3024
Epoch: [17/20], Batch Num: [211/600]
Discriminator Loss: 0.9152, Generator Loss: 2.1397
D(x): 0.7064, D(G(z)): 0.2668
Epoch: [17/20], Batch Num: [212/600]
Discriminator Loss: 0.8287, Generator Loss: 2.1472
D(x): 0.6966, D(G(z)): 0.2235
Epoch: [17/20], Batch Num: [213/600]
Discriminator Loss: 0.8087, Generator Loss: 1.9410
D(x): 0.6924, D(G(z)): 0.2285
Epoch: [17/20], Batch Num: [214/600]
Discriminator Loss: 0.8647, Generator Loss: 1.6624
D(x): 0.6933, D(G(z)): 0.2480
Epoch: [17/20], Batch Num: [215/600]
Discriminator Loss: 0.7907, Generator Loss: 1.4996
D(x): 0.7560, D(G(z)): 0.3165
Epoch: [17/20], Batch Num: [216/600]
Discriminator Loss: 0.8972, Generator Loss: 1.6426
D(x): 0.8132, D(G(z)): 0.3575
Epoch: [17/20], Batch Num: [217/600]
Discriminator Loss: 0.8289, Generator Loss: 1.8418
D(x): 0.7769, D(G(z)): 0.3084
Epoch: [17/20], Batch Num: [218/600]
Discriminator Loss: 0.9084, Generator Loss: 2.1604
D(x): 0.6833, D(G(z)): 0.2696
Epoch: [17/20], Batch Num: [219/600]
Discriminator Loss: 0.9612, Generator Loss: 1.9710
D(x): 0.6129, D(G(z)): 0.2132
Epoch: [17/20], Batch Num: [220/600]
Discriminator Loss: 0.8017, Generator Loss: 1.6518
D(x): 0.6800, D(G(z)): 0.2125
Epoch: [17/20], Batch Num: [221/600]
Discriminator Loss: 0.8230, Generator Loss: 1.4694
D(x): 0.7556, D(G(z)): 0.3097
Epoch: [17/20], Batch Num: [222/600]
Discriminator Loss: 0.8098, Generator Loss: 1.4935
D(x): 0.8349, D(G(z)): 0.3699
Epoch: [17/20], Batch Num: [223/600]
Discriminator Loss: 0.8399, Generator Loss: 2.0200
D(x): 0.7923, D(G(z)): 0.3509
Epoch: [17/20], Batch Num: [224/600]
Discriminator Loss: 0.8106, Generator Loss: 2.1990
D(x): 0.6919, D(G(z)): 0.2187
Epoch: [17/20], Batch Num: [225/600]
Discriminator Loss: 0.9051, Generator Loss: 2.0031
D(x): 0.6725, D(G(z)): 0.2475
Epoch: [17/20], Batch Num: [226/600]
Discriminator Loss: 0.8554, Generator Loss: 1.8749
D(x): 0.6798, D(G(z)): 0.2425
Epoch: [17/20], Batch Num: [227/600]
Discriminator Loss: 0.8393, Generator Loss: 1.4760
D(x): 0.6939, D(G(z)): 0.2340
Epoch: [17/20], Batch Num: [228/600]
Discriminator Loss: 0.9501, Generator Loss: 1.4046
D(x): 0.7449, D(G(z)): 0.3384
Epoch: [17/20], Batch Num: [229/600]
Discriminator Loss: 0.7970, Generator Loss: 1.5733
D(x): 0.8531, D(G(z)): 0.3851
Epoch: [17/20], Batch Num: [230/600]
Discriminator Loss: 0.7619, Generator Loss: 2.0186
D(x): 0.7742, D(G(z)): 0.2976
Epoch: [17/20], Batch Num: [231/600]
Discriminator Loss: 0.7567, Generator Loss: 2.0496
D(x): 0.7359, D(G(z)): 0.2437
Epoch: [17/20], Batch Num: [232/600]
Discriminator Loss: 0.8556, Generator Loss: 1.8758
D(x): 0.7046, D(G(z)): 0.2169
Epoch: [17/20], Batch Num: [233/600]
Discriminator Loss: 0.7705, Generator Loss: 1.8400
D(x): 0.7157, D(G(z)): 0.2100
Epoch: [17/20], Batch Num: [234/600]
Discriminator Loss: 0.7962, Generator Loss: 1.5569
D(x): 0.7165, D(G(z)): 0.2475
Epoch: [17/20], Batch Num: [235/600]
Discriminator Loss: 0.6886, Generator Loss: 1.4787
D(x): 0.8084, D(G(z)): 0.2932
Epoch: [17/20], Batch Num: [236/600]
Discriminator Loss: 1.0511, Generator Loss: 1.6635
D(x): 0.7328, D(G(z)): 0.3532
Epoch: [17/20], Batch Num: [237/600]
Discriminator Loss: 0.8522, Generator Loss: 1.8479
D(x): 0.7810, D(G(z)): 0.3398
Epoch: [17/20], Batch Num: [238/600]
Discriminator Loss: 0.7581, Generator Loss: 2.0161
D(x): 0.7706, D(G(z)): 0.2690
Epoch: [17/20], Batch Num: [239/600]
Discriminator Loss: 0.8799, Generator Loss: 2.1927
D(x): 0.6902, D(G(z)): 0.2320
Epoch: [17/20], Batch Num: [240/600]
Discriminator Loss: 0.7996, Generator Loss: 1.9114
D(x): 0.6760, D(G(z)): 0.1606
Epoch: [17/20], Batch Num: [241/600]
Discriminator Loss: 0.7507, Generator Loss: 1.7921
D(x): 0.7583, D(G(z)): 0.2450
Epoch: [17/20], Batch Num: [242/600]
Discriminator Loss: 0.8261, Generator Loss: 1.5070
D(x): 0.7334, D(G(z)): 0.2500
Epoch: [17/20], Batch Num: [243/600]
Discriminator Loss: 0.7709, Generator Loss: 1.5291
D(x): 0.8657, D(G(z)): 0.3628
Epoch: [17/20], Batch Num: [244/600]
Discriminator Loss: 0.8594, Generator Loss: 1.6252
D(x): 0.7666, D(G(z)): 0.3126
Epoch: [17/20], Batch Num: [245/600]
Discriminator Loss: 0.7996, Generator Loss: 1.9359
D(x): 0.7729, D(G(z)): 0.2968
Epoch: [17/20], Batch Num: [246/600]
Discriminator Loss: 0.7739, Generator Loss: 1.9791
D(x): 0.7228, D(G(z)): 0.2200
Epoch: [17/20], Batch Num: [247/600]
Discriminator Loss: 0.8021, Generator Loss: 1.9577
D(x): 0.6915, D(G(z)): 0.2118
Epoch: [17/20], Batch Num: [248/600]
Discriminator Loss: 0.8006, Generator Loss: 1.9398
D(x): 0.7171, D(G(z)): 0.2035
Epoch: [17/20], Batch Num: [249/600]
Discriminator Loss: 0.9413, Generator Loss: 1.5718
D(x): 0.6836, D(G(z)): 0.2728
Epoch: [17/20], Batch Num: [250/600]
Discriminator Loss: 0.8826, Generator Loss: 1.4046
D(x): 0.8268, D(G(z)): 0.3444
Epoch: [17/20], Batch Num: [251/600]
Discriminator Loss: 0.8216, Generator Loss: 1.6947
D(x): 0.8029, D(G(z)): 0.3333
Epoch: [17/20], Batch Num: [252/600]
Discriminator Loss: 0.7822, Generator Loss: 2.1290
D(x): 0.7910, D(G(z)): 0.2934
Epoch: [17/20], Batch Num: [253/600]
Discriminator Loss: 0.8154, Generator Loss: 2.1573
D(x): 0.7057, D(G(z)): 0.2150
Epoch: [17/20], Batch Num: [254/600]
Discriminator Loss: 0.6997, Generator Loss: 2.1732
D(x): 0.7204, D(G(z)): 0.1550
Epoch: [17/20], Batch Num: [255/600]
Discriminator Loss: 0.8879, Generator Loss: 1.8969
D(x): 0.6886, D(G(z)): 0.2336
Epoch: [17/20], Batch Num: [256/600]
Discriminator Loss: 0.6793, Generator Loss: 1.5803
D(x): 0.7673, D(G(z)): 0.2309
Epoch: [17/20], Batch Num: [257/600]
Discriminator Loss: 0.7894, Generator Loss: 1.4671
D(x): 0.7883, D(G(z)): 0.3096
Epoch: [17/20], Batch Num: [258/600]
Discriminator Loss: 0.8238, Generator Loss: 1.6113
D(x): 0.7949, D(G(z)): 0.3181
Epoch: [17/20], Batch Num: [259/600]
Discriminator Loss: 0.7086, Generator Loss: 1.8251
D(x): 0.8143, D(G(z)): 0.2985
Epoch: [17/20], Batch Num: [260/600]
Discriminator Loss: 0.9010, Generator Loss: 2.1439
D(x): 0.7243, D(G(z)): 0.2554
Epoch: [17/20], Batch Num: [261/600]
Discriminator Loss: 0.8353, Generator Loss: 2.0725
D(x): 0.6931, D(G(z)): 0.2252
Epoch: [17/20], Batch Num: [262/600]
Discriminator Loss: 0.8248, Generator Loss: 1.8720
D(x): 0.6957, D(G(z)): 0.2134
Epoch: [17/20], Batch Num: [263/600]
Discriminator Loss: 0.8326, Generator Loss: 1.8436
D(x): 0.7556, D(G(z)): 0.2607
Epoch: [17/20], Batch Num: [264/600]
Discriminator Loss: 0.7661, Generator Loss: 1.8134
D(x): 0.7701, D(G(z)): 0.2612
Epoch: [17/20], Batch Num: [265/600]
Discriminator Loss: 0.8040, Generator Loss: 1.8636
D(x): 0.7425, D(G(z)): 0.2741
Epoch: [17/20], Batch Num: [266/600]
Discriminator Loss: 0.9007, Generator Loss: 1.8905
D(x): 0.7709, D(G(z)): 0.3101
Epoch: [17/20], Batch Num: [267/600]
Discriminator Loss: 0.9573, Generator Loss: 1.9927
D(x): 0.7625, D(G(z)): 0.3179
Epoch: [17/20], Batch Num: [268/600]
Discriminator Loss: 0.8053, Generator Loss: 1.8967
D(x): 0.7044, D(G(z)): 0.2057
Epoch: [17/20], Batch Num: [269/600]
Discriminator Loss: 0.9865, Generator Loss: 2.0733
D(x): 0.6624, D(G(z)): 0.2533
Epoch: [17/20], Batch Num: [270/600]
Discriminator Loss: 0.7359, Generator Loss: 1.7739
D(x): 0.7246, D(G(z)): 0.2187
Epoch: [17/20], Batch Num: [271/600]
Discriminator Loss: 0.9455, Generator Loss: 1.5475
D(x): 0.7258, D(G(z)): 0.2982
Epoch: [17/20], Batch Num: [272/600]
Discriminator Loss: 1.0272, Generator Loss: 1.6140
D(x): 0.7296, D(G(z)): 0.3535
Epoch: [17/20], Batch Num: [273/600]
Discriminator Loss: 0.9259, Generator Loss: 1.6820
D(x): 0.7130, D(G(z)): 0.3083
Epoch: [17/20], Batch Num: [274/600]
Discriminator Loss: 0.9406, Generator Loss: 1.6156
D(x): 0.7247, D(G(z)): 0.3137
Epoch: [17/20], Batch Num: [275/600]
Discriminator Loss: 1.0890, Generator Loss: 2.0659
D(x): 0.7592, D(G(z)): 0.3771
Epoch: [17/20], Batch Num: [276/600]
Discriminator Loss: 1.0081, Generator Loss: 2.0931
D(x): 0.6707, D(G(z)): 0.2925
Epoch: [17/20], Batch Num: [277/600]
Discriminator Loss: 1.1580, Generator Loss: 2.0139
D(x): 0.5991, D(G(z)): 0.2563
Epoch: [17/20], Batch Num: [278/600]
Discriminator Loss: 0.9447, Generator Loss: 1.8060
D(x): 0.6267, D(G(z)): 0.2165
Epoch: [17/20], Batch Num: [279/600]
Discriminator Loss: 1.2655, Generator Loss: 1.5107
D(x): 0.6220, D(G(z)): 0.3364
Epoch: [17/20], Batch Num: [280/600]
Discriminator Loss: 0.9383, Generator Loss: 1.5019
D(x): 0.7336, D(G(z)): 0.3291
Epoch: [17/20], Batch Num: [281/600]
Discriminator Loss: 1.1235, Generator Loss: 1.4746
D(x): 0.6914, D(G(z)): 0.3495
Epoch: [17/20], Batch Num: [282/600]
Discriminator Loss: 1.0253, Generator Loss: 1.4999
D(x): 0.6910, D(G(z)): 0.3124
Epoch: [17/20], Batch Num: [283/600]
Discriminator Loss: 0.9598, Generator Loss: 1.2262
D(x): 0.7136, D(G(z)): 0.3087
Epoch: [17/20], Batch Num: [284/600]
Discriminator Loss: 1.0681, Generator Loss: 1.5564
D(x): 0.6520, D(G(z)): 0.3189
Epoch: [17/20], Batch Num: [285/600]
Discriminator Loss: 0.9921, Generator Loss: 1.3148
D(x): 0.6560, D(G(z)): 0.3188
Epoch: [17/20], Batch Num: [286/600]
Discriminator Loss: 0.9826, Generator Loss: 1.4685
D(x): 0.6990, D(G(z)): 0.3190
Epoch: [17/20], Batch Num: [287/600]
Discriminator Loss: 1.0190, Generator Loss: 1.4429
D(x): 0.6621, D(G(z)): 0.3094
Epoch: [17/20], Batch Num: [288/600]
Discriminator Loss: 1.1314, Generator Loss: 1.3629
D(x): 0.6357, D(G(z)): 0.3127
Epoch: [17/20], Batch Num: [289/600]
Discriminator Loss: 0.9879, Generator Loss: 1.4677
D(x): 0.6804, D(G(z)): 0.3121
Epoch: [17/20], Batch Num: [290/600]
Discriminator Loss: 0.8983, Generator Loss: 1.5563
D(x): 0.7002, D(G(z)): 0.2903
Epoch: [17/20], Batch Num: [291/600]
Discriminator Loss: 1.0047, Generator Loss: 1.5250
D(x): 0.6543, D(G(z)): 0.2960
Epoch: [17/20], Batch Num: [292/600]
Discriminator Loss: 0.9891, Generator Loss: 1.4908
D(x): 0.6700, D(G(z)): 0.3260
Epoch: [17/20], Batch Num: [293/600]
Discriminator Loss: 0.9874, Generator Loss: 1.4952
D(x): 0.6838, D(G(z)): 0.3260
Epoch: [17/20], Batch Num: [294/600]
Discriminator Loss: 0.9518, Generator Loss: 1.4087
D(x): 0.6781, D(G(z)): 0.3311
Epoch: [17/20], Batch Num: [295/600]
Discriminator Loss: 0.9961, Generator Loss: 1.6360
D(x): 0.6618, D(G(z)): 0.3071
Epoch: [17/20], Batch Num: [296/600]
Discriminator Loss: 0.9727, Generator Loss: 1.8411
D(x): 0.6727, D(G(z)): 0.3218
Epoch: [17/20], Batch Num: [297/600]
Discriminator Loss: 0.8706, Generator Loss: 1.7889
D(x): 0.6742, D(G(z)): 0.2671
Epoch: [17/20], Batch Num: [298/600]
Discriminator Loss: 0.8598, Generator Loss: 1.6680
D(x): 0.6834, D(G(z)): 0.2695
Epoch: [17/20], Batch Num: [299/600]
Discriminator Loss: 0.8059, Generator Loss: 1.7731
D(x): 0.6917, D(G(z)): 0.2507
Epoch: 17, Batch Num: [300/600]

Epoch: [17/20], Batch Num: [300/600]
Discriminator Loss: 0.9017, Generator Loss: 1.6835
D(x): 0.7112, D(G(z)): 0.2964
Epoch: [17/20], Batch Num: [301/600]
Discriminator Loss: 0.9306, Generator Loss: 1.6905
D(x): 0.6841, D(G(z)): 0.2995
Epoch: [17/20], Batch Num: [302/600]
Discriminator Loss: 0.9094, Generator Loss: 1.8773
D(x): 0.7149, D(G(z)): 0.3189
Epoch: [17/20], Batch Num: [303/600]
Discriminator Loss: 0.8795, Generator Loss: 1.7648
D(x): 0.6836, D(G(z)): 0.2565
Epoch: [17/20], Batch Num: [304/600]
Discriminator Loss: 0.7620, Generator Loss: 1.9576
D(x): 0.7430, D(G(z)): 0.2740
Epoch: [17/20], Batch Num: [305/600]
Discriminator Loss: 0.7079, Generator Loss: 1.7724
D(x): 0.7227, D(G(z)): 0.2363
Epoch: [17/20], Batch Num: [306/600]
Discriminator Loss: 0.8529, Generator Loss: 1.7698
D(x): 0.6899, D(G(z)): 0.2589
Epoch: [17/20], Batch Num: [307/600]
Discriminator Loss: 0.7541, Generator Loss: 1.7789
D(x): 0.7504, D(G(z)): 0.2701
Epoch: [17/20], Batch Num: [308/600]
Discriminator Loss: 0.8816, Generator Loss: 1.6280
D(x): 0.6995, D(G(z)): 0.2846
Epoch: [17/20], Batch Num: [309/600]
Discriminator Loss: 0.8001, Generator Loss: 1.8443
D(x): 0.7154, D(G(z)): 0.2612
Epoch: [17/20], Batch Num: [310/600]
Discriminator Loss: 0.8839, Generator Loss: 1.5685
D(x): 0.7011, D(G(z)): 0.2774
Epoch: [17/20], Batch Num: [311/600]
Discriminator Loss: 0.9243, Generator Loss: 1.7417
D(x): 0.7095, D(G(z)): 0.3215
Epoch: [17/20], Batch Num: [312/600]
Discriminator Loss: 0.8262, Generator Loss: 1.9539
D(x): 0.7236, D(G(z)): 0.2834
Epoch: [17/20], Batch Num: [313/600]
Discriminator Loss: 0.8752, Generator Loss: 1.7461
D(x): 0.7088, D(G(z)): 0.2853
Epoch: [17/20], Batch Num: [314/600]
Discriminator Loss: 0.9995, Generator Loss: 1.8796
D(x): 0.6602, D(G(z)): 0.2964
Epoch: [17/20], Batch Num: [315/600]
Discriminator Loss: 0.7318, Generator Loss: 1.7440
D(x): 0.7367, D(G(z)): 0.2561
Epoch: [17/20], Batch Num: [316/600]
Discriminator Loss: 0.8533, Generator Loss: 1.5897
D(x): 0.7024, D(G(z)): 0.2737
Epoch: [17/20], Batch Num: [317/600]
Discriminator Loss: 0.9545, Generator Loss: 1.5637
D(x): 0.7108, D(G(z)): 0.3248
Epoch: [17/20], Batch Num: [318/600]
Discriminator Loss: 0.8399, Generator Loss: 1.6514
D(x): 0.7125, D(G(z)): 0.2515
Epoch: [17/20], Batch Num: [319/600]
Discriminator Loss: 1.0288, Generator Loss: 1.7577
D(x): 0.6971, D(G(z)): 0.3273
Epoch: [17/20], Batch Num: [320/600]
Discriminator Loss: 0.9917, Generator Loss: 1.4574
D(x): 0.6586, D(G(z)): 0.2856
Epoch: [17/20], Batch Num: [321/600]
Discriminator Loss: 0.9064, Generator Loss: 1.5755
D(x): 0.7134, D(G(z)): 0.3191
Epoch: [17/20], Batch Num: [322/600]
Discriminator Loss: 1.1012, Generator Loss: 1.3787
D(x): 0.6639, D(G(z)): 0.3241
Epoch: [17/20], Batch Num: [323/600]
Discriminator Loss: 0.9765, Generator Loss: 1.3160
D(x): 0.6535, D(G(z)): 0.2890
Epoch: [17/20], Batch Num: [324/600]
Discriminator Loss: 0.9360, Generator Loss: 1.4035
D(x): 0.7114, D(G(z)): 0.3538
Epoch: [17/20], Batch Num: [325/600]
Discriminator Loss: 0.9553, Generator Loss: 1.5395
D(x): 0.7481, D(G(z)): 0.3555
Epoch: [17/20], Batch Num: [326/600]
Discriminator Loss: 1.0487, Generator Loss: 1.5100
D(x): 0.6577, D(G(z)): 0.3142
Epoch: [17/20], Batch Num: [327/600]
Discriminator Loss: 1.0909, Generator Loss: 1.3364
D(x): 0.6167, D(G(z)): 0.2852
Epoch: [17/20], Batch Num: [328/600]
Discriminator Loss: 1.0107, Generator Loss: 1.3868
D(x): 0.6881, D(G(z)): 0.3431
Epoch: [17/20], Batch Num: [329/600]
Discriminator Loss: 1.0992, Generator Loss: 1.4366
D(x): 0.6229, D(G(z)): 0.3199
Epoch: [17/20], Batch Num: [330/600]
Discriminator Loss: 0.9556, Generator Loss: 1.4010
D(x): 0.6897, D(G(z)): 0.3388
Epoch: [17/20], Batch Num: [331/600]
Discriminator Loss: 0.9831, Generator Loss: 1.3234
D(x): 0.6797, D(G(z)): 0.3405
Epoch: [17/20], Batch Num: [332/600]
Discriminator Loss: 0.9798, Generator Loss: 1.2250
D(x): 0.6823, D(G(z)): 0.3493
Epoch: [17/20], Batch Num: [333/600]
Discriminator Loss: 0.8760, Generator Loss: 1.2765
D(x): 0.7466, D(G(z)): 0.3489
Epoch: [17/20], Batch Num: [334/600]
Discriminator Loss: 0.7734, Generator Loss: 1.4645
D(x): 0.7572, D(G(z)): 0.3176
Epoch: [17/20], Batch Num: [335/600]
Discriminator Loss: 0.9315, Generator Loss: 1.4400
D(x): 0.6785, D(G(z)): 0.3269
Epoch: [17/20], Batch Num: [336/600]
Discriminator Loss: 0.9254, Generator Loss: 1.4682
D(x): 0.6790, D(G(z)): 0.3133
Epoch: [17/20], Batch Num: [337/600]
Discriminator Loss: 0.8161, Generator Loss: 1.5457
D(x): 0.7412, D(G(z)): 0.3118
Epoch: [17/20], Batch Num: [338/600]
Discriminator Loss: 0.7422, Generator Loss: 1.4838
D(x): 0.7098, D(G(z)): 0.2560
Epoch: [17/20], Batch Num: [339/600]
Discriminator Loss: 0.8210, Generator Loss: 1.2670
D(x): 0.6996, D(G(z)): 0.2847
Epoch: [17/20], Batch Num: [340/600]
Discriminator Loss: 0.8933, Generator Loss: 1.3502
D(x): 0.7248, D(G(z)): 0.3500
Epoch: [17/20], Batch Num: [341/600]
Discriminator Loss: 0.9082, Generator Loss: 1.3834
D(x): 0.7550, D(G(z)): 0.3569
Epoch: [17/20], Batch Num: [342/600]
Discriminator Loss: 0.8493, Generator Loss: 1.4313
D(x): 0.7321, D(G(z)): 0.3142
Epoch: [17/20], Batch Num: [343/600]
Discriminator Loss: 0.8664, Generator Loss: 1.6769
D(x): 0.7361, D(G(z)): 0.3394
Epoch: [17/20], Batch Num: [344/600]
Discriminator Loss: 0.8186, Generator Loss: 1.6579
D(x): 0.7038, D(G(z)): 0.2480
Epoch: [17/20], Batch Num: [345/600]
Discriminator Loss: 0.7659, Generator Loss: 1.6666
D(x): 0.7240, D(G(z)): 0.2618
Epoch: [17/20], Batch Num: [346/600]
Discriminator Loss: 0.7634, Generator Loss: 1.5376
D(x): 0.7177, D(G(z)): 0.2462
Epoch: [17/20], Batch Num: [347/600]
Discriminator Loss: 0.7253, Generator Loss: 1.6445
D(x): 0.7444, D(G(z)): 0.2605
Epoch: [17/20], Batch Num: [348/600]
Discriminator Loss: 0.7398, Generator Loss: 1.2946
D(x): 0.7687, D(G(z)): 0.2990
Epoch: [17/20], Batch Num: [349/600]
Discriminator Loss: 0.8578, Generator Loss: 1.5991
D(x): 0.7770, D(G(z)): 0.3466
Epoch: [17/20], Batch Num: [350/600]
Discriminator Loss: 0.7897, Generator Loss: 1.6796
D(x): 0.7730, D(G(z)): 0.3221
Epoch: [17/20], Batch Num: [351/600]
Discriminator Loss: 0.7644, Generator Loss: 1.5882
D(x): 0.7130, D(G(z)): 0.2564
Epoch: [17/20], Batch Num: [352/600]
Discriminator Loss: 0.8946, Generator Loss: 1.7307
D(x): 0.7155, D(G(z)): 0.2920
Epoch: [17/20], Batch Num: [353/600]
Discriminator Loss: 0.6682, Generator Loss: 1.7568
D(x): 0.7514, D(G(z)): 0.2120
Epoch: [17/20], Batch Num: [354/600]
Discriminator Loss: 0.7846, Generator Loss: 1.6053
D(x): 0.7358, D(G(z)): 0.2638
Epoch: [17/20], Batch Num: [355/600]
Discriminator Loss: 0.7083, Generator Loss: 1.4841
D(x): 0.7903, D(G(z)): 0.2741
Epoch: [17/20], Batch Num: [356/600]
Discriminator Loss: 0.7137, Generator Loss: 1.7284
D(x): 0.8192, D(G(z)): 0.3061
Epoch: [17/20], Batch Num: [357/600]
Discriminator Loss: 0.7511, Generator Loss: 1.5736
D(x): 0.7578, D(G(z)): 0.2642
Epoch: [17/20], Batch Num: [358/600]
Discriminator Loss: 0.8951, Generator Loss: 1.6636
D(x): 0.7075, D(G(z)): 0.2579
Epoch: [17/20], Batch Num: [359/600]
Discriminator Loss: 0.6536, Generator Loss: 1.8326
D(x): 0.7853, D(G(z)): 0.2441
Epoch: [17/20], Batch Num: [360/600]
Discriminator Loss: 0.7337, Generator Loss: 1.7742
D(x): 0.8008, D(G(z)): 0.2676
Epoch: [17/20], Batch Num: [361/600]
Discriminator Loss: 0.7468, Generator Loss: 1.8637
D(x): 0.7887, D(G(z)): 0.2819
Epoch: [17/20], Batch Num: [362/600]
Discriminator Loss: 0.7334, Generator Loss: 1.8092
D(x): 0.7470, D(G(z)): 0.2320
Epoch: [17/20], Batch Num: [363/600]
Discriminator Loss: 0.7956, Generator Loss: 1.9257
D(x): 0.7531, D(G(z)): 0.2478
Epoch: [17/20], Batch Num: [364/600]
Discriminator Loss: 0.7412, Generator Loss: 1.8282
D(x): 0.7842, D(G(z)): 0.2847
Epoch: [17/20], Batch Num: [365/600]
Discriminator Loss: 0.6679, Generator Loss: 1.8506
D(x): 0.7599, D(G(z)): 0.2315
Epoch: [17/20], Batch Num: [366/600]
Discriminator Loss: 0.7112, Generator Loss: 1.6906
D(x): 0.7748, D(G(z)): 0.2524
Epoch: [17/20], Batch Num: [367/600]
Discriminator Loss: 0.6345, Generator Loss: 1.9491
D(x): 0.7879, D(G(z)): 0.2366
Epoch: [17/20], Batch Num: [368/600]
Discriminator Loss: 0.7728, Generator Loss: 1.8804
D(x): 0.7490, D(G(z)): 0.2471
Epoch: [17/20], Batch Num: [369/600]
Discriminator Loss: 0.6657, Generator Loss: 1.6741
D(x): 0.7828, D(G(z)): 0.2417
Epoch: [17/20], Batch Num: [370/600]
Discriminator Loss: 0.8165, Generator Loss: 1.9058
D(x): 0.7987, D(G(z)): 0.2862
Epoch: [17/20], Batch Num: [371/600]
Discriminator Loss: 0.5877, Generator Loss: 1.8905
D(x): 0.8232, D(G(z)): 0.2314
Epoch: [17/20], Batch Num: [372/600]
Discriminator Loss: 0.6845, Generator Loss: 2.0099
D(x): 0.7995, D(G(z)): 0.2503
Epoch: [17/20], Batch Num: [373/600]
Discriminator Loss: 0.6874, Generator Loss: 2.1872
D(x): 0.7876, D(G(z)): 0.2425
Epoch: [17/20], Batch Num: [374/600]
Discriminator Loss: 0.9022, Generator Loss: 1.9776
D(x): 0.6690, D(G(z)): 0.2089
Epoch: [17/20], Batch Num: [375/600]
Discriminator Loss: 0.7370, Generator Loss: 1.8093
D(x): 0.7809, D(G(z)): 0.2240
Epoch: [17/20], Batch Num: [376/600]
Discriminator Loss: 0.6461, Generator Loss: 1.5736
D(x): 0.8143, D(G(z)): 0.2402
Epoch: [17/20], Batch Num: [377/600]
Discriminator Loss: 0.6767, Generator Loss: 1.8413
D(x): 0.8288, D(G(z)): 0.2713
Epoch: [17/20], Batch Num: [378/600]
Discriminator Loss: 0.6575, Generator Loss: 2.1914
D(x): 0.8592, D(G(z)): 0.2856
Epoch: [17/20], Batch Num: [379/600]
Discriminator Loss: 0.6661, Generator Loss: 2.0252
D(x): 0.7556, D(G(z)): 0.1935
Epoch: [17/20], Batch Num: [380/600]
Discriminator Loss: 0.9229, Generator Loss: 1.7643
D(x): 0.6690, D(G(z)): 0.2124
Epoch: [17/20], Batch Num: [381/600]
Discriminator Loss: 0.7206, Generator Loss: 1.7467
D(x): 0.7777, D(G(z)): 0.2430
Epoch: [17/20], Batch Num: [382/600]
Discriminator Loss: 0.7307, Generator Loss: 1.4886
D(x): 0.8362, D(G(z)): 0.3125
Epoch: [17/20], Batch Num: [383/600]
Discriminator Loss: 0.7638, Generator Loss: 1.8674
D(x): 0.8180, D(G(z)): 0.3210
Epoch: [17/20], Batch Num: [384/600]
Discriminator Loss: 0.8724, Generator Loss: 2.0952
D(x): 0.7497, D(G(z)): 0.2766
Epoch: [17/20], Batch Num: [385/600]
Discriminator Loss: 0.8735, Generator Loss: 2.0826
D(x): 0.7413, D(G(z)): 0.2496
Epoch: [17/20], Batch Num: [386/600]
Discriminator Loss: 0.7721, Generator Loss: 2.0607
D(x): 0.7407, D(G(z)): 0.2309
Epoch: [17/20], Batch Num: [387/600]
Discriminator Loss: 0.8754, Generator Loss: 1.7367
D(x): 0.6510, D(G(z)): 0.1897
Epoch: [17/20], Batch Num: [388/600]
Discriminator Loss: 0.9281, Generator Loss: 1.5179
D(x): 0.6830, D(G(z)): 0.2496
Epoch: [17/20], Batch Num: [389/600]
Discriminator Loss: 0.9605, Generator Loss: 1.2819
D(x): 0.7406, D(G(z)): 0.3132
Epoch: [17/20], Batch Num: [390/600]
Discriminator Loss: 0.8765, Generator Loss: 1.4693
D(x): 0.8336, D(G(z)): 0.3449
Epoch: [17/20], Batch Num: [391/600]
Discriminator Loss: 0.9000, Generator Loss: 1.9441
D(x): 0.7955, D(G(z)): 0.3471
Epoch: [17/20], Batch Num: [392/600]
Discriminator Loss: 0.8433, Generator Loss: 2.1016
D(x): 0.6872, D(G(z)): 0.2223
Epoch: [17/20], Batch Num: [393/600]
Discriminator Loss: 0.8739, Generator Loss: 1.9633
D(x): 0.6652, D(G(z)): 0.2105
Epoch: [17/20], Batch Num: [394/600]
Discriminator Loss: 0.9175, Generator Loss: 1.7033
D(x): 0.6223, D(G(z)): 0.1904
Epoch: [17/20], Batch Num: [395/600]
Discriminator Loss: 1.0256, Generator Loss: 1.2747
D(x): 0.6960, D(G(z)): 0.3139
Epoch: [17/20], Batch Num: [396/600]
Discriminator Loss: 1.1259, Generator Loss: 1.4680
D(x): 0.7788, D(G(z)): 0.4125
Epoch: [17/20], Batch Num: [397/600]
Discriminator Loss: 1.1721, Generator Loss: 1.5911
D(x): 0.7109, D(G(z)): 0.3979
Epoch: [17/20], Batch Num: [398/600]
Discriminator Loss: 1.0438, Generator Loss: 1.7825
D(x): 0.6808, D(G(z)): 0.3033
Epoch: [17/20], Batch Num: [399/600]
Discriminator Loss: 0.8530, Generator Loss: 1.8835
D(x): 0.7097, D(G(z)): 0.2594
Epoch: 17, Batch Num: [400/600]

Epoch: [17/20], Batch Num: [400/600]
Discriminator Loss: 0.8536, Generator Loss: 1.8059
D(x): 0.6731, D(G(z)): 0.2289
Epoch: [17/20], Batch Num: [401/600]
Discriminator Loss: 1.0162, Generator Loss: 1.5490
D(x): 0.6375, D(G(z)): 0.2640
Epoch: [17/20], Batch Num: [402/600]
Discriminator Loss: 0.8065, Generator Loss: 1.6368
D(x): 0.7395, D(G(z)): 0.2807
Epoch: [17/20], Batch Num: [403/600]
Discriminator Loss: 0.8106, Generator Loss: 1.6499
D(x): 0.7737, D(G(z)): 0.3004
Epoch: [17/20], Batch Num: [404/600]
Discriminator Loss: 1.0147, Generator Loss: 1.8000
D(x): 0.7243, D(G(z)): 0.3264
Epoch: [17/20], Batch Num: [405/600]
Discriminator Loss: 0.9143, Generator Loss: 1.9032
D(x): 0.7236, D(G(z)): 0.3022
Epoch: [17/20], Batch Num: [406/600]
Discriminator Loss: 0.8633, Generator Loss: 2.0134
D(x): 0.6644, D(G(z)): 0.2472
Epoch: [17/20], Batch Num: [407/600]
Discriminator Loss: 0.7223, Generator Loss: 1.9101
D(x): 0.7403, D(G(z)): 0.2296
Epoch: [17/20], Batch Num: [408/600]
Discriminator Loss: 0.7762, Generator Loss: 1.8914
D(x): 0.7255, D(G(z)): 0.2471
Epoch: [17/20], Batch Num: [409/600]
Discriminator Loss: 0.8835, Generator Loss: 1.8803
D(x): 0.6913, D(G(z)): 0.2714
Epoch: [17/20], Batch Num: [410/600]
Discriminator Loss: 0.8663, Generator Loss: 1.6659
D(x): 0.7291, D(G(z)): 0.2697
Epoch: [17/20], Batch Num: [411/600]
Discriminator Loss: 0.8873, Generator Loss: 1.6529
D(x): 0.7217, D(G(z)): 0.2962
Epoch: [17/20], Batch Num: [412/600]
Discriminator Loss: 0.9223, Generator Loss: 1.7099
D(x): 0.7355, D(G(z)): 0.3368
Epoch: [17/20], Batch Num: [413/600]
Discriminator Loss: 0.8080, Generator Loss: 1.8761
D(x): 0.7536, D(G(z)): 0.2959
Epoch: [17/20], Batch Num: [414/600]
Discriminator Loss: 0.8319, Generator Loss: 2.0103
D(x): 0.7012, D(G(z)): 0.2663
Epoch: [17/20], Batch Num: [415/600]
Discriminator Loss: 0.7207, Generator Loss: 1.9680
D(x): 0.7238, D(G(z)): 0.2211
Epoch: [17/20], Batch Num: [416/600]
Discriminator Loss: 0.8908, Generator Loss: 2.0165
D(x): 0.6490, D(G(z)): 0.2400
Epoch: [17/20], Batch Num: [417/600]
Discriminator Loss: 0.8588, Generator Loss: 1.6405
D(x): 0.7081, D(G(z)): 0.2616
Epoch: [17/20], Batch Num: [418/600]
Discriminator Loss: 0.9575, Generator Loss: 1.4971
D(x): 0.6617, D(G(z)): 0.2652
Epoch: [17/20], Batch Num: [419/600]
Discriminator Loss: 0.9044, Generator Loss: 1.4542
D(x): 0.7215, D(G(z)): 0.3203
Epoch: [17/20], Batch Num: [420/600]
Discriminator Loss: 0.8053, Generator Loss: 1.4699
D(x): 0.7684, D(G(z)): 0.3208
Epoch: [17/20], Batch Num: [421/600]
Discriminator Loss: 0.8948, Generator Loss: 1.7847
D(x): 0.7287, D(G(z)): 0.3166
Epoch: [17/20], Batch Num: [422/600]
Discriminator Loss: 1.0093, Generator Loss: 1.9235
D(x): 0.7183, D(G(z)): 0.3447
Epoch: [17/20], Batch Num: [423/600]
Discriminator Loss: 1.0505, Generator Loss: 2.1143
D(x): 0.6503, D(G(z)): 0.2987
Epoch: [17/20], Batch Num: [424/600]
Discriminator Loss: 1.0327, Generator Loss: 1.6000
D(x): 0.5964, D(G(z)): 0.2388
Epoch: [17/20], Batch Num: [425/600]
Discriminator Loss: 0.9586, Generator Loss: 1.4674
D(x): 0.6384, D(G(z)): 0.2397
Epoch: [17/20], Batch Num: [426/600]
Discriminator Loss: 1.0237, Generator Loss: 1.4164
D(x): 0.6961, D(G(z)): 0.3601
Epoch: [17/20], Batch Num: [427/600]
Discriminator Loss: 1.1706, Generator Loss: 1.2224
D(x): 0.6596, D(G(z)): 0.3761
Epoch: [17/20], Batch Num: [428/600]
Discriminator Loss: 1.0340, Generator Loss: 1.3718
D(x): 0.7292, D(G(z)): 0.3927
Epoch: [17/20], Batch Num: [429/600]
Discriminator Loss: 0.9987, Generator Loss: 1.2997
D(x): 0.7136, D(G(z)): 0.3481
Epoch: [17/20], Batch Num: [430/600]
Discriminator Loss: 0.9148, Generator Loss: 1.5695
D(x): 0.7048, D(G(z)): 0.3308
Epoch: [17/20], Batch Num: [431/600]
Discriminator Loss: 0.9046, Generator Loss: 1.6711
D(x): 0.6965, D(G(z)): 0.3026
Epoch: [17/20], Batch Num: [432/600]
Discriminator Loss: 0.8612, Generator Loss: 1.6147
D(x): 0.6709, D(G(z)): 0.2545
Epoch: [17/20], Batch Num: [433/600]
Discriminator Loss: 0.9269, Generator Loss: 1.5911
D(x): 0.6463, D(G(z)): 0.2514
Epoch: [17/20], Batch Num: [434/600]
Discriminator Loss: 0.9752, Generator Loss: 1.4550
D(x): 0.6415, D(G(z)): 0.2679
Epoch: [17/20], Batch Num: [435/600]
Discriminator Loss: 1.0531, Generator Loss: 1.0245
D(x): 0.6713, D(G(z)): 0.3632
Epoch: [17/20], Batch Num: [436/600]
Discriminator Loss: 1.0439, Generator Loss: 1.0832
D(x): 0.7468, D(G(z)): 0.4163
Epoch: [17/20], Batch Num: [437/600]
Discriminator Loss: 1.0514, Generator Loss: 1.2322
D(x): 0.7402, D(G(z)): 0.4161
Epoch: [17/20], Batch Num: [438/600]
Discriminator Loss: 0.9854, Generator Loss: 1.4845
D(x): 0.6924, D(G(z)): 0.3612
Epoch: [17/20], Batch Num: [439/600]
Discriminator Loss: 0.9104, Generator Loss: 1.5661
D(x): 0.7137, D(G(z)): 0.3349
Epoch: [17/20], Batch Num: [440/600]
Discriminator Loss: 0.9080, Generator Loss: 1.6874
D(x): 0.6609, D(G(z)): 0.2778
Epoch: [17/20], Batch Num: [441/600]
Discriminator Loss: 0.9174, Generator Loss: 1.7114
D(x): 0.6584, D(G(z)): 0.2667
Epoch: [17/20], Batch Num: [442/600]
Discriminator Loss: 0.9184, Generator Loss: 1.6998
D(x): 0.6689, D(G(z)): 0.2796
Epoch: [17/20], Batch Num: [443/600]
Discriminator Loss: 0.7143, Generator Loss: 1.4622
D(x): 0.7291, D(G(z)): 0.2389
Epoch: [17/20], Batch Num: [444/600]
Discriminator Loss: 0.9060, Generator Loss: 1.2319
D(x): 0.6772, D(G(z)): 0.2860
Epoch: [17/20], Batch Num: [445/600]
Discriminator Loss: 0.8223, Generator Loss: 1.3623
D(x): 0.7429, D(G(z)): 0.3218
Epoch: [17/20], Batch Num: [446/600]
Discriminator Loss: 0.7581, Generator Loss: 1.3301
D(x): 0.7730, D(G(z)): 0.3075
Epoch: [17/20], Batch Num: [447/600]
Discriminator Loss: 0.8633, Generator Loss: 1.2608
D(x): 0.6958, D(G(z)): 0.2891
Epoch: [17/20], Batch Num: [448/600]
Discriminator Loss: 0.7268, Generator Loss: 1.3912
D(x): 0.7867, D(G(z)): 0.3057
Epoch: [17/20], Batch Num: [449/600]
Discriminator Loss: 0.7191, Generator Loss: 1.6655
D(x): 0.7907, D(G(z)): 0.3173
Epoch: [17/20], Batch Num: [450/600]
Discriminator Loss: 0.7485, Generator Loss: 1.4332
D(x): 0.7372, D(G(z)): 0.2712
Epoch: [17/20], Batch Num: [451/600]
Discriminator Loss: 0.8024, Generator Loss: 1.7081
D(x): 0.7151, D(G(z)): 0.2520
Epoch: [17/20], Batch Num: [452/600]
Discriminator Loss: 0.7269, Generator Loss: 1.7824
D(x): 0.7719, D(G(z)): 0.2657
Epoch: [17/20], Batch Num: [453/600]
Discriminator Loss: 0.6887, Generator Loss: 1.8758
D(x): 0.7767, D(G(z)): 0.2586
Epoch: [17/20], Batch Num: [454/600]
Discriminator Loss: 0.8320, Generator Loss: 1.8601
D(x): 0.6825, D(G(z)): 0.2080
Epoch: [17/20], Batch Num: [455/600]
Discriminator Loss: 0.7070, Generator Loss: 1.6589
D(x): 0.7150, D(G(z)): 0.1996
Epoch: [17/20], Batch Num: [456/600]
Discriminator Loss: 0.7680, Generator Loss: 1.6076
D(x): 0.7323, D(G(z)): 0.2466
Epoch: [17/20], Batch Num: [457/600]
Discriminator Loss: 0.6982, Generator Loss: 1.5136
D(x): 0.8076, D(G(z)): 0.3010
Epoch: [17/20], Batch Num: [458/600]
Discriminator Loss: 0.7371, Generator Loss: 1.6818
D(x): 0.8172, D(G(z)): 0.3077
Epoch: [17/20], Batch Num: [459/600]
Discriminator Loss: 0.8224, Generator Loss: 1.7941
D(x): 0.8125, D(G(z)): 0.3581
Epoch: [17/20], Batch Num: [460/600]
Discriminator Loss: 0.7838, Generator Loss: 2.0426
D(x): 0.7545, D(G(z)): 0.2752
Epoch: [17/20], Batch Num: [461/600]
Discriminator Loss: 0.7662, Generator Loss: 2.1114
D(x): 0.7261, D(G(z)): 0.2236
Epoch: [17/20], Batch Num: [462/600]
Discriminator Loss: 0.5980, Generator Loss: 2.1225
D(x): 0.7568, D(G(z)): 0.1671
Epoch: [17/20], Batch Num: [463/600]
Discriminator Loss: 0.7020, Generator Loss: 2.1717
D(x): 0.7055, D(G(z)): 0.1687
Epoch: [17/20], Batch Num: [464/600]
Discriminator Loss: 0.7110, Generator Loss: 1.7402
D(x): 0.7252, D(G(z)): 0.2118
Epoch: [17/20], Batch Num: [465/600]
Discriminator Loss: 0.7186, Generator Loss: 1.3584
D(x): 0.7819, D(G(z)): 0.2625
Epoch: [17/20], Batch Num: [466/600]
Discriminator Loss: 0.8262, Generator Loss: 1.4710
D(x): 0.7969, D(G(z)): 0.3265
Epoch: [17/20], Batch Num: [467/600]
Discriminator Loss: 0.8083, Generator Loss: 1.7921
D(x): 0.8178, D(G(z)): 0.3403
Epoch: [17/20], Batch Num: [468/600]
Discriminator Loss: 0.8246, Generator Loss: 2.0363
D(x): 0.7572, D(G(z)): 0.2887
Epoch: [17/20], Batch Num: [469/600]
Discriminator Loss: 0.7533, Generator Loss: 1.9467
D(x): 0.7257, D(G(z)): 0.2370
Epoch: [17/20], Batch Num: [470/600]
Discriminator Loss: 1.0156, Generator Loss: 2.0850
D(x): 0.6288, D(G(z)): 0.2619
Epoch: [17/20], Batch Num: [471/600]
Discriminator Loss: 0.7225, Generator Loss: 2.0240
D(x): 0.6957, D(G(z)): 0.1641
Epoch: [17/20], Batch Num: [472/600]
Discriminator Loss: 0.8413, Generator Loss: 1.5871
D(x): 0.7221, D(G(z)): 0.2764
Epoch: [17/20], Batch Num: [473/600]
Discriminator Loss: 1.0171, Generator Loss: 1.5289
D(x): 0.7285, D(G(z)): 0.3398
Epoch: [17/20], Batch Num: [474/600]
Discriminator Loss: 0.9201, Generator Loss: 1.5537
D(x): 0.7805, D(G(z)): 0.3388
Epoch: [17/20], Batch Num: [475/600]
Discriminator Loss: 0.8641, Generator Loss: 1.6797
D(x): 0.7808, D(G(z)): 0.3409
Epoch: [17/20], Batch Num: [476/600]
Discriminator Loss: 0.8486, Generator Loss: 1.7916
D(x): 0.7226, D(G(z)): 0.2579
Epoch: [17/20], Batch Num: [477/600]
Discriminator Loss: 0.9534, Generator Loss: 1.9081
D(x): 0.6990, D(G(z)): 0.2728
Epoch: [17/20], Batch Num: [478/600]
Discriminator Loss: 0.9516, Generator Loss: 1.9814
D(x): 0.7051, D(G(z)): 0.2524
Epoch: [17/20], Batch Num: [479/600]
Discriminator Loss: 0.9447, Generator Loss: 1.8760
D(x): 0.6388, D(G(z)): 0.2351
Epoch: [17/20], Batch Num: [480/600]
Discriminator Loss: 0.9923, Generator Loss: 1.4017
D(x): 0.6751, D(G(z)): 0.2996
Epoch: [17/20], Batch Num: [481/600]
Discriminator Loss: 0.9876, Generator Loss: 1.3968
D(x): 0.6792, D(G(z)): 0.2904
Epoch: [17/20], Batch Num: [482/600]
Discriminator Loss: 1.0110, Generator Loss: 1.2836
D(x): 0.7011, D(G(z)): 0.3364
Epoch: [17/20], Batch Num: [483/600]
Discriminator Loss: 1.2319, Generator Loss: 1.3379
D(x): 0.7055, D(G(z)): 0.4136
Epoch: [17/20], Batch Num: [484/600]
Discriminator Loss: 1.1762, Generator Loss: 1.5270
D(x): 0.6805, D(G(z)): 0.3722
Epoch: [17/20], Batch Num: [485/600]
Discriminator Loss: 0.9331, Generator Loss: 1.6116
D(x): 0.6857, D(G(z)): 0.2923
Epoch: [17/20], Batch Num: [486/600]
Discriminator Loss: 0.9374, Generator Loss: 1.7868
D(x): 0.7002, D(G(z)): 0.2836
Epoch: [17/20], Batch Num: [487/600]
Discriminator Loss: 0.9902, Generator Loss: 1.6172
D(x): 0.6434, D(G(z)): 0.2602
Epoch: [17/20], Batch Num: [488/600]
Discriminator Loss: 0.8949, Generator Loss: 1.5050
D(x): 0.6635, D(G(z)): 0.2484
Epoch: [17/20], Batch Num: [489/600]
Discriminator Loss: 0.8766, Generator Loss: 1.4078
D(x): 0.7098, D(G(z)): 0.2930
Epoch: [17/20], Batch Num: [490/600]
Discriminator Loss: 0.8371, Generator Loss: 1.3369
D(x): 0.7243, D(G(z)): 0.3040
Epoch: [17/20], Batch Num: [491/600]
Discriminator Loss: 0.9485, Generator Loss: 1.4575
D(x): 0.7542, D(G(z)): 0.3549
Epoch: [17/20], Batch Num: [492/600]
Discriminator Loss: 0.9622, Generator Loss: 1.4172
D(x): 0.7166, D(G(z)): 0.3367
Epoch: [17/20], Batch Num: [493/600]
Discriminator Loss: 0.8604, Generator Loss: 1.8113
D(x): 0.7681, D(G(z)): 0.3413
Epoch: [17/20], Batch Num: [494/600]
Discriminator Loss: 0.8289, Generator Loss: 1.9419
D(x): 0.6782, D(G(z)): 0.2409
Epoch: [17/20], Batch Num: [495/600]
Discriminator Loss: 0.7638, Generator Loss: 1.9778
D(x): 0.6806, D(G(z)): 0.2128
Epoch: [17/20], Batch Num: [496/600]
Discriminator Loss: 0.7849, Generator Loss: 1.7264
D(x): 0.7217, D(G(z)): 0.2206
Epoch: [17/20], Batch Num: [497/600]
Discriminator Loss: 0.7758, Generator Loss: 1.5626
D(x): 0.6995, D(G(z)): 0.2294
Epoch: [17/20], Batch Num: [498/600]
Discriminator Loss: 0.7205, Generator Loss: 1.3618
D(x): 0.7404, D(G(z)): 0.2434
Epoch: [17/20], Batch Num: [499/600]
Discriminator Loss: 0.9276, Generator Loss: 1.5755
D(x): 0.7623, D(G(z)): 0.3662
Epoch: 17, Batch Num: [500/600]

Epoch: [17/20], Batch Num: [500/600]
Discriminator Loss: 0.8105, Generator Loss: 1.6720
D(x): 0.7804, D(G(z)): 0.3328
Epoch: [17/20], Batch Num: [501/600]
Discriminator Loss: 0.6954, Generator Loss: 1.9987
D(x): 0.8021, D(G(z)): 0.2843
Epoch: [17/20], Batch Num: [502/600]
Discriminator Loss: 0.7234, Generator Loss: 2.0454
D(x): 0.7146, D(G(z)): 0.2110
Epoch: [17/20], Batch Num: [503/600]
Discriminator Loss: 0.8218, Generator Loss: 2.0531
D(x): 0.6707, D(G(z)): 0.2078
Epoch: [17/20], Batch Num: [504/600]
Discriminator Loss: 0.7165, Generator Loss: 1.8588
D(x): 0.6816, D(G(z)): 0.1859
Epoch: [17/20], Batch Num: [505/600]
Discriminator Loss: 0.6915, Generator Loss: 1.8664
D(x): 0.7472, D(G(z)): 0.2305
Epoch: [17/20], Batch Num: [506/600]
Discriminator Loss: 0.7482, Generator Loss: 1.6461
D(x): 0.7581, D(G(z)): 0.2756
Epoch: [17/20], Batch Num: [507/600]
Discriminator Loss: 0.7648, Generator Loss: 1.5405
D(x): 0.8023, D(G(z)): 0.2972
Epoch: [17/20], Batch Num: [508/600]
Discriminator Loss: 0.6656, Generator Loss: 1.8270
D(x): 0.8328, D(G(z)): 0.3068
Epoch: [17/20], Batch Num: [509/600]
Discriminator Loss: 0.7180, Generator Loss: 1.9890
D(x): 0.7492, D(G(z)): 0.2334
Epoch: [17/20], Batch Num: [510/600]
Discriminator Loss: 0.7399, Generator Loss: 2.3149
D(x): 0.7427, D(G(z)): 0.2186
Epoch: [17/20], Batch Num: [511/600]
Discriminator Loss: 0.7380, Generator Loss: 2.1840
D(x): 0.7273, D(G(z)): 0.2033
Epoch: [17/20], Batch Num: [512/600]
Discriminator Loss: 0.7897, Generator Loss: 2.1890
D(x): 0.6945, D(G(z)): 0.2010
Epoch: [17/20], Batch Num: [513/600]
Discriminator Loss: 0.6723, Generator Loss: 2.0714
D(x): 0.8193, D(G(z)): 0.2629
Epoch: [17/20], Batch Num: [514/600]
Discriminator Loss: 0.7634, Generator Loss: 1.9260
D(x): 0.7433, D(G(z)): 0.2299
Epoch: [17/20], Batch Num: [515/600]
Discriminator Loss: 0.8706, Generator Loss: 1.5870
D(x): 0.7854, D(G(z)): 0.3128
Epoch: [17/20], Batch Num: [516/600]
Discriminator Loss: 0.7917, Generator Loss: 1.9549
D(x): 0.7377, D(G(z)): 0.2446
Epoch: [17/20], Batch Num: [517/600]
Discriminator Loss: 0.8769, Generator Loss: 2.0264
D(x): 0.7129, D(G(z)): 0.2702
Epoch: [17/20], Batch Num: [518/600]
Discriminator Loss: 0.9164, Generator Loss: 1.5371
D(x): 0.6395, D(G(z)): 0.2187
Epoch: [17/20], Batch Num: [519/600]
Discriminator Loss: 0.7308, Generator Loss: 1.6556
D(x): 0.7569, D(G(z)): 0.2373
Epoch: [17/20], Batch Num: [520/600]
Discriminator Loss: 1.0509, Generator Loss: 1.5235
D(x): 0.7748, D(G(z)): 0.3983
Epoch: [17/20], Batch Num: [521/600]
Discriminator Loss: 0.9588, Generator Loss: 1.9387
D(x): 0.7465, D(G(z)): 0.3257
Epoch: [17/20], Batch Num: [522/600]
Discriminator Loss: 1.0631, Generator Loss: 1.9810
D(x): 0.6758, D(G(z)): 0.3007
Epoch: [17/20], Batch Num: [523/600]
Discriminator Loss: 1.1568, Generator Loss: 1.6750
D(x): 0.5830, D(G(z)): 0.2379
Epoch: [17/20], Batch Num: [524/600]
Discriminator Loss: 1.0831, Generator Loss: 1.3397
D(x): 0.6296, D(G(z)): 0.2943
Epoch: [17/20], Batch Num: [525/600]
Discriminator Loss: 0.9923, Generator Loss: 1.0612
D(x): 0.7099, D(G(z)): 0.3353
Epoch: [17/20], Batch Num: [526/600]
Discriminator Loss: 0.9266, Generator Loss: 1.1725
D(x): 0.7995, D(G(z)): 0.3897
Epoch: [17/20], Batch Num: [527/600]
Discriminator Loss: 0.9665, Generator Loss: 1.4813
D(x): 0.8022, D(G(z)): 0.4252
Epoch: [17/20], Batch Num: [528/600]
Discriminator Loss: 1.1012, Generator Loss: 1.5992
D(x): 0.6725, D(G(z)): 0.3302
Epoch: [17/20], Batch Num: [529/600]
Discriminator Loss: 0.8911, Generator Loss: 1.7140
D(x): 0.6798, D(G(z)): 0.2795
Epoch: [17/20], Batch Num: [530/600]
Discriminator Loss: 0.9738, Generator Loss: 1.7178
D(x): 0.6152, D(G(z)): 0.2172
Epoch: [17/20], Batch Num: [531/600]
Discriminator Loss: 1.0409, Generator Loss: 1.4575
D(x): 0.5866, D(G(z)): 0.2487
Epoch: [17/20], Batch Num: [532/600]
Discriminator Loss: 0.9477, Generator Loss: 1.1542
D(x): 0.6498, D(G(z)): 0.2841
Epoch: [17/20], Batch Num: [533/600]
Discriminator Loss: 0.7684, Generator Loss: 1.0808
D(x): 0.8117, D(G(z)): 0.3467
Epoch: [17/20], Batch Num: [534/600]
Discriminator Loss: 0.9190, Generator Loss: 1.0113
D(x): 0.7477, D(G(z)): 0.3664
Epoch: [17/20], Batch Num: [535/600]
Discriminator Loss: 0.9222, Generator Loss: 1.2970
D(x): 0.8203, D(G(z)): 0.4057
Epoch: [17/20], Batch Num: [536/600]
Discriminator Loss: 0.8299, Generator Loss: 1.5353
D(x): 0.7835, D(G(z)): 0.3522
Epoch: [17/20], Batch Num: [537/600]
Discriminator Loss: 0.7905, Generator Loss: 1.9567
D(x): 0.7425, D(G(z)): 0.2894
Epoch: [17/20], Batch Num: [538/600]
Discriminator Loss: 0.7577, Generator Loss: 2.2025
D(x): 0.7038, D(G(z)): 0.2176
Epoch: [17/20], Batch Num: [539/600]
Discriminator Loss: 0.8315, Generator Loss: 2.0438
D(x): 0.6287, D(G(z)): 0.1781
Epoch: [17/20], Batch Num: [540/600]
Discriminator Loss: 0.8372, Generator Loss: 1.9311
D(x): 0.6319, D(G(z)): 0.1679
Epoch: [17/20], Batch Num: [541/600]
Discriminator Loss: 0.7487, Generator Loss: 1.6363
D(x): 0.7154, D(G(z)): 0.2189
Epoch: [17/20], Batch Num: [542/600]
Discriminator Loss: 0.6387, Generator Loss: 1.4587
D(x): 0.7845, D(G(z)): 0.2517
Epoch: [17/20], Batch Num: [543/600]
Discriminator Loss: 0.6755, Generator Loss: 1.4015
D(x): 0.8344, D(G(z)): 0.3116
Epoch: [17/20], Batch Num: [544/600]
Discriminator Loss: 0.8303, Generator Loss: 1.2999
D(x): 0.7978, D(G(z)): 0.3552
Epoch: [17/20], Batch Num: [545/600]
Discriminator Loss: 0.6532, Generator Loss: 1.7146
D(x): 0.8793, D(G(z)): 0.3308
Epoch: [17/20], Batch Num: [546/600]
Discriminator Loss: 0.6915, Generator Loss: 1.9805
D(x): 0.7746, D(G(z)): 0.2667
Epoch: [17/20], Batch Num: [547/600]
Discriminator Loss: 0.5655, Generator Loss: 2.2189
D(x): 0.7650, D(G(z)): 0.1855
Epoch: [17/20], Batch Num: [548/600]
Discriminator Loss: 0.6843, Generator Loss: 2.0505
D(x): 0.7029, D(G(z)): 0.1655
Epoch: [17/20], Batch Num: [549/600]
Discriminator Loss: 0.7613, Generator Loss: 1.9847
D(x): 0.6783, D(G(z)): 0.1682
Epoch: [17/20], Batch Num: [550/600]
Discriminator Loss: 0.6872, Generator Loss: 1.7848
D(x): 0.7610, D(G(z)): 0.2190
Epoch: [17/20], Batch Num: [551/600]
Discriminator Loss: 0.7374, Generator Loss: 1.6297
D(x): 0.7939, D(G(z)): 0.2890
Epoch: [17/20], Batch Num: [552/600]
Discriminator Loss: 0.8316, Generator Loss: 1.7166
D(x): 0.8304, D(G(z)): 0.3389
Epoch: [17/20], Batch Num: [553/600]
Discriminator Loss: 0.6945, Generator Loss: 2.0005
D(x): 0.8207, D(G(z)): 0.2645
Epoch: [17/20], Batch Num: [554/600]
Discriminator Loss: 0.5988, Generator Loss: 2.1298
D(x): 0.8111, D(G(z)): 0.2424
Epoch: [17/20], Batch Num: [555/600]
Discriminator Loss: 0.8034, Generator Loss: 2.2481
D(x): 0.7131, D(G(z)): 0.2100
Epoch: [17/20], Batch Num: [556/600]
Discriminator Loss: 0.7453, Generator Loss: 2.1072
D(x): 0.7355, D(G(z)): 0.2117
Epoch: [17/20], Batch Num: [557/600]
Discriminator Loss: 0.6795, Generator Loss: 1.9689
D(x): 0.7621, D(G(z)): 0.1986
Epoch: [17/20], Batch Num: [558/600]
Discriminator Loss: 0.7028, Generator Loss: 1.8405
D(x): 0.7851, D(G(z)): 0.2477
Epoch: [17/20], Batch Num: [559/600]
Discriminator Loss: 0.8122, Generator Loss: 2.0468
D(x): 0.7105, D(G(z)): 0.2427
Epoch: [17/20], Batch Num: [560/600]
Discriminator Loss: 0.8921, Generator Loss: 1.6599
D(x): 0.7554, D(G(z)): 0.3129
Epoch: [17/20], Batch Num: [561/600]
Discriminator Loss: 0.9056, Generator Loss: 1.6868
D(x): 0.7565, D(G(z)): 0.3124
Epoch: [17/20], Batch Num: [562/600]
Discriminator Loss: 0.8107, Generator Loss: 1.7072
D(x): 0.7600, D(G(z)): 0.2746
Epoch: [17/20], Batch Num: [563/600]
Discriminator Loss: 0.9231, Generator Loss: 1.8349
D(x): 0.7323, D(G(z)): 0.3066
Epoch: [17/20], Batch Num: [564/600]
Discriminator Loss: 0.8477, Generator Loss: 1.9917
D(x): 0.7126, D(G(z)): 0.2622
Epoch: [17/20], Batch Num: [565/600]
Discriminator Loss: 0.8811, Generator Loss: 1.8363
D(x): 0.7031, D(G(z)): 0.2283
Epoch: [17/20], Batch Num: [566/600]
Discriminator Loss: 0.8553, Generator Loss: 1.7376
D(x): 0.7088, D(G(z)): 0.2479
Epoch: [17/20], Batch Num: [567/600]
Discriminator Loss: 0.9841, Generator Loss: 1.5086
D(x): 0.7500, D(G(z)): 0.3155
Epoch: [17/20], Batch Num: [568/600]
Discriminator Loss: 0.9547, Generator Loss: 1.4537
D(x): 0.6704, D(G(z)): 0.2757
Epoch: [17/20], Batch Num: [569/600]
Discriminator Loss: 0.8031, Generator Loss: 1.4615
D(x): 0.7879, D(G(z)): 0.3392
Epoch: [17/20], Batch Num: [570/600]
Discriminator Loss: 1.1474, Generator Loss: 1.5162
D(x): 0.6789, D(G(z)): 0.3524
Epoch: [17/20], Batch Num: [571/600]
Discriminator Loss: 1.0794, Generator Loss: 1.7353
D(x): 0.7118, D(G(z)): 0.3686
Epoch: [17/20], Batch Num: [572/600]
Discriminator Loss: 1.0080, Generator Loss: 1.6622
D(x): 0.6408, D(G(z)): 0.2590
Epoch: [17/20], Batch Num: [573/600]
Discriminator Loss: 1.0252, Generator Loss: 1.4948
D(x): 0.6465, D(G(z)): 0.2699
Epoch: [17/20], Batch Num: [574/600]
Discriminator Loss: 1.0094, Generator Loss: 1.4727
D(x): 0.6927, D(G(z)): 0.2928
Epoch: [17/20], Batch Num: [575/600]
Discriminator Loss: 1.0027, Generator Loss: 1.3192
D(x): 0.6409, D(G(z)): 0.2940
Epoch: [17/20], Batch Num: [576/600]
Discriminator Loss: 0.7785, Generator Loss: 1.3032
D(x): 0.8171, D(G(z)): 0.3498
Epoch: [17/20], Batch Num: [577/600]
Discriminator Loss: 1.1386, Generator Loss: 1.4329
D(x): 0.6903, D(G(z)): 0.3667
Epoch: [17/20], Batch Num: [578/600]
Discriminator Loss: 0.9191, Generator Loss: 1.5025
D(x): 0.7468, D(G(z)): 0.3385
Epoch: [17/20], Batch Num: [579/600]
Discriminator Loss: 1.0168, Generator Loss: 1.6745
D(x): 0.6722, D(G(z)): 0.3166
Epoch: [17/20], Batch Num: [580/600]
Discriminator Loss: 0.8506, Generator Loss: 1.9007
D(x): 0.6767, D(G(z)): 0.2306
Epoch: [17/20], Batch Num: [581/600]
Discriminator Loss: 0.8769, Generator Loss: 1.8211
D(x): 0.6453, D(G(z)): 0.2276
Epoch: [17/20], Batch Num: [582/600]
Discriminator Loss: 0.7783, Generator Loss: 1.4873
D(x): 0.6870, D(G(z)): 0.2417
Epoch: [17/20], Batch Num: [583/600]
Discriminator Loss: 0.7861, Generator Loss: 1.3333
D(x): 0.7138, D(G(z)): 0.2681
Epoch: [17/20], Batch Num: [584/600]
Discriminator Loss: 0.9024, Generator Loss: 1.2477
D(x): 0.7433, D(G(z)): 0.3443
Epoch: [17/20], Batch Num: [585/600]
Discriminator Loss: 0.7610, Generator Loss: 1.4746
D(x): 0.8101, D(G(z)): 0.3471
Epoch: [17/20], Batch Num: [586/600]
Discriminator Loss: 0.7792, Generator Loss: 1.8242
D(x): 0.7522, D(G(z)): 0.2967
Epoch: [17/20], Batch Num: [587/600]
Discriminator Loss: 0.7835, Generator Loss: 2.0134
D(x): 0.7533, D(G(z)): 0.3024
Epoch: [17/20], Batch Num: [588/600]
Discriminator Loss: 0.7624, Generator Loss: 2.0769
D(x): 0.6988, D(G(z)): 0.2322
Epoch: [17/20], Batch Num: [589/600]
Discriminator Loss: 0.8009, Generator Loss: 2.2336
D(x): 0.6622, D(G(z)): 0.1886
Epoch: [17/20], Batch Num: [590/600]
Discriminator Loss: 0.7192, Generator Loss: 2.1547
D(x): 0.7203, D(G(z)): 0.2119
Epoch: [17/20], Batch Num: [591/600]
Discriminator Loss: 0.5334, Generator Loss: 1.9715
D(x): 0.8080, D(G(z)): 0.2065
Epoch: [17/20], Batch Num: [592/600]
Discriminator Loss: 0.5756, Generator Loss: 1.9217
D(x): 0.8240, D(G(z)): 0.2345
Epoch: [17/20], Batch Num: [593/600]
Discriminator Loss: 0.6042, Generator Loss: 2.1405
D(x): 0.7801, D(G(z)): 0.2047
Epoch: [17/20], Batch Num: [594/600]
Discriminator Loss: 0.6254, Generator Loss: 2.1464
D(x): 0.8221, D(G(z)): 0.2407
Epoch: [17/20], Batch Num: [595/600]
Discriminator Loss: 0.5172, Generator Loss: 2.1769
D(x): 0.8164, D(G(z)): 0.1912
Epoch: [17/20], Batch Num: [596/600]
Discriminator Loss: 0.4874, Generator Loss: 2.0646
D(x): 0.8454, D(G(z)): 0.2012
Epoch: [17/20], Batch Num: [597/600]
Discriminator Loss: 0.5712, Generator Loss: 2.4152
D(x): 0.8017, D(G(z)): 0.1860
Epoch: [17/20], Batch Num: [598/600]
Discriminator Loss: 0.6407, Generator Loss: 2.5058
D(x): 0.7747, D(G(z)): 0.1699
Epoch: [17/20], Batch Num: [599/600]
Discriminator Loss: 0.6137, Generator Loss: 2.4721
D(x): 0.7558, D(G(z)): 0.1362
Epoch: 18, Batch Num: [0/600]

Epoch: [18/20], Batch Num: [0/600]
Discriminator Loss: 0.6399, Generator Loss: 2.0375
D(x): 0.7959, D(G(z)): 0.2082
Epoch: [18/20], Batch Num: [1/600]
Discriminator Loss: 0.5535, Generator Loss: 2.1049
D(x): 0.8344, D(G(z)): 0.1944
Epoch: [18/20], Batch Num: [2/600]
Discriminator Loss: 0.7699, Generator Loss: 2.3058
D(x): 0.8004, D(G(z)): 0.2653
Epoch: [18/20], Batch Num: [3/600]
Discriminator Loss: 0.7719, Generator Loss: 2.7096
D(x): 0.8138, D(G(z)): 0.2709
Epoch: [18/20], Batch Num: [4/600]
Discriminator Loss: 0.7990, Generator Loss: 2.7461
D(x): 0.7123, D(G(z)): 0.1512
Epoch: [18/20], Batch Num: [5/600]
Discriminator Loss: 0.8381, Generator Loss: 2.5747
D(x): 0.6939, D(G(z)): 0.1799
Epoch: [18/20], Batch Num: [6/600]
Discriminator Loss: 0.7971, Generator Loss: 1.9757
D(x): 0.7282, D(G(z)): 0.2061
Epoch: [18/20], Batch Num: [7/600]
Discriminator Loss: 0.9744, Generator Loss: 1.8371
D(x): 0.8175, D(G(z)): 0.3580
Epoch: [18/20], Batch Num: [8/600]
Discriminator Loss: 0.9198, Generator Loss: 2.4928
D(x): 0.8140, D(G(z)): 0.3246
Epoch: [18/20], Batch Num: [9/600]
Discriminator Loss: 1.0058, Generator Loss: 2.4809
D(x): 0.6450, D(G(z)): 0.1964
Epoch: [18/20], Batch Num: [10/600]
Discriminator Loss: 0.9831, Generator Loss: 2.0286
D(x): 0.6542, D(G(z)): 0.2323
Epoch: [18/20], Batch Num: [11/600]
Discriminator Loss: 0.9845, Generator Loss: 1.9907
D(x): 0.7408, D(G(z)): 0.3167
Epoch: [18/20], Batch Num: [12/600]
Discriminator Loss: 1.0592, Generator Loss: 1.3071
D(x): 0.6618, D(G(z)): 0.2904
Epoch: [18/20], Batch Num: [13/600]
Discriminator Loss: 1.1758, Generator Loss: 1.5963
D(x): 0.7135, D(G(z)): 0.3867
Epoch: [18/20], Batch Num: [14/600]
Discriminator Loss: 1.2402, Generator Loss: 1.8140
D(x): 0.7279, D(G(z)): 0.4031
Epoch: [18/20], Batch Num: [15/600]
Discriminator Loss: 1.4390, Generator Loss: 1.6335
D(x): 0.5405, D(G(z)): 0.3321
Epoch: [18/20], Batch Num: [16/600]
Discriminator Loss: 1.3707, Generator Loss: 1.6032
D(x): 0.5970, D(G(z)): 0.3426
Epoch: [18/20], Batch Num: [17/600]
Discriminator Loss: 1.2363, Generator Loss: 1.3871
D(x): 0.6672, D(G(z)): 0.3572
Epoch: [18/20], Batch Num: [18/600]
Discriminator Loss: 1.2935, Generator Loss: 1.4305
D(x): 0.6328, D(G(z)): 0.3601
Epoch: [18/20], Batch Num: [19/600]
Discriminator Loss: 1.3601, Generator Loss: 1.2787
D(x): 0.5909, D(G(z)): 0.3580
Epoch: [18/20], Batch Num: [20/600]
Discriminator Loss: 1.3129, Generator Loss: 1.1084
D(x): 0.5770, D(G(z)): 0.3512
Epoch: [18/20], Batch Num: [21/600]
Discriminator Loss: 1.3507, Generator Loss: 1.1226
D(x): 0.6123, D(G(z)): 0.3835
Epoch: [18/20], Batch Num: [22/600]
Discriminator Loss: 1.4170, Generator Loss: 1.0368
D(x): 0.6198, D(G(z)): 0.4670
Epoch: [18/20], Batch Num: [23/600]
Discriminator Loss: 1.3354, Generator Loss: 1.1172
D(x): 0.6234, D(G(z)): 0.4450
Epoch: [18/20], Batch Num: [24/600]
Discriminator Loss: 1.2251, Generator Loss: 1.1894
D(x): 0.6191, D(G(z)): 0.3989
Epoch: [18/20], Batch Num: [25/600]
Discriminator Loss: 1.1293, Generator Loss: 1.2790
D(x): 0.6317, D(G(z)): 0.3697
Epoch: [18/20], Batch Num: [26/600]
Discriminator Loss: 1.1583, Generator Loss: 1.2148
D(x): 0.5742, D(G(z)): 0.3147
Epoch: [18/20], Batch Num: [27/600]
Discriminator Loss: 1.1593, Generator Loss: 1.2632
D(x): 0.5500, D(G(z)): 0.3173
Epoch: [18/20], Batch Num: [28/600]
Discriminator Loss: 1.1138, Generator Loss: 1.1716
D(x): 0.6029, D(G(z)): 0.3488
Epoch: [18/20], Batch Num: [29/600]
Discriminator Loss: 0.9979, Generator Loss: 1.1508
D(x): 0.6363, D(G(z)): 0.3271
Epoch: [18/20], Batch Num: [30/600]
Discriminator Loss: 0.9457, Generator Loss: 1.0430
D(x): 0.7206, D(G(z)): 0.3658
Epoch: [18/20], Batch Num: [31/600]
Discriminator Loss: 0.9655, Generator Loss: 1.1077
D(x): 0.7419, D(G(z)): 0.4032
Epoch: [18/20], Batch Num: [32/600]
Discriminator Loss: 0.9983, Generator Loss: 1.1787
D(x): 0.6845, D(G(z)): 0.3738
Epoch: [18/20], Batch Num: [33/600]
Discriminator Loss: 1.0246, Generator Loss: 1.2509
D(x): 0.6948, D(G(z)): 0.3810
Epoch: [18/20], Batch Num: [34/600]
Discriminator Loss: 0.8268, Generator Loss: 1.4090
D(x): 0.7300, D(G(z)): 0.3301
Epoch: [18/20], Batch Num: [35/600]
Discriminator Loss: 0.8646, Generator Loss: 1.4515
D(x): 0.7090, D(G(z)): 0.3264
Epoch: [18/20], Batch Num: [36/600]
Discriminator Loss: 0.7904, Generator Loss: 1.4574
D(x): 0.6944, D(G(z)): 0.2584
Epoch: [18/20], Batch Num: [37/600]
Discriminator Loss: 0.7973, Generator Loss: 1.4710
D(x): 0.6812, D(G(z)): 0.2549
Epoch: [18/20], Batch Num: [38/600]
Discriminator Loss: 0.7249, Generator Loss: 1.4811
D(x): 0.7158, D(G(z)): 0.2450
Epoch: [18/20], Batch Num: [39/600]
Discriminator Loss: 0.8057, Generator Loss: 1.4654
D(x): 0.7210, D(G(z)): 0.2872
Epoch: [18/20], Batch Num: [40/600]
Discriminator Loss: 0.6285, Generator Loss: 1.5668
D(x): 0.8150, D(G(z)): 0.2899
Epoch: [18/20], Batch Num: [41/600]
Discriminator Loss: 0.7676, Generator Loss: 1.6550
D(x): 0.7671, D(G(z)): 0.3099
Epoch: [18/20], Batch Num: [42/600]
Discriminator Loss: 0.6985, Generator Loss: 1.6467
D(x): 0.7940, D(G(z)): 0.2867
Epoch: [18/20], Batch Num: [43/600]
Discriminator Loss: 0.7287, Generator Loss: 1.9257
D(x): 0.7657, D(G(z)): 0.2900
Epoch: [18/20], Batch Num: [44/600]
Discriminator Loss: 0.6515, Generator Loss: 1.8602
D(x): 0.7829, D(G(z)): 0.2585
Epoch: [18/20], Batch Num: [45/600]
Discriminator Loss: 0.6720, Generator Loss: 2.1536
D(x): 0.7484, D(G(z)): 0.2049
Epoch: [18/20], Batch Num: [46/600]
Discriminator Loss: 0.5566, Generator Loss: 2.0992
D(x): 0.7593, D(G(z)): 0.1519
Epoch: [18/20], Batch Num: [47/600]
Discriminator Loss: 0.5993, Generator Loss: 2.1727
D(x): 0.7513, D(G(z)): 0.1756
Epoch: [18/20], Batch Num: [48/600]
Discriminator Loss: 0.6212, Generator Loss: 2.0334
D(x): 0.7841, D(G(z)): 0.2167
Epoch: [18/20], Batch Num: [49/600]
Discriminator Loss: 0.5332, Generator Loss: 1.9410
D(x): 0.8034, D(G(z)): 0.1922
Epoch: [18/20], Batch Num: [50/600]
Discriminator Loss: 0.6344, Generator Loss: 1.8501
D(x): 0.8716, D(G(z)): 0.2702
Epoch: [18/20], Batch Num: [51/600]
Discriminator Loss: 0.5679, Generator Loss: 2.2996
D(x): 0.8324, D(G(z)): 0.2418
Epoch: [18/20], Batch Num: [52/600]
Discriminator Loss: 0.7056, Generator Loss: 2.2024
D(x): 0.7968, D(G(z)): 0.2570
Epoch: [18/20], Batch Num: [53/600]
Discriminator Loss: 0.6912, Generator Loss: 2.3393
D(x): 0.7180, D(G(z)): 0.1718
Epoch: [18/20], Batch Num: [54/600]
Discriminator Loss: 0.5943, Generator Loss: 2.4182
D(x): 0.8030, D(G(z)): 0.2069
Epoch: [18/20], Batch Num: [55/600]
Discriminator Loss: 0.7095, Generator Loss: 2.2598
D(x): 0.7519, D(G(z)): 0.1889
Epoch: [18/20], Batch Num: [56/600]
Discriminator Loss: 0.6485, Generator Loss: 1.9595
D(x): 0.8258, D(G(z)): 0.2532
Epoch: [18/20], Batch Num: [57/600]
Discriminator Loss: 0.6829, Generator Loss: 1.8994
D(x): 0.7805, D(G(z)): 0.2354
Epoch: [18/20], Batch Num: [58/600]
Discriminator Loss: 0.9866, Generator Loss: 1.8082
D(x): 0.6845, D(G(z)): 0.2782
Epoch: [18/20], Batch Num: [59/600]
Discriminator Loss: 0.9357, Generator Loss: 1.8862
D(x): 0.7523, D(G(z)): 0.3117
Epoch: [18/20], Batch Num: [60/600]
Discriminator Loss: 0.9525, Generator Loss: 1.7912
D(x): 0.7333, D(G(z)): 0.3021
Epoch: [18/20], Batch Num: [61/600]
Discriminator Loss: 0.7828, Generator Loss: 1.9846
D(x): 0.7633, D(G(z)): 0.2700
Epoch: [18/20], Batch Num: [62/600]
Discriminator Loss: 0.9876, Generator Loss: 1.7165
D(x): 0.6645, D(G(z)): 0.2545
Epoch: [18/20], Batch Num: [63/600]
Discriminator Loss: 0.9641, Generator Loss: 1.8397
D(x): 0.7499, D(G(z)): 0.3155
Epoch: [18/20], Batch Num: [64/600]
Discriminator Loss: 0.9497, Generator Loss: 1.8855
D(x): 0.6460, D(G(z)): 0.2301
Epoch: [18/20], Batch Num: [65/600]
Discriminator Loss: 1.1124, Generator Loss: 1.5378
D(x): 0.6544, D(G(z)): 0.2885
Epoch: [18/20], Batch Num: [66/600]
Discriminator Loss: 1.1624, Generator Loss: 1.4846
D(x): 0.7175, D(G(z)): 0.3741
Epoch: [18/20], Batch Num: [67/600]
Discriminator Loss: 1.2326, Generator Loss: 1.6853
D(x): 0.6662, D(G(z)): 0.3693
Epoch: [18/20], Batch Num: [68/600]
Discriminator Loss: 1.2264, Generator Loss: 1.5384
D(x): 0.6586, D(G(z)): 0.3467
Epoch: [18/20], Batch Num: [69/600]
Discriminator Loss: 1.4715, Generator Loss: 1.4143
D(x): 0.5699, D(G(z)): 0.3819
Epoch: [18/20], Batch Num: [70/600]
Discriminator Loss: 1.2332, Generator Loss: 1.3957
D(x): 0.6412, D(G(z)): 0.3663
Epoch: [18/20], Batch Num: [71/600]
Discriminator Loss: 1.2325, Generator Loss: 1.3964
D(x): 0.6232, D(G(z)): 0.3510
Epoch: [18/20], Batch Num: [72/600]
Discriminator Loss: 1.3787, Generator Loss: 1.2762
D(x): 0.6006, D(G(z)): 0.4010
Epoch: [18/20], Batch Num: [73/600]
Discriminator Loss: 1.0431, Generator Loss: 1.2262
D(x): 0.6631, D(G(z)): 0.3145
Epoch: [18/20], Batch Num: [74/600]
Discriminator Loss: 1.3729, Generator Loss: 1.2782
D(x): 0.6283, D(G(z)): 0.3815
Epoch: [18/20], Batch Num: [75/600]
Discriminator Loss: 1.2119, Generator Loss: 1.2652
D(x): 0.6297, D(G(z)): 0.3849
Epoch: [18/20], Batch Num: [76/600]
Discriminator Loss: 1.1376, Generator Loss: 1.2042
D(x): 0.6328, D(G(z)): 0.3429
Epoch: [18/20], Batch Num: [77/600]
Discriminator Loss: 1.1827, Generator Loss: 1.2206
D(x): 0.6238, D(G(z)): 0.3680
Epoch: [18/20], Batch Num: [78/600]
Discriminator Loss: 1.1568, Generator Loss: 1.1628
D(x): 0.6108, D(G(z)): 0.3522
Epoch: [18/20], Batch Num: [79/600]
Discriminator Loss: 1.2113, Generator Loss: 1.2003
D(x): 0.6527, D(G(z)): 0.4049
Epoch: [18/20], Batch Num: [80/600]
Discriminator Loss: 0.9702, Generator Loss: 1.2421
D(x): 0.6852, D(G(z)): 0.3407
Epoch: [18/20], Batch Num: [81/600]
Discriminator Loss: 0.9751, Generator Loss: 1.2685
D(x): 0.6652, D(G(z)): 0.3381
Epoch: [18/20], Batch Num: [82/600]
Discriminator Loss: 0.9989, Generator Loss: 1.4914
D(x): 0.6295, D(G(z)): 0.3040
Epoch: [18/20], Batch Num: [83/600]
Discriminator Loss: 0.9473, Generator Loss: 1.3335
D(x): 0.6618, D(G(z)): 0.3173
Epoch: [18/20], Batch Num: [84/600]
Discriminator Loss: 0.9324, Generator Loss: 1.4349
D(x): 0.6843, D(G(z)): 0.3385
Epoch: [18/20], Batch Num: [85/600]
Discriminator Loss: 0.9186, Generator Loss: 1.3945
D(x): 0.6688, D(G(z)): 0.3134
Epoch: [18/20], Batch Num: [86/600]
Discriminator Loss: 0.8721, Generator Loss: 1.2444
D(x): 0.6781, D(G(z)): 0.2906
Epoch: [18/20], Batch Num: [87/600]
Discriminator Loss: 0.8144, Generator Loss: 1.3334
D(x): 0.7230, D(G(z)): 0.3084
Epoch: [18/20], Batch Num: [88/600]
Discriminator Loss: 0.8216, Generator Loss: 1.4215
D(x): 0.7569, D(G(z)): 0.3561
Epoch: [18/20], Batch Num: [89/600]
Discriminator Loss: 0.7938, Generator Loss: 1.5829
D(x): 0.7477, D(G(z)): 0.3169
Epoch: [18/20], Batch Num: [90/600]
Discriminator Loss: 0.8078, Generator Loss: 1.6377
D(x): 0.7187, D(G(z)): 0.2964
Epoch: [18/20], Batch Num: [91/600]
Discriminator Loss: 0.7263, Generator Loss: 1.5848
D(x): 0.7264, D(G(z)): 0.2494
Epoch: [18/20], Batch Num: [92/600]
Discriminator Loss: 0.7757, Generator Loss: 1.7734
D(x): 0.7324, D(G(z)): 0.2912
Epoch: [18/20], Batch Num: [93/600]
Discriminator Loss: 0.7375, Generator Loss: 1.7972
D(x): 0.6880, D(G(z)): 0.2111
Epoch: [18/20], Batch Num: [94/600]
Discriminator Loss: 0.5969, Generator Loss: 1.6700
D(x): 0.7668, D(G(z)): 0.2004
Epoch: [18/20], Batch Num: [95/600]
Discriminator Loss: 0.5906, Generator Loss: 1.8452
D(x): 0.8032, D(G(z)): 0.2555
Epoch: [18/20], Batch Num: [96/600]
Discriminator Loss: 0.6955, Generator Loss: 1.7727
D(x): 0.7554, D(G(z)): 0.2551
Epoch: [18/20], Batch Num: [97/600]
Discriminator Loss: 0.6122, Generator Loss: 1.6591
D(x): 0.7955, D(G(z)): 0.2395
Epoch: [18/20], Batch Num: [98/600]
Discriminator Loss: 0.6406, Generator Loss: 1.8094
D(x): 0.8024, D(G(z)): 0.2817
Epoch: [18/20], Batch Num: [99/600]
Discriminator Loss: 0.5631, Generator Loss: 1.8769
D(x): 0.8116, D(G(z)): 0.2234
Epoch: 18, Batch Num: [100/600]

Epoch: [18/20], Batch Num: [100/600]
Discriminator Loss: 0.6351, Generator Loss: 2.0929
D(x): 0.7788, D(G(z)): 0.2346
Epoch: [18/20], Batch Num: [101/600]
Discriminator Loss: 0.6108, Generator Loss: 2.2042
D(x): 0.7921, D(G(z)): 0.2349
Epoch: [18/20], Batch Num: [102/600]
Discriminator Loss: 0.7500, Generator Loss: 2.1964
D(x): 0.6898, D(G(z)): 0.1997
Epoch: [18/20], Batch Num: [103/600]
Discriminator Loss: 0.5899, Generator Loss: 2.1303
D(x): 0.8024, D(G(z)): 0.2179
Epoch: [18/20], Batch Num: [104/600]
Discriminator Loss: 0.5711, Generator Loss: 2.1713
D(x): 0.7995, D(G(z)): 0.1961
Epoch: [18/20], Batch Num: [105/600]
Discriminator Loss: 0.6045, Generator Loss: 2.1272
D(x): 0.8357, D(G(z)): 0.2482
Epoch: [18/20], Batch Num: [106/600]
Discriminator Loss: 0.6803, Generator Loss: 2.1625
D(x): 0.7730, D(G(z)): 0.2169
Epoch: [18/20], Batch Num: [107/600]
Discriminator Loss: 0.5801, Generator Loss: 2.5777
D(x): 0.8023, D(G(z)): 0.2032
Epoch: [18/20], Batch Num: [108/600]
Discriminator Loss: 0.5470, Generator Loss: 2.2146
D(x): 0.8028, D(G(z)): 0.1845
Epoch: [18/20], Batch Num: [109/600]
Discriminator Loss: 0.7604, Generator Loss: 2.0523
D(x): 0.7527, D(G(z)): 0.2205
Epoch: [18/20], Batch Num: [110/600]
Discriminator Loss: 0.6290, Generator Loss: 1.9340
D(x): 0.8073, D(G(z)): 0.2366
Epoch: [18/20], Batch Num: [111/600]
Discriminator Loss: 0.7258, Generator Loss: 2.3180
D(x): 0.8255, D(G(z)): 0.2939
Epoch: [18/20], Batch Num: [112/600]
Discriminator Loss: 0.7615, Generator Loss: 2.5288
D(x): 0.7543, D(G(z)): 0.2361
Epoch: [18/20], Batch Num: [113/600]
Discriminator Loss: 0.8372, Generator Loss: 2.3439
D(x): 0.6842, D(G(z)): 0.1672
Epoch: [18/20], Batch Num: [114/600]
Discriminator Loss: 0.9922, Generator Loss: 1.8902
D(x): 0.6943, D(G(z)): 0.2627
Epoch: [18/20], Batch Num: [115/600]
Discriminator Loss: 0.7750, Generator Loss: 2.0699
D(x): 0.7582, D(G(z)): 0.2542
Epoch: [18/20], Batch Num: [116/600]
Discriminator Loss: 0.8524, Generator Loss: 1.7424
D(x): 0.7685, D(G(z)): 0.2880
Epoch: [18/20], Batch Num: [117/600]
Discriminator Loss: 0.9585, Generator Loss: 1.8089
D(x): 0.7173, D(G(z)): 0.2700
Epoch: [18/20], Batch Num: [118/600]
Discriminator Loss: 1.3203, Generator Loss: 1.6994
D(x): 0.6492, D(G(z)): 0.3615
Epoch: [18/20], Batch Num: [119/600]
Discriminator Loss: 0.8967, Generator Loss: 1.7741
D(x): 0.6856, D(G(z)): 0.2650
Epoch: [18/20], Batch Num: [120/600]
Discriminator Loss: 1.2477, Generator Loss: 1.4891
D(x): 0.6174, D(G(z)): 0.3248
Epoch: [18/20], Batch Num: [121/600]
Discriminator Loss: 1.3287, Generator Loss: 1.4196
D(x): 0.6287, D(G(z)): 0.3384
Epoch: [18/20], Batch Num: [122/600]
Discriminator Loss: 1.1347, Generator Loss: 1.2588
D(x): 0.7087, D(G(z)): 0.3828
Epoch: [18/20], Batch Num: [123/600]
Discriminator Loss: 1.2259, Generator Loss: 1.5047
D(x): 0.6693, D(G(z)): 0.3800
Epoch: [18/20], Batch Num: [124/600]
Discriminator Loss: 1.1359, Generator Loss: 1.5243
D(x): 0.5938, D(G(z)): 0.2975
Epoch: [18/20], Batch Num: [125/600]
Discriminator Loss: 1.2284, Generator Loss: 1.4120
D(x): 0.6098, D(G(z)): 0.3371
Epoch: [18/20], Batch Num: [126/600]
Discriminator Loss: 1.0699, Generator Loss: 1.1730
D(x): 0.6509, D(G(z)): 0.3293
Epoch: [18/20], Batch Num: [127/600]
Discriminator Loss: 1.0662, Generator Loss: 1.2586
D(x): 0.7137, D(G(z)): 0.4052
Epoch: [18/20], Batch Num: [128/600]
Discriminator Loss: 1.2105, Generator Loss: 1.4492
D(x): 0.6813, D(G(z)): 0.4295
Epoch: [18/20], Batch Num: [129/600]
Discriminator Loss: 1.2075, Generator Loss: 1.7152
D(x): 0.6377, D(G(z)): 0.3706
Epoch: [18/20], Batch Num: [130/600]
Discriminator Loss: 1.0919, Generator Loss: 1.7169
D(x): 0.6057, D(G(z)): 0.2698
Epoch: [18/20], Batch Num: [131/600]
Discriminator Loss: 1.0993, Generator Loss: 1.3651
D(x): 0.5753, D(G(z)): 0.2617
Epoch: [18/20], Batch Num: [132/600]
Discriminator Loss: 1.1428, Generator Loss: 1.2331
D(x): 0.6421, D(G(z)): 0.3480
Epoch: [18/20], Batch Num: [133/600]
Discriminator Loss: 0.9400, Generator Loss: 1.2257
D(x): 0.7173, D(G(z)): 0.3453
Epoch: [18/20], Batch Num: [134/600]
Discriminator Loss: 1.1186, Generator Loss: 1.2584
D(x): 0.7079, D(G(z)): 0.4201
Epoch: [18/20], Batch Num: [135/600]
Discriminator Loss: 0.8697, Generator Loss: 1.3878
D(x): 0.7377, D(G(z)): 0.3397
Epoch: [18/20], Batch Num: [136/600]
Discriminator Loss: 1.0714, Generator Loss: 1.4483
D(x): 0.6346, D(G(z)): 0.3132
Epoch: [18/20], Batch Num: [137/600]
Discriminator Loss: 0.8432, Generator Loss: 1.6143
D(x): 0.7255, D(G(z)): 0.2992
Epoch: [18/20], Batch Num: [138/600]
Discriminator Loss: 0.8988, Generator Loss: 1.6838
D(x): 0.6826, D(G(z)): 0.2697
Epoch: [18/20], Batch Num: [139/600]
Discriminator Loss: 0.8393, Generator Loss: 1.6889
D(x): 0.6914, D(G(z)): 0.2639
Epoch: [18/20], Batch Num: [140/600]
Discriminator Loss: 0.8670, Generator Loss: 1.3909
D(x): 0.6582, D(G(z)): 0.2544
Epoch: [18/20], Batch Num: [141/600]
Discriminator Loss: 0.7936, Generator Loss: 1.4030
D(x): 0.7379, D(G(z)): 0.2871
Epoch: [18/20], Batch Num: [142/600]
Discriminator Loss: 0.7657, Generator Loss: 1.3306
D(x): 0.7387, D(G(z)): 0.2925
Epoch: [18/20], Batch Num: [143/600]
Discriminator Loss: 0.9068, Generator Loss: 1.5485
D(x): 0.7288, D(G(z)): 0.3268
Epoch: [18/20], Batch Num: [144/600]
Discriminator Loss: 0.7567, Generator Loss: 1.6211
D(x): 0.7625, D(G(z)): 0.2915
Epoch: [18/20], Batch Num: [145/600]
Discriminator Loss: 0.7401, Generator Loss: 1.7376
D(x): 0.7464, D(G(z)): 0.2555
Epoch: [18/20], Batch Num: [146/600]
Discriminator Loss: 0.7749, Generator Loss: 1.6131
D(x): 0.7589, D(G(z)): 0.2858
Epoch: [18/20], Batch Num: [147/600]
Discriminator Loss: 0.7221, Generator Loss: 1.8235
D(x): 0.7098, D(G(z)): 0.2325
Epoch: [18/20], Batch Num: [148/600]
Discriminator Loss: 0.7486, Generator Loss: 1.7039
D(x): 0.6840, D(G(z)): 0.2200
Epoch: [18/20], Batch Num: [149/600]
Discriminator Loss: 0.7593, Generator Loss: 1.6184
D(x): 0.7487, D(G(z)): 0.2763
Epoch: [18/20], Batch Num: [150/600]
Discriminator Loss: 0.6806, Generator Loss: 1.5402
D(x): 0.7578, D(G(z)): 0.2388
Epoch: [18/20], Batch Num: [151/600]
Discriminator Loss: 0.7715, Generator Loss: 1.6659
D(x): 0.7685, D(G(z)): 0.3092
Epoch: [18/20], Batch Num: [152/600]
Discriminator Loss: 0.7313, Generator Loss: 1.4878
D(x): 0.7525, D(G(z)): 0.2730
Epoch: [18/20], Batch Num: [153/600]
Discriminator Loss: 0.7094, Generator Loss: 1.6767
D(x): 0.7962, D(G(z)): 0.3031
Epoch: [18/20], Batch Num: [154/600]
Discriminator Loss: 0.6949, Generator Loss: 1.8771
D(x): 0.8095, D(G(z)): 0.3105
Epoch: [18/20], Batch Num: [155/600]
Discriminator Loss: 0.6801, Generator Loss: 1.9237
D(x): 0.7430, D(G(z)): 0.2198
Epoch: [18/20], Batch Num: [156/600]
Discriminator Loss: 0.7156, Generator Loss: 1.7934
D(x): 0.7047, D(G(z)): 0.2099
Epoch: [18/20], Batch Num: [157/600]
Discriminator Loss: 0.8302, Generator Loss: 1.6250
D(x): 0.7158, D(G(z)): 0.2395
Epoch: [18/20], Batch Num: [158/600]
Discriminator Loss: 0.7998, Generator Loss: 1.4655
D(x): 0.7092, D(G(z)): 0.2408
Epoch: [18/20], Batch Num: [159/600]
Discriminator Loss: 0.8494, Generator Loss: 1.5296
D(x): 0.8025, D(G(z)): 0.3452
Epoch: [18/20], Batch Num: [160/600]
Discriminator Loss: 0.8007, Generator Loss: 1.5586
D(x): 0.7943, D(G(z)): 0.3205
Epoch: [18/20], Batch Num: [161/600]
Discriminator Loss: 0.8618, Generator Loss: 1.8565
D(x): 0.7455, D(G(z)): 0.2989
Epoch: [18/20], Batch Num: [162/600]
Discriminator Loss: 0.9533, Generator Loss: 1.9355
D(x): 0.7014, D(G(z)): 0.3047
Epoch: [18/20], Batch Num: [163/600]
Discriminator Loss: 0.9140, Generator Loss: 1.8141
D(x): 0.6933, D(G(z)): 0.2393
Epoch: [18/20], Batch Num: [164/600]
Discriminator Loss: 1.0381, Generator Loss: 1.5010
D(x): 0.6145, D(G(z)): 0.2408
Epoch: [18/20], Batch Num: [165/600]
Discriminator Loss: 0.7938, Generator Loss: 1.6405
D(x): 0.7787, D(G(z)): 0.3220
Epoch: [18/20], Batch Num: [166/600]
Discriminator Loss: 1.0672, Generator Loss: 1.6029
D(x): 0.6629, D(G(z)): 0.3103
Epoch: [18/20], Batch Num: [167/600]
Discriminator Loss: 1.1873, Generator Loss: 1.3933
D(x): 0.6256, D(G(z)): 0.3458
Epoch: [18/20], Batch Num: [168/600]
Discriminator Loss: 1.0685, Generator Loss: 1.3248
D(x): 0.6929, D(G(z)): 0.3814
Epoch: [18/20], Batch Num: [169/600]
Discriminator Loss: 1.1461, Generator Loss: 1.4755
D(x): 0.6692, D(G(z)): 0.3704
Epoch: [18/20], Batch Num: [170/600]
Discriminator Loss: 1.1691, Generator Loss: 1.4690
D(x): 0.6302, D(G(z)): 0.3430
Epoch: [18/20], Batch Num: [171/600]
Discriminator Loss: 1.1288, Generator Loss: 1.3444
D(x): 0.6336, D(G(z)): 0.3469
Epoch: [18/20], Batch Num: [172/600]
Discriminator Loss: 1.0753, Generator Loss: 1.2601
D(x): 0.6013, D(G(z)): 0.2917
Epoch: [18/20], Batch Num: [173/600]
Discriminator Loss: 1.1412, Generator Loss: 1.2307
D(x): 0.6365, D(G(z)): 0.3586
Epoch: [18/20], Batch Num: [174/600]
Discriminator Loss: 1.0784, Generator Loss: 1.3852
D(x): 0.6810, D(G(z)): 0.3878
Epoch: [18/20], Batch Num: [175/600]
Discriminator Loss: 0.9613, Generator Loss: 1.2304
D(x): 0.6531, D(G(z)): 0.3186
Epoch: [18/20], Batch Num: [176/600]
Discriminator Loss: 1.1285, Generator Loss: 1.1636
D(x): 0.6494, D(G(z)): 0.3660
Epoch: [18/20], Batch Num: [177/600]
Discriminator Loss: 1.1540, Generator Loss: 1.2512
D(x): 0.6439, D(G(z)): 0.3854
Epoch: [18/20], Batch Num: [178/600]
Discriminator Loss: 1.1968, Generator Loss: 1.1824
D(x): 0.6704, D(G(z)): 0.4382
Epoch: [18/20], Batch Num: [179/600]
Discriminator Loss: 0.9185, Generator Loss: 1.5922
D(x): 0.6977, D(G(z)): 0.3388
Epoch: [18/20], Batch Num: [180/600]
Discriminator Loss: 1.0991, Generator Loss: 1.3771
D(x): 0.5822, D(G(z)): 0.3173
Epoch: [18/20], Batch Num: [181/600]
Discriminator Loss: 0.9318, Generator Loss: 1.3367
D(x): 0.6710, D(G(z)): 0.3084
Epoch: [18/20], Batch Num: [182/600]
Discriminator Loss: 1.1480, Generator Loss: 1.3479
D(x): 0.6172, D(G(z)): 0.3762
Epoch: [18/20], Batch Num: [183/600]
Discriminator Loss: 0.9642, Generator Loss: 1.2567
D(x): 0.6689, D(G(z)): 0.3418
Epoch: [18/20], Batch Num: [184/600]
Discriminator Loss: 1.0222, Generator Loss: 1.2983
D(x): 0.6900, D(G(z)): 0.3707
Epoch: [18/20], Batch Num: [185/600]
Discriminator Loss: 1.0158, Generator Loss: 1.3022
D(x): 0.6944, D(G(z)): 0.3751
Epoch: [18/20], Batch Num: [186/600]
Discriminator Loss: 0.9381, Generator Loss: 1.4417
D(x): 0.7022, D(G(z)): 0.3425
Epoch: [18/20], Batch Num: [187/600]
Discriminator Loss: 0.9337, Generator Loss: 1.4314
D(x): 0.6538, D(G(z)): 0.3058
Epoch: [18/20], Batch Num: [188/600]
Discriminator Loss: 0.9200, Generator Loss: 1.4151
D(x): 0.6480, D(G(z)): 0.2744
Epoch: [18/20], Batch Num: [189/600]
Discriminator Loss: 0.7561, Generator Loss: 1.4773
D(x): 0.6998, D(G(z)): 0.2527
Epoch: [18/20], Batch Num: [190/600]
Discriminator Loss: 0.8508, Generator Loss: 1.2629
D(x): 0.7591, D(G(z)): 0.3461
Epoch: [18/20], Batch Num: [191/600]
Discriminator Loss: 0.7982, Generator Loss: 1.3514
D(x): 0.7255, D(G(z)): 0.2953
Epoch: [18/20], Batch Num: [192/600]
Discriminator Loss: 0.8550, Generator Loss: 1.4064
D(x): 0.7049, D(G(z)): 0.2954
Epoch: [18/20], Batch Num: [193/600]
Discriminator Loss: 0.8247, Generator Loss: 1.6973
D(x): 0.7699, D(G(z)): 0.3474
Epoch: [18/20], Batch Num: [194/600]
Discriminator Loss: 0.7803, Generator Loss: 1.6990
D(x): 0.7291, D(G(z)): 0.2877
Epoch: [18/20], Batch Num: [195/600]
Discriminator Loss: 0.7292, Generator Loss: 1.7933
D(x): 0.7210, D(G(z)): 0.2480
Epoch: [18/20], Batch Num: [196/600]
Discriminator Loss: 0.7169, Generator Loss: 1.6142
D(x): 0.7161, D(G(z)): 0.2313
Epoch: [18/20], Batch Num: [197/600]
Discriminator Loss: 0.7198, Generator Loss: 1.6801
D(x): 0.7526, D(G(z)): 0.2611
Epoch: [18/20], Batch Num: [198/600]
Discriminator Loss: 0.8665, Generator Loss: 1.7106
D(x): 0.7086, D(G(z)): 0.2749
Epoch: [18/20], Batch Num: [199/600]
Discriminator Loss: 0.8043, Generator Loss: 1.5915
D(x): 0.7123, D(G(z)): 0.2729
Epoch: 18, Batch Num: [200/600]

Epoch: [18/20], Batch Num: [200/600]
Discriminator Loss: 0.8063, Generator Loss: 1.5657
D(x): 0.7636, D(G(z)): 0.3079
Epoch: [18/20], Batch Num: [201/600]
Discriminator Loss: 0.6182, Generator Loss: 1.5041
D(x): 0.8033, D(G(z)): 0.2684
Epoch: [18/20], Batch Num: [202/600]
Discriminator Loss: 0.7626, Generator Loss: 1.7966
D(x): 0.7878, D(G(z)): 0.3125
Epoch: [18/20], Batch Num: [203/600]
Discriminator Loss: 0.7341, Generator Loss: 1.8360
D(x): 0.7450, D(G(z)): 0.2503
Epoch: [18/20], Batch Num: [204/600]
Discriminator Loss: 0.6738, Generator Loss: 2.2826
D(x): 0.7780, D(G(z)): 0.2531
Epoch: [18/20], Batch Num: [205/600]
Discriminator Loss: 0.8510, Generator Loss: 2.0449
D(x): 0.6503, D(G(z)): 0.2121
Epoch: [18/20], Batch Num: [206/600]
Discriminator Loss: 0.8770, Generator Loss: 1.7132
D(x): 0.6696, D(G(z)): 0.2469
Epoch: [18/20], Batch Num: [207/600]
Discriminator Loss: 0.6459, Generator Loss: 1.5319
D(x): 0.8045, D(G(z)): 0.2512
Epoch: [18/20], Batch Num: [208/600]
Discriminator Loss: 0.9612, Generator Loss: 1.5011
D(x): 0.7298, D(G(z)): 0.3097
Epoch: [18/20], Batch Num: [209/600]
Discriminator Loss: 0.7879, Generator Loss: 1.4640
D(x): 0.7886, D(G(z)): 0.3067
Epoch: [18/20], Batch Num: [210/600]
Discriminator Loss: 0.7859, Generator Loss: 1.5358
D(x): 0.7580, D(G(z)): 0.2979
Epoch: [18/20], Batch Num: [211/600]
Discriminator Loss: 0.8591, Generator Loss: 1.9189
D(x): 0.7437, D(G(z)): 0.2927
Epoch: [18/20], Batch Num: [212/600]
Discriminator Loss: 0.8518, Generator Loss: 1.7341
D(x): 0.6822, D(G(z)): 0.2380
Epoch: [18/20], Batch Num: [213/600]
Discriminator Loss: 0.8617, Generator Loss: 1.6854
D(x): 0.7016, D(G(z)): 0.2650
Epoch: [18/20], Batch Num: [214/600]
Discriminator Loss: 0.8681, Generator Loss: 1.6107
D(x): 0.7736, D(G(z)): 0.3220
Epoch: [18/20], Batch Num: [215/600]
Discriminator Loss: 0.9014, Generator Loss: 1.8494
D(x): 0.6849, D(G(z)): 0.2901
Epoch: [18/20], Batch Num: [216/600]
Discriminator Loss: 1.0617, Generator Loss: 1.6938
D(x): 0.6864, D(G(z)): 0.3044
Epoch: [18/20], Batch Num: [217/600]
Discriminator Loss: 1.0582, Generator Loss: 1.6630
D(x): 0.6493, D(G(z)): 0.2845
Epoch: [18/20], Batch Num: [218/600]
Discriminator Loss: 1.1096, Generator Loss: 1.3699
D(x): 0.6406, D(G(z)): 0.3038
Epoch: [18/20], Batch Num: [219/600]
Discriminator Loss: 1.0052, Generator Loss: 1.3155
D(x): 0.7393, D(G(z)): 0.3515
Epoch: [18/20], Batch Num: [220/600]
Discriminator Loss: 0.9922, Generator Loss: 1.4325
D(x): 0.7134, D(G(z)): 0.3539
Epoch: [18/20], Batch Num: [221/600]
Discriminator Loss: 1.0943, Generator Loss: 1.4511
D(x): 0.6756, D(G(z)): 0.3489
Epoch: [18/20], Batch Num: [222/600]
Discriminator Loss: 1.0666, Generator Loss: 1.5202
D(x): 0.6325, D(G(z)): 0.3101
Epoch: [18/20], Batch Num: [223/600]
Discriminator Loss: 1.1282, Generator Loss: 1.3996
D(x): 0.6476, D(G(z)): 0.3318
Epoch: [18/20], Batch Num: [224/600]
Discriminator Loss: 1.0333, Generator Loss: 1.5878
D(x): 0.6555, D(G(z)): 0.3188
Epoch: [18/20], Batch Num: [225/600]
Discriminator Loss: 1.3095, Generator Loss: 1.4097
D(x): 0.5563, D(G(z)): 0.3297
Epoch: [18/20], Batch Num: [226/600]
Discriminator Loss: 1.0637, Generator Loss: 1.3196
D(x): 0.6738, D(G(z)): 0.3384
Epoch: [18/20], Batch Num: [227/600]
Discriminator Loss: 0.9876, Generator Loss: 1.3028
D(x): 0.6927, D(G(z)): 0.3551
Epoch: [18/20], Batch Num: [228/600]
Discriminator Loss: 1.0207, Generator Loss: 1.2318
D(x): 0.6879, D(G(z)): 0.3385
Epoch: [18/20], Batch Num: [229/600]
Discriminator Loss: 1.0545, Generator Loss: 1.3292
D(x): 0.7039, D(G(z)): 0.3689
Epoch: [18/20], Batch Num: [230/600]
Discriminator Loss: 1.1368, Generator Loss: 1.5041
D(x): 0.6435, D(G(z)): 0.3575
Epoch: [18/20], Batch Num: [231/600]
Discriminator Loss: 0.9832, Generator Loss: 1.5019
D(x): 0.6607, D(G(z)): 0.2760
Epoch: [18/20], Batch Num: [232/600]
Discriminator Loss: 0.8578, Generator Loss: 1.5263
D(x): 0.7044, D(G(z)): 0.2847
Epoch: [18/20], Batch Num: [233/600]
Discriminator Loss: 0.9826, Generator Loss: 1.5865
D(x): 0.6395, D(G(z)): 0.2913
Epoch: [18/20], Batch Num: [234/600]
Discriminator Loss: 0.8262, Generator Loss: 1.5601
D(x): 0.7186, D(G(z)): 0.2941
Epoch: [18/20], Batch Num: [235/600]
Discriminator Loss: 0.9447, Generator Loss: 1.5637
D(x): 0.6267, D(G(z)): 0.2693
Epoch: [18/20], Batch Num: [236/600]
Discriminator Loss: 0.7602, Generator Loss: 1.4764
D(x): 0.7362, D(G(z)): 0.2821
Epoch: [18/20], Batch Num: [237/600]
Discriminator Loss: 0.8360, Generator Loss: 1.3707
D(x): 0.7453, D(G(z)): 0.3217
Epoch: [18/20], Batch Num: [238/600]
Discriminator Loss: 0.8355, Generator Loss: 1.4201
D(x): 0.7548, D(G(z)): 0.3184
Epoch: [18/20], Batch Num: [239/600]
Discriminator Loss: 0.8336, Generator Loss: 1.5545
D(x): 0.7507, D(G(z)): 0.3089
Epoch: [18/20], Batch Num: [240/600]
Discriminator Loss: 0.8137, Generator Loss: 1.5704
D(x): 0.7131, D(G(z)): 0.2797
Epoch: [18/20], Batch Num: [241/600]
Discriminator Loss: 0.7047, Generator Loss: 1.8111
D(x): 0.7554, D(G(z)): 0.2440
Epoch: [18/20], Batch Num: [242/600]
Discriminator Loss: 0.8091, Generator Loss: 1.8272
D(x): 0.7212, D(G(z)): 0.2646
Epoch: [18/20], Batch Num: [243/600]
Discriminator Loss: 0.9089, Generator Loss: 1.7237
D(x): 0.6757, D(G(z)): 0.2720
Epoch: [18/20], Batch Num: [244/600]
Discriminator Loss: 0.8272, Generator Loss: 1.5166
D(x): 0.7151, D(G(z)): 0.2322
Epoch: [18/20], Batch Num: [245/600]
Discriminator Loss: 0.8637, Generator Loss: 1.5389
D(x): 0.6950, D(G(z)): 0.2551
Epoch: [18/20], Batch Num: [246/600]
Discriminator Loss: 0.7500, Generator Loss: 1.4484
D(x): 0.7853, D(G(z)): 0.2896
Epoch: [18/20], Batch Num: [247/600]
Discriminator Loss: 0.7699, Generator Loss: 1.5583
D(x): 0.8018, D(G(z)): 0.3332
Epoch: [18/20], Batch Num: [248/600]
Discriminator Loss: 0.7753, Generator Loss: 1.7951
D(x): 0.7418, D(G(z)): 0.2834
Epoch: [18/20], Batch Num: [249/600]
Discriminator Loss: 0.8293, Generator Loss: 1.6362
D(x): 0.6766, D(G(z)): 0.2313
Epoch: [18/20], Batch Num: [250/600]
Discriminator Loss: 0.8608, Generator Loss: 1.5967
D(x): 0.7147, D(G(z)): 0.2698
Epoch: [18/20], Batch Num: [251/600]
Discriminator Loss: 0.8643, Generator Loss: 1.4981
D(x): 0.7339, D(G(z)): 0.3053
Epoch: [18/20], Batch Num: [252/600]
Discriminator Loss: 0.7217, Generator Loss: 1.4869
D(x): 0.7807, D(G(z)): 0.2985
Epoch: [18/20], Batch Num: [253/600]
Discriminator Loss: 0.8983, Generator Loss: 1.4444
D(x): 0.7191, D(G(z)): 0.2983
Epoch: [18/20], Batch Num: [254/600]
Discriminator Loss: 0.8060, Generator Loss: 1.6215
D(x): 0.7392, D(G(z)): 0.2860
Epoch: [18/20], Batch Num: [255/600]
Discriminator Loss: 0.7369, Generator Loss: 1.6520
D(x): 0.7901, D(G(z)): 0.3047
Epoch: [18/20], Batch Num: [256/600]
Discriminator Loss: 0.7555, Generator Loss: 1.7308
D(x): 0.7563, D(G(z)): 0.2840
Epoch: [18/20], Batch Num: [257/600]
Discriminator Loss: 0.8792, Generator Loss: 1.9147
D(x): 0.6637, D(G(z)): 0.2233
Epoch: [18/20], Batch Num: [258/600]
Discriminator Loss: 0.7905, Generator Loss: 1.5776
D(x): 0.7236, D(G(z)): 0.2498
Epoch: [18/20], Batch Num: [259/600]
Discriminator Loss: 0.8620, Generator Loss: 1.4679
D(x): 0.7134, D(G(z)): 0.2738
Epoch: [18/20], Batch Num: [260/600]
Discriminator Loss: 0.9813, Generator Loss: 1.6635
D(x): 0.7308, D(G(z)): 0.3578
Epoch: [18/20], Batch Num: [261/600]
Discriminator Loss: 0.8556, Generator Loss: 1.5310
D(x): 0.7402, D(G(z)): 0.2838
Epoch: [18/20], Batch Num: [262/600]
Discriminator Loss: 1.0335, Generator Loss: 1.8540
D(x): 0.6777, D(G(z)): 0.3221
Epoch: [18/20], Batch Num: [263/600]
Discriminator Loss: 0.9471, Generator Loss: 1.5336
D(x): 0.6701, D(G(z)): 0.2847
Epoch: [18/20], Batch Num: [264/600]
Discriminator Loss: 1.0052, Generator Loss: 1.6306
D(x): 0.7019, D(G(z)): 0.3355
Epoch: [18/20], Batch Num: [265/600]
Discriminator Loss: 0.9153, Generator Loss: 1.5616
D(x): 0.7337, D(G(z)): 0.3282
Epoch: [18/20], Batch Num: [266/600]
Discriminator Loss: 0.9467, Generator Loss: 1.6031
D(x): 0.7153, D(G(z)): 0.3183
Epoch: [18/20], Batch Num: [267/600]
Discriminator Loss: 0.8399, Generator Loss: 1.7044
D(x): 0.7344, D(G(z)): 0.2757
Epoch: [18/20], Batch Num: [268/600]
Discriminator Loss: 0.9818, Generator Loss: 1.8776
D(x): 0.7258, D(G(z)): 0.3300
Epoch: [18/20], Batch Num: [269/600]
Discriminator Loss: 1.0805, Generator Loss: 1.9673
D(x): 0.6301, D(G(z)): 0.3049
Epoch: [18/20], Batch Num: [270/600]
Discriminator Loss: 0.9375, Generator Loss: 1.7928
D(x): 0.6383, D(G(z)): 0.2370
Epoch: [18/20], Batch Num: [271/600]
Discriminator Loss: 0.7568, Generator Loss: 1.4464
D(x): 0.7253, D(G(z)): 0.2329
Epoch: [18/20], Batch Num: [272/600]
Discriminator Loss: 0.7566, Generator Loss: 1.3328
D(x): 0.7340, D(G(z)): 0.2720
Epoch: [18/20], Batch Num: [273/600]
Discriminator Loss: 0.8734, Generator Loss: 1.2766
D(x): 0.7659, D(G(z)): 0.3521
Epoch: [18/20], Batch Num: [274/600]
Discriminator Loss: 0.8506, Generator Loss: 1.4925
D(x): 0.7885, D(G(z)): 0.3488
Epoch: [18/20], Batch Num: [275/600]
Discriminator Loss: 1.0909, Generator Loss: 1.7202
D(x): 0.7369, D(G(z)): 0.3890
Epoch: [18/20], Batch Num: [276/600]
Discriminator Loss: 0.9200, Generator Loss: 1.7801
D(x): 0.6604, D(G(z)): 0.2573
Epoch: [18/20], Batch Num: [277/600]
Discriminator Loss: 0.8129, Generator Loss: 1.8308
D(x): 0.6935, D(G(z)): 0.2405
Epoch: [18/20], Batch Num: [278/600]
Discriminator Loss: 0.7266, Generator Loss: 1.6652
D(x): 0.7046, D(G(z)): 0.2071
Epoch: [18/20], Batch Num: [279/600]
Discriminator Loss: 0.9164, Generator Loss: 1.3580
D(x): 0.6904, D(G(z)): 0.2971
Epoch: [18/20], Batch Num: [280/600]
Discriminator Loss: 0.8336, Generator Loss: 1.4076
D(x): 0.7494, D(G(z)): 0.3192
Epoch: [18/20], Batch Num: [281/600]
Discriminator Loss: 0.9107, Generator Loss: 1.5013
D(x): 0.7507, D(G(z)): 0.3495
Epoch: [18/20], Batch Num: [282/600]
Discriminator Loss: 0.9483, Generator Loss: 1.5735
D(x): 0.7194, D(G(z)): 0.3384
Epoch: [18/20], Batch Num: [283/600]
Discriminator Loss: 0.7679, Generator Loss: 1.7470
D(x): 0.7522, D(G(z)): 0.2819
Epoch: [18/20], Batch Num: [284/600]
Discriminator Loss: 0.8737, Generator Loss: 1.9107
D(x): 0.6796, D(G(z)): 0.2576
Epoch: [18/20], Batch Num: [285/600]
Discriminator Loss: 0.8076, Generator Loss: 1.8922
D(x): 0.7314, D(G(z)): 0.2652
Epoch: [18/20], Batch Num: [286/600]
Discriminator Loss: 0.8644, Generator Loss: 1.7100
D(x): 0.6745, D(G(z)): 0.2405
Epoch: [18/20], Batch Num: [287/600]
Discriminator Loss: 0.8046, Generator Loss: 1.7618
D(x): 0.7348, D(G(z)): 0.2786
Epoch: [18/20], Batch Num: [288/600]
Discriminator Loss: 0.8802, Generator Loss: 1.5438
D(x): 0.6950, D(G(z)): 0.2658
Epoch: [18/20], Batch Num: [289/600]
Discriminator Loss: 0.7589, Generator Loss: 1.4246
D(x): 0.7597, D(G(z)): 0.2910
Epoch: [18/20], Batch Num: [290/600]
Discriminator Loss: 1.0561, Generator Loss: 1.3978
D(x): 0.7048, D(G(z)): 0.3574
Epoch: [18/20], Batch Num: [291/600]
Discriminator Loss: 0.9076, Generator Loss: 1.4865
D(x): 0.7147, D(G(z)): 0.3069
Epoch: [18/20], Batch Num: [292/600]
Discriminator Loss: 0.8296, Generator Loss: 1.7893
D(x): 0.7312, D(G(z)): 0.2949
Epoch: [18/20], Batch Num: [293/600]
Discriminator Loss: 0.7842, Generator Loss: 1.8143
D(x): 0.7369, D(G(z)): 0.2612
Epoch: [18/20], Batch Num: [294/600]
Discriminator Loss: 0.9938, Generator Loss: 1.8428
D(x): 0.6548, D(G(z)): 0.2651
Epoch: [18/20], Batch Num: [295/600]
Discriminator Loss: 0.7182, Generator Loss: 1.6055
D(x): 0.7336, D(G(z)): 0.2395
Epoch: [18/20], Batch Num: [296/600]
Discriminator Loss: 0.8239, Generator Loss: 1.5667
D(x): 0.7241, D(G(z)): 0.2871
Epoch: [18/20], Batch Num: [297/600]
Discriminator Loss: 0.9565, Generator Loss: 1.4891
D(x): 0.7593, D(G(z)): 0.3584
Epoch: [18/20], Batch Num: [298/600]
Discriminator Loss: 0.9475, Generator Loss: 1.4044
D(x): 0.7227, D(G(z)): 0.3197
Epoch: [18/20], Batch Num: [299/600]
Discriminator Loss: 0.9202, Generator Loss: 1.6288
D(x): 0.7146, D(G(z)): 0.3067
Epoch: 18, Batch Num: [300/600]

Epoch: [18/20], Batch Num: [300/600]
Discriminator Loss: 0.7430, Generator Loss: 1.5287
D(x): 0.7516, D(G(z)): 0.2590
Epoch: [18/20], Batch Num: [301/600]
Discriminator Loss: 0.8934, Generator Loss: 1.4504
D(x): 0.7045, D(G(z)): 0.2764
Epoch: [18/20], Batch Num: [302/600]
Discriminator Loss: 1.0098, Generator Loss: 1.5216
D(x): 0.6837, D(G(z)): 0.3106
Epoch: [18/20], Batch Num: [303/600]
Discriminator Loss: 0.8715, Generator Loss: 1.5190
D(x): 0.7427, D(G(z)): 0.2750
Epoch: [18/20], Batch Num: [304/600]
Discriminator Loss: 1.0266, Generator Loss: 1.7612
D(x): 0.7228, D(G(z)): 0.3320
Epoch: [18/20], Batch Num: [305/600]
Discriminator Loss: 1.0635, Generator Loss: 1.5776
D(x): 0.6763, D(G(z)): 0.3161
Epoch: [18/20], Batch Num: [306/600]
Discriminator Loss: 0.9536, Generator Loss: 1.4576
D(x): 0.6655, D(G(z)): 0.2764
Epoch: [18/20], Batch Num: [307/600]
Discriminator Loss: 1.1373, Generator Loss: 1.6396
D(x): 0.6732, D(G(z)): 0.3661
Epoch: [18/20], Batch Num: [308/600]
Discriminator Loss: 1.0324, Generator Loss: 1.4868
D(x): 0.6611, D(G(z)): 0.2925
Epoch: [18/20], Batch Num: [309/600]
Discriminator Loss: 1.0391, Generator Loss: 1.4364
D(x): 0.6751, D(G(z)): 0.3149
Epoch: [18/20], Batch Num: [310/600]
Discriminator Loss: 0.9431, Generator Loss: 1.4158
D(x): 0.6965, D(G(z)): 0.3260
Epoch: [18/20], Batch Num: [311/600]
Discriminator Loss: 0.9823, Generator Loss: 1.5650
D(x): 0.7161, D(G(z)): 0.3619
Epoch: [18/20], Batch Num: [312/600]
Discriminator Loss: 0.8814, Generator Loss: 1.3790
D(x): 0.6834, D(G(z)): 0.2647
Epoch: [18/20], Batch Num: [313/600]
Discriminator Loss: 0.8688, Generator Loss: 1.4275
D(x): 0.7176, D(G(z)): 0.3037
Epoch: [18/20], Batch Num: [314/600]
Discriminator Loss: 1.0255, Generator Loss: 1.5520
D(x): 0.7349, D(G(z)): 0.3844
Epoch: [18/20], Batch Num: [315/600]
Discriminator Loss: 0.9539, Generator Loss: 1.5972
D(x): 0.6706, D(G(z)): 0.2884
Epoch: [18/20], Batch Num: [316/600]
Discriminator Loss: 0.9035, Generator Loss: 1.4999
D(x): 0.6472, D(G(z)): 0.2500
Epoch: [18/20], Batch Num: [317/600]
Discriminator Loss: 0.9325, Generator Loss: 1.3281
D(x): 0.6972, D(G(z)): 0.2965
Epoch: [18/20], Batch Num: [318/600]
Discriminator Loss: 0.8314, Generator Loss: 1.3733
D(x): 0.7319, D(G(z)): 0.2967
Epoch: [18/20], Batch Num: [319/600]
Discriminator Loss: 0.9062, Generator Loss: 1.2753
D(x): 0.7402, D(G(z)): 0.3525
Epoch: [18/20], Batch Num: [320/600]
Discriminator Loss: 0.9085, Generator Loss: 1.4142
D(x): 0.7371, D(G(z)): 0.3502
Epoch: [18/20], Batch Num: [321/600]
Discriminator Loss: 0.9259, Generator Loss: 1.6801
D(x): 0.7644, D(G(z)): 0.3691
Epoch: [18/20], Batch Num: [322/600]
Discriminator Loss: 0.9062, Generator Loss: 1.9149
D(x): 0.6700, D(G(z)): 0.2666
Epoch: [18/20], Batch Num: [323/600]
Discriminator Loss: 0.9652, Generator Loss: 1.7705
D(x): 0.6479, D(G(z)): 0.2553
Epoch: [18/20], Batch Num: [324/600]
Discriminator Loss: 0.8077, Generator Loss: 1.8113
D(x): 0.6979, D(G(z)): 0.2482
Epoch: [18/20], Batch Num: [325/600]
Discriminator Loss: 0.9253, Generator Loss: 1.4571
D(x): 0.6328, D(G(z)): 0.2346
Epoch: [18/20], Batch Num: [326/600]
Discriminator Loss: 0.8415, Generator Loss: 1.2814
D(x): 0.6977, D(G(z)): 0.2535
Epoch: [18/20], Batch Num: [327/600]
Discriminator Loss: 0.9837, Generator Loss: 1.1820
D(x): 0.7768, D(G(z)): 0.4067
Epoch: [18/20], Batch Num: [328/600]
Discriminator Loss: 0.7487, Generator Loss: 1.3946
D(x): 0.8453, D(G(z)): 0.3694
Epoch: [18/20], Batch Num: [329/600]
Discriminator Loss: 0.7622, Generator Loss: 1.6566
D(x): 0.7879, D(G(z)): 0.3213
Epoch: [18/20], Batch Num: [330/600]
Discriminator Loss: 0.7819, Generator Loss: 1.9368
D(x): 0.7019, D(G(z)): 0.2264
Epoch: [18/20], Batch Num: [331/600]
Discriminator Loss: 0.8840, Generator Loss: 2.0492
D(x): 0.6668, D(G(z)): 0.2317
Epoch: [18/20], Batch Num: [332/600]
Discriminator Loss: 0.8549, Generator Loss: 1.7998
D(x): 0.6724, D(G(z)): 0.2424
Epoch: [18/20], Batch Num: [333/600]
Discriminator Loss: 0.9149, Generator Loss: 1.6401
D(x): 0.7027, D(G(z)): 0.2792
Epoch: [18/20], Batch Num: [334/600]
Discriminator Loss: 0.8228, Generator Loss: 1.4699
D(x): 0.7308, D(G(z)): 0.2926
Epoch: [18/20], Batch Num: [335/600]
Discriminator Loss: 0.8059, Generator Loss: 1.3401
D(x): 0.7315, D(G(z)): 0.2834
Epoch: [18/20], Batch Num: [336/600]
Discriminator Loss: 0.7833, Generator Loss: 1.3816
D(x): 0.8426, D(G(z)): 0.3597
Epoch: [18/20], Batch Num: [337/600]
Discriminator Loss: 0.7852, Generator Loss: 1.6714
D(x): 0.7693, D(G(z)): 0.2986
Epoch: [18/20], Batch Num: [338/600]
Discriminator Loss: 0.9047, Generator Loss: 1.6576
D(x): 0.7369, D(G(z)): 0.3159
Epoch: [18/20], Batch Num: [339/600]
Discriminator Loss: 0.9489, Generator Loss: 1.9368
D(x): 0.6505, D(G(z)): 0.2353
Epoch: [18/20], Batch Num: [340/600]
Discriminator Loss: 0.9732, Generator Loss: 1.6272
D(x): 0.6071, D(G(z)): 0.2000
Epoch: [18/20], Batch Num: [341/600]
Discriminator Loss: 0.7382, Generator Loss: 1.4482
D(x): 0.7443, D(G(z)): 0.2408
Epoch: [18/20], Batch Num: [342/600]
Discriminator Loss: 0.9589, Generator Loss: 1.2769
D(x): 0.7136, D(G(z)): 0.3140
Epoch: [18/20], Batch Num: [343/600]
Discriminator Loss: 0.9307, Generator Loss: 1.3161
D(x): 0.8200, D(G(z)): 0.3980
Epoch: [18/20], Batch Num: [344/600]
Discriminator Loss: 0.8913, Generator Loss: 1.7302
D(x): 0.7519, D(G(z)): 0.3474
Epoch: [18/20], Batch Num: [345/600]
Discriminator Loss: 0.8341, Generator Loss: 1.9013
D(x): 0.7341, D(G(z)): 0.2823
Epoch: [18/20], Batch Num: [346/600]
Discriminator Loss: 1.0614, Generator Loss: 2.0182
D(x): 0.6126, D(G(z)): 0.2452
Epoch: [18/20], Batch Num: [347/600]
Discriminator Loss: 1.0394, Generator Loss: 1.7494
D(x): 0.5834, D(G(z)): 0.2063
Epoch: [18/20], Batch Num: [348/600]
Discriminator Loss: 0.8273, Generator Loss: 1.4850
D(x): 0.7329, D(G(z)): 0.2796
Epoch: [18/20], Batch Num: [349/600]
Discriminator Loss: 0.9208, Generator Loss: 1.3114
D(x): 0.7546, D(G(z)): 0.3511
Epoch: [18/20], Batch Num: [350/600]
Discriminator Loss: 0.9821, Generator Loss: 1.3094
D(x): 0.7573, D(G(z)): 0.3778
Epoch: [18/20], Batch Num: [351/600]
Discriminator Loss: 0.8950, Generator Loss: 1.5126
D(x): 0.7374, D(G(z)): 0.3323
Epoch: [18/20], Batch Num: [352/600]
Discriminator Loss: 0.8366, Generator Loss: 1.6514
D(x): 0.7393, D(G(z)): 0.3071
Epoch: [18/20], Batch Num: [353/600]
Discriminator Loss: 0.8645, Generator Loss: 1.7220
D(x): 0.6541, D(G(z)): 0.2517
Epoch: [18/20], Batch Num: [354/600]
Discriminator Loss: 0.9020, Generator Loss: 1.6516
D(x): 0.6986, D(G(z)): 0.2735
Epoch: [18/20], Batch Num: [355/600]
Discriminator Loss: 0.7832, Generator Loss: 1.4962
D(x): 0.6950, D(G(z)): 0.2363
Epoch: [18/20], Batch Num: [356/600]
Discriminator Loss: 0.9564, Generator Loss: 1.3194
D(x): 0.6632, D(G(z)): 0.2866
Epoch: [18/20], Batch Num: [357/600]
Discriminator Loss: 0.9229, Generator Loss: 1.3014
D(x): 0.7220, D(G(z)): 0.3618
Epoch: [18/20], Batch Num: [358/600]
Discriminator Loss: 0.9640, Generator Loss: 1.2923
D(x): 0.7138, D(G(z)): 0.3499
Epoch: [18/20], Batch Num: [359/600]
Discriminator Loss: 0.9041, Generator Loss: 1.5461
D(x): 0.7600, D(G(z)): 0.3828
Epoch: [18/20], Batch Num: [360/600]
Discriminator Loss: 0.9561, Generator Loss: 1.5815
D(x): 0.7150, D(G(z)): 0.3284
Epoch: [18/20], Batch Num: [361/600]
Discriminator Loss: 0.9386, Generator Loss: 1.7638
D(x): 0.6553, D(G(z)): 0.2609
Epoch: [18/20], Batch Num: [362/600]
Discriminator Loss: 0.9006, Generator Loss: 1.8175
D(x): 0.6372, D(G(z)): 0.2276
Epoch: [18/20], Batch Num: [363/600]
Discriminator Loss: 0.9080, Generator Loss: 1.4328
D(x): 0.6317, D(G(z)): 0.2285
Epoch: [18/20], Batch Num: [364/600]
Discriminator Loss: 0.8023, Generator Loss: 1.1445
D(x): 0.7854, D(G(z)): 0.3252
Epoch: [18/20], Batch Num: [365/600]
Discriminator Loss: 0.7650, Generator Loss: 1.2863
D(x): 0.7785, D(G(z)): 0.3185
Epoch: [18/20], Batch Num: [366/600]
Discriminator Loss: 0.9387, Generator Loss: 1.4063
D(x): 0.7442, D(G(z)): 0.3521
Epoch: [18/20], Batch Num: [367/600]
Discriminator Loss: 0.8714, Generator Loss: 1.8104
D(x): 0.7166, D(G(z)): 0.2969
Epoch: [18/20], Batch Num: [368/600]
Discriminator Loss: 0.9243, Generator Loss: 1.7759
D(x): 0.7136, D(G(z)): 0.3053
Epoch: [18/20], Batch Num: [369/600]
Discriminator Loss: 0.9140, Generator Loss: 1.6466
D(x): 0.6179, D(G(z)): 0.2252
Epoch: [18/20], Batch Num: [370/600]
Discriminator Loss: 0.9539, Generator Loss: 1.2799
D(x): 0.6377, D(G(z)): 0.2520
Epoch: [18/20], Batch Num: [371/600]
Discriminator Loss: 0.9512, Generator Loss: 1.4305
D(x): 0.7180, D(G(z)): 0.3173
Epoch: [18/20], Batch Num: [372/600]
Discriminator Loss: 0.8100, Generator Loss: 1.3789
D(x): 0.7733, D(G(z)): 0.3279
Epoch: [18/20], Batch Num: [373/600]
Discriminator Loss: 1.0204, Generator Loss: 1.4574
D(x): 0.7116, D(G(z)): 0.3480
Epoch: [18/20], Batch Num: [374/600]
Discriminator Loss: 0.8222, Generator Loss: 1.7042
D(x): 0.7584, D(G(z)): 0.3169
Epoch: [18/20], Batch Num: [375/600]
Discriminator Loss: 0.8620, Generator Loss: 1.8548
D(x): 0.7027, D(G(z)): 0.2672
Epoch: [18/20], Batch Num: [376/600]
Discriminator Loss: 0.9953, Generator Loss: 1.7066
D(x): 0.6906, D(G(z)): 0.2930
Epoch: [18/20], Batch Num: [377/600]
Discriminator Loss: 0.8594, Generator Loss: 1.6299
D(x): 0.6732, D(G(z)): 0.2402
Epoch: [18/20], Batch Num: [378/600]
Discriminator Loss: 0.8192, Generator Loss: 1.7088
D(x): 0.7267, D(G(z)): 0.2910
Epoch: [18/20], Batch Num: [379/600]
Discriminator Loss: 0.7513, Generator Loss: 1.4218
D(x): 0.7611, D(G(z)): 0.2759
Epoch: [18/20], Batch Num: [380/600]
Discriminator Loss: 0.8970, Generator Loss: 1.4023
D(x): 0.7559, D(G(z)): 0.3406
Epoch: [18/20], Batch Num: [381/600]
Discriminator Loss: 0.8174, Generator Loss: 1.6160
D(x): 0.7827, D(G(z)): 0.3422
Epoch: [18/20], Batch Num: [382/600]
Discriminator Loss: 0.9635, Generator Loss: 1.9322
D(x): 0.6829, D(G(z)): 0.2756
Epoch: [18/20], Batch Num: [383/600]
Discriminator Loss: 1.0550, Generator Loss: 1.8342
D(x): 0.6532, D(G(z)): 0.2899
Epoch: [18/20], Batch Num: [384/600]
Discriminator Loss: 0.9037, Generator Loss: 1.7817
D(x): 0.6997, D(G(z)): 0.2869
Epoch: [18/20], Batch Num: [385/600]
Discriminator Loss: 1.0186, Generator Loss: 1.6213
D(x): 0.6471, D(G(z)): 0.2635
Epoch: [18/20], Batch Num: [386/600]
Discriminator Loss: 0.8552, Generator Loss: 1.2786
D(x): 0.7264, D(G(z)): 0.2983
Epoch: [18/20], Batch Num: [387/600]
Discriminator Loss: 0.8645, Generator Loss: 1.4079
D(x): 0.7298, D(G(z)): 0.2927
Epoch: [18/20], Batch Num: [388/600]
Discriminator Loss: 1.0685, Generator Loss: 1.4111
D(x): 0.7549, D(G(z)): 0.4061
Epoch: [18/20], Batch Num: [389/600]
Discriminator Loss: 1.0404, Generator Loss: 1.5644
D(x): 0.6932, D(G(z)): 0.3536
Epoch: [18/20], Batch Num: [390/600]
Discriminator Loss: 1.1850, Generator Loss: 1.5817
D(x): 0.5891, D(G(z)): 0.2876
Epoch: [18/20], Batch Num: [391/600]
Discriminator Loss: 1.1033, Generator Loss: 1.5414
D(x): 0.6887, D(G(z)): 0.3686
Epoch: [18/20], Batch Num: [392/600]
Discriminator Loss: 1.0619, Generator Loss: 1.2815
D(x): 0.6454, D(G(z)): 0.2951
Epoch: [18/20], Batch Num: [393/600]
Discriminator Loss: 1.0338, Generator Loss: 1.3281
D(x): 0.6705, D(G(z)): 0.3162
Epoch: [18/20], Batch Num: [394/600]
Discriminator Loss: 0.9220, Generator Loss: 1.4843
D(x): 0.7035, D(G(z)): 0.3174
Epoch: [18/20], Batch Num: [395/600]
Discriminator Loss: 1.2084, Generator Loss: 1.3956
D(x): 0.6837, D(G(z)): 0.3843
Epoch: [18/20], Batch Num: [396/600]
Discriminator Loss: 1.0682, Generator Loss: 1.6232
D(x): 0.6844, D(G(z)): 0.3521
Epoch: [18/20], Batch Num: [397/600]
Discriminator Loss: 0.9108, Generator Loss: 1.3779
D(x): 0.6788, D(G(z)): 0.2866
Epoch: [18/20], Batch Num: [398/600]
Discriminator Loss: 0.9997, Generator Loss: 1.2784
D(x): 0.6727, D(G(z)): 0.3286
Epoch: [18/20], Batch Num: [399/600]
Discriminator Loss: 1.1318, Generator Loss: 1.4720
D(x): 0.6977, D(G(z)): 0.3734
Epoch: 18, Batch Num: [400/600]

Epoch: [18/20], Batch Num: [400/600]
Discriminator Loss: 1.0851, Generator Loss: 1.3695
D(x): 0.6370, D(G(z)): 0.3301
Epoch: [18/20], Batch Num: [401/600]
Discriminator Loss: 0.7758, Generator Loss: 1.4059
D(x): 0.7160, D(G(z)): 0.2701
Epoch: [18/20], Batch Num: [402/600]
Discriminator Loss: 0.9632, Generator Loss: 1.2686
D(x): 0.6705, D(G(z)): 0.3171
Epoch: [18/20], Batch Num: [403/600]
Discriminator Loss: 0.9006, Generator Loss: 1.3233
D(x): 0.7239, D(G(z)): 0.3071
Epoch: [18/20], Batch Num: [404/600]
Discriminator Loss: 0.9721, Generator Loss: 1.4122
D(x): 0.7223, D(G(z)): 0.3486
Epoch: [18/20], Batch Num: [405/600]
Discriminator Loss: 0.8932, Generator Loss: 1.5361
D(x): 0.7196, D(G(z)): 0.3348
Epoch: [18/20], Batch Num: [406/600]
Discriminator Loss: 0.9141, Generator Loss: 1.8076
D(x): 0.6903, D(G(z)): 0.3134
Epoch: [18/20], Batch Num: [407/600]
Discriminator Loss: 0.8947, Generator Loss: 1.5462
D(x): 0.6612, D(G(z)): 0.2576
Epoch: [18/20], Batch Num: [408/600]
Discriminator Loss: 0.9801, Generator Loss: 1.4874
D(x): 0.6394, D(G(z)): 0.2920
Epoch: [18/20], Batch Num: [409/600]
Discriminator Loss: 0.9352, Generator Loss: 1.3810
D(x): 0.7152, D(G(z)): 0.3345
Epoch: [18/20], Batch Num: [410/600]
Discriminator Loss: 0.7437, Generator Loss: 1.6399
D(x): 0.8008, D(G(z)): 0.3220
Epoch: [18/20], Batch Num: [411/600]
Discriminator Loss: 0.7346, Generator Loss: 1.6759
D(x): 0.7506, D(G(z)): 0.2703
Epoch: [18/20], Batch Num: [412/600]
Discriminator Loss: 0.7513, Generator Loss: 1.7190
D(x): 0.7484, D(G(z)): 0.2783
Epoch: [18/20], Batch Num: [413/600]
Discriminator Loss: 0.7581, Generator Loss: 1.8739
D(x): 0.7400, D(G(z)): 0.2823
Epoch: [18/20], Batch Num: [414/600]
Discriminator Loss: 0.7594, Generator Loss: 1.7902
D(x): 0.6924, D(G(z)): 0.2242
Epoch: [18/20], Batch Num: [415/600]
Discriminator Loss: 0.7435, Generator Loss: 1.7058
D(x): 0.7466, D(G(z)): 0.2664
Epoch: [18/20], Batch Num: [416/600]
Discriminator Loss: 0.7746, Generator Loss: 1.5372
D(x): 0.7241, D(G(z)): 0.2606
Epoch: [18/20], Batch Num: [417/600]
Discriminator Loss: 0.7155, Generator Loss: 1.6183
D(x): 0.7687, D(G(z)): 0.2629
Epoch: [18/20], Batch Num: [418/600]
Discriminator Loss: 0.8818, Generator Loss: 1.6765
D(x): 0.7231, D(G(z)): 0.3002
Epoch: [18/20], Batch Num: [419/600]
Discriminator Loss: 0.8115, Generator Loss: 1.8470
D(x): 0.7348, D(G(z)): 0.2929
Epoch: [18/20], Batch Num: [420/600]
Discriminator Loss: 0.7027, Generator Loss: 1.9308
D(x): 0.7870, D(G(z)): 0.2714
Epoch: [18/20], Batch Num: [421/600]
Discriminator Loss: 0.7181, Generator Loss: 1.7908
D(x): 0.7278, D(G(z)): 0.2439
Epoch: [18/20], Batch Num: [422/600]
Discriminator Loss: 0.8525, Generator Loss: 1.9067
D(x): 0.7066, D(G(z)): 0.2611
Epoch: [18/20], Batch Num: [423/600]
Discriminator Loss: 0.7415, Generator Loss: 1.9381
D(x): 0.7529, D(G(z)): 0.2598
Epoch: [18/20], Batch Num: [424/600]
Discriminator Loss: 0.8848, Generator Loss: 1.8124
D(x): 0.7131, D(G(z)): 0.2619
Epoch: [18/20], Batch Num: [425/600]
Discriminator Loss: 0.7643, Generator Loss: 1.8700
D(x): 0.7643, D(G(z)): 0.2870
Epoch: [18/20], Batch Num: [426/600]
Discriminator Loss: 0.8220, Generator Loss: 1.8938
D(x): 0.7696, D(G(z)): 0.3102
Epoch: [18/20], Batch Num: [427/600]
Discriminator Loss: 0.9011, Generator Loss: 1.8157
D(x): 0.6807, D(G(z)): 0.2484
Epoch: [18/20], Batch Num: [428/600]
Discriminator Loss: 1.0011, Generator Loss: 1.3517
D(x): 0.6525, D(G(z)): 0.2765
Epoch: [18/20], Batch Num: [429/600]
Discriminator Loss: 0.9879, Generator Loss: 1.4226
D(x): 0.7660, D(G(z)): 0.3578
Epoch: [18/20], Batch Num: [430/600]
Discriminator Loss: 1.0905, Generator Loss: 1.6944
D(x): 0.7173, D(G(z)): 0.3634
Epoch: [18/20], Batch Num: [431/600]
Discriminator Loss: 0.9657, Generator Loss: 1.9355
D(x): 0.6956, D(G(z)): 0.2845
Epoch: [18/20], Batch Num: [432/600]
Discriminator Loss: 1.0842, Generator Loss: 1.7012
D(x): 0.6357, D(G(z)): 0.2638
Epoch: [18/20], Batch Num: [433/600]
Discriminator Loss: 1.1976, Generator Loss: 1.7791
D(x): 0.6222, D(G(z)): 0.2921
Epoch: [18/20], Batch Num: [434/600]
Discriminator Loss: 1.2401, Generator Loss: 1.6060
D(x): 0.6158, D(G(z)): 0.3102
Epoch: [18/20], Batch Num: [435/600]
Discriminator Loss: 1.0634, Generator Loss: 1.2348
D(x): 0.6929, D(G(z)): 0.3447
Epoch: [18/20], Batch Num: [436/600]
Discriminator Loss: 0.9950, Generator Loss: 1.3974
D(x): 0.7325, D(G(z)): 0.3728
Epoch: [18/20], Batch Num: [437/600]
Discriminator Loss: 1.1454, Generator Loss: 1.6113
D(x): 0.7201, D(G(z)): 0.4163
Epoch: [18/20], Batch Num: [438/600]
Discriminator Loss: 1.1031, Generator Loss: 1.5067
D(x): 0.6169, D(G(z)): 0.2955
Epoch: [18/20], Batch Num: [439/600]
Discriminator Loss: 1.1063, Generator Loss: 1.6180
D(x): 0.5982, D(G(z)): 0.2819
Epoch: [18/20], Batch Num: [440/600]
Discriminator Loss: 1.1571, Generator Loss: 1.2833
D(x): 0.5808, D(G(z)): 0.3152
Epoch: [18/20], Batch Num: [441/600]
Discriminator Loss: 1.1444, Generator Loss: 1.2369
D(x): 0.6497, D(G(z)): 0.3587
Epoch: [18/20], Batch Num: [442/600]
Discriminator Loss: 1.0263, Generator Loss: 1.2991
D(x): 0.7144, D(G(z)): 0.3869
Epoch: [18/20], Batch Num: [443/600]
Discriminator Loss: 0.9114, Generator Loss: 1.3268
D(x): 0.7504, D(G(z)): 0.3754
Epoch: [18/20], Batch Num: [444/600]
Discriminator Loss: 0.9923, Generator Loss: 1.6058
D(x): 0.7007, D(G(z)): 0.3674
Epoch: [18/20], Batch Num: [445/600]
Discriminator Loss: 0.8010, Generator Loss: 1.8904
D(x): 0.7137, D(G(z)): 0.2903
Epoch: [18/20], Batch Num: [446/600]
Discriminator Loss: 0.8252, Generator Loss: 1.7465
D(x): 0.6607, D(G(z)): 0.2331
Epoch: [18/20], Batch Num: [447/600]
Discriminator Loss: 0.9268, Generator Loss: 1.7055
D(x): 0.6255, D(G(z)): 0.2468
Epoch: [18/20], Batch Num: [448/600]
Discriminator Loss: 0.7969, Generator Loss: 1.7001
D(x): 0.6789, D(G(z)): 0.2363
Epoch: [18/20], Batch Num: [449/600]
Discriminator Loss: 0.8764, Generator Loss: 1.4269
D(x): 0.6618, D(G(z)): 0.2553
Epoch: [18/20], Batch Num: [450/600]
Discriminator Loss: 0.7457, Generator Loss: 1.4470
D(x): 0.7459, D(G(z)): 0.2807
Epoch: [18/20], Batch Num: [451/600]
Discriminator Loss: 0.7692, Generator Loss: 1.4806
D(x): 0.7934, D(G(z)): 0.3311
Epoch: [18/20], Batch Num: [452/600]
Discriminator Loss: 0.7248, Generator Loss: 1.5656
D(x): 0.7878, D(G(z)): 0.3174
Epoch: [18/20], Batch Num: [453/600]
Discriminator Loss: 0.6545, Generator Loss: 1.8499
D(x): 0.7933, D(G(z)): 0.2783
Epoch: [18/20], Batch Num: [454/600]
Discriminator Loss: 0.5938, Generator Loss: 2.1809
D(x): 0.7691, D(G(z)): 0.2054
Epoch: [18/20], Batch Num: [455/600]
Discriminator Loss: 0.5706, Generator Loss: 2.2926
D(x): 0.7898, D(G(z)): 0.2211
Epoch: [18/20], Batch Num: [456/600]
Discriminator Loss: 0.5932, Generator Loss: 2.2773
D(x): 0.7568, D(G(z)): 0.1932
Epoch: [18/20], Batch Num: [457/600]
Discriminator Loss: 0.6056, Generator Loss: 2.4190
D(x): 0.7224, D(G(z)): 0.1490
Epoch: [18/20], Batch Num: [458/600]
Discriminator Loss: 0.5193, Generator Loss: 1.8992
D(x): 0.8053, D(G(z)): 0.1974
Epoch: [18/20], Batch Num: [459/600]
Discriminator Loss: 0.5920, Generator Loss: 2.0036
D(x): 0.8346, D(G(z)): 0.2405
Epoch: [18/20], Batch Num: [460/600]
Discriminator Loss: 0.5886, Generator Loss: 2.1957
D(x): 0.8356, D(G(z)): 0.2633
Epoch: [18/20], Batch Num: [461/600]
Discriminator Loss: 0.4958, Generator Loss: 2.5220
D(x): 0.8497, D(G(z)): 0.2026
Epoch: [18/20], Batch Num: [462/600]
Discriminator Loss: 0.7103, Generator Loss: 2.5105
D(x): 0.7337, D(G(z)): 0.1807
Epoch: [18/20], Batch Num: [463/600]
Discriminator Loss: 0.6093, Generator Loss: 2.3070
D(x): 0.7491, D(G(z)): 0.1787
Epoch: [18/20], Batch Num: [464/600]
Discriminator Loss: 0.7134, Generator Loss: 2.1093
D(x): 0.7787, D(G(z)): 0.2240
Epoch: [18/20], Batch Num: [465/600]
Discriminator Loss: 0.6980, Generator Loss: 1.9498
D(x): 0.7837, D(G(z)): 0.2355
Epoch: [18/20], Batch Num: [466/600]
Discriminator Loss: 0.8157, Generator Loss: 1.7966
D(x): 0.7409, D(G(z)): 0.2541
Epoch: [18/20], Batch Num: [467/600]
Discriminator Loss: 0.8517, Generator Loss: 1.9166
D(x): 0.7890, D(G(z)): 0.2903
Epoch: [18/20], Batch Num: [468/600]
Discriminator Loss: 0.8206, Generator Loss: 2.0881
D(x): 0.7671, D(G(z)): 0.2809
Epoch: [18/20], Batch Num: [469/600]
Discriminator Loss: 0.8628, Generator Loss: 1.9888
D(x): 0.7080, D(G(z)): 0.2518
Epoch: [18/20], Batch Num: [470/600]
Discriminator Loss: 1.0118, Generator Loss: 1.8205
D(x): 0.6621, D(G(z)): 0.2590
Epoch: [18/20], Batch Num: [471/600]
Discriminator Loss: 0.9238, Generator Loss: 1.6161
D(x): 0.7137, D(G(z)): 0.2882
Epoch: [18/20], Batch Num: [472/600]
Discriminator Loss: 1.2495, Generator Loss: 1.6051
D(x): 0.7201, D(G(z)): 0.4216
Epoch: [18/20], Batch Num: [473/600]
Discriminator Loss: 1.1778, Generator Loss: 1.6473
D(x): 0.7353, D(G(z)): 0.3921
Epoch: [18/20], Batch Num: [474/600]
Discriminator Loss: 1.2156, Generator Loss: 1.8788
D(x): 0.6004, D(G(z)): 0.2745
Epoch: [18/20], Batch Num: [475/600]
Discriminator Loss: 1.3216, Generator Loss: 1.6495
D(x): 0.5760, D(G(z)): 0.2729
Epoch: [18/20], Batch Num: [476/600]
Discriminator Loss: 1.3761, Generator Loss: 1.2985
D(x): 0.6041, D(G(z)): 0.3433
Epoch: [18/20], Batch Num: [477/600]
Discriminator Loss: 1.1333, Generator Loss: 1.2615
D(x): 0.7330, D(G(z)): 0.3942
Epoch: [18/20], Batch Num: [478/600]
Discriminator Loss: 1.3146, Generator Loss: 1.2781
D(x): 0.6435, D(G(z)): 0.4246
Epoch: [18/20], Batch Num: [479/600]
Discriminator Loss: 1.1107, Generator Loss: 1.3327
D(x): 0.6886, D(G(z)): 0.3775
Epoch: [18/20], Batch Num: [480/600]
Discriminator Loss: 1.2420, Generator Loss: 1.4911
D(x): 0.5746, D(G(z)): 0.3453
Epoch: [18/20], Batch Num: [481/600]
Discriminator Loss: 1.2259, Generator Loss: 1.6639
D(x): 0.6124, D(G(z)): 0.3211
Epoch: [18/20], Batch Num: [482/600]
Discriminator Loss: 1.1580, Generator Loss: 1.6163
D(x): 0.5910, D(G(z)): 0.2844
Epoch: [18/20], Batch Num: [483/600]
Discriminator Loss: 1.1286, Generator Loss: 1.4246
D(x): 0.6701, D(G(z)): 0.3221
Epoch: [18/20], Batch Num: [484/600]
Discriminator Loss: 1.1095, Generator Loss: 1.5179
D(x): 0.6511, D(G(z)): 0.3247
Epoch: [18/20], Batch Num: [485/600]
Discriminator Loss: 1.0421, Generator Loss: 1.3415
D(x): 0.6536, D(G(z)): 0.3091
Epoch: [18/20], Batch Num: [486/600]
Discriminator Loss: 0.8868, Generator Loss: 1.2162
D(x): 0.7198, D(G(z)): 0.3074
Epoch: [18/20], Batch Num: [487/600]
Discriminator Loss: 0.9145, Generator Loss: 1.2855
D(x): 0.7472, D(G(z)): 0.3567
Epoch: [18/20], Batch Num: [488/600]
Discriminator Loss: 1.0472, Generator Loss: 1.5430
D(x): 0.7107, D(G(z)): 0.3781
Epoch: [18/20], Batch Num: [489/600]
Discriminator Loss: 0.9831, Generator Loss: 1.6769
D(x): 0.6927, D(G(z)): 0.3188
Epoch: [18/20], Batch Num: [490/600]
Discriminator Loss: 0.8215, Generator Loss: 1.7247
D(x): 0.7031, D(G(z)): 0.2597
Epoch: [18/20], Batch Num: [491/600]
Discriminator Loss: 0.8204, Generator Loss: 1.6140
D(x): 0.6523, D(G(z)): 0.2153
Epoch: [18/20], Batch Num: [492/600]
Discriminator Loss: 0.9245, Generator Loss: 1.7214
D(x): 0.6544, D(G(z)): 0.2478
Epoch: [18/20], Batch Num: [493/600]
Discriminator Loss: 0.7911, Generator Loss: 1.4614
D(x): 0.7412, D(G(z)): 0.2958
Epoch: [18/20], Batch Num: [494/600]
Discriminator Loss: 0.8323, Generator Loss: 1.4390
D(x): 0.7770, D(G(z)): 0.3333
Epoch: [18/20], Batch Num: [495/600]
Discriminator Loss: 0.7372, Generator Loss: 1.6239
D(x): 0.8062, D(G(z)): 0.3154
Epoch: [18/20], Batch Num: [496/600]
Discriminator Loss: 0.6783, Generator Loss: 1.7719
D(x): 0.8070, D(G(z)): 0.2882
Epoch: [18/20], Batch Num: [497/600]
Discriminator Loss: 0.7416, Generator Loss: 1.9953
D(x): 0.7396, D(G(z)): 0.2471
Epoch: [18/20], Batch Num: [498/600]
Discriminator Loss: 0.7670, Generator Loss: 1.9689
D(x): 0.7318, D(G(z)): 0.2384
Epoch: [18/20], Batch Num: [499/600]
Discriminator Loss: 0.7272, Generator Loss: 1.9289
D(x): 0.7411, D(G(z)): 0.2374
Epoch: 18, Batch Num: [500/600]

Epoch: [18/20], Batch Num: [500/600]
Discriminator Loss: 0.7671, Generator Loss: 2.1376
D(x): 0.7304, D(G(z)): 0.2473
Epoch: [18/20], Batch Num: [501/600]
Discriminator Loss: 0.6903, Generator Loss: 1.7244
D(x): 0.7443, D(G(z)): 0.2193
Epoch: [18/20], Batch Num: [502/600]
Discriminator Loss: 0.7758, Generator Loss: 1.9445
D(x): 0.7914, D(G(z)): 0.2964
Epoch: [18/20], Batch Num: [503/600]
Discriminator Loss: 0.7521, Generator Loss: 1.8163
D(x): 0.8072, D(G(z)): 0.3065
Epoch: [18/20], Batch Num: [504/600]
Discriminator Loss: 0.8310, Generator Loss: 1.9296
D(x): 0.7468, D(G(z)): 0.2801
Epoch: [18/20], Batch Num: [505/600]
Discriminator Loss: 0.6541, Generator Loss: 2.1627
D(x): 0.8049, D(G(z)): 0.2351
Epoch: [18/20], Batch Num: [506/600]
Discriminator Loss: 0.7001, Generator Loss: 1.9350
D(x): 0.7658, D(G(z)): 0.2191
Epoch: [18/20], Batch Num: [507/600]
Discriminator Loss: 0.8128, Generator Loss: 1.9661
D(x): 0.6813, D(G(z)): 0.2039
Epoch: [18/20], Batch Num: [508/600]
Discriminator Loss: 0.8291, Generator Loss: 1.8610
D(x): 0.7456, D(G(z)): 0.2674
Epoch: [18/20], Batch Num: [509/600]
Discriminator Loss: 0.7225, Generator Loss: 1.7667
D(x): 0.8252, D(G(z)): 0.2865
Epoch: [18/20], Batch Num: [510/600]
Discriminator Loss: 0.8422, Generator Loss: 1.7967
D(x): 0.7412, D(G(z)): 0.2775
Epoch: [18/20], Batch Num: [511/600]
Discriminator Loss: 0.8856, Generator Loss: 1.8008
D(x): 0.7543, D(G(z)): 0.3318
Epoch: [18/20], Batch Num: [512/600]
Discriminator Loss: 0.9019, Generator Loss: 1.6759
D(x): 0.7195, D(G(z)): 0.2683
Epoch: [18/20], Batch Num: [513/600]
Discriminator Loss: 0.8802, Generator Loss: 1.7180
D(x): 0.6828, D(G(z)): 0.2586
Epoch: [18/20], Batch Num: [514/600]
Discriminator Loss: 0.9071, Generator Loss: 1.5535
D(x): 0.7155, D(G(z)): 0.3035
Epoch: [18/20], Batch Num: [515/600]
Discriminator Loss: 1.0956, Generator Loss: 1.5054
D(x): 0.7012, D(G(z)): 0.3685
Epoch: [18/20], Batch Num: [516/600]
Discriminator Loss: 0.9263, Generator Loss: 1.5144
D(x): 0.7725, D(G(z)): 0.3418
Epoch: [18/20], Batch Num: [517/600]
Discriminator Loss: 1.0886, Generator Loss: 1.6027
D(x): 0.6980, D(G(z)): 0.3504
Epoch: [18/20], Batch Num: [518/600]
Discriminator Loss: 0.9964, Generator Loss: 1.6350
D(x): 0.6555, D(G(z)): 0.2836
Epoch: [18/20], Batch Num: [519/600]
Discriminator Loss: 0.9881, Generator Loss: 1.5821
D(x): 0.6734, D(G(z)): 0.2789
Epoch: [18/20], Batch Num: [520/600]
Discriminator Loss: 1.0662, Generator Loss: 1.4763
D(x): 0.6957, D(G(z)): 0.3531
Epoch: [18/20], Batch Num: [521/600]
Discriminator Loss: 0.9461, Generator Loss: 1.4898
D(x): 0.7339, D(G(z)): 0.3470
Epoch: [18/20], Batch Num: [522/600]
Discriminator Loss: 0.8388, Generator Loss: 1.5882
D(x): 0.7660, D(G(z)): 0.3328
Epoch: [18/20], Batch Num: [523/600]
Discriminator Loss: 0.9442, Generator Loss: 1.5664
D(x): 0.7070, D(G(z)): 0.3221
Epoch: [18/20], Batch Num: [524/600]
Discriminator Loss: 1.0245, Generator Loss: 1.6605
D(x): 0.6895, D(G(z)): 0.3121
Epoch: [18/20], Batch Num: [525/600]
Discriminator Loss: 1.1460, Generator Loss: 1.7940
D(x): 0.6800, D(G(z)): 0.3522
Epoch: [18/20], Batch Num: [526/600]
Discriminator Loss: 0.8871, Generator Loss: 1.7256
D(x): 0.6843, D(G(z)): 0.2796
Epoch: [18/20], Batch Num: [527/600]
Discriminator Loss: 0.8794, Generator Loss: 1.7136
D(x): 0.7000, D(G(z)): 0.2762
Epoch: [18/20], Batch Num: [528/600]
Discriminator Loss: 0.9501, Generator Loss: 1.5916
D(x): 0.7143, D(G(z)): 0.3477
Epoch: [18/20], Batch Num: [529/600]
Discriminator Loss: 1.0202, Generator Loss: 1.6776
D(x): 0.7125, D(G(z)): 0.3462
Epoch: [18/20], Batch Num: [530/600]
Discriminator Loss: 0.9742, Generator Loss: 1.5367
D(x): 0.6848, D(G(z)): 0.3148
Epoch: [18/20], Batch Num: [531/600]
Discriminator Loss: 0.9112, Generator Loss: 1.6301
D(x): 0.6969, D(G(z)): 0.3028
Epoch: [18/20], Batch Num: [532/600]
Discriminator Loss: 0.8128, Generator Loss: 1.7248
D(x): 0.7324, D(G(z)): 0.2783
Epoch: [18/20], Batch Num: [533/600]
Discriminator Loss: 0.8706, Generator Loss: 1.5990
D(x): 0.7126, D(G(z)): 0.3030
Epoch: [18/20], Batch Num: [534/600]
Discriminator Loss: 0.9073, Generator Loss: 1.5244
D(x): 0.6947, D(G(z)): 0.3095
Epoch: [18/20], Batch Num: [535/600]
Discriminator Loss: 0.9218, Generator Loss: 1.6160
D(x): 0.6815, D(G(z)): 0.2935
Epoch: [18/20], Batch Num: [536/600]
Discriminator Loss: 0.8838, Generator Loss: 1.6288
D(x): 0.7289, D(G(z)): 0.3151
Epoch: [18/20], Batch Num: [537/600]
Discriminator Loss: 0.7947, Generator Loss: 1.4561
D(x): 0.7158, D(G(z)): 0.2723
Epoch: [18/20], Batch Num: [538/600]
Discriminator Loss: 0.8848, Generator Loss: 1.3638
D(x): 0.7313, D(G(z)): 0.3352
Epoch: [18/20], Batch Num: [539/600]
Discriminator Loss: 0.7915, Generator Loss: 1.4984
D(x): 0.7396, D(G(z)): 0.2994
Epoch: [18/20], Batch Num: [540/600]
Discriminator Loss: 0.8093, Generator Loss: 1.7333
D(x): 0.7530, D(G(z)): 0.3123
Epoch: [18/20], Batch Num: [541/600]
Discriminator Loss: 0.8747, Generator Loss: 1.5590
D(x): 0.7114, D(G(z)): 0.3102
Epoch: [18/20], Batch Num: [542/600]
Discriminator Loss: 0.6890, Generator Loss: 1.5678
D(x): 0.7348, D(G(z)): 0.2402
Epoch: [18/20], Batch Num: [543/600]
Discriminator Loss: 0.7962, Generator Loss: 1.5131
D(x): 0.7303, D(G(z)): 0.2886
Epoch: [18/20], Batch Num: [544/600]
Discriminator Loss: 0.8539, Generator Loss: 1.5636
D(x): 0.6745, D(G(z)): 0.2647
Epoch: [18/20], Batch Num: [545/600]
Discriminator Loss: 0.8794, Generator Loss: 1.3293
D(x): 0.7729, D(G(z)): 0.3411
Epoch: [18/20], Batch Num: [546/600]
Discriminator Loss: 0.8312, Generator Loss: 1.3123
D(x): 0.7343, D(G(z)): 0.2890
Epoch: [18/20], Batch Num: [547/600]
Discriminator Loss: 0.7896, Generator Loss: 1.5260
D(x): 0.7643, D(G(z)): 0.3016
Epoch: [18/20], Batch Num: [548/600]
Discriminator Loss: 0.9219, Generator Loss: 1.7091
D(x): 0.6976, D(G(z)): 0.3058
Epoch: [18/20], Batch Num: [549/600]
Discriminator Loss: 0.8785, Generator Loss: 1.7058
D(x): 0.7075, D(G(z)): 0.3014
Epoch: [18/20], Batch Num: [550/600]
Discriminator Loss: 0.8648, Generator Loss: 1.6163
D(x): 0.6847, D(G(z)): 0.2645
Epoch: [18/20], Batch Num: [551/600]
Discriminator Loss: 0.7745, Generator Loss: 1.4642
D(x): 0.7226, D(G(z)): 0.2626
Epoch: [18/20], Batch Num: [552/600]
Discriminator Loss: 0.8601, Generator Loss: 1.5806
D(x): 0.7051, D(G(z)): 0.2945
Epoch: [18/20], Batch Num: [553/600]
Discriminator Loss: 0.9394, Generator Loss: 1.4691
D(x): 0.7323, D(G(z)): 0.3255
Epoch: [18/20], Batch Num: [554/600]
Discriminator Loss: 0.9026, Generator Loss: 1.4370
D(x): 0.6927, D(G(z)): 0.3102
Epoch: [18/20], Batch Num: [555/600]
Discriminator Loss: 0.9057, Generator Loss: 1.4255
D(x): 0.7280, D(G(z)): 0.3232
Epoch: [18/20], Batch Num: [556/600]
Discriminator Loss: 0.8126, Generator Loss: 1.5944
D(x): 0.7272, D(G(z)): 0.2883
Epoch: [18/20], Batch Num: [557/600]
Discriminator Loss: 0.9713, Generator Loss: 1.5431
D(x): 0.6955, D(G(z)): 0.3246
Epoch: [18/20], Batch Num: [558/600]
Discriminator Loss: 0.8905, Generator Loss: 1.5992
D(x): 0.7002, D(G(z)): 0.2749
Epoch: [18/20], Batch Num: [559/600]
Discriminator Loss: 0.8469, Generator Loss: 1.6455
D(x): 0.7004, D(G(z)): 0.2643
Epoch: [18/20], Batch Num: [560/600]
Discriminator Loss: 0.7092, Generator Loss: 1.4840
D(x): 0.7292, D(G(z)): 0.2339
Epoch: [18/20], Batch Num: [561/600]
Discriminator Loss: 0.7952, Generator Loss: 1.2913
D(x): 0.7501, D(G(z)): 0.2996
Epoch: [18/20], Batch Num: [562/600]
Discriminator Loss: 0.7662, Generator Loss: 1.4495
D(x): 0.7538, D(G(z)): 0.2846
Epoch: [18/20], Batch Num: [563/600]
Discriminator Loss: 0.7821, Generator Loss: 1.4154
D(x): 0.7467, D(G(z)): 0.2888
Epoch: [18/20], Batch Num: [564/600]
Discriminator Loss: 0.7503, Generator Loss: 1.5201
D(x): 0.8092, D(G(z)): 0.3249
Epoch: [18/20], Batch Num: [565/600]
Discriminator Loss: 0.7820, Generator Loss: 1.5684
D(x): 0.7405, D(G(z)): 0.2735
Epoch: [18/20], Batch Num: [566/600]
Discriminator Loss: 0.6845, Generator Loss: 1.7464
D(x): 0.7719, D(G(z)): 0.2579
Epoch: [18/20], Batch Num: [567/600]
Discriminator Loss: 0.7689, Generator Loss: 1.9196
D(x): 0.7508, D(G(z)): 0.2523
Epoch: [18/20], Batch Num: [568/600]
Discriminator Loss: 0.8377, Generator Loss: 1.8726
D(x): 0.6813, D(G(z)): 0.2197
Epoch: [18/20], Batch Num: [569/600]
Discriminator Loss: 0.7985, Generator Loss: 1.5868
D(x): 0.7208, D(G(z)): 0.2343
Epoch: [18/20], Batch Num: [570/600]
Discriminator Loss: 0.7457, Generator Loss: 1.5124
D(x): 0.7942, D(G(z)): 0.2916
Epoch: [18/20], Batch Num: [571/600]
Discriminator Loss: 0.7359, Generator Loss: 1.4482
D(x): 0.8162, D(G(z)): 0.3070
Epoch: [18/20], Batch Num: [572/600]
Discriminator Loss: 0.8671, Generator Loss: 1.7578
D(x): 0.7624, D(G(z)): 0.3129
Epoch: [18/20], Batch Num: [573/600]
Discriminator Loss: 0.6878, Generator Loss: 1.8516
D(x): 0.7567, D(G(z)): 0.2279
Epoch: [18/20], Batch Num: [574/600]
Discriminator Loss: 0.7513, Generator Loss: 1.7052
D(x): 0.7165, D(G(z)): 0.2071
Epoch: [18/20], Batch Num: [575/600]
Discriminator Loss: 0.6144, Generator Loss: 1.7305
D(x): 0.7747, D(G(z)): 0.2174
Epoch: [18/20], Batch Num: [576/600]
Discriminator Loss: 0.7391, Generator Loss: 1.7356
D(x): 0.7578, D(G(z)): 0.2332
Epoch: [18/20], Batch Num: [577/600]
Discriminator Loss: 0.7999, Generator Loss: 1.7672
D(x): 0.8024, D(G(z)): 0.2729
Epoch: [18/20], Batch Num: [578/600]
Discriminator Loss: 0.6661, Generator Loss: 1.6993
D(x): 0.8016, D(G(z)): 0.2649
Epoch: [18/20], Batch Num: [579/600]
Discriminator Loss: 0.7310, Generator Loss: 2.0720
D(x): 0.8091, D(G(z)): 0.2715
Epoch: [18/20], Batch Num: [580/600]
Discriminator Loss: 0.7215, Generator Loss: 2.1385
D(x): 0.7587, D(G(z)): 0.2305
Epoch: [18/20], Batch Num: [581/600]
Discriminator Loss: 0.7711, Generator Loss: 2.2761
D(x): 0.6998, D(G(z)): 0.1798
Epoch: [18/20], Batch Num: [582/600]
Discriminator Loss: 0.7325, Generator Loss: 2.0871
D(x): 0.7513, D(G(z)): 0.2121
Epoch: [18/20], Batch Num: [583/600]
Discriminator Loss: 0.8584, Generator Loss: 1.7651
D(x): 0.7047, D(G(z)): 0.2398
Epoch: [18/20], Batch Num: [584/600]
Discriminator Loss: 0.7465, Generator Loss: 1.6427
D(x): 0.8004, D(G(z)): 0.2709
Epoch: [18/20], Batch Num: [585/600]
Discriminator Loss: 0.8698, Generator Loss: 1.7373
D(x): 0.7846, D(G(z)): 0.3191
Epoch: [18/20], Batch Num: [586/600]
Discriminator Loss: 0.8439, Generator Loss: 1.8807
D(x): 0.7321, D(G(z)): 0.2606
Epoch: [18/20], Batch Num: [587/600]
Discriminator Loss: 0.7964, Generator Loss: 1.7790
D(x): 0.7030, D(G(z)): 0.1938
Epoch: [18/20], Batch Num: [588/600]
Discriminator Loss: 0.8408, Generator Loss: 1.6149
D(x): 0.7608, D(G(z)): 0.2588
Epoch: [18/20], Batch Num: [589/600]
Discriminator Loss: 0.8045, Generator Loss: 1.6744
D(x): 0.7773, D(G(z)): 0.2873
Epoch: [18/20], Batch Num: [590/600]
Discriminator Loss: 0.8193, Generator Loss: 1.8829
D(x): 0.7403, D(G(z)): 0.2656
Epoch: [18/20], Batch Num: [591/600]
Discriminator Loss: 0.7852, Generator Loss: 1.6330
D(x): 0.7260, D(G(z)): 0.2397
Epoch: [18/20], Batch Num: [592/600]
Discriminator Loss: 0.8629, Generator Loss: 1.6924
D(x): 0.7354, D(G(z)): 0.2798
Epoch: [18/20], Batch Num: [593/600]
Discriminator Loss: 1.0506, Generator Loss: 1.6499
D(x): 0.7158, D(G(z)): 0.3465
Epoch: [18/20], Batch Num: [594/600]
Discriminator Loss: 1.0282, Generator Loss: 1.5503
D(x): 0.7017, D(G(z)): 0.3227
Epoch: [18/20], Batch Num: [595/600]
Discriminator Loss: 0.9381, Generator Loss: 1.5707
D(x): 0.7322, D(G(z)): 0.2949
Epoch: [18/20], Batch Num: [596/600]
Discriminator Loss: 0.9599, Generator Loss: 1.7038
D(x): 0.6720, D(G(z)): 0.2587
Epoch: [18/20], Batch Num: [597/600]
Discriminator Loss: 0.8253, Generator Loss: 1.5857
D(x): 0.7755, D(G(z)): 0.3068
Epoch: [18/20], Batch Num: [598/600]
Discriminator Loss: 0.8891, Generator Loss: 1.5669
D(x): 0.6769, D(G(z)): 0.2610
Epoch: [18/20], Batch Num: [599/600]
Discriminator Loss: 0.8574, Generator Loss: 1.5426
D(x): 0.7056, D(G(z)): 0.2566
Epoch: 19, Batch Num: [0/600]

Epoch: [19/20], Batch Num: [0/600]
Discriminator Loss: 0.8065, Generator Loss: 1.4889
D(x): 0.7634, D(G(z)): 0.2949
Epoch: [19/20], Batch Num: [1/600]
Discriminator Loss: 0.8236, Generator Loss: 1.5840
D(x): 0.7798, D(G(z)): 0.3136
Epoch: [19/20], Batch Num: [2/600]
Discriminator Loss: 0.8944, Generator Loss: 1.6808
D(x): 0.7277, D(G(z)): 0.2880
Epoch: [19/20], Batch Num: [3/600]
Discriminator Loss: 0.7793, Generator Loss: 1.7087
D(x): 0.7372, D(G(z)): 0.2722
Epoch: [19/20], Batch Num: [4/600]
Discriminator Loss: 1.0048, Generator Loss: 1.5033
D(x): 0.6214, D(G(z)): 0.2449
Epoch: [19/20], Batch Num: [5/600]
Discriminator Loss: 0.8765, Generator Loss: 1.6611
D(x): 0.7104, D(G(z)): 0.2888
Epoch: [19/20], Batch Num: [6/600]
Discriminator Loss: 0.7085, Generator Loss: 1.6443
D(x): 0.7849, D(G(z)): 0.2575
Epoch: [19/20], Batch Num: [7/600]
Discriminator Loss: 1.0260, Generator Loss: 1.7832
D(x): 0.7366, D(G(z)): 0.3155
Epoch: [19/20], Batch Num: [8/600]
Discriminator Loss: 0.8103, Generator Loss: 1.8992
D(x): 0.7175, D(G(z)): 0.2226
Epoch: [19/20], Batch Num: [9/600]
Discriminator Loss: 0.6147, Generator Loss: 1.6959
D(x): 0.7872, D(G(z)): 0.2244
Epoch: [19/20], Batch Num: [10/600]
Discriminator Loss: 0.8385, Generator Loss: 1.9109
D(x): 0.7242, D(G(z)): 0.2750
Epoch: [19/20], Batch Num: [11/600]
Discriminator Loss: 0.8924, Generator Loss: 1.7062
D(x): 0.6860, D(G(z)): 0.2509
Epoch: [19/20], Batch Num: [12/600]
Discriminator Loss: 0.7144, Generator Loss: 1.6796
D(x): 0.7619, D(G(z)): 0.2407
Epoch: [19/20], Batch Num: [13/600]
Discriminator Loss: 0.7507, Generator Loss: 1.5446
D(x): 0.7558, D(G(z)): 0.2728
Epoch: [19/20], Batch Num: [14/600]
Discriminator Loss: 0.7699, Generator Loss: 1.5849
D(x): 0.7566, D(G(z)): 0.2665
Epoch: [19/20], Batch Num: [15/600]
Discriminator Loss: 0.8277, Generator Loss: 1.7511
D(x): 0.7489, D(G(z)): 0.2926
Epoch: [19/20], Batch Num: [16/600]
Discriminator Loss: 0.6833, Generator Loss: 1.8718
D(x): 0.7700, D(G(z)): 0.2548
Epoch: [19/20], Batch Num: [17/600]
Discriminator Loss: 0.7863, Generator Loss: 1.7719
D(x): 0.7548, D(G(z)): 0.2405
Epoch: [19/20], Batch Num: [18/600]
Discriminator Loss: 0.9406, Generator Loss: 1.7554
D(x): 0.7007, D(G(z)): 0.2789
Epoch: [19/20], Batch Num: [19/600]
Discriminator Loss: 0.9947, Generator Loss: 1.9316
D(x): 0.7263, D(G(z)): 0.3404
Epoch: [19/20], Batch Num: [20/600]
Discriminator Loss: 0.7103, Generator Loss: 1.9422
D(x): 0.7660, D(G(z)): 0.2494
Epoch: [19/20], Batch Num: [21/600]
Discriminator Loss: 0.8481, Generator Loss: 2.1508
D(x): 0.7243, D(G(z)): 0.2557
Epoch: [19/20], Batch Num: [22/600]
Discriminator Loss: 0.7381, Generator Loss: 1.7825
D(x): 0.7341, D(G(z)): 0.2235
Epoch: [19/20], Batch Num: [23/600]
Discriminator Loss: 0.7693, Generator Loss: 1.8799
D(x): 0.7014, D(G(z)): 0.2152
Epoch: [19/20], Batch Num: [24/600]
Discriminator Loss: 0.9008, Generator Loss: 1.6473
D(x): 0.7070, D(G(z)): 0.2535
Epoch: [19/20], Batch Num: [25/600]
Discriminator Loss: 0.8922, Generator Loss: 1.2551
D(x): 0.7633, D(G(z)): 0.3399
Epoch: [19/20], Batch Num: [26/600]
Discriminator Loss: 0.9397, Generator Loss: 1.4166
D(x): 0.7553, D(G(z)): 0.3239
Epoch: [19/20], Batch Num: [27/600]
Discriminator Loss: 0.7859, Generator Loss: 1.4850
D(x): 0.7784, D(G(z)): 0.2855
Epoch: [19/20], Batch Num: [28/600]
Discriminator Loss: 1.0356, Generator Loss: 1.7624
D(x): 0.7153, D(G(z)): 0.3419
Epoch: [19/20], Batch Num: [29/600]
Discriminator Loss: 0.9983, Generator Loss: 1.9158
D(x): 0.6329, D(G(z)): 0.2458
Epoch: [19/20], Batch Num: [30/600]
Discriminator Loss: 0.8656, Generator Loss: 1.7409
D(x): 0.6596, D(G(z)): 0.2387
Epoch: [19/20], Batch Num: [31/600]
Discriminator Loss: 0.9759, Generator Loss: 1.3177
D(x): 0.7008, D(G(z)): 0.3084
Epoch: [19/20], Batch Num: [32/600]
Discriminator Loss: 1.0075, Generator Loss: 1.4641
D(x): 0.7231, D(G(z)): 0.3456
Epoch: [19/20], Batch Num: [33/600]
Discriminator Loss: 1.0547, Generator Loss: 1.3753
D(x): 0.7050, D(G(z)): 0.3597
Epoch: [19/20], Batch Num: [34/600]
Discriminator Loss: 1.0376, Generator Loss: 1.3083
D(x): 0.7116, D(G(z)): 0.3897
Epoch: [19/20], Batch Num: [35/600]
Discriminator Loss: 1.0056, Generator Loss: 1.6128
D(x): 0.7266, D(G(z)): 0.3540
Epoch: [19/20], Batch Num: [36/600]
Discriminator Loss: 1.0469, Generator Loss: 1.6036
D(x): 0.6396, D(G(z)): 0.2894
Epoch: [19/20], Batch Num: [37/600]
Discriminator Loss: 1.2197, Generator Loss: 1.6140
D(x): 0.5868, D(G(z)): 0.3233
Epoch: [19/20], Batch Num: [38/600]
Discriminator Loss: 0.9846, Generator Loss: 1.4780
D(x): 0.6434, D(G(z)): 0.2782
Epoch: [19/20], Batch Num: [39/600]
Discriminator Loss: 0.9664, Generator Loss: 1.2445
D(x): 0.6766, D(G(z)): 0.3200
Epoch: [19/20], Batch Num: [40/600]
Discriminator Loss: 0.9144, Generator Loss: 1.1931
D(x): 0.7188, D(G(z)): 0.3285
Epoch: [19/20], Batch Num: [41/600]
Discriminator Loss: 1.0206, Generator Loss: 1.1803
D(x): 0.6868, D(G(z)): 0.3564
Epoch: [19/20], Batch Num: [42/600]
Discriminator Loss: 1.2267, Generator Loss: 1.4603
D(x): 0.7114, D(G(z)): 0.4529
Epoch: [19/20], Batch Num: [43/600]
Discriminator Loss: 1.1307, Generator Loss: 1.4770
D(x): 0.6379, D(G(z)): 0.3419
Epoch: [19/20], Batch Num: [44/600]
Discriminator Loss: 0.9525, Generator Loss: 1.5439
D(x): 0.6648, D(G(z)): 0.2850
Epoch: [19/20], Batch Num: [45/600]
Discriminator Loss: 0.9674, Generator Loss: 1.5344
D(x): 0.6282, D(G(z)): 0.2711
Epoch: [19/20], Batch Num: [46/600]
Discriminator Loss: 0.7841, Generator Loss: 1.5153
D(x): 0.6832, D(G(z)): 0.2304
Epoch: [19/20], Batch Num: [47/600]
Discriminator Loss: 0.9416, Generator Loss: 1.3940
D(x): 0.6668, D(G(z)): 0.2951
Epoch: [19/20], Batch Num: [48/600]
Discriminator Loss: 0.8781, Generator Loss: 1.3485
D(x): 0.6939, D(G(z)): 0.2960
Epoch: [19/20], Batch Num: [49/600]
Discriminator Loss: 0.9282, Generator Loss: 1.3402
D(x): 0.7509, D(G(z)): 0.3644
Epoch: [19/20], Batch Num: [50/600]
Discriminator Loss: 0.8604, Generator Loss: 1.3946
D(x): 0.7607, D(G(z)): 0.3456
Epoch: [19/20], Batch Num: [51/600]
Discriminator Loss: 0.9105, Generator Loss: 1.5981
D(x): 0.7129, D(G(z)): 0.3248
Epoch: [19/20], Batch Num: [52/600]
Discriminator Loss: 0.8427, Generator Loss: 1.6380
D(x): 0.7016, D(G(z)): 0.2534
Epoch: [19/20], Batch Num: [53/600]
Discriminator Loss: 0.8366, Generator Loss: 1.9318
D(x): 0.6331, D(G(z)): 0.2024
Epoch: [19/20], Batch Num: [54/600]
Discriminator Loss: 0.7582, Generator Loss: 1.5980
D(x): 0.6992, D(G(z)): 0.2268
Epoch: [19/20], Batch Num: [55/600]
Discriminator Loss: 0.9004, Generator Loss: 1.6298
D(x): 0.6889, D(G(z)): 0.2711
Epoch: [19/20], Batch Num: [56/600]
Discriminator Loss: 0.8094, Generator Loss: 1.6130
D(x): 0.7616, D(G(z)): 0.2824
Epoch: [19/20], Batch Num: [57/600]
Discriminator Loss: 0.6626, Generator Loss: 1.7088
D(x): 0.8269, D(G(z)): 0.2921
Epoch: [19/20], Batch Num: [58/600]
Discriminator Loss: 0.7664, Generator Loss: 2.0554
D(x): 0.7642, D(G(z)): 0.2769
Epoch: [19/20], Batch Num: [59/600]
Discriminator Loss: 0.6324, Generator Loss: 1.9850
D(x): 0.7874, D(G(z)): 0.2297
Epoch: [19/20], Batch Num: [60/600]
Discriminator Loss: 0.6837, Generator Loss: 2.0686
D(x): 0.7289, D(G(z)): 0.2124
Epoch: [19/20], Batch Num: [61/600]
Discriminator Loss: 0.5716, Generator Loss: 2.2225
D(x): 0.7602, D(G(z)): 0.1737
Epoch: [19/20], Batch Num: [62/600]
Discriminator Loss: 0.7291, Generator Loss: 1.9038
D(x): 0.7431, D(G(z)): 0.2231
Epoch: [19/20], Batch Num: [63/600]
Discriminator Loss: 0.6996, Generator Loss: 1.7017
D(x): 0.7602, D(G(z)): 0.2486
Epoch: [19/20], Batch Num: [64/600]
Discriminator Loss: 0.8144, Generator Loss: 1.9092
D(x): 0.7510, D(G(z)): 0.2752
Epoch: [19/20], Batch Num: [65/600]
Discriminator Loss: 0.8279, Generator Loss: 1.7650
D(x): 0.7782, D(G(z)): 0.2881
Epoch: [19/20], Batch Num: [66/600]
Discriminator Loss: 0.7978, Generator Loss: 1.8810
D(x): 0.7395, D(G(z)): 0.2435
Epoch: [19/20], Batch Num: [67/600]
Discriminator Loss: 1.0002, Generator Loss: 2.0395
D(x): 0.6829, D(G(z)): 0.2732
Epoch: [19/20], Batch Num: [68/600]
Discriminator Loss: 0.9007, Generator Loss: 2.0050
D(x): 0.7239, D(G(z)): 0.2741
Epoch: [19/20], Batch Num: [69/600]
Discriminator Loss: 0.8665, Generator Loss: 1.9148
D(x): 0.7193, D(G(z)): 0.2709
Epoch: [19/20], Batch Num: [70/600]
Discriminator Loss: 0.9622, Generator Loss: 1.4599
D(x): 0.6600, D(G(z)): 0.2495
Epoch: [19/20], Batch Num: [71/600]
Discriminator Loss: 0.8731, Generator Loss: 1.3821
D(x): 0.7395, D(G(z)): 0.3314
Epoch: [19/20], Batch Num: [72/600]
Discriminator Loss: 1.0592, Generator Loss: 1.3875
D(x): 0.7018, D(G(z)): 0.3432
Epoch: [19/20], Batch Num: [73/600]
Discriminator Loss: 1.0448, Generator Loss: 1.8816
D(x): 0.7524, D(G(z)): 0.3933
Epoch: [19/20], Batch Num: [74/600]
Discriminator Loss: 1.2349, Generator Loss: 1.7531
D(x): 0.5737, D(G(z)): 0.2783
Epoch: [19/20], Batch Num: [75/600]
Discriminator Loss: 1.3396, Generator Loss: 1.5970
D(x): 0.5674, D(G(z)): 0.3425
Epoch: [19/20], Batch Num: [76/600]
Discriminator Loss: 1.0722, Generator Loss: 1.4616
D(x): 0.6424, D(G(z)): 0.2761
Epoch: [19/20], Batch Num: [77/600]
Discriminator Loss: 1.2007, Generator Loss: 1.1620
D(x): 0.6487, D(G(z)): 0.3670
Epoch: [19/20], Batch Num: [78/600]
Discriminator Loss: 1.3270, Generator Loss: 1.1871
D(x): 0.6608, D(G(z)): 0.4047
Epoch: [19/20], Batch Num: [79/600]
Discriminator Loss: 1.3872, Generator Loss: 1.2187
D(x): 0.6695, D(G(z)): 0.4532
Epoch: [19/20], Batch Num: [80/600]
Discriminator Loss: 1.2105, Generator Loss: 1.4933
D(x): 0.6586, D(G(z)): 0.3941
Epoch: [19/20], Batch Num: [81/600]
Discriminator Loss: 1.3375, Generator Loss: 1.6639
D(x): 0.5871, D(G(z)): 0.3711
Epoch: [19/20], Batch Num: [82/600]
Discriminator Loss: 1.1563, Generator Loss: 1.5470
D(x): 0.5982, D(G(z)): 0.3100
Epoch: [19/20], Batch Num: [83/600]
Discriminator Loss: 1.0917, Generator Loss: 1.3955
D(x): 0.6081, D(G(z)): 0.2876
Epoch: [19/20], Batch Num: [84/600]
Discriminator Loss: 1.0660, Generator Loss: 1.2237
D(x): 0.5937, D(G(z)): 0.2778
Epoch: [19/20], Batch Num: [85/600]
Discriminator Loss: 1.2161, Generator Loss: 1.1543
D(x): 0.6812, D(G(z)): 0.4315
Epoch: [19/20], Batch Num: [86/600]
Discriminator Loss: 0.9675, Generator Loss: 1.2425
D(x): 0.7163, D(G(z)): 0.3730
Epoch: [19/20], Batch Num: [87/600]
Discriminator Loss: 1.0464, Generator Loss: 1.2352
D(x): 0.6772, D(G(z)): 0.3817
Epoch: [19/20], Batch Num: [88/600]
Discriminator Loss: 0.8049, Generator Loss: 1.3117
D(x): 0.7351, D(G(z)): 0.3198
Epoch: [19/20], Batch Num: [89/600]
Discriminator Loss: 0.8581, Generator Loss: 1.5317
D(x): 0.7018, D(G(z)): 0.2993
Epoch: [19/20], Batch Num: [90/600]
Discriminator Loss: 0.8462, Generator Loss: 1.7719
D(x): 0.7069, D(G(z)): 0.2865
Epoch: [19/20], Batch Num: [91/600]
Discriminator Loss: 0.8275, Generator Loss: 1.6311
D(x): 0.6584, D(G(z)): 0.2399
Epoch: [19/20], Batch Num: [92/600]
Discriminator Loss: 0.8606, Generator Loss: 1.5739
D(x): 0.6546, D(G(z)): 0.2377
Epoch: [19/20], Batch Num: [93/600]
Discriminator Loss: 0.8616, Generator Loss: 1.3670
D(x): 0.6793, D(G(z)): 0.2796
Epoch: [19/20], Batch Num: [94/600]
Discriminator Loss: 0.7305, Generator Loss: 1.4723
D(x): 0.7888, D(G(z)): 0.3088
Epoch: [19/20], Batch Num: [95/600]
Discriminator Loss: 0.8984, Generator Loss: 1.5595
D(x): 0.7783, D(G(z)): 0.3725
Epoch: [19/20], Batch Num: [96/600]
Discriminator Loss: 0.7710, Generator Loss: 1.7966
D(x): 0.7649, D(G(z)): 0.2812
Epoch: [19/20], Batch Num: [97/600]
Discriminator Loss: 0.7213, Generator Loss: 1.8804
D(x): 0.7336, D(G(z)): 0.2346
Epoch: [19/20], Batch Num: [98/600]
Discriminator Loss: 0.8629, Generator Loss: 2.0306
D(x): 0.6527, D(G(z)): 0.2144
Epoch: [19/20], Batch Num: [99/600]
Discriminator Loss: 0.6809, Generator Loss: 1.8251
D(x): 0.6934, D(G(z)): 0.1604
Epoch: 19, Batch Num: [100/600]

Epoch: [19/20], Batch Num: [100/600]
Discriminator Loss: 0.7053, Generator Loss: 1.7620
D(x): 0.7292, D(G(z)): 0.2255
Epoch: [19/20], Batch Num: [101/600]
Discriminator Loss: 0.6703, Generator Loss: 1.6210
D(x): 0.7671, D(G(z)): 0.2450
Epoch: [19/20], Batch Num: [102/600]
Discriminator Loss: 0.6969, Generator Loss: 1.6197
D(x): 0.8145, D(G(z)): 0.2926
Epoch: [19/20], Batch Num: [103/600]
Discriminator Loss: 0.6175, Generator Loss: 1.6030
D(x): 0.8060, D(G(z)): 0.2483
Epoch: [19/20], Batch Num: [104/600]
Discriminator Loss: 0.6167, Generator Loss: 1.8721
D(x): 0.8397, D(G(z)): 0.2768
Epoch: [19/20], Batch Num: [105/600]
Discriminator Loss: 0.5497, Generator Loss: 2.1355
D(x): 0.8033, D(G(z)): 0.2155
Epoch: [19/20], Batch Num: [106/600]
Discriminator Loss: 0.6319, Generator Loss: 2.2003
D(x): 0.7946, D(G(z)): 0.2281
Epoch: [19/20], Batch Num: [107/600]
Discriminator Loss: 0.7340, Generator Loss: 2.4192
D(x): 0.7296, D(G(z)): 0.2026
Epoch: [19/20], Batch Num: [108/600]
Discriminator Loss: 0.6103, Generator Loss: 2.1323
D(x): 0.7585, D(G(z)): 0.1772
Epoch: [19/20], Batch Num: [109/600]
Discriminator Loss: 0.6243, Generator Loss: 2.1507
D(x): 0.7397, D(G(z)): 0.1784
Epoch: [19/20], Batch Num: [110/600]
Discriminator Loss: 0.5439, Generator Loss: 1.9665
D(x): 0.8012, D(G(z)): 0.1985
Epoch: [19/20], Batch Num: [111/600]
Discriminator Loss: 0.6291, Generator Loss: 1.7002
D(x): 0.8302, D(G(z)): 0.2591
Epoch: [19/20], Batch Num: [112/600]
Discriminator Loss: 0.5591, Generator Loss: 1.9355
D(x): 0.8481, D(G(z)): 0.2546
Epoch: [19/20], Batch Num: [113/600]
Discriminator Loss: 0.7223, Generator Loss: 1.8268
D(x): 0.7953, D(G(z)): 0.3066
Epoch: [19/20], Batch Num: [114/600]
Discriminator Loss: 0.7161, Generator Loss: 2.0952
D(x): 0.7614, D(G(z)): 0.2250
Epoch: [19/20], Batch Num: [115/600]
Discriminator Loss: 0.8746, Generator Loss: 2.1795
D(x): 0.7160, D(G(z)): 0.2569
Epoch: [19/20], Batch Num: [116/600]
Discriminator Loss: 0.9116, Generator Loss: 1.8085
D(x): 0.6805, D(G(z)): 0.2244
Epoch: [19/20], Batch Num: [117/600]
Discriminator Loss: 0.9549, Generator Loss: 1.4758
D(x): 0.6901, D(G(z)): 0.2746
Epoch: [19/20], Batch Num: [118/600]
Discriminator Loss: 0.9731, Generator Loss: 1.3865
D(x): 0.7332, D(G(z)): 0.3275
Epoch: [19/20], Batch Num: [119/600]
Discriminator Loss: 1.1953, Generator Loss: 1.4456
D(x): 0.6565, D(G(z)): 0.3666
Epoch: [19/20], Batch Num: [120/600]
Discriminator Loss: 1.1355, Generator Loss: 1.7622
D(x): 0.7345, D(G(z)): 0.3987
Epoch: [19/20], Batch Num: [121/600]
Discriminator Loss: 1.3167, Generator Loss: 1.5885
D(x): 0.6173, D(G(z)): 0.3953
Epoch: [19/20], Batch Num: [122/600]
Discriminator Loss: 1.1077, Generator Loss: 1.7562
D(x): 0.6292, D(G(z)): 0.2887
Epoch: [19/20], Batch Num: [123/600]
Discriminator Loss: 1.5374, Generator Loss: 1.3534
D(x): 0.5080, D(G(z)): 0.3329
Epoch: [19/20], Batch Num: [124/600]
Discriminator Loss: 1.2670, Generator Loss: 1.1318
D(x): 0.6410, D(G(z)): 0.3700
Epoch: [19/20], Batch Num: [125/600]
Discriminator Loss: 1.4267, Generator Loss: 1.1237
D(x): 0.6430, D(G(z)): 0.4488
Epoch: [19/20], Batch Num: [126/600]
Discriminator Loss: 1.3584, Generator Loss: 1.1378
D(x): 0.6711, D(G(z)): 0.4675
Epoch: [19/20], Batch Num: [127/600]
Discriminator Loss: 1.3251, Generator Loss: 1.3506
D(x): 0.6414, D(G(z)): 0.4464
Epoch: [19/20], Batch Num: [128/600]
Discriminator Loss: 1.2212, Generator Loss: 1.6069
D(x): 0.6526, D(G(z)): 0.3994
Epoch: [19/20], Batch Num: [129/600]
Discriminator Loss: 1.3970, Generator Loss: 1.7207
D(x): 0.5053, D(G(z)): 0.3285
Epoch: [19/20], Batch Num: [130/600]
Discriminator Loss: 1.3037, Generator Loss: 1.4927
D(x): 0.5068, D(G(z)): 0.2808
Epoch: [19/20], Batch Num: [131/600]
Discriminator Loss: 1.0616, Generator Loss: 1.1732
D(x): 0.6388, D(G(z)): 0.3161
Epoch: [19/20], Batch Num: [132/600]
Discriminator Loss: 1.1464, Generator Loss: 1.0926
D(x): 0.6146, D(G(z)): 0.3634
Epoch: [19/20], Batch Num: [133/600]
Discriminator Loss: 1.2074, Generator Loss: 1.0473
D(x): 0.6502, D(G(z)): 0.4075
Epoch: [19/20], Batch Num: [134/600]
Discriminator Loss: 1.0757, Generator Loss: 1.1641
D(x): 0.6919, D(G(z)): 0.3928
Epoch: [19/20], Batch Num: [135/600]
Discriminator Loss: 0.8822, Generator Loss: 1.2301
D(x): 0.7524, D(G(z)): 0.3659
Epoch: [19/20], Batch Num: [136/600]
Discriminator Loss: 0.9674, Generator Loss: 1.3495
D(x): 0.7011, D(G(z)): 0.3692
Epoch: [19/20], Batch Num: [137/600]
Discriminator Loss: 0.7766, Generator Loss: 1.4787
D(x): 0.7376, D(G(z)): 0.2995
Epoch: [19/20], Batch Num: [138/600]
Discriminator Loss: 0.8846, Generator Loss: 1.4911
D(x): 0.7004, D(G(z)): 0.3206
Epoch: [19/20], Batch Num: [139/600]
Discriminator Loss: 0.9143, Generator Loss: 1.7573
D(x): 0.6108, D(G(z)): 0.2494
Epoch: [19/20], Batch Num: [140/600]
Discriminator Loss: 0.7177, Generator Loss: 1.5953
D(x): 0.6902, D(G(z)): 0.2117
Epoch: [19/20], Batch Num: [141/600]
Discriminator Loss: 0.7528, Generator Loss: 1.5514
D(x): 0.7256, D(G(z)): 0.2803
Epoch: [19/20], Batch Num: [142/600]
Discriminator Loss: 0.7025, Generator Loss: 1.5736
D(x): 0.7415, D(G(z)): 0.2583
Epoch: [19/20], Batch Num: [143/600]
Discriminator Loss: 0.6675, Generator Loss: 1.5253
D(x): 0.7502, D(G(z)): 0.2535
Epoch: [19/20], Batch Num: [144/600]
Discriminator Loss: 0.6285, Generator Loss: 1.5639
D(x): 0.8272, D(G(z)): 0.2857
Epoch: [19/20], Batch Num: [145/600]
Discriminator Loss: 0.7165, Generator Loss: 1.6663
D(x): 0.7663, D(G(z)): 0.2750
Epoch: [19/20], Batch Num: [146/600]
Discriminator Loss: 0.6213, Generator Loss: 1.9862
D(x): 0.8169, D(G(z)): 0.2739
Epoch: [19/20], Batch Num: [147/600]
Discriminator Loss: 0.5787, Generator Loss: 1.8699
D(x): 0.7910, D(G(z)): 0.2249
Epoch: [19/20], Batch Num: [148/600]
Discriminator Loss: 0.6550, Generator Loss: 2.0723
D(x): 0.7683, D(G(z)): 0.2443
Epoch: [19/20], Batch Num: [149/600]
Discriminator Loss: 0.6773, Generator Loss: 2.1316
D(x): 0.7519, D(G(z)): 0.2316
Epoch: [19/20], Batch Num: [150/600]
Discriminator Loss: 0.6481, Generator Loss: 2.0754
D(x): 0.7378, D(G(z)): 0.1916
Epoch: [19/20], Batch Num: [151/600]
Discriminator Loss: 0.6484, Generator Loss: 1.9411
D(x): 0.7346, D(G(z)): 0.1904
Epoch: [19/20], Batch Num: [152/600]
Discriminator Loss: 0.5495, Generator Loss: 1.9704
D(x): 0.8159, D(G(z)): 0.2121
Epoch: [19/20], Batch Num: [153/600]
Discriminator Loss: 0.6587, Generator Loss: 1.8447
D(x): 0.8207, D(G(z)): 0.2727
Epoch: [19/20], Batch Num: [154/600]
Discriminator Loss: 0.5437, Generator Loss: 1.9331
D(x): 0.8461, D(G(z)): 0.2414
Epoch: [19/20], Batch Num: [155/600]
Discriminator Loss: 0.5959, Generator Loss: 2.1290
D(x): 0.7925, D(G(z)): 0.2200
Epoch: [19/20], Batch Num: [156/600]
Discriminator Loss: 0.6192, Generator Loss: 2.3818
D(x): 0.7768, D(G(z)): 0.2085
Epoch: [19/20], Batch Num: [157/600]
Discriminator Loss: 0.5939, Generator Loss: 2.2606
D(x): 0.7978, D(G(z)): 0.2052
Epoch: [19/20], Batch Num: [158/600]
Discriminator Loss: 0.6966, Generator Loss: 2.2866
D(x): 0.7269, D(G(z)): 0.1966
Epoch: [19/20], Batch Num: [159/600]
Discriminator Loss: 0.6777, Generator Loss: 1.9915
D(x): 0.7441, D(G(z)): 0.1799
Epoch: [19/20], Batch Num: [160/600]
Discriminator Loss: 0.8231, Generator Loss: 2.0169
D(x): 0.7832, D(G(z)): 0.3022
Epoch: [19/20], Batch Num: [161/600]
Discriminator Loss: 0.7561, Generator Loss: 1.9345
D(x): 0.7998, D(G(z)): 0.2695
Epoch: [19/20], Batch Num: [162/600]
Discriminator Loss: 0.8036, Generator Loss: 1.9335
D(x): 0.7625, D(G(z)): 0.2473
Epoch: [19/20], Batch Num: [163/600]
Discriminator Loss: 0.9103, Generator Loss: 1.9233
D(x): 0.7045, D(G(z)): 0.2707
Epoch: [19/20], Batch Num: [164/600]
Discriminator Loss: 0.8898, Generator Loss: 1.8999
D(x): 0.7335, D(G(z)): 0.2914
Epoch: [19/20], Batch Num: [165/600]
Discriminator Loss: 0.8267, Generator Loss: 1.8609
D(x): 0.7075, D(G(z)): 0.2562
Epoch: [19/20], Batch Num: [166/600]
Discriminator Loss: 1.2356, Generator Loss: 1.6197
D(x): 0.6717, D(G(z)): 0.3227
Epoch: [19/20], Batch Num: [167/600]
Discriminator Loss: 1.2459, Generator Loss: 1.5400
D(x): 0.7117, D(G(z)): 0.3766
Epoch: [19/20], Batch Num: [168/600]
Discriminator Loss: 1.1514, Generator Loss: 1.6152
D(x): 0.6868, D(G(z)): 0.3518
Epoch: [19/20], Batch Num: [169/600]
Discriminator Loss: 1.1809, Generator Loss: 1.8555
D(x): 0.6348, D(G(z)): 0.2922
Epoch: [19/20], Batch Num: [170/600]
Discriminator Loss: 1.0874, Generator Loss: 1.5936
D(x): 0.6516, D(G(z)): 0.2640
Epoch: [19/20], Batch Num: [171/600]
Discriminator Loss: 1.4142, Generator Loss: 1.4673
D(x): 0.5796, D(G(z)): 0.3438
Epoch: [19/20], Batch Num: [172/600]
Discriminator Loss: 1.4498, Generator Loss: 1.4484
D(x): 0.6213, D(G(z)): 0.4035
Epoch: [19/20], Batch Num: [173/600]
Discriminator Loss: 1.2155, Generator Loss: 1.6547
D(x): 0.7162, D(G(z)): 0.4381
Epoch: [19/20], Batch Num: [174/600]
Discriminator Loss: 1.3721, Generator Loss: 1.6907
D(x): 0.5764, D(G(z)): 0.3486
Epoch: [19/20], Batch Num: [175/600]
Discriminator Loss: 1.3235, Generator Loss: 1.5954
D(x): 0.5553, D(G(z)): 0.2891
Epoch: [19/20], Batch Num: [176/600]
Discriminator Loss: 1.0857, Generator Loss: 1.4266
D(x): 0.5772, D(G(z)): 0.2483
Epoch: [19/20], Batch Num: [177/600]
Discriminator Loss: 1.2060, Generator Loss: 1.3625
D(x): 0.5769, D(G(z)): 0.3136
Epoch: [19/20], Batch Num: [178/600]
Discriminator Loss: 1.3482, Generator Loss: 1.0332
D(x): 0.5889, D(G(z)): 0.3888
Epoch: [19/20], Batch Num: [179/600]
Discriminator Loss: 1.2985, Generator Loss: 1.0412
D(x): 0.6925, D(G(z)): 0.4655
Epoch: [19/20], Batch Num: [180/600]
Discriminator Loss: 1.3168, Generator Loss: 1.1626
D(x): 0.6501, D(G(z)): 0.4482
Epoch: [19/20], Batch Num: [181/600]
Discriminator Loss: 1.1549, Generator Loss: 1.3964
D(x): 0.6736, D(G(z)): 0.4033
Epoch: [19/20], Batch Num: [182/600]
Discriminator Loss: 0.9482, Generator Loss: 1.4515
D(x): 0.6616, D(G(z)): 0.3036
Epoch: [19/20], Batch Num: [183/600]
Discriminator Loss: 1.0901, Generator Loss: 1.6642
D(x): 0.5987, D(G(z)): 0.2853
Epoch: [19/20], Batch Num: [184/600]
Discriminator Loss: 1.1826, Generator Loss: 1.4314
D(x): 0.5554, D(G(z)): 0.2748
Epoch: [19/20], Batch Num: [185/600]
Discriminator Loss: 1.0294, Generator Loss: 1.4563
D(x): 0.5938, D(G(z)): 0.2624
Epoch: [19/20], Batch Num: [186/600]
Discriminator Loss: 1.0411, Generator Loss: 1.2679
D(x): 0.6031, D(G(z)): 0.2781
Epoch: [19/20], Batch Num: [187/600]
Discriminator Loss: 0.8812, Generator Loss: 1.2294
D(x): 0.7081, D(G(z)): 0.3097
Epoch: [19/20], Batch Num: [188/600]
Discriminator Loss: 0.9749, Generator Loss: 1.2769
D(x): 0.7019, D(G(z)): 0.3644
Epoch: [19/20], Batch Num: [189/600]
Discriminator Loss: 0.8909, Generator Loss: 1.2007
D(x): 0.7217, D(G(z)): 0.3483
Epoch: [19/20], Batch Num: [190/600]
Discriminator Loss: 0.9184, Generator Loss: 1.2286
D(x): 0.7426, D(G(z)): 0.3599
Epoch: [19/20], Batch Num: [191/600]
Discriminator Loss: 0.7933, Generator Loss: 1.3149
D(x): 0.7175, D(G(z)): 0.2961
Epoch: [19/20], Batch Num: [192/600]
Discriminator Loss: 0.8036, Generator Loss: 1.3562
D(x): 0.7156, D(G(z)): 0.2943
Epoch: [19/20], Batch Num: [193/600]
Discriminator Loss: 0.9524, Generator Loss: 1.5617
D(x): 0.6671, D(G(z)): 0.3192
Epoch: [19/20], Batch Num: [194/600]
Discriminator Loss: 0.9362, Generator Loss: 1.7083
D(x): 0.6643, D(G(z)): 0.2854
Epoch: [19/20], Batch Num: [195/600]
Discriminator Loss: 0.8356, Generator Loss: 1.7340
D(x): 0.6896, D(G(z)): 0.2623
Epoch: [19/20], Batch Num: [196/600]
Discriminator Loss: 0.8032, Generator Loss: 1.6039
D(x): 0.7060, D(G(z)): 0.2584
Epoch: [19/20], Batch Num: [197/600]
Discriminator Loss: 0.7637, Generator Loss: 1.4490
D(x): 0.6900, D(G(z)): 0.2336
Epoch: [19/20], Batch Num: [198/600]
Discriminator Loss: 0.6751, Generator Loss: 1.5234
D(x): 0.7995, D(G(z)): 0.2922
Epoch: [19/20], Batch Num: [199/600]
Discriminator Loss: 0.6978, Generator Loss: 1.6748
D(x): 0.7588, D(G(z)): 0.2539
Epoch: 19, Batch Num: [200/600]

Epoch: [19/20], Batch Num: [200/600]
Discriminator Loss: 0.8048, Generator Loss: 1.6375
D(x): 0.7213, D(G(z)): 0.2872
Epoch: [19/20], Batch Num: [201/600]
Discriminator Loss: 0.7735, Generator Loss: 1.6536
D(x): 0.7479, D(G(z)): 0.2956
Epoch: [19/20], Batch Num: [202/600]
Discriminator Loss: 0.7042, Generator Loss: 1.7043
D(x): 0.7368, D(G(z)): 0.2239
Epoch: [19/20], Batch Num: [203/600]
Discriminator Loss: 0.7054, Generator Loss: 1.6667
D(x): 0.7291, D(G(z)): 0.2282
Epoch: [19/20], Batch Num: [204/600]
Discriminator Loss: 0.6599, Generator Loss: 1.7062
D(x): 0.7630, D(G(z)): 0.2430
Epoch: [19/20], Batch Num: [205/600]
Discriminator Loss: 0.6066, Generator Loss: 1.6894
D(x): 0.7858, D(G(z)): 0.2412
Epoch: [19/20], Batch Num: [206/600]
Discriminator Loss: 0.6428, Generator Loss: 1.7828
D(x): 0.8005, D(G(z)): 0.2694
Epoch: [19/20], Batch Num: [207/600]
Discriminator Loss: 0.7856, Generator Loss: 1.8369
D(x): 0.7446, D(G(z)): 0.2724
Epoch: [19/20], Batch Num: [208/600]
Discriminator Loss: 0.6155, Generator Loss: 1.9584
D(x): 0.7955, D(G(z)): 0.2457
Epoch: [19/20], Batch Num: [209/600]
Discriminator Loss: 0.6584, Generator Loss: 1.9791
D(x): 0.7772, D(G(z)): 0.2434
Epoch: [19/20], Batch Num: [210/600]
Discriminator Loss: 0.7519, Generator Loss: 1.8356
D(x): 0.7316, D(G(z)): 0.2496
Epoch: [19/20], Batch Num: [211/600]
Discriminator Loss: 0.6499, Generator Loss: 1.7829
D(x): 0.7881, D(G(z)): 0.2423
Epoch: [19/20], Batch Num: [212/600]
Discriminator Loss: 0.7755, Generator Loss: 1.6819
D(x): 0.7509, D(G(z)): 0.2703
Epoch: [19/20], Batch Num: [213/600]
Discriminator Loss: 0.7127, Generator Loss: 1.8099
D(x): 0.7548, D(G(z)): 0.2644
Epoch: [19/20], Batch Num: [214/600]
Discriminator Loss: 0.8055, Generator Loss: 1.9379
D(x): 0.7570, D(G(z)): 0.2953
Epoch: [19/20], Batch Num: [215/600]
Discriminator Loss: 0.8068, Generator Loss: 1.9731
D(x): 0.7513, D(G(z)): 0.2852
Epoch: [19/20], Batch Num: [216/600]
Discriminator Loss: 0.7027, Generator Loss: 1.8751
D(x): 0.7227, D(G(z)): 0.1984
Epoch: [19/20], Batch Num: [217/600]
Discriminator Loss: 0.7797, Generator Loss: 1.5773
D(x): 0.7411, D(G(z)): 0.2772
Epoch: [19/20], Batch Num: [218/600]
Discriminator Loss: 0.9851, Generator Loss: 1.7302
D(x): 0.7405, D(G(z)): 0.3175
Epoch: [19/20], Batch Num: [219/600]
Discriminator Loss: 1.0648, Generator Loss: 1.7146
D(x): 0.6885, D(G(z)): 0.3256
Epoch: [19/20], Batch Num: [220/600]
Discriminator Loss: 0.9169, Generator Loss: 1.4877
D(x): 0.7129, D(G(z)): 0.2959
Epoch: [19/20], Batch Num: [221/600]
Discriminator Loss: 1.0733, Generator Loss: 1.7320
D(x): 0.6794, D(G(z)): 0.3457
Epoch: [19/20], Batch Num: [222/600]
Discriminator Loss: 1.0979, Generator Loss: 1.5881
D(x): 0.6897, D(G(z)): 0.3390
Epoch: [19/20], Batch Num: [223/600]
Discriminator Loss: 1.0495, Generator Loss: 1.7649
D(x): 0.7061, D(G(z)): 0.3314
Epoch: [19/20], Batch Num: [224/600]
Discriminator Loss: 1.0618, Generator Loss: 1.6935
D(x): 0.6528, D(G(z)): 0.3044
Epoch: [19/20], Batch Num: [225/600]
Discriminator Loss: 1.1165, Generator Loss: 1.5028
D(x): 0.6262, D(G(z)): 0.3012
Epoch: [19/20], Batch Num: [226/600]
Discriminator Loss: 1.2213, Generator Loss: 1.2566
D(x): 0.6094, D(G(z)): 0.3543
Epoch: [19/20], Batch Num: [227/600]
Discriminator Loss: 1.1816, Generator Loss: 1.1567
D(x): 0.6577, D(G(z)): 0.3748
Epoch: [19/20], Batch Num: [228/600]
Discriminator Loss: 1.3795, Generator Loss: 1.1448
D(x): 0.6102, D(G(z)): 0.4245
Epoch: [19/20], Batch Num: [229/600]
Discriminator Loss: 1.1907, Generator Loss: 1.2037
D(x): 0.6988, D(G(z)): 0.4120
Epoch: [19/20], Batch Num: [230/600]
Discriminator Loss: 1.3686, Generator Loss: 1.3378
D(x): 0.6895, D(G(z)): 0.4779
Epoch: [19/20], Batch Num: [231/600]
Discriminator Loss: 1.3392, Generator Loss: 1.4030
D(x): 0.5558, D(G(z)): 0.3458
Epoch: [19/20], Batch Num: [232/600]
Discriminator Loss: 1.2876, Generator Loss: 1.2534
D(x): 0.5309, D(G(z)): 0.3027
Epoch: [19/20], Batch Num: [233/600]
Discriminator Loss: 1.3800, Generator Loss: 1.1632
D(x): 0.5605, D(G(z)): 0.3849
Epoch: [19/20], Batch Num: [234/600]
Discriminator Loss: 1.3701, Generator Loss: 1.0235
D(x): 0.5618, D(G(z)): 0.3914
Epoch: [19/20], Batch Num: [235/600]
Discriminator Loss: 1.2011, Generator Loss: 1.0859
D(x): 0.6451, D(G(z)): 0.4080
Epoch: [19/20], Batch Num: [236/600]
Discriminator Loss: 1.3292, Generator Loss: 0.9709
D(x): 0.6081, D(G(z)): 0.4455
Epoch: [19/20], Batch Num: [237/600]
Discriminator Loss: 1.3584, Generator Loss: 1.0510
D(x): 0.6701, D(G(z)): 0.4845
Epoch: [19/20], Batch Num: [238/600]
Discriminator Loss: 1.1717, Generator Loss: 1.2176
D(x): 0.6059, D(G(z)): 0.3815
Epoch: [19/20], Batch Num: [239/600]
Discriminator Loss: 1.1817, Generator Loss: 1.2989
D(x): 0.5939, D(G(z)): 0.3697
Epoch: [19/20], Batch Num: [240/600]
Discriminator Loss: 1.1027, Generator Loss: 1.4470
D(x): 0.5931, D(G(z)): 0.3205
Epoch: [19/20], Batch Num: [241/600]
Discriminator Loss: 1.1760, Generator Loss: 1.2113
D(x): 0.5537, D(G(z)): 0.2970
Epoch: [19/20], Batch Num: [242/600]
Discriminator Loss: 1.0943, Generator Loss: 1.1686
D(x): 0.6092, D(G(z)): 0.3469
Epoch: [19/20], Batch Num: [243/600]
Discriminator Loss: 0.9503, Generator Loss: 1.2068
D(x): 0.6707, D(G(z)): 0.3315
Epoch: [19/20], Batch Num: [244/600]
Discriminator Loss: 0.9729, Generator Loss: 1.1218
D(x): 0.6823, D(G(z)): 0.3712
Epoch: [19/20], Batch Num: [245/600]
Discriminator Loss: 1.0874, Generator Loss: 1.0633
D(x): 0.6861, D(G(z)): 0.4158
Epoch: [19/20], Batch Num: [246/600]
Discriminator Loss: 0.9606, Generator Loss: 1.2311
D(x): 0.7419, D(G(z)): 0.4111
Epoch: [19/20], Batch Num: [247/600]
Discriminator Loss: 0.9723, Generator Loss: 1.2661
D(x): 0.6999, D(G(z)): 0.3764
Epoch: [19/20], Batch Num: [248/600]
Discriminator Loss: 0.8702, Generator Loss: 1.4116
D(x): 0.6939, D(G(z)): 0.3277
Epoch: [19/20], Batch Num: [249/600]
Discriminator Loss: 0.9046, Generator Loss: 1.5261
D(x): 0.6505, D(G(z)): 0.2901
Epoch: [19/20], Batch Num: [250/600]
Discriminator Loss: 0.9660, Generator Loss: 1.6816
D(x): 0.6304, D(G(z)): 0.3033
Epoch: [19/20], Batch Num: [251/600]
Discriminator Loss: 0.8444, Generator Loss: 1.4860
D(x): 0.6918, D(G(z)): 0.2942
Epoch: [19/20], Batch Num: [252/600]
Discriminator Loss: 0.7343, Generator Loss: 1.5240
D(x): 0.6950, D(G(z)): 0.2387
Epoch: [19/20], Batch Num: [253/600]
Discriminator Loss: 0.7634, Generator Loss: 1.4091
D(x): 0.6962, D(G(z)): 0.2560
Epoch: [19/20], Batch Num: [254/600]
Discriminator Loss: 0.7302, Generator Loss: 1.3793
D(x): 0.7883, D(G(z)): 0.3220
Epoch: [19/20], Batch Num: [255/600]
Discriminator Loss: 0.7483, Generator Loss: 1.5009
D(x): 0.8038, D(G(z)): 0.3328
Epoch: [19/20], Batch Num: [256/600]
Discriminator Loss: 0.7383, Generator Loss: 1.6104
D(x): 0.7588, D(G(z)): 0.2975
Epoch: [19/20], Batch Num: [257/600]
Discriminator Loss: 0.7058, Generator Loss: 1.8217
D(x): 0.7340, D(G(z)): 0.2506
Epoch: [19/20], Batch Num: [258/600]
Discriminator Loss: 0.7172, Generator Loss: 1.8825
D(x): 0.7435, D(G(z)): 0.2679
Epoch: [19/20], Batch Num: [259/600]
Discriminator Loss: 0.7178, Generator Loss: 1.8826
D(x): 0.6853, D(G(z)): 0.1917
Epoch: [19/20], Batch Num: [260/600]
Discriminator Loss: 0.6670, Generator Loss: 1.8957
D(x): 0.7540, D(G(z)): 0.2259
Epoch: [19/20], Batch Num: [261/600]
Discriminator Loss: 0.7888, Generator Loss: 1.7844
D(x): 0.7523, D(G(z)): 0.2749
Epoch: [19/20], Batch Num: [262/600]
Discriminator Loss: 0.6374, Generator Loss: 2.0400
D(x): 0.7789, D(G(z)): 0.2447
Epoch: [19/20], Batch Num: [263/600]
Discriminator Loss: 0.7426, Generator Loss: 1.9547
D(x): 0.7899, D(G(z)): 0.2784
Epoch: [19/20], Batch Num: [264/600]
Discriminator Loss: 0.6635, Generator Loss: 1.8671
D(x): 0.8249, D(G(z)): 0.2698
Epoch: [19/20], Batch Num: [265/600]
Discriminator Loss: 0.7740, Generator Loss: 2.2324
D(x): 0.7406, D(G(z)): 0.2545
Epoch: [19/20], Batch Num: [266/600]
Discriminator Loss: 0.7263, Generator Loss: 2.3991
D(x): 0.7641, D(G(z)): 0.2117
Epoch: [19/20], Batch Num: [267/600]
Discriminator Loss: 0.7483, Generator Loss: 2.2157
D(x): 0.7402, D(G(z)): 0.2200
Epoch: [19/20], Batch Num: [268/600]
Discriminator Loss: 0.6562, Generator Loss: 1.8679
D(x): 0.7467, D(G(z)): 0.1841
Epoch: [19/20], Batch Num: [269/600]
Discriminator Loss: 0.6798, Generator Loss: 1.9238
D(x): 0.8055, D(G(z)): 0.2433
Epoch: [19/20], Batch Num: [270/600]
Discriminator Loss: 0.8899, Generator Loss: 1.8052
D(x): 0.7357, D(G(z)): 0.2786
Epoch: [19/20], Batch Num: [271/600]
Discriminator Loss: 0.7379, Generator Loss: 2.0400
D(x): 0.8231, D(G(z)): 0.2925
Epoch: [19/20], Batch Num: [272/600]
Discriminator Loss: 0.9941, Generator Loss: 1.9821
D(x): 0.7021, D(G(z)): 0.2938
Epoch: [19/20], Batch Num: [273/600]
Discriminator Loss: 0.9425, Generator Loss: 1.6935
D(x): 0.6311, D(G(z)): 0.2112
Epoch: [19/20], Batch Num: [274/600]
Discriminator Loss: 0.8832, Generator Loss: 1.2359
D(x): 0.6834, D(G(z)): 0.2492
Epoch: [19/20], Batch Num: [275/600]
Discriminator Loss: 1.2315, Generator Loss: 1.2138
D(x): 0.7658, D(G(z)): 0.4555
Epoch: [19/20], Batch Num: [276/600]
Discriminator Loss: 1.0684, Generator Loss: 1.7421
D(x): 0.8112, D(G(z)): 0.4265
Epoch: [19/20], Batch Num: [277/600]
Discriminator Loss: 1.2532, Generator Loss: 2.3071
D(x): 0.6698, D(G(z)): 0.3421
Epoch: [19/20], Batch Num: [278/600]
Discriminator Loss: 1.2289, Generator Loss: 2.2757
D(x): 0.5598, D(G(z)): 0.2467
Epoch: [19/20], Batch Num: [279/600]
Discriminator Loss: 1.3933, Generator Loss: 1.5096
D(x): 0.5017, D(G(z)): 0.2242
Epoch: [19/20], Batch Num: [280/600]
Discriminator Loss: 1.0402, Generator Loss: 1.0766
D(x): 0.6729, D(G(z)): 0.3196
Epoch: [19/20], Batch Num: [281/600]
Discriminator Loss: 1.1390, Generator Loss: 0.9661
D(x): 0.7458, D(G(z)): 0.4344
Epoch: [19/20], Batch Num: [282/600]
Discriminator Loss: 1.0489, Generator Loss: 1.0450
D(x): 0.7870, D(G(z)): 0.4503
Epoch: [19/20], Batch Num: [283/600]
Discriminator Loss: 1.2192, Generator Loss: 1.1717
D(x): 0.6836, D(G(z)): 0.4490
Epoch: [19/20], Batch Num: [284/600]
Discriminator Loss: 1.1651, Generator Loss: 1.4287
D(x): 0.6339, D(G(z)): 0.3806
Epoch: [19/20], Batch Num: [285/600]
Discriminator Loss: 1.2974, Generator Loss: 1.5166
D(x): 0.5379, D(G(z)): 0.3206
Epoch: [19/20], Batch Num: [286/600]
Discriminator Loss: 1.3313, Generator Loss: 1.1698
D(x): 0.5235, D(G(z)): 0.3045
Epoch: [19/20], Batch Num: [287/600]
Discriminator Loss: 1.2529, Generator Loss: 1.0547
D(x): 0.5747, D(G(z)): 0.3582
Epoch: [19/20], Batch Num: [288/600]
Discriminator Loss: 1.0725, Generator Loss: 1.0175
D(x): 0.6805, D(G(z)): 0.3964
Epoch: [19/20], Batch Num: [289/600]
Discriminator Loss: 1.1590, Generator Loss: 0.8877
D(x): 0.6864, D(G(z)): 0.4459
Epoch: [19/20], Batch Num: [290/600]
Discriminator Loss: 0.9569, Generator Loss: 0.9556
D(x): 0.7471, D(G(z)): 0.4221
Epoch: [19/20], Batch Num: [291/600]
Discriminator Loss: 1.0605, Generator Loss: 1.1293
D(x): 0.7049, D(G(z)): 0.4310
Epoch: [19/20], Batch Num: [292/600]
Discriminator Loss: 1.0498, Generator Loss: 1.3404
D(x): 0.7015, D(G(z)): 0.4084
Epoch: [19/20], Batch Num: [293/600]
Discriminator Loss: 1.0571, Generator Loss: 1.5319
D(x): 0.6310, D(G(z)): 0.3390
Epoch: [19/20], Batch Num: [294/600]
Discriminator Loss: 1.1298, Generator Loss: 1.5511
D(x): 0.5573, D(G(z)): 0.2847
Epoch: [19/20], Batch Num: [295/600]
Discriminator Loss: 0.9121, Generator Loss: 1.5457
D(x): 0.6328, D(G(z)): 0.2521
Epoch: [19/20], Batch Num: [296/600]
Discriminator Loss: 0.9562, Generator Loss: 1.4115
D(x): 0.5935, D(G(z)): 0.2379
Epoch: [19/20], Batch Num: [297/600]
Discriminator Loss: 0.8971, Generator Loss: 1.4356
D(x): 0.6360, D(G(z)): 0.2662
Epoch: [19/20], Batch Num: [298/600]
Discriminator Loss: 0.8573, Generator Loss: 1.2141
D(x): 0.6744, D(G(z)): 0.2923
Epoch: [19/20], Batch Num: [299/600]
Discriminator Loss: 0.8095, Generator Loss: 1.2579
D(x): 0.7213, D(G(z)): 0.3007
Epoch: 19, Batch Num: [300/600]

Epoch: [19/20], Batch Num: [300/600]
Discriminator Loss: 0.7406, Generator Loss: 1.2175
D(x): 0.8158, D(G(z)): 0.3493
Epoch: [19/20], Batch Num: [301/600]
Discriminator Loss: 0.7702, Generator Loss: 1.3613
D(x): 0.7834, D(G(z)): 0.3362
Epoch: [19/20], Batch Num: [302/600]
Discriminator Loss: 0.7389, Generator Loss: 1.5799
D(x): 0.7708, D(G(z)): 0.3058
Epoch: [19/20], Batch Num: [303/600]
Discriminator Loss: 0.7262, Generator Loss: 1.8607
D(x): 0.7600, D(G(z)): 0.2758
Epoch: [19/20], Batch Num: [304/600]
Discriminator Loss: 0.6645, Generator Loss: 1.7655
D(x): 0.7574, D(G(z)): 0.2449
Epoch: [19/20], Batch Num: [305/600]
Discriminator Loss: 0.7054, Generator Loss: 2.0519
D(x): 0.7137, D(G(z)): 0.2151
Epoch: [19/20], Batch Num: [306/600]
Discriminator Loss: 0.7385, Generator Loss: 2.0903
D(x): 0.7063, D(G(z)): 0.2315
Epoch: [19/20], Batch Num: [307/600]
Discriminator Loss: 0.6042, Generator Loss: 1.9334
D(x): 0.7797, D(G(z)): 0.2296
Epoch: [19/20], Batch Num: [308/600]
Discriminator Loss: 0.6633, Generator Loss: 1.7645
D(x): 0.6916, D(G(z)): 0.1569
Epoch: [19/20], Batch Num: [309/600]
Discriminator Loss: 0.6845, Generator Loss: 1.8759
D(x): 0.7644, D(G(z)): 0.2456
Epoch: [19/20], Batch Num: [310/600]
Discriminator Loss: 0.7594, Generator Loss: 1.4064
D(x): 0.7726, D(G(z)): 0.2905
Epoch: [19/20], Batch Num: [311/600]
Discriminator Loss: 0.5178, Generator Loss: 1.8764
D(x): 0.8439, D(G(z)): 0.2354
Epoch: [19/20], Batch Num: [312/600]
Discriminator Loss: 0.6560, Generator Loss: 1.9100
D(x): 0.8345, D(G(z)): 0.2756
Epoch: [19/20], Batch Num: [313/600]
Discriminator Loss: 0.6496, Generator Loss: 2.2883
D(x): 0.7878, D(G(z)): 0.2497
Epoch: [19/20], Batch Num: [314/600]
Discriminator Loss: 0.5591, Generator Loss: 2.1716
D(x): 0.8410, D(G(z)): 0.2194
Epoch: [19/20], Batch Num: [315/600]
Discriminator Loss: 0.5949, Generator Loss: 2.6169
D(x): 0.7847, D(G(z)): 0.1647
Epoch: [19/20], Batch Num: [316/600]
Discriminator Loss: 0.6182, Generator Loss: 2.7224
D(x): 0.7402, D(G(z)): 0.1598
Epoch: [19/20], Batch Num: [317/600]
Discriminator Loss: 0.7609, Generator Loss: 2.0311
D(x): 0.7185, D(G(z)): 0.1778
Epoch: [19/20], Batch Num: [318/600]
Discriminator Loss: 0.6048, Generator Loss: 1.8678
D(x): 0.8118, D(G(z)): 0.2382
Epoch: [19/20], Batch Num: [319/600]
Discriminator Loss: 0.6576, Generator Loss: 1.9534
D(x): 0.8255, D(G(z)): 0.2694
Epoch: [19/20], Batch Num: [320/600]
Discriminator Loss: 0.6350, Generator Loss: 1.7597
D(x): 0.8440, D(G(z)): 0.2767
Epoch: [19/20], Batch Num: [321/600]
Discriminator Loss: 0.8734, Generator Loss: 2.1202
D(x): 0.8072, D(G(z)): 0.3088
Epoch: [19/20], Batch Num: [322/600]
Discriminator Loss: 0.8927, Generator Loss: 2.2110
D(x): 0.7203, D(G(z)): 0.2491
Epoch: [19/20], Batch Num: [323/600]
Discriminator Loss: 1.0044, Generator Loss: 2.2287
D(x): 0.6364, D(G(z)): 0.2142
Epoch: [19/20], Batch Num: [324/600]
Discriminator Loss: 1.0007, Generator Loss: 2.0007
D(x): 0.6845, D(G(z)): 0.2546
Epoch: [19/20], Batch Num: [325/600]
Discriminator Loss: 0.9364, Generator Loss: 1.7679
D(x): 0.7056, D(G(z)): 0.2899
Epoch: [19/20], Batch Num: [326/600]
Discriminator Loss: 0.9088, Generator Loss: 1.6924
D(x): 0.7573, D(G(z)): 0.3040
Epoch: [19/20], Batch Num: [327/600]
Discriminator Loss: 1.0315, Generator Loss: 1.5254
D(x): 0.6530, D(G(z)): 0.2911
Epoch: [19/20], Batch Num: [328/600]
Discriminator Loss: 1.1575, Generator Loss: 1.5268
D(x): 0.7111, D(G(z)): 0.3831
Epoch: [19/20], Batch Num: [329/600]
Discriminator Loss: 1.0380, Generator Loss: 1.6413
D(x): 0.7330, D(G(z)): 0.3490
Epoch: [19/20], Batch Num: [330/600]
Discriminator Loss: 1.2236, Generator Loss: 1.6012
D(x): 0.6230, D(G(z)): 0.3327
Epoch: [19/20], Batch Num: [331/600]
Discriminator Loss: 0.9488, Generator Loss: 1.6197
D(x): 0.6827, D(G(z)): 0.2820
Epoch: [19/20], Batch Num: [332/600]
Discriminator Loss: 1.0601, Generator Loss: 1.3667
D(x): 0.6784, D(G(z)): 0.3151
Epoch: [19/20], Batch Num: [333/600]
Discriminator Loss: 1.1906, Generator Loss: 1.4689
D(x): 0.6644, D(G(z)): 0.3622
Epoch: [19/20], Batch Num: [334/600]
Discriminator Loss: 1.1827, Generator Loss: 1.2485
D(x): 0.6977, D(G(z)): 0.3686
Epoch: [19/20], Batch Num: [335/600]
Discriminator Loss: 1.2115, Generator Loss: 1.4837
D(x): 0.6521, D(G(z)): 0.3759
Epoch: [19/20], Batch Num: [336/600]
Discriminator Loss: 1.1373, Generator Loss: 1.1967
D(x): 0.6173, D(G(z)): 0.3198
Epoch: [19/20], Batch Num: [337/600]
Discriminator Loss: 1.2324, Generator Loss: 1.4200
D(x): 0.6190, D(G(z)): 0.3581
Epoch: [19/20], Batch Num: [338/600]
Discriminator Loss: 1.1885, Generator Loss: 1.0914
D(x): 0.6261, D(G(z)): 0.3604
Epoch: [19/20], Batch Num: [339/600]
Discriminator Loss: 1.3621, Generator Loss: 1.1496
D(x): 0.6104, D(G(z)): 0.4041
Epoch: [19/20], Batch Num: [340/600]
Discriminator Loss: 1.1533, Generator Loss: 1.1132
D(x): 0.6975, D(G(z)): 0.4148
Epoch: [19/20], Batch Num: [341/600]
Discriminator Loss: 1.3221, Generator Loss: 1.3235
D(x): 0.6771, D(G(z)): 0.4505
Epoch: [19/20], Batch Num: [342/600]
Discriminator Loss: 1.1474, Generator Loss: 1.3428
D(x): 0.6473, D(G(z)): 0.3630
Epoch: [19/20], Batch Num: [343/600]
Discriminator Loss: 1.2025, Generator Loss: 1.4419
D(x): 0.5830, D(G(z)): 0.3415
Epoch: [19/20], Batch Num: [344/600]
Discriminator Loss: 1.2031, Generator Loss: 1.4385
D(x): 0.5530, D(G(z)): 0.3170
Epoch: [19/20], Batch Num: [345/600]
Discriminator Loss: 1.1630, Generator Loss: 1.4274
D(x): 0.5883, D(G(z)): 0.3353
Epoch: [19/20], Batch Num: [346/600]
Discriminator Loss: 1.0209, Generator Loss: 1.2482
D(x): 0.6108, D(G(z)): 0.3018
Epoch: [19/20], Batch Num: [347/600]
Discriminator Loss: 1.0117, Generator Loss: 1.2806
D(x): 0.6680, D(G(z)): 0.3592
Epoch: [19/20], Batch Num: [348/600]
Discriminator Loss: 1.0425, Generator Loss: 1.2048
D(x): 0.6890, D(G(z)): 0.3739
Epoch: [19/20], Batch Num: [349/600]
Discriminator Loss: 1.1158, Generator Loss: 1.1573
D(x): 0.6893, D(G(z)): 0.4084
Epoch: [19/20], Batch Num: [350/600]
Discriminator Loss: 1.0718, Generator Loss: 1.4089
D(x): 0.6525, D(G(z)): 0.3677
Epoch: [19/20], Batch Num: [351/600]
Discriminator Loss: 1.0954, Generator Loss: 1.4015
D(x): 0.6136, D(G(z)): 0.3589
Epoch: [19/20], Batch Num: [352/600]
Discriminator Loss: 1.0225, Generator Loss: 1.1015
D(x): 0.6171, D(G(z)): 0.3259
Epoch: [19/20], Batch Num: [353/600]
Discriminator Loss: 1.0848, Generator Loss: 1.2838
D(x): 0.6533, D(G(z)): 0.3708
Epoch: [19/20], Batch Num: [354/600]
Discriminator Loss: 1.0323, Generator Loss: 1.2376
D(x): 0.6081, D(G(z)): 0.3167
Epoch: [19/20], Batch Num: [355/600]
Discriminator Loss: 0.8844, Generator Loss: 1.3935
D(x): 0.6892, D(G(z)): 0.3184
Epoch: [19/20], Batch Num: [356/600]
Discriminator Loss: 0.9949, Generator Loss: 1.2427
D(x): 0.6600, D(G(z)): 0.3613
Epoch: [19/20], Batch Num: [357/600]
Discriminator Loss: 1.0168, Generator Loss: 1.3054
D(x): 0.6756, D(G(z)): 0.3691
Epoch: [19/20], Batch Num: [358/600]
Discriminator Loss: 0.9037, Generator Loss: 1.3318
D(x): 0.6985, D(G(z)): 0.3354
Epoch: [19/20], Batch Num: [359/600]
Discriminator Loss: 0.9169, Generator Loss: 1.4794
D(x): 0.6989, D(G(z)): 0.3403
Epoch: [19/20], Batch Num: [360/600]
Discriminator Loss: 0.7814, Generator Loss: 1.3775
D(x): 0.7172, D(G(z)): 0.2941
Epoch: [19/20], Batch Num: [361/600]
Discriminator Loss: 0.8725, Generator Loss: 1.3793
D(x): 0.7287, D(G(z)): 0.3468
Epoch: [19/20], Batch Num: [362/600]
Discriminator Loss: 0.9483, Generator Loss: 1.5057
D(x): 0.6424, D(G(z)): 0.2956
Epoch: [19/20], Batch Num: [363/600]
Discriminator Loss: 0.9737, Generator Loss: 1.4630
D(x): 0.6206, D(G(z)): 0.2821
Epoch: [19/20], Batch Num: [364/600]
Discriminator Loss: 0.8975, Generator Loss: 1.4589
D(x): 0.6778, D(G(z)): 0.2938
Epoch: [19/20], Batch Num: [365/600]
Discriminator Loss: 0.8297, Generator Loss: 1.3705
D(x): 0.7090, D(G(z)): 0.3024
Epoch: [19/20], Batch Num: [366/600]
Discriminator Loss: 0.9064, Generator Loss: 1.4395
D(x): 0.7029, D(G(z)): 0.3269
Epoch: [19/20], Batch Num: [367/600]
Discriminator Loss: 0.8285, Generator Loss: 1.4950
D(x): 0.7093, D(G(z)): 0.3047
Epoch: [19/20], Batch Num: [368/600]
Discriminator Loss: 0.8152, Generator Loss: 1.4417
D(x): 0.7408, D(G(z)): 0.3359
Epoch: [19/20], Batch Num: [369/600]
Discriminator Loss: 0.8164, Generator Loss: 1.5468
D(x): 0.7620, D(G(z)): 0.3290
Epoch: [19/20], Batch Num: [370/600]
Discriminator Loss: 0.8843, Generator Loss: 1.6996
D(x): 0.7064, D(G(z)): 0.3180
Epoch: [19/20], Batch Num: [371/600]
Discriminator Loss: 0.7622, Generator Loss: 1.8586
D(x): 0.7258, D(G(z)): 0.2846
Epoch: [19/20], Batch Num: [372/600]
Discriminator Loss: 0.8836, Generator Loss: 1.8462
D(x): 0.6597, D(G(z)): 0.2716
Epoch: [19/20], Batch Num: [373/600]
Discriminator Loss: 0.7680, Generator Loss: 1.6433
D(x): 0.6890, D(G(z)): 0.2339
Epoch: [19/20], Batch Num: [374/600]
Discriminator Loss: 0.8702, Generator Loss: 1.5495
D(x): 0.6554, D(G(z)): 0.2497
Epoch: [19/20], Batch Num: [375/600]
Discriminator Loss: 0.8386, Generator Loss: 1.3985
D(x): 0.7380, D(G(z)): 0.3216
Epoch: [19/20], Batch Num: [376/600]
Discriminator Loss: 0.7218, Generator Loss: 1.3592
D(x): 0.7939, D(G(z)): 0.3049
Epoch: [19/20], Batch Num: [377/600]
Discriminator Loss: 0.9134, Generator Loss: 1.4886
D(x): 0.7507, D(G(z)): 0.3447
Epoch: [19/20], Batch Num: [378/600]
Discriminator Loss: 0.8027, Generator Loss: 1.7541
D(x): 0.7341, D(G(z)): 0.2931
Epoch: [19/20], Batch Num: [379/600]
Discriminator Loss: 0.7291, Generator Loss: 1.7840
D(x): 0.7358, D(G(z)): 0.2542
Epoch: [19/20], Batch Num: [380/600]
Discriminator Loss: 0.7799, Generator Loss: 1.8754
D(x): 0.6929, D(G(z)): 0.2246
Epoch: [19/20], Batch Num: [381/600]
Discriminator Loss: 0.7668, Generator Loss: 1.6391
D(x): 0.7057, D(G(z)): 0.2266
Epoch: [19/20], Batch Num: [382/600]
Discriminator Loss: 0.7494, Generator Loss: 1.6067
D(x): 0.7291, D(G(z)): 0.2487
Epoch: [19/20], Batch Num: [383/600]
Discriminator Loss: 0.8630, Generator Loss: 1.5376
D(x): 0.7605, D(G(z)): 0.3321
Epoch: [19/20], Batch Num: [384/600]
Discriminator Loss: 0.8626, Generator Loss: 1.5353
D(x): 0.7450, D(G(z)): 0.3062
Epoch: [19/20], Batch Num: [385/600]
Discriminator Loss: 1.0408, Generator Loss: 1.7557
D(x): 0.6837, D(G(z)): 0.3005
Epoch: [19/20], Batch Num: [386/600]
Discriminator Loss: 0.8515, Generator Loss: 1.6839
D(x): 0.7312, D(G(z)): 0.2872
Epoch: [19/20], Batch Num: [387/600]
Discriminator Loss: 0.7714, Generator Loss: 1.9232
D(x): 0.7567, D(G(z)): 0.2804
Epoch: [19/20], Batch Num: [388/600]
Discriminator Loss: 0.8172, Generator Loss: 1.9240
D(x): 0.7423, D(G(z)): 0.2775
Epoch: [19/20], Batch Num: [389/600]
Discriminator Loss: 0.8881, Generator Loss: 1.8195
D(x): 0.6699, D(G(z)): 0.2032
Epoch: [19/20], Batch Num: [390/600]
Discriminator Loss: 0.9324, Generator Loss: 1.7065
D(x): 0.7014, D(G(z)): 0.2919
Epoch: [19/20], Batch Num: [391/600]
Discriminator Loss: 1.0496, Generator Loss: 1.4651
D(x): 0.6839, D(G(z)): 0.3469
Epoch: [19/20], Batch Num: [392/600]
Discriminator Loss: 0.8711, Generator Loss: 1.5443
D(x): 0.7549, D(G(z)): 0.3417
Epoch: [19/20], Batch Num: [393/600]
Discriminator Loss: 1.1962, Generator Loss: 1.4886
D(x): 0.6830, D(G(z)): 0.3701
Epoch: [19/20], Batch Num: [394/600]
Discriminator Loss: 1.0240, Generator Loss: 1.4869
D(x): 0.6721, D(G(z)): 0.2847
Epoch: [19/20], Batch Num: [395/600]
Discriminator Loss: 1.2964, Generator Loss: 1.4815
D(x): 0.5948, D(G(z)): 0.3150
Epoch: [19/20], Batch Num: [396/600]
Discriminator Loss: 1.0872, Generator Loss: 1.4412
D(x): 0.7020, D(G(z)): 0.3720
Epoch: [19/20], Batch Num: [397/600]
Discriminator Loss: 1.2796, Generator Loss: 1.4773
D(x): 0.6659, D(G(z)): 0.4206
Epoch: [19/20], Batch Num: [398/600]
Discriminator Loss: 1.1388, Generator Loss: 1.5129
D(x): 0.6193, D(G(z)): 0.3124
Epoch: [19/20], Batch Num: [399/600]
Discriminator Loss: 1.1479, Generator Loss: 1.1329
D(x): 0.6063, D(G(z)): 0.3067
Epoch: 19, Batch Num: [400/600]

Epoch: [19/20], Batch Num: [400/600]
Discriminator Loss: 1.1430, Generator Loss: 1.0576
D(x): 0.6478, D(G(z)): 0.3530
Epoch: [19/20], Batch Num: [401/600]
Discriminator Loss: 1.2323, Generator Loss: 1.1386
D(x): 0.6442, D(G(z)): 0.4009
Epoch: [19/20], Batch Num: [402/600]
Discriminator Loss: 1.2581, Generator Loss: 1.0183
D(x): 0.6598, D(G(z)): 0.4402
Epoch: [19/20], Batch Num: [403/600]
Discriminator Loss: 1.0680, Generator Loss: 1.2100
D(x): 0.7040, D(G(z)): 0.3971
Epoch: [19/20], Batch Num: [404/600]
Discriminator Loss: 1.1130, Generator Loss: 1.2472
D(x): 0.6196, D(G(z)): 0.3476
Epoch: [19/20], Batch Num: [405/600]
Discriminator Loss: 1.0809, Generator Loss: 1.3481
D(x): 0.6059, D(G(z)): 0.3208
Epoch: [19/20], Batch Num: [406/600]
Discriminator Loss: 1.0959, Generator Loss: 1.3706
D(x): 0.5934, D(G(z)): 0.3135
Epoch: [19/20], Batch Num: [407/600]
Discriminator Loss: 1.0261, Generator Loss: 1.1407
D(x): 0.6133, D(G(z)): 0.3160
Epoch: [19/20], Batch Num: [408/600]
Discriminator Loss: 1.1048, Generator Loss: 1.2057
D(x): 0.6332, D(G(z)): 0.3772
Epoch: [19/20], Batch Num: [409/600]
Discriminator Loss: 1.0087, Generator Loss: 1.1231
D(x): 0.6780, D(G(z)): 0.3878
Epoch: [19/20], Batch Num: [410/600]
Discriminator Loss: 0.9525, Generator Loss: 1.2287
D(x): 0.6898, D(G(z)): 0.3595
Epoch: [19/20], Batch Num: [411/600]
Discriminator Loss: 0.9166, Generator Loss: 1.2781
D(x): 0.7232, D(G(z)): 0.3864
Epoch: [19/20], Batch Num: [412/600]
Discriminator Loss: 0.8081, Generator Loss: 1.2819
D(x): 0.7332, D(G(z)): 0.3179
Epoch: [19/20], Batch Num: [413/600]
Discriminator Loss: 0.8277, Generator Loss: 1.4259
D(x): 0.7199, D(G(z)): 0.3304
Epoch: [19/20], Batch Num: [414/600]
Discriminator Loss: 0.8485, Generator Loss: 1.4363
D(x): 0.6748, D(G(z)): 0.2789
Epoch: [19/20], Batch Num: [415/600]
Discriminator Loss: 0.8641, Generator Loss: 1.3253
D(x): 0.6632, D(G(z)): 0.2822
Epoch: [19/20], Batch Num: [416/600]
Discriminator Loss: 0.6994, Generator Loss: 1.4047
D(x): 0.7260, D(G(z)): 0.2465
Epoch: [19/20], Batch Num: [417/600]
Discriminator Loss: 0.6886, Generator Loss: 1.3025
D(x): 0.7733, D(G(z)): 0.2971
Epoch: [19/20], Batch Num: [418/600]
Discriminator Loss: 0.8497, Generator Loss: 1.4340
D(x): 0.6787, D(G(z)): 0.2939
Epoch: [19/20], Batch Num: [419/600]
Discriminator Loss: 0.7652, Generator Loss: 1.3831
D(x): 0.7638, D(G(z)): 0.3114
Epoch: [19/20], Batch Num: [420/600]
Discriminator Loss: 0.7583, Generator Loss: 1.5689
D(x): 0.7982, D(G(z)): 0.3329
Epoch: [19/20], Batch Num: [421/600]
Discriminator Loss: 0.5775, Generator Loss: 1.7462
D(x): 0.8027, D(G(z)): 0.2443
Epoch: [19/20], Batch Num: [422/600]
Discriminator Loss: 0.6833, Generator Loss: 2.0599
D(x): 0.7316, D(G(z)): 0.2221
Epoch: [19/20], Batch Num: [423/600]
Discriminator Loss: 0.7151, Generator Loss: 2.0467
D(x): 0.6894, D(G(z)): 0.1936
Epoch: [19/20], Batch Num: [424/600]
Discriminator Loss: 0.7396, Generator Loss: 2.0318
D(x): 0.7166, D(G(z)): 0.2224
Epoch: [19/20], Batch Num: [425/600]
Discriminator Loss: 0.5977, Generator Loss: 1.8008
D(x): 0.7566, D(G(z)): 0.2077
Epoch: [19/20], Batch Num: [426/600]
Discriminator Loss: 0.6499, Generator Loss: 1.6257
D(x): 0.7883, D(G(z)): 0.2694
Epoch: [19/20], Batch Num: [427/600]
Discriminator Loss: 0.6591, Generator Loss: 1.7680
D(x): 0.8188, D(G(z)): 0.2974
Epoch: [19/20], Batch Num: [428/600]
Discriminator Loss: 0.7065, Generator Loss: 1.8813
D(x): 0.8000, D(G(z)): 0.2924
Epoch: [19/20], Batch Num: [429/600]
Discriminator Loss: 0.6618, Generator Loss: 2.3108
D(x): 0.8281, D(G(z)): 0.2681
Epoch: [19/20], Batch Num: [430/600]
Discriminator Loss: 0.6638, Generator Loss: 2.0303
D(x): 0.7391, D(G(z)): 0.2084
Epoch: [19/20], Batch Num: [431/600]
Discriminator Loss: 0.6141, Generator Loss: 1.9542
D(x): 0.7594, D(G(z)): 0.1953
Epoch: [19/20], Batch Num: [432/600]
Discriminator Loss: 0.5891, Generator Loss: 1.8805
D(x): 0.7582, D(G(z)): 0.1829
Epoch: [19/20], Batch Num: [433/600]
Discriminator Loss: 0.6981, Generator Loss: 1.8272
D(x): 0.7566, D(G(z)): 0.2226
Epoch: [19/20], Batch Num: [434/600]
Discriminator Loss: 0.7153, Generator Loss: 1.7152
D(x): 0.8256, D(G(z)): 0.3081
Epoch: [19/20], Batch Num: [435/600]
Discriminator Loss: 0.6168, Generator Loss: 1.8138
D(x): 0.8228, D(G(z)): 0.2652
Epoch: [19/20], Batch Num: [436/600]
Discriminator Loss: 0.6450, Generator Loss: 1.8928
D(x): 0.8016, D(G(z)): 0.2542
Epoch: [19/20], Batch Num: [437/600]
Discriminator Loss: 0.7351, Generator Loss: 2.0411
D(x): 0.7738, D(G(z)): 0.2630
Epoch: [19/20], Batch Num: [438/600]
Discriminator Loss: 0.7690, Generator Loss: 2.0952
D(x): 0.7391, D(G(z)): 0.2153
Epoch: [19/20], Batch Num: [439/600]
Discriminator Loss: 0.7085, Generator Loss: 2.0692
D(x): 0.7781, D(G(z)): 0.2516
Epoch: [19/20], Batch Num: [440/600]
Discriminator Loss: 0.8413, Generator Loss: 1.8952
D(x): 0.7147, D(G(z)): 0.2542
Epoch: [19/20], Batch Num: [441/600]
Discriminator Loss: 0.8348, Generator Loss: 1.5360
D(x): 0.7317, D(G(z)): 0.2655
Epoch: [19/20], Batch Num: [442/600]
Discriminator Loss: 0.9459, Generator Loss: 1.5744
D(x): 0.7332, D(G(z)): 0.3095
Epoch: [19/20], Batch Num: [443/600]
Discriminator Loss: 0.9906, Generator Loss: 1.6191
D(x): 0.7747, D(G(z)): 0.3675
Epoch: [19/20], Batch Num: [444/600]
Discriminator Loss: 0.9593, Generator Loss: 1.9427
D(x): 0.7666, D(G(z)): 0.3358
Epoch: [19/20], Batch Num: [445/600]
Discriminator Loss: 1.0675, Generator Loss: 2.0338
D(x): 0.6212, D(G(z)): 0.2480
Epoch: [19/20], Batch Num: [446/600]
Discriminator Loss: 1.1503, Generator Loss: 1.6898
D(x): 0.6377, D(G(z)): 0.3052
Epoch: [19/20], Batch Num: [447/600]
Discriminator Loss: 0.9727, Generator Loss: 1.5199
D(x): 0.7154, D(G(z)): 0.2879
Epoch: [19/20], Batch Num: [448/600]
Discriminator Loss: 1.0455, Generator Loss: 1.3173
D(x): 0.6912, D(G(z)): 0.3206
Epoch: [19/20], Batch Num: [449/600]
Discriminator Loss: 1.0581, Generator Loss: 1.3760
D(x): 0.7063, D(G(z)): 0.3542
Epoch: [19/20], Batch Num: [450/600]
Discriminator Loss: 1.1476, Generator Loss: 1.2433
D(x): 0.6938, D(G(z)): 0.3791
Epoch: [19/20], Batch Num: [451/600]
Discriminator Loss: 1.1482, Generator Loss: 1.5462
D(x): 0.6779, D(G(z)): 0.3397
Epoch: [19/20], Batch Num: [452/600]
Discriminator Loss: 1.1667, Generator Loss: 1.6449
D(x): 0.6314, D(G(z)): 0.3135
Epoch: [19/20], Batch Num: [453/600]
Discriminator Loss: 1.1297, Generator Loss: 1.2728
D(x): 0.6027, D(G(z)): 0.2870
Epoch: [19/20], Batch Num: [454/600]
Discriminator Loss: 1.0613, Generator Loss: 1.1951
D(x): 0.6299, D(G(z)): 0.3030
Epoch: [19/20], Batch Num: [455/600]
Discriminator Loss: 1.1492, Generator Loss: 1.4634
D(x): 0.7483, D(G(z)): 0.4276
Epoch: [19/20], Batch Num: [456/600]
Discriminator Loss: 1.0190, Generator Loss: 1.3144
D(x): 0.7212, D(G(z)): 0.3662
Epoch: [19/20], Batch Num: [457/600]
Discriminator Loss: 0.9295, Generator Loss: 1.4952
D(x): 0.6827, D(G(z)): 0.2877
Epoch: [19/20], Batch Num: [458/600]
Discriminator Loss: 1.0894, Generator Loss: 1.4731
D(x): 0.6426, D(G(z)): 0.3166
Epoch: [19/20], Batch Num: [459/600]
Discriminator Loss: 1.0709, Generator Loss: 1.5543
D(x): 0.6595, D(G(z)): 0.3264
Epoch: [19/20], Batch Num: [460/600]
Discriminator Loss: 0.9085, Generator Loss: 1.4682
D(x): 0.6645, D(G(z)): 0.2660
Epoch: [19/20], Batch Num: [461/600]
Discriminator Loss: 0.9505, Generator Loss: 1.5637
D(x): 0.6739, D(G(z)): 0.3042
Epoch: [19/20], Batch Num: [462/600]
Discriminator Loss: 0.8298, Generator Loss: 1.4351
D(x): 0.7081, D(G(z)): 0.2676
Epoch: [19/20], Batch Num: [463/600]
Discriminator Loss: 1.0004, Generator Loss: 1.2868
D(x): 0.7057, D(G(z)): 0.3518
Epoch: [19/20], Batch Num: [464/600]
Discriminator Loss: 0.9397, Generator Loss: 1.3720
D(x): 0.7055, D(G(z)): 0.3352
Epoch: [19/20], Batch Num: [465/600]
Discriminator Loss: 0.9144, Generator Loss: 1.4521
D(x): 0.7297, D(G(z)): 0.3241
Epoch: [19/20], Batch Num: [466/600]
Discriminator Loss: 0.9396, Generator Loss: 1.6906
D(x): 0.7177, D(G(z)): 0.3271
Epoch: [19/20], Batch Num: [467/600]
Discriminator Loss: 0.9663, Generator Loss: 1.6292
D(x): 0.7087, D(G(z)): 0.3034
Epoch: [19/20], Batch Num: [468/600]
Discriminator Loss: 0.9335, Generator Loss: 1.6829
D(x): 0.6653, D(G(z)): 0.2775
Epoch: [19/20], Batch Num: [469/600]
Discriminator Loss: 0.9745, Generator Loss: 1.7356
D(x): 0.6433, D(G(z)): 0.2740
Epoch: [19/20], Batch Num: [470/600]
Discriminator Loss: 0.7545, Generator Loss: 1.5573
D(x): 0.7208, D(G(z)): 0.2520
Epoch: [19/20], Batch Num: [471/600]
Discriminator Loss: 0.7792, Generator Loss: 1.5342
D(x): 0.7451, D(G(z)): 0.2847
Epoch: [19/20], Batch Num: [472/600]
Discriminator Loss: 0.7459, Generator Loss: 1.4640
D(x): 0.7469, D(G(z)): 0.2667
Epoch: [19/20], Batch Num: [473/600]
Discriminator Loss: 0.7776, Generator Loss: 1.4440
D(x): 0.7849, D(G(z)): 0.3315
Epoch: [19/20], Batch Num: [474/600]
Discriminator Loss: 0.8483, Generator Loss: 1.7544
D(x): 0.7726, D(G(z)): 0.3447
Epoch: [19/20], Batch Num: [475/600]
Discriminator Loss: 0.6893, Generator Loss: 1.8095
D(x): 0.7676, D(G(z)): 0.2563
Epoch: [19/20], Batch Num: [476/600]
Discriminator Loss: 0.8335, Generator Loss: 1.8741
D(x): 0.6947, D(G(z)): 0.2701
Epoch: [19/20], Batch Num: [477/600]
Discriminator Loss: 0.8141, Generator Loss: 1.7797
D(x): 0.6869, D(G(z)): 0.2287
Epoch: [19/20], Batch Num: [478/600]
Discriminator Loss: 0.7571, Generator Loss: 1.6053
D(x): 0.6886, D(G(z)): 0.2069
Epoch: [19/20], Batch Num: [479/600]
Discriminator Loss: 0.6979, Generator Loss: 1.4046
D(x): 0.7676, D(G(z)): 0.2496
Epoch: [19/20], Batch Num: [480/600]
Discriminator Loss: 0.7928, Generator Loss: 1.3950
D(x): 0.7831, D(G(z)): 0.3182
Epoch: [19/20], Batch Num: [481/600]
Discriminator Loss: 0.8393, Generator Loss: 1.4629
D(x): 0.8034, D(G(z)): 0.3549
Epoch: [19/20], Batch Num: [482/600]
Discriminator Loss: 0.7367, Generator Loss: 1.6630
D(x): 0.7760, D(G(z)): 0.3118
Epoch: [19/20], Batch Num: [483/600]
Discriminator Loss: 0.7875, Generator Loss: 1.8296
D(x): 0.7293, D(G(z)): 0.2751
Epoch: [19/20], Batch Num: [484/600]
Discriminator Loss: 0.8203, Generator Loss: 1.7279
D(x): 0.6699, D(G(z)): 0.2269
Epoch: [19/20], Batch Num: [485/600]
Discriminator Loss: 0.7582, Generator Loss: 1.6209
D(x): 0.7326, D(G(z)): 0.2425
Epoch: [19/20], Batch Num: [486/600]
Discriminator Loss: 0.8538, Generator Loss: 1.5973
D(x): 0.6895, D(G(z)): 0.2650
Epoch: [19/20], Batch Num: [487/600]
Discriminator Loss: 0.7973, Generator Loss: 1.3222
D(x): 0.7422, D(G(z)): 0.2947
Epoch: [19/20], Batch Num: [488/600]
Discriminator Loss: 0.8141, Generator Loss: 1.4024
D(x): 0.7876, D(G(z)): 0.3308
Epoch: [19/20], Batch Num: [489/600]
Discriminator Loss: 1.0341, Generator Loss: 1.6490
D(x): 0.6875, D(G(z)): 0.3482
Epoch: [19/20], Batch Num: [490/600]
Discriminator Loss: 0.9442, Generator Loss: 1.4621
D(x): 0.6831, D(G(z)): 0.2941
Epoch: [19/20], Batch Num: [491/600]
Discriminator Loss: 1.0052, Generator Loss: 1.7219
D(x): 0.6818, D(G(z)): 0.3289
Epoch: [19/20], Batch Num: [492/600]
Discriminator Loss: 1.0702, Generator Loss: 1.9309
D(x): 0.6693, D(G(z)): 0.3191
Epoch: [19/20], Batch Num: [493/600]
Discriminator Loss: 1.0141, Generator Loss: 1.5435
D(x): 0.6302, D(G(z)): 0.2468
Epoch: [19/20], Batch Num: [494/600]
Discriminator Loss: 1.0456, Generator Loss: 1.5426
D(x): 0.6437, D(G(z)): 0.2824
Epoch: [19/20], Batch Num: [495/600]
Discriminator Loss: 0.9256, Generator Loss: 1.3038
D(x): 0.7052, D(G(z)): 0.3039
Epoch: [19/20], Batch Num: [496/600]
Discriminator Loss: 0.9601, Generator Loss: 1.4516
D(x): 0.7283, D(G(z)): 0.3472
Epoch: [19/20], Batch Num: [497/600]
Discriminator Loss: 1.2345, Generator Loss: 1.2015
D(x): 0.6283, D(G(z)): 0.3880
Epoch: [19/20], Batch Num: [498/600]
Discriminator Loss: 1.0582, Generator Loss: 1.4377
D(x): 0.6794, D(G(z)): 0.3522
Epoch: [19/20], Batch Num: [499/600]
Discriminator Loss: 1.0993, Generator Loss: 1.4208
D(x): 0.6251, D(G(z)): 0.3039
Epoch: 19, Batch Num: [500/600]

Epoch: [19/20], Batch Num: [500/600]
Discriminator Loss: 1.2123, Generator Loss: 1.4360
D(x): 0.6330, D(G(z)): 0.3629
Epoch: [19/20], Batch Num: [501/600]
Discriminator Loss: 1.0496, Generator Loss: 1.3733
D(x): 0.6239, D(G(z)): 0.2911
Epoch: [19/20], Batch Num: [502/600]
Discriminator Loss: 1.1945, Generator Loss: 1.2529
D(x): 0.6271, D(G(z)): 0.3651
Epoch: [19/20], Batch Num: [503/600]
Discriminator Loss: 1.1034, Generator Loss: 1.2581
D(x): 0.7125, D(G(z)): 0.3953
Epoch: [19/20], Batch Num: [504/600]
Discriminator Loss: 1.2862, Generator Loss: 1.3268
D(x): 0.5909, D(G(z)): 0.3629
Epoch: [19/20], Batch Num: [505/600]
Discriminator Loss: 1.1162, Generator Loss: 1.2550
D(x): 0.6068, D(G(z)): 0.3426
Epoch: [19/20], Batch Num: [506/600]
Discriminator Loss: 1.0175, Generator Loss: 1.2394
D(x): 0.6556, D(G(z)): 0.3306
Epoch: [19/20], Batch Num: [507/600]
Discriminator Loss: 1.0836, Generator Loss: 1.3380
D(x): 0.6100, D(G(z)): 0.3190
Epoch: [19/20], Batch Num: [508/600]
Discriminator Loss: 1.0662, Generator Loss: 1.2089
D(x): 0.6611, D(G(z)): 0.3618
Epoch: [19/20], Batch Num: [509/600]
Discriminator Loss: 1.0378, Generator Loss: 1.3123
D(x): 0.6837, D(G(z)): 0.3590
Epoch: [19/20], Batch Num: [510/600]
Discriminator Loss: 1.0663, Generator Loss: 1.4470
D(x): 0.6682, D(G(z)): 0.3707
Epoch: [19/20], Batch Num: [511/600]
Discriminator Loss: 1.0660, Generator Loss: 1.5540
D(x): 0.6710, D(G(z)): 0.3304
Epoch: [19/20], Batch Num: [512/600]
Discriminator Loss: 0.9384, Generator Loss: 1.5787
D(x): 0.6587, D(G(z)): 0.2944
Epoch: [19/20], Batch Num: [513/600]
Discriminator Loss: 0.8867, Generator Loss: 1.5009
D(x): 0.6141, D(G(z)): 0.2429
Epoch: [19/20], Batch Num: [514/600]
Discriminator Loss: 0.8903, Generator Loss: 1.3630
D(x): 0.6981, D(G(z)): 0.2898
Epoch: [19/20], Batch Num: [515/600]
Discriminator Loss: 0.8867, Generator Loss: 1.4166
D(x): 0.7076, D(G(z)): 0.3239
Epoch: [19/20], Batch Num: [516/600]
Discriminator Loss: 0.7876, Generator Loss: 1.5023
D(x): 0.7622, D(G(z)): 0.3181
Epoch: [19/20], Batch Num: [517/600]
Discriminator Loss: 0.7420, Generator Loss: 1.6500
D(x): 0.7811, D(G(z)): 0.3152
Epoch: [19/20], Batch Num: [518/600]
Discriminator Loss: 0.7894, Generator Loss: 1.7330
D(x): 0.7531, D(G(z)): 0.3187
Epoch: [19/20], Batch Num: [519/600]
Discriminator Loss: 0.8346, Generator Loss: 1.8748
D(x): 0.6993, D(G(z)): 0.2770
Epoch: [19/20], Batch Num: [520/600]
Discriminator Loss: 0.6639, Generator Loss: 1.9772
D(x): 0.7379, D(G(z)): 0.2100
Epoch: [19/20], Batch Num: [521/600]
Discriminator Loss: 0.7018, Generator Loss: 1.9790
D(x): 0.7278, D(G(z)): 0.2111
Epoch: [19/20], Batch Num: [522/600]
Discriminator Loss: 0.5861, Generator Loss: 1.9908
D(x): 0.7387, D(G(z)): 0.1699
Epoch: [19/20], Batch Num: [523/600]
Discriminator Loss: 0.7945, Generator Loss: 1.9463
D(x): 0.6930, D(G(z)): 0.2218
Epoch: [19/20], Batch Num: [524/600]
Discriminator Loss: 0.5872, Generator Loss: 1.7116
D(x): 0.8012, D(G(z)): 0.2407
Epoch: [19/20], Batch Num: [525/600]
Discriminator Loss: 0.6589, Generator Loss: 1.7123
D(x): 0.8355, D(G(z)): 0.2963
Epoch: [19/20], Batch Num: [526/600]
Discriminator Loss: 0.7329, Generator Loss: 1.9514
D(x): 0.8002, D(G(z)): 0.3004
Epoch: [19/20], Batch Num: [527/600]
Discriminator Loss: 0.6749, Generator Loss: 2.1324
D(x): 0.7571, D(G(z)): 0.2069
Epoch: [19/20], Batch Num: [528/600]
Discriminator Loss: 0.6914, Generator Loss: 1.8686
D(x): 0.7190, D(G(z)): 0.1763
Epoch: [19/20], Batch Num: [529/600]
Discriminator Loss: 0.6486, Generator Loss: 1.8802
D(x): 0.7606, D(G(z)): 0.1965
Epoch: [19/20], Batch Num: [530/600]
Discriminator Loss: 0.6989, Generator Loss: 1.6876
D(x): 0.7907, D(G(z)): 0.2654
Epoch: [19/20], Batch Num: [531/600]
Discriminator Loss: 0.7234, Generator Loss: 1.8666
D(x): 0.8238, D(G(z)): 0.3087
Epoch: [19/20], Batch Num: [532/600]
Discriminator Loss: 0.7718, Generator Loss: 2.1400
D(x): 0.7270, D(G(z)): 0.2467
Epoch: [19/20], Batch Num: [533/600]
Discriminator Loss: 0.7613, Generator Loss: 2.2053
D(x): 0.7525, D(G(z)): 0.2491
Epoch: [19/20], Batch Num: [534/600]
Discriminator Loss: 0.9341, Generator Loss: 2.0956
D(x): 0.7134, D(G(z)): 0.2753
Epoch: [19/20], Batch Num: [535/600]
Discriminator Loss: 0.8480, Generator Loss: 2.1701
D(x): 0.6981, D(G(z)): 0.2299
Epoch: [19/20], Batch Num: [536/600]
Discriminator Loss: 0.7552, Generator Loss: 1.9178
D(x): 0.7339, D(G(z)): 0.2262
Epoch: [19/20], Batch Num: [537/600]
Discriminator Loss: 0.8879, Generator Loss: 1.6510
D(x): 0.7124, D(G(z)): 0.2949
Epoch: [19/20], Batch Num: [538/600]
Discriminator Loss: 0.7806, Generator Loss: 1.5903
D(x): 0.7896, D(G(z)): 0.3059
Epoch: [19/20], Batch Num: [539/600]
Discriminator Loss: 0.9595, Generator Loss: 1.8046
D(x): 0.7784, D(G(z)): 0.3265
Epoch: [19/20], Batch Num: [540/600]
Discriminator Loss: 1.1138, Generator Loss: 1.9388
D(x): 0.6394, D(G(z)): 0.2879
Epoch: [19/20], Batch Num: [541/600]
Discriminator Loss: 1.2334, Generator Loss: 1.6791
D(x): 0.6034, D(G(z)): 0.3172
Epoch: [19/20], Batch Num: [542/600]
Discriminator Loss: 1.0401, Generator Loss: 1.3505
D(x): 0.6622, D(G(z)): 0.2785
Epoch: [19/20], Batch Num: [543/600]
Discriminator Loss: 1.1570, Generator Loss: 1.3046
D(x): 0.6652, D(G(z)): 0.3673
Epoch: [19/20], Batch Num: [544/600]
Discriminator Loss: 1.1744, Generator Loss: 1.3375
D(x): 0.7303, D(G(z)): 0.4003
Epoch: [19/20], Batch Num: [545/600]
Discriminator Loss: 1.3463, Generator Loss: 1.5984
D(x): 0.6416, D(G(z)): 0.4152
Epoch: [19/20], Batch Num: [546/600]
Discriminator Loss: 0.9980, Generator Loss: 1.6772
D(x): 0.6734, D(G(z)): 0.3107
Epoch: [19/20], Batch Num: [547/600]
Discriminator Loss: 1.4051, Generator Loss: 1.5312
D(x): 0.5526, D(G(z)): 0.3443
Epoch: [19/20], Batch Num: [548/600]
Discriminator Loss: 1.3780, Generator Loss: 1.2980
D(x): 0.5526, D(G(z)): 0.3417
Epoch: [19/20], Batch Num: [549/600]
Discriminator Loss: 1.2995, Generator Loss: 1.0388
D(x): 0.6481, D(G(z)): 0.4267
Epoch: [19/20], Batch Num: [550/600]
Discriminator Loss: 1.1723, Generator Loss: 1.0714
D(x): 0.6710, D(G(z)): 0.4061
Epoch: [19/20], Batch Num: [551/600]
Discriminator Loss: 1.2337, Generator Loss: 1.3285
D(x): 0.6576, D(G(z)): 0.4215
Epoch: [19/20], Batch Num: [552/600]
Discriminator Loss: 1.2417, Generator Loss: 1.2414
D(x): 0.6410, D(G(z)): 0.4041
Epoch: [19/20], Batch Num: [553/600]
Discriminator Loss: 1.2684, Generator Loss: 1.2917
D(x): 0.5938, D(G(z)): 0.3777
Epoch: [19/20], Batch Num: [554/600]
Discriminator Loss: 1.2039, Generator Loss: 1.2395
D(x): 0.5859, D(G(z)): 0.3543
Epoch: [19/20], Batch Num: [555/600]
Discriminator Loss: 1.2787, Generator Loss: 1.2015
D(x): 0.5631, D(G(z)): 0.3466
Epoch: [19/20], Batch Num: [556/600]
Discriminator Loss: 1.0911, Generator Loss: 1.1064
D(x): 0.6549, D(G(z)): 0.3786
Epoch: [19/20], Batch Num: [557/600]
Discriminator Loss: 0.9817, Generator Loss: 1.1959
D(x): 0.7308, D(G(z)): 0.3911
Epoch: [19/20], Batch Num: [558/600]
Discriminator Loss: 1.0052, Generator Loss: 1.3896
D(x): 0.6616, D(G(z)): 0.3508
Epoch: [19/20], Batch Num: [559/600]
Discriminator Loss: 0.8900, Generator Loss: 1.3077
D(x): 0.6667, D(G(z)): 0.2905
Epoch: [19/20], Batch Num: [560/600]
Discriminator Loss: 1.0482, Generator Loss: 1.3857
D(x): 0.6468, D(G(z)): 0.3460
Epoch: [19/20], Batch Num: [561/600]
Discriminator Loss: 0.8739, Generator Loss: 1.4251
D(x): 0.6883, D(G(z)): 0.2966
Epoch: [19/20], Batch Num: [562/600]
Discriminator Loss: 0.8516, Generator Loss: 1.3480
D(x): 0.6759, D(G(z)): 0.2875
Epoch: [19/20], Batch Num: [563/600]
Discriminator Loss: 0.7960, Generator Loss: 1.3136
D(x): 0.7076, D(G(z)): 0.2881
Epoch: [19/20], Batch Num: [564/600]
Discriminator Loss: 0.9109, Generator Loss: 1.3364
D(x): 0.6991, D(G(z)): 0.3265
Epoch: [19/20], Batch Num: [565/600]
Discriminator Loss: 0.8068, Generator Loss: 1.3640
D(x): 0.7331, D(G(z)): 0.3050
Epoch: [19/20], Batch Num: [566/600]
Discriminator Loss: 0.9127, Generator Loss: 1.5714
D(x): 0.7351, D(G(z)): 0.3619
Epoch: [19/20], Batch Num: [567/600]
Discriminator Loss: 0.7679, Generator Loss: 1.6232
D(x): 0.7339, D(G(z)): 0.2937
Epoch: [19/20], Batch Num: [568/600]
Discriminator Loss: 0.8184, Generator Loss: 1.7472
D(x): 0.7163, D(G(z)): 0.2828
Epoch: [19/20], Batch Num: [569/600]
Discriminator Loss: 0.7515, Generator Loss: 1.7664
D(x): 0.7228, D(G(z)): 0.2626
Epoch: [19/20], Batch Num: [570/600]
Discriminator Loss: 0.6611, Generator Loss: 1.7803
D(x): 0.7245, D(G(z)): 0.2049
Epoch: [19/20], Batch Num: [571/600]
Discriminator Loss: 0.7443, Generator Loss: 1.6673
D(x): 0.7322, D(G(z)): 0.2594
Epoch: [19/20], Batch Num: [572/600]
Discriminator Loss: 0.6545, Generator Loss: 1.6892
D(x): 0.7666, D(G(z)): 0.2439
Epoch: [19/20], Batch Num: [573/600]
Discriminator Loss: 0.7020, Generator Loss: 1.6756
D(x): 0.7621, D(G(z)): 0.2649
Epoch: [19/20], Batch Num: [574/600]
Discriminator Loss: 0.5811, Generator Loss: 1.7233
D(x): 0.8101, D(G(z)): 0.2381
Epoch: [19/20], Batch Num: [575/600]
Discriminator Loss: 0.5564, Generator Loss: 1.6994
D(x): 0.8291, D(G(z)): 0.2477
Epoch: [19/20], Batch Num: [576/600]
Discriminator Loss: 0.6270, Generator Loss: 1.8780
D(x): 0.8029, D(G(z)): 0.2497
Epoch: [19/20], Batch Num: [577/600]
Discriminator Loss: 0.5730, Generator Loss: 1.9453
D(x): 0.8422, D(G(z)): 0.2302
Epoch: [19/20], Batch Num: [578/600]
Discriminator Loss: 0.6666, Generator Loss: 2.1847
D(x): 0.7638, D(G(z)): 0.2106
Epoch: [19/20], Batch Num: [579/600]
Discriminator Loss: 0.9107, Generator Loss: 2.1905
D(x): 0.6920, D(G(z)): 0.2115
Epoch: [19/20], Batch Num: [580/600]
Discriminator Loss: 0.7421, Generator Loss: 2.0320
D(x): 0.7085, D(G(z)): 0.1799
Epoch: [19/20], Batch Num: [581/600]
Discriminator Loss: 0.6634, Generator Loss: 1.7490
D(x): 0.7805, D(G(z)): 0.2348
Epoch: [19/20], Batch Num: [582/600]
Discriminator Loss: 0.7568, Generator Loss: 1.5655
D(x): 0.8327, D(G(z)): 0.3090
Epoch: [19/20], Batch Num: [583/600]
Discriminator Loss: 0.9200, Generator Loss: 2.0812
D(x): 0.8594, D(G(z)): 0.4153
Epoch: [19/20], Batch Num: [584/600]
Discriminator Loss: 0.6927, Generator Loss: 2.2304
D(x): 0.7448, D(G(z)): 0.1930
Epoch: [19/20], Batch Num: [585/600]
Discriminator Loss: 0.9652, Generator Loss: 2.1165
D(x): 0.6070, D(G(z)): 0.1502
Epoch: [19/20], Batch Num: [586/600]
Discriminator Loss: 0.8291, Generator Loss: 1.8675
D(x): 0.6688, D(G(z)): 0.2002
Epoch: [19/20], Batch Num: [587/600]
Discriminator Loss: 1.0683, Generator Loss: 1.4481
D(x): 0.6948, D(G(z)): 0.3280
Epoch: [19/20], Batch Num: [588/600]
Discriminator Loss: 1.1444, Generator Loss: 1.6732
D(x): 0.7482, D(G(z)): 0.3918
Epoch: [19/20], Batch Num: [589/600]
Discriminator Loss: 1.0642, Generator Loss: 1.6954
D(x): 0.7479, D(G(z)): 0.3794
Epoch: [19/20], Batch Num: [590/600]
Discriminator Loss: 0.9667, Generator Loss: 2.1212
D(x): 0.6676, D(G(z)): 0.2773
Epoch: [19/20], Batch Num: [591/600]
Discriminator Loss: 1.1921, Generator Loss: 1.9527
D(x): 0.5605, D(G(z)): 0.2443
Epoch: [19/20], Batch Num: [592/600]
Discriminator Loss: 1.2480, Generator Loss: 1.4698
D(x): 0.6022, D(G(z)): 0.3037
Epoch: [19/20], Batch Num: [593/600]
Discriminator Loss: 1.1892, Generator Loss: 1.4608
D(x): 0.6780, D(G(z)): 0.4041
Epoch: [19/20], Batch Num: [594/600]
Discriminator Loss: 1.2107, Generator Loss: 1.2343
D(x): 0.6400, D(G(z)): 0.3724
Epoch: [19/20], Batch Num: [595/600]
Discriminator Loss: 1.4099, Generator Loss: 1.3747
D(x): 0.6256, D(G(z)): 0.4273
Epoch: [19/20], Batch Num: [596/600]
Discriminator Loss: 1.3370, Generator Loss: 1.3535
D(x): 0.5912, D(G(z)): 0.3709
Epoch: [19/20], Batch Num: [597/600]
Discriminator Loss: 1.3764, Generator Loss: 1.3767
D(x): 0.5435, D(G(z)): 0.3305
Epoch: [19/20], Batch Num: [598/600]
Discriminator Loss: 1.0920, Generator Loss: 1.5166
D(x): 0.6477, D(G(z)): 0.3489
Epoch: [19/20], Batch Num: [599/600]
Discriminator Loss: 1.3190, Generator Loss: 1.4301
D(x): 0.5624, D(G(z)): 0.3680

Au début, les images générées sont du pur bruit, mais elles s'améliorent ensuite, jusqu'à ce qu'on obtienne des images assez bonnes.

Il est également possible de visualiser le processus d'apprentissage. Comme vous pouvez le voir dans le bloc précédent, l'erreur du discriminateur est très élevée au début, car il ne sait pas comment classer correctement les images comme étant réelles ou fausses. Au fur et à mesure que le discriminateur s'améliore et que son erreur diminue à environ 0,4 . Quant à l'erreur du générateur, elle augmente, ce qui prouve que le discriminateur est plus performant que le générateur et qu'il peut classer correctement les échantillons faux.

En poursuivant l'entraînement, l'erreur du générateur diminue, ce qui implique que les images qu'il génère sont de mieux en mieux. Alors que le générateur s'améliore, l'erreur du discriminateur augmente, car les images deviennent chaque fois plus réalistes.

def train_discriminator(optimizer, real_data, fake_data):
    N = real_data.size(0)
    # Reset gradients
    optimizer.zero_grad()

    # 1.1 Train on Real Data
    prediction_real = discriminator(real_data)
    # Calculate error and backpropagate
    error_real = loss(prediction_real.cuda(), ones_target(N).cuda())
    error_real.backward()

    # 1.2 Train on Fake Data
    prediction_fake = discriminator(fake_data)
    # Calculate error and backpropagate
    error_fake = loss(prediction_fake.cuda(), zeros_target(N).cuda())
    error_fake.backward()

    # 1.3 Update weights with gradients
    optimizer.step()

    # Return error and predictions for real and fake inputs
    return error_real + error_fake, prediction_real, prediction_fake

def train_generator(optimizer, fake_data):
    N = fake_data.size(0)
    # Reset gradients
    optimizer.zero_grad()
    # Sample noise and generate fake data
    prediction = discriminator(fake_data)
    # Calculate error and backpropagate
    error = loss(prediction.cuda(), ones_target(N).cuda())
    error.backward()
    # Update weights with gradients
    optimizer.step()
    # Return error
    return error

7. Exploration et optimisation

Modifiez la structure des deux réseaux (discriminateur et générateur) en ajoutant ou enlevant des couches cachées, en modifiant le nombre de neurones dans ces couches et en jouant sur les paramètres des fonctions LeakyReLU et Dropout.

Après avoir expliqué comment évaluer la qualité des images générées par le générateur, comparez la qualité des résultats obtenus avec les modifications que vous avez testé et essayez d’obtenir le meilleur résultat possible.

class DiscriminatorNet(nn.Module):
#On ajoute une couche cachée + Modifiant le nombre de neurones dans ces couches
 #LeakyReLU à alpha = 0.10 et Dropout0.5
    def __init__(self):
        super(DiscriminatorNet, self).__init__()
        n_features = 784
        n_out = 1
        
        self.hidden0 = nn.Sequential( 
            nn.Linear(n_features, 1024),
            nn.LeakyReLU(0.1),
            nn.Dropout(0.5)
        )
      

        self.hidden1 = nn.Sequential(
            nn.Linear(1024, 768),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.3)
        )

        self.hidden2 = nn.Sequential(
            nn.Linear(768, 512),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.3)
        )

        self.hidden3 = nn.Sequential(
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.3)
        )
            
        self.out = nn.Sequential(
            torch.nn.Linear(256, n_out),
            torch.nn.Sigmoid()
        )

    def forward(self, x):
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = self.hidden2(x)
        x = self.hidden3(x)
        x = self.out(x)
        return x
    
discriminator = DiscriminatorNet()

class GeneratorNet(nn.Module):
    """
    4 couches cachées generative neural network
    """

    def __init__(self):
        super(GeneratorNet, self).__init__()
        n_features = 100
        n_out = 784

        self.hidden0 = nn.Sequential(
            nn.Linear(n_features, 256),
            nn.LeakyReLU(0.1)
        )

        self.hidden1 = nn.Sequential(
            nn.Linear(256, 512),
            nn.LeakyReLU(0.1)
        )

        self.hidden2 = nn.Sequential(
            nn.Linear(512, 768 ),
            nn.LeakyReLU(0.1)
        )
        
        self.hidden3 = nn.Sequential(
            nn.Linear(768, 1024),
            nn.LeakyReLU(0.1),
            
        )
         

        self.out = nn.Sequential(
            nn.Linear(1024, n_out),
            nn.Tanh()
        )

    def forward(self, x):
       
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = self.hidden2(x)
        x = self.hidden3(x)
        x = self.out(x)
        return x
generator = GeneratorNet()

if torch.cuda.is_available():
    discriminator.cuda()
    generator.cuda()
    
# Optimizers
d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), lr=0.0002)

# Loss function 
loss = nn.BCELoss()

def train_discriminator(optimizer, real_data, fake_data):
    # Reset gradients
    optimizer.zero_grad()
    
    # 1.1 Train on Real Data
    prediction_real = discriminator(real_data)
    # Calculate error and backpropagate
    error_real = loss(prediction_real, gt.real_data_target(real_data.size(0)))
    error_real.backward()

    # 1.2 Train on Fake Data
    prediction_fake = discriminator(fake_data)
    # Calculate error and backpropagate
    error_fake = loss(prediction_fake, gt.fake_data_target(real_data.size(0)))
    error_fake.backward()
    
    # 1.3 Update weights with gradients
    optimizer.step()
    
    # Return error
    return error_real + error_fake, prediction_real, prediction_fake

def train_generator(optimizer, fake_data):
    # 2. Train Generator
    # Reset gradients
    optimizer.zero_grad()
    # Sample noise and generate fake data
    prediction = discriminator(fake_data)
    # Calculate error and backpropagate
    error = loss(prediction, gt.real_data_target(prediction.size(0)))
    error.backward()
    # Update weights with gradients
    optimizer.step()
    # Return error
    return error

num_epochs=20
for epoch in range(num_epochs):
  
    for n_batch, (real_batch,_) in enumerate(data_loader):
        n = real_batch.size(0)
        #Transformez le lot de données en variables Torch à l'aide des fonctions Variable et gt.images_to_vectors. 
        #Ce sont les vraies données pour l'étape suivante.
        # 1 Train Discriminator
        # 1.1 Prepare real data
        real_data = Variable(gt.images_to_vectors(real_batch))
       
        
            
        # 1.2 Generate fake data with the Generator
        #""" A COMPLETER : Créer des vecteurs bruit """
        #(les gradients ne sont donc pas calculés pour le générateur)
        fake_data = generator(gt.noise(n)).detach()
        #""" A COMPLETER : Générer des images avec le générateur """
        
        # 1.3 Train Discriminator 
       # """ A COMPLETER : Entrainer le discriminateur """
        d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, real_data, fake_data)

 
        # 2 Train Generator
        # 2.1 Generate noise
        fake_data = generator(gt.noise(n))
                      
        # 2.2 Train Generator
        #"""  A COMPLETER : Entrainer le generateur """
        g_error = train_generator(g_optimizer, fake_data)
                              
        # Generate images from a fixed noise input and visualize the output
        gt.plot_gan(epoch, n_batch, num_batches, generator)

        # Display status Logs
        gt.logger.display_status(
                epoch, num_epochs, n_batch, num_batches,
                d_error, g_error, d_pred_real, d_pred_fake
            )

  

Epoch: 0, Batch Num: [0/600]

Epoch: [0/20], Batch Num: [0/600]
Discriminator Loss: 1.3811, Generator Loss: 0.7151
D(x): 0.4914, D(G(z)): 0.4885
Epoch: [0/20], Batch Num: [1/600]
Discriminator Loss: 1.3041, Generator Loss: 0.7127
D(x): 0.5318, D(G(z)): 0.4896
Epoch: [0/20], Batch Num: [2/600]
Discriminator Loss: 1.2142, Generator Loss: 0.7083
D(x): 0.5841, D(G(z)): 0.4914
Epoch: [0/20], Batch Num: [3/600]
Discriminator Loss: 1.1055, Generator Loss: 0.7016
D(x): 0.6549, D(G(z)): 0.4943
Epoch: [0/20], Batch Num: [4/600]
Discriminator Loss: 0.9954, Generator Loss: 0.6876
D(x): 0.7392, D(G(z)): 0.4996
Epoch: [0/20], Batch Num: [5/600]
Discriminator Loss: 0.9013, Generator Loss: 0.6620
D(x): 0.8275, D(G(z)): 0.5090
Epoch: [0/20], Batch Num: [6/600]
Discriminator Loss: 0.8470, Generator Loss: 0.6230
D(x): 0.9068, D(G(z)): 0.5270
Epoch: [0/20], Batch Num: [7/600]
Discriminator Loss: 0.8443, Generator Loss: 0.5659
D(x): 0.9633, D(G(z)): 0.5536
Epoch: [0/20], Batch Num: [8/600]
Discriminator Loss: 0.9213, Generator Loss: 0.5061
D(x): 0.9856, D(G(z)): 0.5959
Epoch: [0/20], Batch Num: [9/600]
Discriminator Loss: 1.0543, Generator Loss: 0.4480
D(x): 0.9919, D(G(z)): 0.6479
Epoch: [0/20], Batch Num: [10/600]
Discriminator Loss: 1.1811, Generator Loss: 0.3996
D(x): 0.9944, D(G(z)): 0.6900
Epoch: [0/20], Batch Num: [11/600]
Discriminator Loss: 1.3527, Generator Loss: 0.3732
D(x): 0.9947, D(G(z)): 0.7378
Epoch: [0/20], Batch Num: [12/600]
Discriminator Loss: 1.4119, Generator Loss: 0.3642
D(x): 0.9912, D(G(z)): 0.7519
Epoch: [0/20], Batch Num: [13/600]
Discriminator Loss: 1.4805, Generator Loss: 0.3713
D(x): 0.9832, D(G(z)): 0.7657
Epoch: [0/20], Batch Num: [14/600]
Discriminator Loss: 1.4603, Generator Loss: 0.4004
D(x): 0.9670, D(G(z)): 0.7574
Epoch: [0/20], Batch Num: [15/600]
Discriminator Loss: 1.3578, Generator Loss: 0.4560
D(x): 0.9332, D(G(z)): 0.7224
Epoch: [0/20], Batch Num: [16/600]
Discriminator Loss: 1.2953, Generator Loss: 0.5225
D(x): 0.8803, D(G(z)): 0.6873
Epoch: [0/20], Batch Num: [17/600]
Discriminator Loss: 1.2335, Generator Loss: 0.6099
D(x): 0.8052, D(G(z)): 0.6367
Epoch: [0/20], Batch Num: [18/600]
Discriminator Loss: 1.1781, Generator Loss: 0.7066
D(x): 0.7261, D(G(z)): 0.5746
Epoch: [0/20], Batch Num: [19/600]
Discriminator Loss: 1.1698, Generator Loss: 0.7903
D(x): 0.6431, D(G(z)): 0.5161
Epoch: [0/20], Batch Num: [20/600]
Discriminator Loss: 1.1910, Generator Loss: 0.8743
D(x): 0.5761, D(G(z)): 0.4711
Epoch: [0/20], Batch Num: [21/600]
Discriminator Loss: 1.2092, Generator Loss: 0.9240
D(x): 0.5252, D(G(z)): 0.4307
Epoch: [0/20], Batch Num: [22/600]
Discriminator Loss: 1.2407, Generator Loss: 0.9584
D(x): 0.4939, D(G(z)): 0.4131
Epoch: [0/20], Batch Num: [23/600]
Discriminator Loss: 1.2493, Generator Loss: 0.9705
D(x): 0.4773, D(G(z)): 0.3976
Epoch: [0/20], Batch Num: [24/600]
Discriminator Loss: 1.2410, Generator Loss: 0.9657
D(x): 0.4815, D(G(z)): 0.3975
Epoch: [0/20], Batch Num: [25/600]
Discriminator Loss: 1.2087, Generator Loss: 0.9312
D(x): 0.5061, D(G(z)): 0.4073
Epoch: [0/20], Batch Num: [26/600]
Discriminator Loss: 1.2134, Generator Loss: 0.8800
D(x): 0.5240, D(G(z)): 0.4296
Epoch: [0/20], Batch Num: [27/600]
Discriminator Loss: 1.2263, Generator Loss: 0.8646
D(x): 0.5505, D(G(z)): 0.4634
Epoch: [0/20], Batch Num: [28/600]
Discriminator Loss: 1.2099, Generator Loss: 0.8966
D(x): 0.5705, D(G(z)): 0.4733
Epoch: [0/20], Batch Num: [29/600]
Discriminator Loss: 1.1969, Generator Loss: 1.0007
D(x): 0.5664, D(G(z)): 0.4590
Epoch: [0/20], Batch Num: [30/600]
Discriminator Loss: 1.1857, Generator Loss: 1.1693
D(x): 0.5278, D(G(z)): 0.4103
Epoch: [0/20], Batch Num: [31/600]
Discriminator Loss: 1.1678, Generator Loss: 1.3387
D(x): 0.4925, D(G(z)): 0.3506
Epoch: [0/20], Batch Num: [32/600]
Discriminator Loss: 1.1432, Generator Loss: 1.4207
D(x): 0.4866, D(G(z)): 0.3130
Epoch: [0/20], Batch Num: [33/600]
Discriminator Loss: 1.0824, Generator Loss: 1.3478
D(x): 0.5019, D(G(z)): 0.2987
Epoch: [0/20], Batch Num: [34/600]
Discriminator Loss: 0.9774, Generator Loss: 1.2812
D(x): 0.5893, D(G(z)): 0.3355
Epoch: [0/20], Batch Num: [35/600]
Discriminator Loss: 1.0033, Generator Loss: 1.4289
D(x): 0.6163, D(G(z)): 0.3760
Epoch: [0/20], Batch Num: [36/600]
Discriminator Loss: 0.7920, Generator Loss: 1.9065
D(x): 0.6889, D(G(z)): 0.3233
Epoch: [0/20], Batch Num: [37/600]
Discriminator Loss: 0.6211, Generator Loss: 2.5281
D(x): 0.7147, D(G(z)): 0.2273
Epoch: [0/20], Batch Num: [38/600]
Discriminator Loss: 0.5293, Generator Loss: 2.7788
D(x): 0.7052, D(G(z)): 0.1295
Epoch: [0/20], Batch Num: [39/600]
Discriminator Loss: 0.3876, Generator Loss: 2.9894
D(x): 0.7764, D(G(z)): 0.1047
Epoch: [0/20], Batch Num: [40/600]
Discriminator Loss: 0.2676, Generator Loss: 3.0837
D(x): 0.8559, D(G(z)): 0.0945
Epoch: [0/20], Batch Num: [41/600]
Discriminator Loss: 0.1882, Generator Loss: 3.5170
D(x): 0.9240, D(G(z)): 0.0972
Epoch: [0/20], Batch Num: [42/600]
Discriminator Loss: 0.1259, Generator Loss: 4.3066
D(x): 0.9553, D(G(z)): 0.0742
Epoch: [0/20], Batch Num: [43/600]
Discriminator Loss: 0.0847, Generator Loss: 5.3372
D(x): 0.9673, D(G(z)): 0.0480
Epoch: [0/20], Batch Num: [44/600]
Discriminator Loss: 0.0377, Generator Loss: 6.3888
D(x): 0.9789, D(G(z)): 0.0154
Epoch: [0/20], Batch Num: [45/600]
Discriminator Loss: 0.0258, Generator Loss: 7.2192
D(x): 0.9825, D(G(z)): 0.0078
Epoch: [0/20], Batch Num: [46/600]
Discriminator Loss: 0.0162, Generator Loss: 7.7835
D(x): 0.9894, D(G(z)): 0.0054
Epoch: [0/20], Batch Num: [47/600]
Discriminator Loss: 0.0097, Generator Loss: 8.2731
D(x): 0.9935, D(G(z)): 0.0031
Epoch: [0/20], Batch Num: [48/600]
Discriminator Loss: 0.0122, Generator Loss: 7.8538
D(x): 0.9939, D(G(z)): 0.0058
Epoch: [0/20], Batch Num: [49/600]
Discriminator Loss: 0.0146, Generator Loss: 7.8559
D(x): 0.9982, D(G(z)): 0.0120
Epoch: [0/20], Batch Num: [50/600]
Discriminator Loss: 0.0248, Generator Loss: 8.0070
D(x): 0.9977, D(G(z)): 0.0205
Epoch: [0/20], Batch Num: [51/600]
Discriminator Loss: 0.0654, Generator Loss: 8.2111
D(x): 0.9835, D(G(z)): 0.0363
Epoch: [0/20], Batch Num: [52/600]
Discriminator Loss: 0.0496, Generator Loss: 8.4913
D(x): 0.9935, D(G(z)): 0.0402
Epoch: [0/20], Batch Num: [53/600]
Discriminator Loss: 0.1565, Generator Loss: 8.1644
D(x): 0.9430, D(G(z)): 0.0508
Epoch: [0/20], Batch Num: [54/600]
Discriminator Loss: 0.3960, Generator Loss: 8.5984
D(x): 0.9349, D(G(z)): 0.1189
Epoch: [0/20], Batch Num: [55/600]
Discriminator Loss: 0.4522, Generator Loss: 6.3314
D(x): 0.8841, D(G(z)): 0.1173
Epoch: [0/20], Batch Num: [56/600]
Discriminator Loss: 0.5697, Generator Loss: 5.7827
D(x): 0.8875, D(G(z)): 0.2431
Epoch: [0/20], Batch Num: [57/600]
Discriminator Loss: 0.6221, Generator Loss: 5.7328
D(x): 0.9352, D(G(z)): 0.3180
Epoch: [0/20], Batch Num: [58/600]
Discriminator Loss: 0.8373, Generator Loss: 4.0031
D(x): 0.8110, D(G(z)): 0.2348
Epoch: [0/20], Batch Num: [59/600]
Discriminator Loss: 0.7660, Generator Loss: 2.9691
D(x): 0.8925, D(G(z)): 0.3443
Epoch: [0/20], Batch Num: [60/600]
Discriminator Loss: 0.9578, Generator Loss: 2.3757
D(x): 0.9151, D(G(z)): 0.4391
Epoch: [0/20], Batch Num: [61/600]
Discriminator Loss: 0.7068, Generator Loss: 1.9959
D(x): 0.9710, D(G(z)): 0.4663
Epoch: [0/20], Batch Num: [62/600]
Discriminator Loss: 0.8702, Generator Loss: 1.4930
D(x): 0.9298, D(G(z)): 0.4644
Epoch: [0/20], Batch Num: [63/600]
Discriminator Loss: 0.9684, Generator Loss: 1.1858
D(x): 0.9401, D(G(z)): 0.5088
Epoch: [0/20], Batch Num: [64/600]
Discriminator Loss: 0.8099, Generator Loss: 0.8860
D(x): 0.9802, D(G(z)): 0.5329
Epoch: [0/20], Batch Num: [65/600]
Discriminator Loss: 0.8279, Generator Loss: 0.7408
D(x): 0.9898, D(G(z)): 0.5550
Epoch: [0/20], Batch Num: [66/600]
Discriminator Loss: 0.9195, Generator Loss: 0.6802
D(x): 0.9892, D(G(z)): 0.5523
Epoch: [0/20], Batch Num: [67/600]
Discriminator Loss: 0.8246, Generator Loss: 0.6427
D(x): 0.9997, D(G(z)): 0.5609
Epoch: [0/20], Batch Num: [68/600]
Discriminator Loss: 0.8403, Generator Loss: 0.6272
D(x): 0.9999, D(G(z)): 0.5680
Epoch: [0/20], Batch Num: [69/600]
Discriminator Loss: 0.8843, Generator Loss: 0.5960
D(x): 0.9999, D(G(z)): 0.5862
Epoch: [0/20], Batch Num: [70/600]
Discriminator Loss: 0.9385, Generator Loss: 0.5654
D(x): 1.0000, D(G(z)): 0.6072
Epoch: [0/20], Batch Num: [71/600]
Discriminator Loss: 1.0292, Generator Loss: 0.5373
D(x): 1.0000, D(G(z)): 0.6399
Epoch: [0/20], Batch Num: [72/600]
Discriminator Loss: 1.1522, Generator Loss: 0.4780
D(x): 1.0000, D(G(z)): 0.6806
Epoch: [0/20], Batch Num: [73/600]
Discriminator Loss: 1.2476, Generator Loss: 0.4406
D(x): 1.0000, D(G(z)): 0.7088
Epoch: [0/20], Batch Num: [74/600]
Discriminator Loss: 1.4058, Generator Loss: 0.3854
D(x): 1.0000, D(G(z)): 0.7488
Epoch: [0/20], Batch Num: [75/600]
Discriminator Loss: 1.4416, Generator Loss: 0.3689
D(x): 1.0000, D(G(z)): 0.7588
Epoch: [0/20], Batch Num: [76/600]
Discriminator Loss: 1.6021, Generator Loss: 0.3777
D(x): 1.0000, D(G(z)): 0.7919
Epoch: [0/20], Batch Num: [77/600]
Discriminator Loss: 1.6174, Generator Loss: 0.3802
D(x): 0.9999, D(G(z)): 0.7950
Epoch: [0/20], Batch Num: [78/600]
Discriminator Loss: 1.4825, Generator Loss: 0.4291
D(x): 0.9996, D(G(z)): 0.7658
Epoch: [0/20], Batch Num: [79/600]
Discriminator Loss: 1.3366, Generator Loss: 0.4892
D(x): 0.9974, D(G(z)): 0.7321
Epoch: [0/20], Batch Num: [80/600]
Discriminator Loss: 1.2044, Generator Loss: 0.5798
D(x): 0.9909, D(G(z)): 0.6936
Epoch: [0/20], Batch Num: [81/600]
Discriminator Loss: 1.0091, Generator Loss: 0.6767
D(x): 0.9614, D(G(z)): 0.6180
Epoch: [0/20], Batch Num: [82/600]
Discriminator Loss: 0.9657, Generator Loss: 0.7829
D(x): 0.8397, D(G(z)): 0.5431
Epoch: [0/20], Batch Num: [83/600]
Discriminator Loss: 0.9927, Generator Loss: 0.8690
D(x): 0.7254, D(G(z)): 0.4826
Epoch: [0/20], Batch Num: [84/600]
Discriminator Loss: 1.0003, Generator Loss: 0.9647
D(x): 0.6785, D(G(z)): 0.4493
Epoch: [0/20], Batch Num: [85/600]
Discriminator Loss: 0.9166, Generator Loss: 1.0330
D(x): 0.6843, D(G(z)): 0.4073
Epoch: [0/20], Batch Num: [86/600]
Discriminator Loss: 0.7847, Generator Loss: 1.1145
D(x): 0.7690, D(G(z)): 0.4018
Epoch: [0/20], Batch Num: [87/600]
Discriminator Loss: 0.6644, Generator Loss: 1.2277
D(x): 0.8325, D(G(z)): 0.3783
Epoch: [0/20], Batch Num: [88/600]
Discriminator Loss: 0.5876, Generator Loss: 1.4620
D(x): 0.8751, D(G(z)): 0.3608
Epoch: [0/20], Batch Num: [89/600]
Discriminator Loss: 0.5050, Generator Loss: 1.7112
D(x): 0.8946, D(G(z)): 0.3200
Epoch: [0/20], Batch Num: [90/600]
Discriminator Loss: 0.4506, Generator Loss: 1.9696
D(x): 0.9059, D(G(z)): 0.2905
Epoch: [0/20], Batch Num: [91/600]
Discriminator Loss: 0.3998, Generator Loss: 2.3500
D(x): 0.9144, D(G(z)): 0.2610
Epoch: [0/20], Batch Num: [92/600]
Discriminator Loss: 0.3741, Generator Loss: 2.6176
D(x): 0.9140, D(G(z)): 0.2412
Epoch: [0/20], Batch Num: [93/600]
Discriminator Loss: 0.4029, Generator Loss: 2.9516
D(x): 0.8816, D(G(z)): 0.2330
Epoch: [0/20], Batch Num: [94/600]
Discriminator Loss: 0.6148, Generator Loss: 2.4229
D(x): 0.8009, D(G(z)): 0.2919
Epoch: [0/20], Batch Num: [95/600]
Discriminator Loss: 0.9966, Generator Loss: 1.5914
D(x): 0.6959, D(G(z)): 0.3994
Epoch: [0/20], Batch Num: [96/600]
Discriminator Loss: 1.4322, Generator Loss: 0.6372
D(x): 0.6878, D(G(z)): 0.5953
Epoch: [0/20], Batch Num: [97/600]
Discriminator Loss: 1.5342, Generator Loss: 0.4426
D(x): 0.8165, D(G(z)): 0.7154
Epoch: [0/20], Batch Num: [98/600]
Discriminator Loss: 1.5331, Generator Loss: 0.4736
D(x): 0.8413, D(G(z)): 0.7262
Epoch: [0/20], Batch Num: [99/600]
Discriminator Loss: 1.5111, Generator Loss: 0.5678
D(x): 0.8085, D(G(z)): 0.6997
Epoch: 0, Batch Num: [100/600]

Epoch: [0/20], Batch Num: [100/600]
Discriminator Loss: 1.2226, Generator Loss: 1.1257
D(x): 0.8606, D(G(z)): 0.6253
Epoch: [0/20], Batch Num: [101/600]
Discriminator Loss: 1.0580, Generator Loss: 1.7958
D(x): 0.8532, D(G(z)): 0.5280
Epoch: [0/20], Batch Num: [102/600]
Discriminator Loss: 0.9436, Generator Loss: 2.2187
D(x): 0.8310, D(G(z)): 0.4480
Epoch: [0/20], Batch Num: [103/600]
Discriminator Loss: 1.0432, Generator Loss: 2.2022
D(x): 0.8228, D(G(z)): 0.4535
Epoch: [0/20], Batch Num: [104/600]
Discriminator Loss: 1.2752, Generator Loss: 1.4897
D(x): 0.7521, D(G(z)): 0.4728
Epoch: [0/20], Batch Num: [105/600]
Discriminator Loss: 1.5371, Generator Loss: 0.8708
D(x): 0.8103, D(G(z)): 0.6400
Epoch: [0/20], Batch Num: [106/600]
Discriminator Loss: 1.7817, Generator Loss: 0.4807
D(x): 0.8405, D(G(z)): 0.7410
Epoch: [0/20], Batch Num: [107/600]
Discriminator Loss: 1.8604, Generator Loss: 0.3794
D(x): 0.8710, D(G(z)): 0.7891
Epoch: [0/20], Batch Num: [108/600]
Discriminator Loss: 2.2591, Generator Loss: 0.3331
D(x): 0.8362, D(G(z)): 0.7944
Epoch: [0/20], Batch Num: [109/600]
Discriminator Loss: 2.0341, Generator Loss: 0.3480
D(x): 0.8260, D(G(z)): 0.7920
Epoch: [0/20], Batch Num: [110/600]
Discriminator Loss: 1.8190, Generator Loss: 0.3775
D(x): 0.8587, D(G(z)): 0.7838
Epoch: [0/20], Batch Num: [111/600]
Discriminator Loss: 1.5523, Generator Loss: 0.4718
D(x): 0.8627, D(G(z)): 0.7357
Epoch: [0/20], Batch Num: [112/600]
Discriminator Loss: 1.4621, Generator Loss: 0.5428
D(x): 0.8378, D(G(z)): 0.6782
Epoch: [0/20], Batch Num: [113/600]
Discriminator Loss: 1.2057, Generator Loss: 0.6851
D(x): 0.8008, D(G(z)): 0.6159
Epoch: [0/20], Batch Num: [114/600]
Discriminator Loss: 1.1665, Generator Loss: 0.8332
D(x): 0.7396, D(G(z)): 0.5434
Epoch: [0/20], Batch Num: [115/600]
Discriminator Loss: 1.0243, Generator Loss: 1.0041
D(x): 0.7124, D(G(z)): 0.4854
Epoch: [0/20], Batch Num: [116/600]
Discriminator Loss: 1.0438, Generator Loss: 1.1815
D(x): 0.6262, D(G(z)): 0.4236
Epoch: [0/20], Batch Num: [117/600]
Discriminator Loss: 1.0243, Generator Loss: 1.3828
D(x): 0.6122, D(G(z)): 0.3641
Epoch: [0/20], Batch Num: [118/600]
Discriminator Loss: 1.0774, Generator Loss: 1.4966
D(x): 0.5688, D(G(z)): 0.3384
Epoch: [0/20], Batch Num: [119/600]
Discriminator Loss: 0.9338, Generator Loss: 1.5858
D(x): 0.5985, D(G(z)): 0.3216
Epoch: [0/20], Batch Num: [120/600]
Discriminator Loss: 0.8181, Generator Loss: 1.5915
D(x): 0.6438, D(G(z)): 0.3000
Epoch: [0/20], Batch Num: [121/600]
Discriminator Loss: 0.7450, Generator Loss: 1.7898
D(x): 0.7026, D(G(z)): 0.3117
Epoch: [0/20], Batch Num: [122/600]
Discriminator Loss: 0.7151, Generator Loss: 1.7474
D(x): 0.7299, D(G(z)): 0.3177
Epoch: [0/20], Batch Num: [123/600]
Discriminator Loss: 0.6915, Generator Loss: 1.9151
D(x): 0.7452, D(G(z)): 0.3121
Epoch: [0/20], Batch Num: [124/600]
Discriminator Loss: 0.6439, Generator Loss: 2.1232
D(x): 0.7618, D(G(z)): 0.2961
Epoch: [0/20], Batch Num: [125/600]
Discriminator Loss: 0.5640, Generator Loss: 2.3685
D(x): 0.8127, D(G(z)): 0.2869
Epoch: [0/20], Batch Num: [126/600]
Discriminator Loss: 0.5303, Generator Loss: 2.7998
D(x): 0.7947, D(G(z)): 0.2366
Epoch: [0/20], Batch Num: [127/600]
Discriminator Loss: 0.4648, Generator Loss: 3.3026
D(x): 0.8077, D(G(z)): 0.2017
Epoch: [0/20], Batch Num: [128/600]
Discriminator Loss: 0.4462, Generator Loss: 3.7398
D(x): 0.7898, D(G(z)): 0.1584
Epoch: [0/20], Batch Num: [129/600]
Discriminator Loss: 0.3589, Generator Loss: 3.9516
D(x): 0.8039, D(G(z)): 0.1177
Epoch: [0/20], Batch Num: [130/600]
Discriminator Loss: 0.3681, Generator Loss: 4.2133
D(x): 0.7965, D(G(z)): 0.1023
Epoch: [0/20], Batch Num: [131/600]
Discriminator Loss: 0.3288, Generator Loss: 4.0830
D(x): 0.8508, D(G(z)): 0.1350
Epoch: [0/20], Batch Num: [132/600]
Discriminator Loss: 0.2671, Generator Loss: 4.1139
D(x): 0.8853, D(G(z)): 0.1193
Epoch: [0/20], Batch Num: [133/600]
Discriminator Loss: 0.3151, Generator Loss: 4.0625
D(x): 0.8768, D(G(z)): 0.1367
Epoch: [0/20], Batch Num: [134/600]
Discriminator Loss: 0.3683, Generator Loss: 4.1866
D(x): 0.8985, D(G(z)): 0.1992
Epoch: [0/20], Batch Num: [135/600]
Discriminator Loss: 0.4387, Generator Loss: 4.2578
D(x): 0.8570, D(G(z)): 0.1934
Epoch: [0/20], Batch Num: [136/600]
Discriminator Loss: 0.5400, Generator Loss: 3.9504
D(x): 0.8585, D(G(z)): 0.2392
Epoch: [0/20], Batch Num: [137/600]
Discriminator Loss: 0.5750, Generator Loss: 3.3380
D(x): 0.8517, D(G(z)): 0.2621
Epoch: [0/20], Batch Num: [138/600]
Discriminator Loss: 1.2209, Generator Loss: 2.5158
D(x): 0.7328, D(G(z)): 0.4037
Epoch: [0/20], Batch Num: [139/600]
Discriminator Loss: 1.5892, Generator Loss: 1.6401
D(x): 0.7605, D(G(z)): 0.6195
Epoch: [0/20], Batch Num: [140/600]
Discriminator Loss: 2.1549, Generator Loss: 0.7661
D(x): 0.7154, D(G(z)): 0.7296
Epoch: [0/20], Batch Num: [141/600]
Discriminator Loss: 2.4962, Generator Loss: 0.3252
D(x): 0.7279, D(G(z)): 0.8351
Epoch: [0/20], Batch Num: [142/600]
Discriminator Loss: 2.6563, Generator Loss: 0.2505
D(x): 0.7841, D(G(z)): 0.8752
Epoch: [0/20], Batch Num: [143/600]
Discriminator Loss: 2.4679, Generator Loss: 0.2096
D(x): 0.8353, D(G(z)): 0.8760
Epoch: [0/20], Batch Num: [144/600]
Discriminator Loss: 2.2033, Generator Loss: 0.2886
D(x): 0.9151, D(G(z)): 0.8694
Epoch: [0/20], Batch Num: [145/600]
Discriminator Loss: 1.9329, Generator Loss: 0.3944
D(x): 0.8916, D(G(z)): 0.8234
Epoch: [0/20], Batch Num: [146/600]
Discriminator Loss: 1.6615, Generator Loss: 0.5933
D(x): 0.8162, D(G(z)): 0.7470
Epoch: [0/20], Batch Num: [147/600]
Discriminator Loss: 1.6146, Generator Loss: 0.7192
D(x): 0.6940, D(G(z)): 0.6618
Epoch: [0/20], Batch Num: [148/600]
Discriminator Loss: 1.4078, Generator Loss: 0.8150
D(x): 0.6651, D(G(z)): 0.5901
Epoch: [0/20], Batch Num: [149/600]
Discriminator Loss: 1.2749, Generator Loss: 0.8655
D(x): 0.6548, D(G(z)): 0.5341
Epoch: [0/20], Batch Num: [150/600]
Discriminator Loss: 1.2782, Generator Loss: 0.8916
D(x): 0.6393, D(G(z)): 0.5051
Epoch: [0/20], Batch Num: [151/600]
Discriminator Loss: 1.0848, Generator Loss: 0.8545
D(x): 0.7005, D(G(z)): 0.5024
Epoch: [0/20], Batch Num: [152/600]
Discriminator Loss: 1.0440, Generator Loss: 0.8735
D(x): 0.7390, D(G(z)): 0.5119
Epoch: [0/20], Batch Num: [153/600]
Discriminator Loss: 1.0660, Generator Loss: 0.9087
D(x): 0.7205, D(G(z)): 0.5040
Epoch: [0/20], Batch Num: [154/600]
Discriminator Loss: 0.9943, Generator Loss: 0.9398
D(x): 0.7536, D(G(z)): 0.4983
Epoch: [0/20], Batch Num: [155/600]
Discriminator Loss: 0.9955, Generator Loss: 0.9795
D(x): 0.7555, D(G(z)): 0.5018
Epoch: [0/20], Batch Num: [156/600]
Discriminator Loss: 0.9993, Generator Loss: 1.1110
D(x): 0.7386, D(G(z)): 0.4911
Epoch: [0/20], Batch Num: [157/600]
Discriminator Loss: 0.9151, Generator Loss: 1.1279
D(x): 0.7581, D(G(z)): 0.4599
Epoch: [0/20], Batch Num: [158/600]
Discriminator Loss: 0.9775, Generator Loss: 1.2546
D(x): 0.7449, D(G(z)): 0.4839
Epoch: [0/20], Batch Num: [159/600]
Discriminator Loss: 1.0250, Generator Loss: 1.1941
D(x): 0.7137, D(G(z)): 0.4781
Epoch: [0/20], Batch Num: [160/600]
Discriminator Loss: 1.0837, Generator Loss: 1.2643
D(x): 0.7031, D(G(z)): 0.4995
Epoch: [0/20], Batch Num: [161/600]
Discriminator Loss: 1.1205, Generator Loss: 1.1803
D(x): 0.6852, D(G(z)): 0.5080
Epoch: [0/20], Batch Num: [162/600]
Discriminator Loss: 1.2976, Generator Loss: 1.1652
D(x): 0.6321, D(G(z)): 0.5302
Epoch: [0/20], Batch Num: [163/600]
Discriminator Loss: 1.4703, Generator Loss: 0.9384
D(x): 0.6237, D(G(z)): 0.5798
Epoch: [0/20], Batch Num: [164/600]
Discriminator Loss: 1.4982, Generator Loss: 0.8734
D(x): 0.6051, D(G(z)): 0.5887
Epoch: [0/20], Batch Num: [165/600]
Discriminator Loss: 1.5670, Generator Loss: 0.9150
D(x): 0.6209, D(G(z)): 0.6042
Epoch: [0/20], Batch Num: [166/600]
Discriminator Loss: 1.5260, Generator Loss: 0.9520
D(x): 0.6309, D(G(z)): 0.6149
Epoch: [0/20], Batch Num: [167/600]
Discriminator Loss: 1.5269, Generator Loss: 1.0771
D(x): 0.6179, D(G(z)): 0.5878
Epoch: [0/20], Batch Num: [168/600]
Discriminator Loss: 1.3705, Generator Loss: 1.4938
D(x): 0.6247, D(G(z)): 0.5285
Epoch: [0/20], Batch Num: [169/600]
Discriminator Loss: 1.2055, Generator Loss: 2.1989
D(x): 0.6209, D(G(z)): 0.4349
Epoch: [0/20], Batch Num: [170/600]
Discriminator Loss: 1.0931, Generator Loss: 3.2201
D(x): 0.6137, D(G(z)): 0.3427
Epoch: [0/20], Batch Num: [171/600]
Discriminator Loss: 1.0912, Generator Loss: 3.4827
D(x): 0.5794, D(G(z)): 0.2449
Epoch: [0/20], Batch Num: [172/600]
Discriminator Loss: 1.1341, Generator Loss: 3.2651
D(x): 0.5853, D(G(z)): 0.2664
Epoch: [0/20], Batch Num: [173/600]
Discriminator Loss: 1.1046, Generator Loss: 2.9091
D(x): 0.5813, D(G(z)): 0.2552
Epoch: [0/20], Batch Num: [174/600]
Discriminator Loss: 1.2303, Generator Loss: 2.2049
D(x): 0.5911, D(G(z)): 0.3463
Epoch: [0/20], Batch Num: [175/600]
Discriminator Loss: 1.3462, Generator Loss: 1.5210
D(x): 0.6054, D(G(z)): 0.4495
Epoch: [0/20], Batch Num: [176/600]
Discriminator Loss: 1.7968, Generator Loss: 1.1442
D(x): 0.6126, D(G(z)): 0.5968
Epoch: [0/20], Batch Num: [177/600]
Discriminator Loss: 2.0809, Generator Loss: 0.9763
D(x): 0.6025, D(G(z)): 0.6671
Epoch: [0/20], Batch Num: [178/600]
Discriminator Loss: 1.9045, Generator Loss: 0.8318
D(x): 0.5702, D(G(z)): 0.6632
Epoch: [0/20], Batch Num: [179/600]
Discriminator Loss: 2.0982, Generator Loss: 0.7664
D(x): 0.5256, D(G(z)): 0.7081
Epoch: [0/20], Batch Num: [180/600]
Discriminator Loss: 2.3049, Generator Loss: 0.6634
D(x): 0.4270, D(G(z)): 0.6958
Epoch: [0/20], Batch Num: [181/600]
Discriminator Loss: 2.6431, Generator Loss: 0.5360
D(x): 0.3644, D(G(z)): 0.7054
Epoch: [0/20], Batch Num: [182/600]
Discriminator Loss: 2.4155, Generator Loss: 0.5168
D(x): 0.3852, D(G(z)): 0.7112
Epoch: [0/20], Batch Num: [183/600]
Discriminator Loss: 2.2592, Generator Loss: 0.5092
D(x): 0.4278, D(G(z)): 0.6988
Epoch: [0/20], Batch Num: [184/600]
Discriminator Loss: 2.1095, Generator Loss: 0.5154
D(x): 0.4331, D(G(z)): 0.6777
Epoch: [0/20], Batch Num: [185/600]
Discriminator Loss: 1.8175, Generator Loss: 0.5502
D(x): 0.4868, D(G(z)): 0.6561
Epoch: [0/20], Batch Num: [186/600]
Discriminator Loss: 1.6045, Generator Loss: 0.6072
D(x): 0.5114, D(G(z)): 0.6022
Epoch: [0/20], Batch Num: [187/600]
Discriminator Loss: 1.4761, Generator Loss: 0.6484
D(x): 0.5284, D(G(z)): 0.5632
Epoch: [0/20], Batch Num: [188/600]
Discriminator Loss: 1.3613, Generator Loss: 0.6710
D(x): 0.5498, D(G(z)): 0.5309
Epoch: [0/20], Batch Num: [189/600]
Discriminator Loss: 1.2875, Generator Loss: 0.6710
D(x): 0.5698, D(G(z)): 0.5132
Epoch: [0/20], Batch Num: [190/600]
Discriminator Loss: 1.2248, Generator Loss: 0.6817
D(x): 0.6024, D(G(z)): 0.5095
Epoch: [0/20], Batch Num: [191/600]
Discriminator Loss: 1.1848, Generator Loss: 0.6687
D(x): 0.6202, D(G(z)): 0.5036
Epoch: [0/20], Batch Num: [192/600]
Discriminator Loss: 1.1614, Generator Loss: 0.6686
D(x): 0.6464, D(G(z)): 0.5126
Epoch: [0/20], Batch Num: [193/600]
Discriminator Loss: 1.1196, Generator Loss: 0.6683
D(x): 0.6584, D(G(z)): 0.5000
Epoch: [0/20], Batch Num: [194/600]
Discriminator Loss: 1.0945, Generator Loss: 0.7196
D(x): 0.6785, D(G(z)): 0.5017
Epoch: [0/20], Batch Num: [195/600]
Discriminator Loss: 1.0430, Generator Loss: 0.7667
D(x): 0.7022, D(G(z)): 0.4925
Epoch: [0/20], Batch Num: [196/600]
Discriminator Loss: 0.9774, Generator Loss: 0.8754
D(x): 0.7077, D(G(z)): 0.4628
Epoch: [0/20], Batch Num: [197/600]
Discriminator Loss: 0.9269, Generator Loss: 0.9732
D(x): 0.7130, D(G(z)): 0.4384
Epoch: [0/20], Batch Num: [198/600]
Discriminator Loss: 0.8342, Generator Loss: 1.1440
D(x): 0.7249, D(G(z)): 0.3936
Epoch: [0/20], Batch Num: [199/600]
Discriminator Loss: 0.7731, Generator Loss: 1.3443
D(x): 0.7064, D(G(z)): 0.3382
Epoch: 0, Batch Num: [200/600]

Epoch: [0/20], Batch Num: [200/600]
Discriminator Loss: 0.6372, Generator Loss: 1.6274
D(x): 0.7398, D(G(z)): 0.2765
Epoch: [0/20], Batch Num: [201/600]
Discriminator Loss: 0.5831, Generator Loss: 1.7845
D(x): 0.7523, D(G(z)): 0.2470
Epoch: [0/20], Batch Num: [202/600]
Discriminator Loss: 0.4591, Generator Loss: 2.0685
D(x): 0.7989, D(G(z)): 0.1995
Epoch: [0/20], Batch Num: [203/600]
Discriminator Loss: 0.3488, Generator Loss: 2.3218
D(x): 0.8416, D(G(z)): 0.1542
Epoch: [0/20], Batch Num: [204/600]
Discriminator Loss: 0.3318, Generator Loss: 2.6426
D(x): 0.8602, D(G(z)): 0.1545
Epoch: [0/20], Batch Num: [205/600]
Discriminator Loss: 0.2266, Generator Loss: 2.7364
D(x): 0.9053, D(G(z)): 0.1133
Epoch: [0/20], Batch Num: [206/600]
Discriminator Loss: 0.1769, Generator Loss: 3.2597
D(x): 0.9372, D(G(z)): 0.0999
Epoch: [0/20], Batch Num: [207/600]
Discriminator Loss: 0.1393, Generator Loss: 3.7013
D(x): 0.9518, D(G(z)): 0.0809
Epoch: [0/20], Batch Num: [208/600]
Discriminator Loss: 0.1107, Generator Loss: 4.1792
D(x): 0.9479, D(G(z)): 0.0517
Epoch: [0/20], Batch Num: [209/600]
Discriminator Loss: 0.0941, Generator Loss: 4.5779
D(x): 0.9687, D(G(z)): 0.0543
Epoch: [0/20], Batch Num: [210/600]
Discriminator Loss: 0.0732, Generator Loss: 5.0145
D(x): 0.9700, D(G(z)): 0.0383
Epoch: [0/20], Batch Num: [211/600]
Discriminator Loss: 0.0553, Generator Loss: 5.7145
D(x): 0.9781, D(G(z)): 0.0307
Epoch: [0/20], Batch Num: [212/600]
Discriminator Loss: 0.0658, Generator Loss: 6.0005
D(x): 0.9679, D(G(z)): 0.0266
Epoch: [0/20], Batch Num: [213/600]
Discriminator Loss: 0.0870, Generator Loss: 6.2063
D(x): 0.9464, D(G(z)): 0.0224
Epoch: [0/20], Batch Num: [214/600]
Discriminator Loss: 0.0587, Generator Loss: 6.0892
D(x): 0.9658, D(G(z)): 0.0197
Epoch: [0/20], Batch Num: [215/600]
Discriminator Loss: 0.0942, Generator Loss: 6.0369
D(x): 0.9519, D(G(z)): 0.0179
Epoch: [0/20], Batch Num: [216/600]
Discriminator Loss: 0.0614, Generator Loss: 5.5680
D(x): 0.9771, D(G(z)): 0.0315
Epoch: [0/20], Batch Num: [217/600]
Discriminator Loss: 0.1094, Generator Loss: 5.6422
D(x): 0.9734, D(G(z)): 0.0632
Epoch: [0/20], Batch Num: [218/600]
Discriminator Loss: 0.2140, Generator Loss: 6.6419
D(x): 0.9577, D(G(z)): 0.0910
Epoch: [0/20], Batch Num: [219/600]
Discriminator Loss: 0.1712, Generator Loss: 6.9988
D(x): 0.9256, D(G(z)): 0.0205
Epoch: [0/20], Batch Num: [220/600]
Discriminator Loss: 0.1803, Generator Loss: 6.4572
D(x): 0.9300, D(G(z)): 0.0504
Epoch: [0/20], Batch Num: [221/600]
Discriminator Loss: 0.2764, Generator Loss: 5.2288
D(x): 0.9009, D(G(z)): 0.0648
Epoch: [0/20], Batch Num: [222/600]
Discriminator Loss: 0.2473, Generator Loss: 5.3386
D(x): 0.9527, D(G(z)): 0.1203
Epoch: [0/20], Batch Num: [223/600]
Discriminator Loss: 0.4398, Generator Loss: 7.4259
D(x): 0.9180, D(G(z)): 0.1365
Epoch: [0/20], Batch Num: [224/600]
Discriminator Loss: 0.4645, Generator Loss: 5.8880
D(x): 0.8233, D(G(z)): 0.0443
Epoch: [0/20], Batch Num: [225/600]
Discriminator Loss: 0.3669, Generator Loss: 4.6943
D(x): 0.8803, D(G(z)): 0.0917
Epoch: [0/20], Batch Num: [226/600]
Discriminator Loss: 0.4313, Generator Loss: 5.2174
D(x): 0.9143, D(G(z)): 0.1687
Epoch: [0/20], Batch Num: [227/600]
Discriminator Loss: 0.4307, Generator Loss: 6.6796
D(x): 0.9009, D(G(z)): 0.1328
Epoch: [0/20], Batch Num: [228/600]
Discriminator Loss: 0.5489, Generator Loss: 6.3142
D(x): 0.8382, D(G(z)): 0.0777
Epoch: [0/20], Batch Num: [229/600]
Discriminator Loss: 0.5555, Generator Loss: 5.0208
D(x): 0.8355, D(G(z)): 0.1094
Epoch: [0/20], Batch Num: [230/600]
Discriminator Loss: 0.6557, Generator Loss: 4.6759
D(x): 0.8549, D(G(z)): 0.1733
Epoch: [0/20], Batch Num: [231/600]
Discriminator Loss: 0.7421, Generator Loss: 4.1968
D(x): 0.8138, D(G(z)): 0.1888
Epoch: [0/20], Batch Num: [232/600]
Discriminator Loss: 0.7906, Generator Loss: 5.4927
D(x): 0.8353, D(G(z)): 0.2665
Epoch: [0/20], Batch Num: [233/600]
Discriminator Loss: 0.7946, Generator Loss: 4.6214
D(x): 0.7579, D(G(z)): 0.0923
Epoch: [0/20], Batch Num: [234/600]
Discriminator Loss: 0.7870, Generator Loss: 4.0354
D(x): 0.8447, D(G(z)): 0.1844
Epoch: [0/20], Batch Num: [235/600]
Discriminator Loss: 0.5195, Generator Loss: 4.5098
D(x): 0.8880, D(G(z)): 0.1957
Epoch: [0/20], Batch Num: [236/600]
Discriminator Loss: 0.6381, Generator Loss: 4.9728
D(x): 0.8363, D(G(z)): 0.1552
Epoch: [0/20], Batch Num: [237/600]
Discriminator Loss: 0.4628, Generator Loss: 4.4459
D(x): 0.8459, D(G(z)): 0.0970
Epoch: [0/20], Batch Num: [238/600]
Discriminator Loss: 0.3829, Generator Loss: 4.0644
D(x): 0.8693, D(G(z)): 0.1097
Epoch: [0/20], Batch Num: [239/600]
Discriminator Loss: 0.3358, Generator Loss: 3.7524
D(x): 0.9219, D(G(z)): 0.1506
Epoch: [0/20], Batch Num: [240/600]
Discriminator Loss: 0.2820, Generator Loss: 4.2262
D(x): 0.9348, D(G(z)): 0.1442
Epoch: [0/20], Batch Num: [241/600]
Discriminator Loss: 0.2246, Generator Loss: 4.4011
D(x): 0.9404, D(G(z)): 0.1135
Epoch: [0/20], Batch Num: [242/600]
Discriminator Loss: 0.1366, Generator Loss: 4.8101
D(x): 0.9470, D(G(z)): 0.0677
Epoch: [0/20], Batch Num: [243/600]
Discriminator Loss: 0.2233, Generator Loss: 4.6371
D(x): 0.9192, D(G(z)): 0.0602
Epoch: [0/20], Batch Num: [244/600]
Discriminator Loss: 0.3001, Generator Loss: 3.8762
D(x): 0.8824, D(G(z)): 0.0718
Epoch: [0/20], Batch Num: [245/600]
Discriminator Loss: 0.1701, Generator Loss: 3.7035
D(x): 0.9634, D(G(z)): 0.1124
Epoch: [0/20], Batch Num: [246/600]
Discriminator Loss: 0.2444, Generator Loss: 3.9616
D(x): 0.9502, D(G(z)): 0.1422
Epoch: [0/20], Batch Num: [247/600]
Discriminator Loss: 0.1228, Generator Loss: 4.7464
D(x): 0.9749, D(G(z)): 0.0832
Epoch: [0/20], Batch Num: [248/600]
Discriminator Loss: 0.1068, Generator Loss: 5.1728
D(x): 0.9629, D(G(z)): 0.0535
Epoch: [0/20], Batch Num: [249/600]
Discriminator Loss: 0.0908, Generator Loss: 5.6589
D(x): 0.9708, D(G(z)): 0.0493
Epoch: [0/20], Batch Num: [250/600]
Discriminator Loss: 0.1111, Generator Loss: 6.1184
D(x): 0.9655, D(G(z)): 0.0555
Epoch: [0/20], Batch Num: [251/600]
Discriminator Loss: 0.0883, Generator Loss: 6.1968
D(x): 0.9678, D(G(z)): 0.0387
Epoch: [0/20], Batch Num: [252/600]
Discriminator Loss: 0.0493, Generator Loss: 6.8078
D(x): 0.9884, D(G(z)): 0.0335
Epoch: [0/20], Batch Num: [253/600]
Discriminator Loss: 0.0422, Generator Loss: 6.5934
D(x): 0.9934, D(G(z)): 0.0316
Epoch: [0/20], Batch Num: [254/600]
Discriminator Loss: 0.0358, Generator Loss: 7.2465
D(x): 0.9907, D(G(z)): 0.0240
Epoch: [0/20], Batch Num: [255/600]
Discriminator Loss: 0.0299, Generator Loss: 7.3889
D(x): 0.9922, D(G(z)): 0.0200
Epoch: [0/20], Batch Num: [256/600]
Discriminator Loss: 0.0197, Generator Loss: 7.3313
D(x): 0.9951, D(G(z)): 0.0143
Epoch: [0/20], Batch Num: [257/600]
Discriminator Loss: 0.0431, Generator Loss: 7.5146
D(x): 0.9943, D(G(z)): 0.0336
Epoch: [0/20], Batch Num: [258/600]
Discriminator Loss: 0.0764, Generator Loss: 8.0872
D(x): 0.9927, D(G(z)): 0.0548
Epoch: [0/20], Batch Num: [259/600]
Discriminator Loss: 0.1265, Generator Loss: 8.7559
D(x): 0.9735, D(G(z)): 0.0557
Epoch: [0/20], Batch Num: [260/600]
Discriminator Loss: 0.1126, Generator Loss: 8.4550
D(x): 0.9685, D(G(z)): 0.0492
Epoch: [0/20], Batch Num: [261/600]
Discriminator Loss: 0.1072, Generator Loss: 8.6820
D(x): 0.9814, D(G(z)): 0.0581
Epoch: [0/20], Batch Num: [262/600]
Discriminator Loss: 0.1566, Generator Loss: 8.4845
D(x): 0.9933, D(G(z)): 0.0728
Epoch: [0/20], Batch Num: [263/600]
Discriminator Loss: 0.3011, Generator Loss: 8.2540
D(x): 0.9781, D(G(z)): 0.1517
Epoch: [0/20], Batch Num: [264/600]
Discriminator Loss: 0.6373, Generator Loss: 8.2599
D(x): 0.9780, D(G(z)): 0.2763
Epoch: [0/20], Batch Num: [265/600]
Discriminator Loss: 0.7151, Generator Loss: 7.1063
D(x): 0.9825, D(G(z)): 0.3511
Epoch: [0/20], Batch Num: [266/600]
Discriminator Loss: 1.8357, Generator Loss: 4.8493
D(x): 0.9414, D(G(z)): 0.6756
Epoch: [0/20], Batch Num: [267/600]
Discriminator Loss: 2.0325, Generator Loss: 2.3313
D(x): 0.9272, D(G(z)): 0.7632
Epoch: [0/20], Batch Num: [268/600]
Discriminator Loss: 0.9170, Generator Loss: 3.3185
D(x): 0.8589, D(G(z)): 0.2861
Epoch: [0/20], Batch Num: [269/600]
Discriminator Loss: 0.2973, Generator Loss: 4.7248
D(x): 0.9093, D(G(z)): 0.0463
Epoch: [0/20], Batch Num: [270/600]
Discriminator Loss: 0.1790, Generator Loss: 3.6432
D(x): 0.9764, D(G(z)): 0.0785
Epoch: [0/20], Batch Num: [271/600]
Discriminator Loss: 0.4566, Generator Loss: 2.0471
D(x): 0.9867, D(G(z)): 0.3000
Epoch: [0/20], Batch Num: [272/600]
Discriminator Loss: 1.3940, Generator Loss: 0.4834
D(x): 0.9702, D(G(z)): 0.7084
Epoch: [0/20], Batch Num: [273/600]
Discriminator Loss: 1.9744, Generator Loss: 0.2293
D(x): 0.9893, D(G(z)): 0.8466
Epoch: [0/20], Batch Num: [274/600]
Discriminator Loss: 2.3239, Generator Loss: 0.1792
D(x): 0.9901, D(G(z)): 0.8915
Epoch: [0/20], Batch Num: [275/600]
Discriminator Loss: 2.4418, Generator Loss: 0.1735
D(x): 0.9768, D(G(z)): 0.8921
Epoch: [0/20], Batch Num: [276/600]
Discriminator Loss: 2.2875, Generator Loss: 0.2200
D(x): 0.9616, D(G(z)): 0.8729
Epoch: [0/20], Batch Num: [277/600]
Discriminator Loss: 1.9377, Generator Loss: 0.2709
D(x): 0.9524, D(G(z)): 0.8390
Epoch: [0/20], Batch Num: [278/600]
Discriminator Loss: 1.8433, Generator Loss: 0.3445
D(x): 0.9396, D(G(z)): 0.8009
Epoch: [0/20], Batch Num: [279/600]
Discriminator Loss: 1.5646, Generator Loss: 0.4928
D(x): 0.9012, D(G(z)): 0.7467
Epoch: [0/20], Batch Num: [280/600]
Discriminator Loss: 1.3410, Generator Loss: 0.6609
D(x): 0.8625, D(G(z)): 0.6733
Epoch: [0/20], Batch Num: [281/600]
Discriminator Loss: 1.1437, Generator Loss: 0.8499
D(x): 0.8164, D(G(z)): 0.5808
Epoch: [0/20], Batch Num: [282/600]
Discriminator Loss: 1.0199, Generator Loss: 1.0402
D(x): 0.7673, D(G(z)): 0.4977
Epoch: [0/20], Batch Num: [283/600]
Discriminator Loss: 0.9153, Generator Loss: 1.2170
D(x): 0.7494, D(G(z)): 0.4399
Epoch: [0/20], Batch Num: [284/600]
Discriminator Loss: 0.8321, Generator Loss: 1.4030
D(x): 0.6863, D(G(z)): 0.3539
Epoch: [0/20], Batch Num: [285/600]
Discriminator Loss: 0.9474, Generator Loss: 1.4884
D(x): 0.5903, D(G(z)): 0.3157
Epoch: [0/20], Batch Num: [286/600]
Discriminator Loss: 1.1746, Generator Loss: 1.4364
D(x): 0.4643, D(G(z)): 0.2789
Epoch: [0/20], Batch Num: [287/600]
Discriminator Loss: 1.3331, Generator Loss: 1.3598
D(x): 0.4157, D(G(z)): 0.2982
Epoch: [0/20], Batch Num: [288/600]
Discriminator Loss: 1.3499, Generator Loss: 1.1514
D(x): 0.4194, D(G(z)): 0.3328
Epoch: [0/20], Batch Num: [289/600]
Discriminator Loss: 1.1918, Generator Loss: 0.9322
D(x): 0.5126, D(G(z)): 0.3843
Epoch: [0/20], Batch Num: [290/600]
Discriminator Loss: 1.1948, Generator Loss: 0.8537
D(x): 0.5744, D(G(z)): 0.4597
Epoch: [0/20], Batch Num: [291/600]
Discriminator Loss: 1.1258, Generator Loss: 0.8725
D(x): 0.6190, D(G(z)): 0.4620
Epoch: [0/20], Batch Num: [292/600]
Discriminator Loss: 0.9844, Generator Loss: 1.3084
D(x): 0.6646, D(G(z)): 0.4234
Epoch: [0/20], Batch Num: [293/600]
Discriminator Loss: 0.7258, Generator Loss: 2.4372
D(x): 0.7080, D(G(z)): 0.3012
Epoch: [0/20], Batch Num: [294/600]
Discriminator Loss: 0.5627, Generator Loss: 3.3807
D(x): 0.7245, D(G(z)): 0.2011
Epoch: [0/20], Batch Num: [295/600]
Discriminator Loss: 0.5262, Generator Loss: 4.0562
D(x): 0.7042, D(G(z)): 0.1471
Epoch: [0/20], Batch Num: [296/600]
Discriminator Loss: 0.5038, Generator Loss: 4.4361
D(x): 0.6946, D(G(z)): 0.1170
Epoch: [0/20], Batch Num: [297/600]
Discriminator Loss: 0.6264, Generator Loss: 4.0507
D(x): 0.6448, D(G(z)): 0.1359
Epoch: [0/20], Batch Num: [298/600]
Discriminator Loss: 0.7965, Generator Loss: 3.0323
D(x): 0.6086, D(G(z)): 0.2166
Epoch: [0/20], Batch Num: [299/600]
Discriminator Loss: 0.9575, Generator Loss: 1.7424
D(x): 0.5892, D(G(z)): 0.2955
Epoch: 0, Batch Num: [300/600]

Epoch: [0/20], Batch Num: [300/600]
Discriminator Loss: 1.1457, Generator Loss: 1.0084
D(x): 0.6261, D(G(z)): 0.4423
Epoch: [0/20], Batch Num: [301/600]
Discriminator Loss: 1.0656, Generator Loss: 0.8392
D(x): 0.7104, D(G(z)): 0.4872
Epoch: [0/20], Batch Num: [302/600]
Discriminator Loss: 1.0440, Generator Loss: 0.9604
D(x): 0.7665, D(G(z)): 0.5038
Epoch: [0/20], Batch Num: [303/600]
Discriminator Loss: 0.9350, Generator Loss: 1.1901
D(x): 0.7899, D(G(z)): 0.4815
Epoch: [0/20], Batch Num: [304/600]
Discriminator Loss: 0.7046, Generator Loss: 1.7125
D(x): 0.7975, D(G(z)): 0.3646
Epoch: [0/20], Batch Num: [305/600]
Discriminator Loss: 0.5929, Generator Loss: 2.0959
D(x): 0.7551, D(G(z)): 0.2400
Epoch: [0/20], Batch Num: [306/600]
Discriminator Loss: 0.5200, Generator Loss: 2.4241
D(x): 0.7456, D(G(z)): 0.1697
Epoch: [0/20], Batch Num: [307/600]
Discriminator Loss: 0.5281, Generator Loss: 2.5764
D(x): 0.7116, D(G(z)): 0.1395
Epoch: [0/20], Batch Num: [308/600]
Discriminator Loss: 0.6211, Generator Loss: 2.4154
D(x): 0.6694, D(G(z)): 0.1216
Epoch: [0/20], Batch Num: [309/600]
Discriminator Loss: 0.5815, Generator Loss: 2.1572
D(x): 0.7014, D(G(z)): 0.1485
Epoch: [0/20], Batch Num: [310/600]
Discriminator Loss: 0.6035, Generator Loss: 1.7924
D(x): 0.7849, D(G(z)): 0.2728
Epoch: [0/20], Batch Num: [311/600]
Discriminator Loss: 0.8159, Generator Loss: 1.6701
D(x): 0.7960, D(G(z)): 0.3971
Epoch: [0/20], Batch Num: [312/600]
Discriminator Loss: 1.0370, Generator Loss: 2.1297
D(x): 0.7660, D(G(z)): 0.4664
Epoch: [0/20], Batch Num: [313/600]
Discriminator Loss: 1.1238, Generator Loss: 2.4030
D(x): 0.6653, D(G(z)): 0.4159
Epoch: [0/20], Batch Num: [314/600]
Discriminator Loss: 1.5520, Generator Loss: 2.1000
D(x): 0.4532, D(G(z)): 0.2957
Epoch: [0/20], Batch Num: [315/600]
Discriminator Loss: 1.9792, Generator Loss: 1.4947
D(x): 0.4142, D(G(z)): 0.4412
Epoch: [0/20], Batch Num: [316/600]
Discriminator Loss: 2.4088, Generator Loss: 0.9838
D(x): 0.4174, D(G(z)): 0.6109
Epoch: [0/20], Batch Num: [317/600]
Discriminator Loss: 2.9703, Generator Loss: 0.5579
D(x): 0.4344, D(G(z)): 0.7902
Epoch: [0/20], Batch Num: [318/600]
Discriminator Loss: 3.3154, Generator Loss: 0.4401
D(x): 0.4398, D(G(z)): 0.8697
Epoch: [0/20], Batch Num: [319/600]
Discriminator Loss: 3.3722, Generator Loss: 0.2857
D(x): 0.4347, D(G(z)): 0.8579
Epoch: [0/20], Batch Num: [320/600]
Discriminator Loss: 3.4677, Generator Loss: 0.2259
D(x): 0.4154, D(G(z)): 0.8689
Epoch: [0/20], Batch Num: [321/600]
Discriminator Loss: 3.4438, Generator Loss: 0.2051
D(x): 0.4135, D(G(z)): 0.8953
Epoch: [0/20], Batch Num: [322/600]
Discriminator Loss: 3.0935, Generator Loss: 0.2398
D(x): 0.5050, D(G(z)): 0.8815
Epoch: [0/20], Batch Num: [323/600]
Discriminator Loss: 2.7907, Generator Loss: 0.2868
D(x): 0.5223, D(G(z)): 0.8491
Epoch: [0/20], Batch Num: [324/600]
Discriminator Loss: 2.3830, Generator Loss: 0.3457
D(x): 0.5663, D(G(z)): 0.8150
Epoch: [0/20], Batch Num: [325/600]
Discriminator Loss: 2.0538, Generator Loss: 0.4407
D(x): 0.5812, D(G(z)): 0.7560
Epoch: [0/20], Batch Num: [326/600]
Discriminator Loss: 1.8237, Generator Loss: 0.6290
D(x): 0.5538, D(G(z)): 0.6824
Epoch: [0/20], Batch Num: [327/600]
Discriminator Loss: 1.5107, Generator Loss: 0.9440
D(x): 0.5627, D(G(z)): 0.5707
Epoch: [0/20], Batch Num: [328/600]
Discriminator Loss: 1.2231, Generator Loss: 1.9982
D(x): 0.5488, D(G(z)): 0.4268
Epoch: [0/20], Batch Num: [329/600]
Discriminator Loss: 0.9789, Generator Loss: 4.3427
D(x): 0.4908, D(G(z)): 0.1862
Epoch: [0/20], Batch Num: [330/600]
Discriminator Loss: 0.8078, Generator Loss: 6.1049
D(x): 0.4995, D(G(z)): 0.0747
Epoch: [0/20], Batch Num: [331/600]
Discriminator Loss: 0.6897, Generator Loss: 7.7258
D(x): 0.5197, D(G(z)): 0.0126
Epoch: [0/20], Batch Num: [332/600]
Discriminator Loss: 0.6267, Generator Loss: 9.1888
D(x): 0.5481, D(G(z)): 0.0094
Epoch: [0/20], Batch Num: [333/600]
Discriminator Loss: 0.6237, Generator Loss: 9.2511
D(x): 0.5480, D(G(z)): 0.0075
Epoch: [0/20], Batch Num: [334/600]
Discriminator Loss: 0.5581, Generator Loss: 9.1493
D(x): 0.5808, D(G(z)): 0.0081
Epoch: [0/20], Batch Num: [335/600]
Discriminator Loss: 0.5307, Generator Loss: 7.5857
D(x): 0.6000, D(G(z)): 0.0105
Epoch: [0/20], Batch Num: [336/600]
Discriminator Loss: 0.5177, Generator Loss: 6.4136
D(x): 0.6197, D(G(z)): 0.0304
Epoch: [0/20], Batch Num: [337/600]
Discriminator Loss: 0.5070, Generator Loss: 5.3811
D(x): 0.6475, D(G(z)): 0.0594
Epoch: [0/20], Batch Num: [338/600]
Discriminator Loss: 0.5448, Generator Loss: 4.3367
D(x): 0.6689, D(G(z)): 0.1114
Epoch: [0/20], Batch Num: [339/600]
Discriminator Loss: 0.6527, Generator Loss: 3.4134
D(x): 0.6887, D(G(z)): 0.2158
Epoch: [0/20], Batch Num: [340/600]
Discriminator Loss: 0.7259, Generator Loss: 3.2213
D(x): 0.6844, D(G(z)): 0.2580
Epoch: [0/20], Batch Num: [341/600]
Discriminator Loss: 0.8634, Generator Loss: 2.9672
D(x): 0.6772, D(G(z)): 0.3353
Epoch: [0/20], Batch Num: [342/600]
Discriminator Loss: 0.9209, Generator Loss: 2.9711
D(x): 0.6555, D(G(z)): 0.3551
Epoch: [0/20], Batch Num: [343/600]
Discriminator Loss: 1.1496, Generator Loss: 2.4085
D(x): 0.5793, D(G(z)): 0.3068
Epoch: [0/20], Batch Num: [344/600]
Discriminator Loss: 1.4808, Generator Loss: 1.3732
D(x): 0.4989, D(G(z)): 0.4272
Epoch: [0/20], Batch Num: [345/600]
Discriminator Loss: 2.3843, Generator Loss: 0.4212
D(x): 0.4368, D(G(z)): 0.5712
Epoch: [0/20], Batch Num: [346/600]
Discriminator Loss: 1.6690, Generator Loss: 0.3008
D(x): 0.6925, D(G(z)): 0.7000
Epoch: [0/20], Batch Num: [347/600]
Discriminator Loss: 1.6785, Generator Loss: 0.2531
D(x): 0.7888, D(G(z)): 0.7559
Epoch: [0/20], Batch Num: [348/600]
Discriminator Loss: 1.7653, Generator Loss: 0.2459
D(x): 0.8181, D(G(z)): 0.7830
Epoch: [0/20], Batch Num: [349/600]
Discriminator Loss: 1.7488, Generator Loss: 0.2513
D(x): 0.8584, D(G(z)): 0.7916
Epoch: [0/20], Batch Num: [350/600]
Discriminator Loss: 1.6800, Generator Loss: 0.2750
D(x): 0.8598, D(G(z)): 0.7781
Epoch: [0/20], Batch Num: [351/600]
Discriminator Loss: 1.6354, Generator Loss: 0.2974
D(x): 0.8499, D(G(z)): 0.7667
Epoch: [0/20], Batch Num: [352/600]
Discriminator Loss: 1.5932, Generator Loss: 0.3248
D(x): 0.8342, D(G(z)): 0.7512
Epoch: [0/20], Batch Num: [353/600]
Discriminator Loss: 1.4724, Generator Loss: 0.3674
D(x): 0.8104, D(G(z)): 0.7124
Epoch: [0/20], Batch Num: [354/600]
Discriminator Loss: 1.4055, Generator Loss: 0.4039
D(x): 0.7936, D(G(z)): 0.6876
Epoch: [0/20], Batch Num: [355/600]
Discriminator Loss: 1.3743, Generator Loss: 0.4287
D(x): 0.7782, D(G(z)): 0.6725
Epoch: [0/20], Batch Num: [356/600]
Discriminator Loss: 1.3383, Generator Loss: 0.4704
D(x): 0.7561, D(G(z)): 0.6510
Epoch: [0/20], Batch Num: [357/600]
Discriminator Loss: 1.3189, Generator Loss: 0.4941
D(x): 0.7219, D(G(z)): 0.6274
Epoch: [0/20], Batch Num: [358/600]
Discriminator Loss: 1.2910, Generator Loss: 0.5287
D(x): 0.7067, D(G(z)): 0.6094
Epoch: [0/20], Batch Num: [359/600]
Discriminator Loss: 1.2859, Generator Loss: 0.5456
D(x): 0.6834, D(G(z)): 0.5938
Epoch: [0/20], Batch Num: [360/600]
Discriminator Loss: 1.2776, Generator Loss: 0.5690
D(x): 0.6694, D(G(z)): 0.5821
Epoch: [0/20], Batch Num: [361/600]
Discriminator Loss: 1.2804, Generator Loss: 0.5814
D(x): 0.6492, D(G(z)): 0.5706
Epoch: [0/20], Batch Num: [362/600]
Discriminator Loss: 1.2620, Generator Loss: 0.5976
D(x): 0.6412, D(G(z)): 0.5574
Epoch: [0/20], Batch Num: [363/600]
Discriminator Loss: 1.2599, Generator Loss: 0.6117
D(x): 0.6324, D(G(z)): 0.5503
Epoch: [0/20], Batch Num: [364/600]
Discriminator Loss: 1.2541, Generator Loss: 0.6171
D(x): 0.6241, D(G(z)): 0.5419
Epoch: [0/20], Batch Num: [365/600]
Discriminator Loss: 1.2522, Generator Loss: 0.6237
D(x): 0.6207, D(G(z)): 0.5385
Epoch: [0/20], Batch Num: [366/600]
Discriminator Loss: 1.2657, Generator Loss: 0.6289
D(x): 0.6128, D(G(z)): 0.5387
Epoch: [0/20], Batch Num: [367/600]
Discriminator Loss: 1.2555, Generator Loss: 0.6327
D(x): 0.6119, D(G(z)): 0.5330
Epoch: [0/20], Batch Num: [368/600]
Discriminator Loss: 1.2428, Generator Loss: 0.6396
D(x): 0.6148, D(G(z)): 0.5294
Epoch: [0/20], Batch Num: [369/600]
Discriminator Loss: 1.2369, Generator Loss: 0.6448
D(x): 0.6178, D(G(z)): 0.5290
Epoch: [0/20], Batch Num: [370/600]
Discriminator Loss: 1.2378, Generator Loss: 0.6431
D(x): 0.6157, D(G(z)): 0.5279
Epoch: [0/20], Batch Num: [371/600]
Discriminator Loss: 1.2351, Generator Loss: 0.6416
D(x): 0.6174, D(G(z)): 0.5276
Epoch: [0/20], Batch Num: [372/600]
Discriminator Loss: 1.2090, Generator Loss: 0.6411
D(x): 0.6334, D(G(z)): 0.5275
Epoch: [0/20], Batch Num: [373/600]
Discriminator Loss: 1.1976, Generator Loss: 0.6413
D(x): 0.6375, D(G(z)): 0.5250
Epoch: [0/20], Batch Num: [374/600]
Discriminator Loss: 1.1977, Generator Loss: 0.6416
D(x): 0.6399, D(G(z)): 0.5270
Epoch: [0/20], Batch Num: [375/600]
Discriminator Loss: 1.1725, Generator Loss: 0.6406
D(x): 0.6533, D(G(z)): 0.5247
Epoch: [0/20], Batch Num: [376/600]
Discriminator Loss: 1.1703, Generator Loss: 0.6370
D(x): 0.6625, D(G(z)): 0.5302
Epoch: [0/20], Batch Num: [377/600]
Discriminator Loss: 1.1649, Generator Loss: 0.6265
D(x): 0.6701, D(G(z)): 0.5329
Epoch: [0/20], Batch Num: [378/600]
Discriminator Loss: 1.1457, Generator Loss: 0.6260
D(x): 0.6878, D(G(z)): 0.5361
Epoch: [0/20], Batch Num: [379/600]
Discriminator Loss: 1.1451, Generator Loss: 0.6299
D(x): 0.6936, D(G(z)): 0.5395
Epoch: [0/20], Batch Num: [380/600]
Discriminator Loss: 1.1376, Generator Loss: 0.6129
D(x): 0.7011, D(G(z)): 0.5408
Epoch: [0/20], Batch Num: [381/600]
Discriminator Loss: 1.1158, Generator Loss: 0.6162
D(x): 0.7161, D(G(z)): 0.5406
Epoch: [0/20], Batch Num: [382/600]
Discriminator Loss: 1.0887, Generator Loss: 0.6130
D(x): 0.7366, D(G(z)): 0.5412
Epoch: [0/20], Batch Num: [383/600]
Discriminator Loss: 1.0828, Generator Loss: 0.6136
D(x): 0.7396, D(G(z)): 0.5403
Epoch: [0/20], Batch Num: [384/600]
Discriminator Loss: 1.0903, Generator Loss: 0.6147
D(x): 0.7542, D(G(z)): 0.5523
Epoch: [0/20], Batch Num: [385/600]
Discriminator Loss: 1.0645, Generator Loss: 0.6021
D(x): 0.7669, D(G(z)): 0.5481
Epoch: [0/20], Batch Num: [386/600]
Discriminator Loss: 1.0498, Generator Loss: 0.6211
D(x): 0.7715, D(G(z)): 0.5444
Epoch: [0/20], Batch Num: [387/600]
Discriminator Loss: 1.0523, Generator Loss: 0.6134
D(x): 0.7720, D(G(z)): 0.5455
Epoch: [0/20], Batch Num: [388/600]
Discriminator Loss: 1.0219, Generator Loss: 0.6219
D(x): 0.7860, D(G(z)): 0.5401
Epoch: [0/20], Batch Num: [389/600]
Discriminator Loss: 1.0236, Generator Loss: 0.6387
D(x): 0.7857, D(G(z)): 0.5408
Epoch: [0/20], Batch Num: [390/600]
Discriminator Loss: 0.9933, Generator Loss: 0.6395
D(x): 0.7987, D(G(z)): 0.5340
Epoch: [0/20], Batch Num: [391/600]
Discriminator Loss: 1.0031, Generator Loss: 0.6398
D(x): 0.7946, D(G(z)): 0.5361
Epoch: [0/20], Batch Num: [392/600]
Discriminator Loss: 0.9773, Generator Loss: 0.6672
D(x): 0.8037, D(G(z)): 0.5291
Epoch: [0/20], Batch Num: [393/600]
Discriminator Loss: 0.9529, Generator Loss: 0.6792
D(x): 0.8037, D(G(z)): 0.5170
Epoch: [0/20], Batch Num: [394/600]
Discriminator Loss: 0.9629, Generator Loss: 0.6942
D(x): 0.8053, D(G(z)): 0.5230
Epoch: [0/20], Batch Num: [395/600]
Discriminator Loss: 0.9287, Generator Loss: 0.7024
D(x): 0.8153, D(G(z)): 0.5124
Epoch: [0/20], Batch Num: [396/600]
Discriminator Loss: 0.9314, Generator Loss: 0.7211
D(x): 0.8048, D(G(z)): 0.5060
Epoch: [0/20], Batch Num: [397/600]
Discriminator Loss: 0.8918, Generator Loss: 0.7215
D(x): 0.8206, D(G(z)): 0.4974
Epoch: [0/20], Batch Num: [398/600]
Discriminator Loss: 0.9088, Generator Loss: 0.7337
D(x): 0.8162, D(G(z)): 0.5016
Epoch: [0/20], Batch Num: [399/600]
Discriminator Loss: 0.8977, Generator Loss: 0.7750
D(x): 0.8175, D(G(z)): 0.4968
Epoch: 0, Batch Num: [400/600]

Epoch: [0/20], Batch Num: [400/600]
Discriminator Loss: 0.9080, Generator Loss: 0.7457
D(x): 0.7964, D(G(z)): 0.4868
Epoch: [0/20], Batch Num: [401/600]
Discriminator Loss: 0.9137, Generator Loss: 0.7577
D(x): 0.8061, D(G(z)): 0.4957
Epoch: [0/20], Batch Num: [402/600]
Discriminator Loss: 0.9307, Generator Loss: 0.8113
D(x): 0.8054, D(G(z)): 0.5029
Epoch: [0/20], Batch Num: [403/600]
Discriminator Loss: 0.9400, Generator Loss: 0.7340
D(x): 0.8127, D(G(z)): 0.5127
Epoch: [0/20], Batch Num: [404/600]
Discriminator Loss: 0.9554, Generator Loss: 0.6937
D(x): 0.8081, D(G(z)): 0.5148
Epoch: [0/20], Batch Num: [405/600]
Discriminator Loss: 1.0475, Generator Loss: 0.7161
D(x): 0.7924, D(G(z)): 0.5439
Epoch: [0/20], Batch Num: [406/600]
Discriminator Loss: 1.0959, Generator Loss: 0.6633
D(x): 0.7812, D(G(z)): 0.5597
Epoch: [0/20], Batch Num: [407/600]
Discriminator Loss: 1.1660, Generator Loss: 0.6539
D(x): 0.7467, D(G(z)): 0.5670
Epoch: [0/20], Batch Num: [408/600]
Discriminator Loss: 1.1915, Generator Loss: 0.6429
D(x): 0.7571, D(G(z)): 0.5837
Epoch: [0/20], Batch Num: [409/600]
Discriminator Loss: 1.3029, Generator Loss: 0.7193
D(x): 0.6799, D(G(z)): 0.5745
Epoch: [0/20], Batch Num: [410/600]
Discriminator Loss: 1.1391, Generator Loss: 1.2184
D(x): 0.7089, D(G(z)): 0.5207
Epoch: [0/20], Batch Num: [411/600]
Discriminator Loss: 0.9690, Generator Loss: 1.9851
D(x): 0.6696, D(G(z)): 0.3877
Epoch: [0/20], Batch Num: [412/600]
Discriminator Loss: 0.8616, Generator Loss: 2.9846
D(x): 0.6494, D(G(z)): 0.2928
Epoch: [0/20], Batch Num: [413/600]
Discriminator Loss: 0.7003, Generator Loss: 3.9145
D(x): 0.6950, D(G(z)): 0.2269
Epoch: [0/20], Batch Num: [414/600]
Discriminator Loss: 0.5345, Generator Loss: 4.0438
D(x): 0.7296, D(G(z)): 0.1632
Epoch: [0/20], Batch Num: [415/600]
Discriminator Loss: 0.4451, Generator Loss: 3.7476
D(x): 0.7611, D(G(z)): 0.1273
Epoch: [0/20], Batch Num: [416/600]
Discriminator Loss: 0.3780, Generator Loss: 3.7805
D(x): 0.8100, D(G(z)): 0.1346
Epoch: [0/20], Batch Num: [417/600]
Discriminator Loss: 0.3761, Generator Loss: 3.9372
D(x): 0.8137, D(G(z)): 0.1315
Epoch: [0/20], Batch Num: [418/600]
Discriminator Loss: 0.4404, Generator Loss: 3.6182
D(x): 0.7944, D(G(z)): 0.1559
Epoch: [0/20], Batch Num: [419/600]
Discriminator Loss: 0.4435, Generator Loss: 3.5426
D(x): 0.8124, D(G(z)): 0.1591
Epoch: [0/20], Batch Num: [420/600]
Discriminator Loss: 0.4450, Generator Loss: 2.9667
D(x): 0.8538, D(G(z)): 0.1838
Epoch: [0/20], Batch Num: [421/600]
Discriminator Loss: 0.5976, Generator Loss: 2.6332
D(x): 0.8337, D(G(z)): 0.2426
Epoch: [0/20], Batch Num: [422/600]
Discriminator Loss: 1.0486, Generator Loss: 1.8920
D(x): 0.7923, D(G(z)): 0.3331
Epoch: [0/20], Batch Num: [423/600]
Discriminator Loss: 1.1211, Generator Loss: 1.5633
D(x): 0.8378, D(G(z)): 0.5391
Epoch: [0/20], Batch Num: [424/600]
Discriminator Loss: 1.7658, Generator Loss: 0.8230
D(x): 0.7296, D(G(z)): 0.5843
Epoch: [0/20], Batch Num: [425/600]
Discriminator Loss: 2.0318, Generator Loss: 0.4280
D(x): 0.7608, D(G(z)): 0.7237
Epoch: [0/20], Batch Num: [426/600]
Discriminator Loss: 2.2572, Generator Loss: 0.2805
D(x): 0.7740, D(G(z)): 0.8038
Epoch: [0/20], Batch Num: [427/600]
Discriminator Loss: 2.1700, Generator Loss: 0.2198
D(x): 0.8189, D(G(z)): 0.8289
Epoch: [0/20], Batch Num: [428/600]
Discriminator Loss: 2.0922, Generator Loss: 0.2573
D(x): 0.8585, D(G(z)): 0.8367
Epoch: [0/20], Batch Num: [429/600]
Discriminator Loss: 1.8686, Generator Loss: 0.3424
D(x): 0.8914, D(G(z)): 0.8127
Epoch: [0/20], Batch Num: [430/600]
Discriminator Loss: 1.6376, Generator Loss: 0.5455
D(x): 0.8403, D(G(z)): 0.7437
Epoch: [0/20], Batch Num: [431/600]
Discriminator Loss: 1.3476, Generator Loss: 1.2229
D(x): 0.8111, D(G(z)): 0.6384
Epoch: [0/20], Batch Num: [432/600]
Discriminator Loss: 0.9832, Generator Loss: 2.4245
D(x): 0.7353, D(G(z)): 0.4192
Epoch: [0/20], Batch Num: [433/600]
Discriminator Loss: 0.7819, Generator Loss: 3.5260
D(x): 0.6643, D(G(z)): 0.2256
Epoch: [0/20], Batch Num: [434/600]
Discriminator Loss: 0.7203, Generator Loss: 3.7445
D(x): 0.6163, D(G(z)): 0.0962
Epoch: [0/20], Batch Num: [435/600]
Discriminator Loss: 0.6157, Generator Loss: 3.6806
D(x): 0.6585, D(G(z)): 0.0998
Epoch: [0/20], Batch Num: [436/600]
Discriminator Loss: 0.5115, Generator Loss: 3.4458
D(x): 0.7295, D(G(z)): 0.1470
Epoch: [0/20], Batch Num: [437/600]
Discriminator Loss: 0.4979, Generator Loss: 2.8961
D(x): 0.7517, D(G(z)): 0.1662
Epoch: [0/20], Batch Num: [438/600]
Discriminator Loss: 0.6132, Generator Loss: 2.4336
D(x): 0.7723, D(G(z)): 0.2562
Epoch: [0/20], Batch Num: [439/600]
Discriminator Loss: 0.6801, Generator Loss: 2.2468
D(x): 0.7796, D(G(z)): 0.3106
Epoch: [0/20], Batch Num: [440/600]
Discriminator Loss: 0.8018, Generator Loss: 2.3036
D(x): 0.7572, D(G(z)): 0.3663
Epoch: [0/20], Batch Num: [441/600]
Discriminator Loss: 0.8693, Generator Loss: 2.5687
D(x): 0.7309, D(G(z)): 0.3836
Epoch: [0/20], Batch Num: [442/600]
Discriminator Loss: 1.1729, Generator Loss: 2.1257
D(x): 0.6255, D(G(z)): 0.4038
Epoch: [0/20], Batch Num: [443/600]
Discriminator Loss: 1.5810, Generator Loss: 1.1754
D(x): 0.5609, D(G(z)): 0.4728
Epoch: [0/20], Batch Num: [444/600]
Discriminator Loss: 1.5162, Generator Loss: 0.7394
D(x): 0.6361, D(G(z)): 0.5802
Epoch: [0/20], Batch Num: [445/600]
Discriminator Loss: 1.6795, Generator Loss: 0.4857
D(x): 0.6526, D(G(z)): 0.6725
Epoch: [0/20], Batch Num: [446/600]
Discriminator Loss: 1.7753, Generator Loss: 0.3625
D(x): 0.6986, D(G(z)): 0.7206
Epoch: [0/20], Batch Num: [447/600]
Discriminator Loss: 1.6902, Generator Loss: 0.3287
D(x): 0.7656, D(G(z)): 0.7462
Epoch: [0/20], Batch Num: [448/600]
Discriminator Loss: 1.7167, Generator Loss: 0.3197
D(x): 0.7631, D(G(z)): 0.7522
Epoch: [0/20], Batch Num: [449/600]
Discriminator Loss: 1.5830, Generator Loss: 0.3299
D(x): 0.8147, D(G(z)): 0.7395
Epoch: [0/20], Batch Num: [450/600]
Discriminator Loss: 1.6295, Generator Loss: 0.3551
D(x): 0.7834, D(G(z)): 0.7406
Epoch: [0/20], Batch Num: [451/600]
Discriminator Loss: 1.5478, Generator Loss: 0.3932
D(x): 0.7851, D(G(z)): 0.7202
Epoch: [0/20], Batch Num: [452/600]
Discriminator Loss: 1.4792, Generator Loss: 0.4502
D(x): 0.7656, D(G(z)): 0.6954
Epoch: [0/20], Batch Num: [453/600]
Discriminator Loss: 1.3862, Generator Loss: 0.5153
D(x): 0.7260, D(G(z)): 0.6477
Epoch: [0/20], Batch Num: [454/600]
Discriminator Loss: 1.3460, Generator Loss: 0.5767
D(x): 0.6870, D(G(z)): 0.6144
Epoch: [0/20], Batch Num: [455/600]
Discriminator Loss: 1.3381, Generator Loss: 0.6763
D(x): 0.6330, D(G(z)): 0.5696
Epoch: [0/20], Batch Num: [456/600]
Discriminator Loss: 1.2417, Generator Loss: 0.7737
D(x): 0.6191, D(G(z)): 0.5256
Epoch: [0/20], Batch Num: [457/600]
Discriminator Loss: 1.3081, Generator Loss: 0.8549
D(x): 0.5482, D(G(z)): 0.4840
Epoch: [0/20], Batch Num: [458/600]
Discriminator Loss: 1.2467, Generator Loss: 0.9327
D(x): 0.5329, D(G(z)): 0.4478
Epoch: [0/20], Batch Num: [459/600]
Discriminator Loss: 1.2695, Generator Loss: 0.9662
D(x): 0.4983, D(G(z)): 0.4147
Epoch: [0/20], Batch Num: [460/600]
Discriminator Loss: 1.2858, Generator Loss: 1.0517
D(x): 0.4736, D(G(z)): 0.4031
Epoch: [0/20], Batch Num: [461/600]
Discriminator Loss: 1.2511, Generator Loss: 0.9759
D(x): 0.4900, D(G(z)): 0.3982
Epoch: [0/20], Batch Num: [462/600]
Discriminator Loss: 1.2982, Generator Loss: 0.9833
D(x): 0.4777, D(G(z)): 0.4153
Epoch: [0/20], Batch Num: [463/600]
Discriminator Loss: 1.2452, Generator Loss: 0.9261
D(x): 0.5052, D(G(z)): 0.4142
Epoch: [0/20], Batch Num: [464/600]
Discriminator Loss: 1.2245, Generator Loss: 0.8822
D(x): 0.5280, D(G(z)): 0.4339
Epoch: [0/20], Batch Num: [465/600]
Discriminator Loss: 1.2031, Generator Loss: 0.8425
D(x): 0.5493, D(G(z)): 0.4413
Epoch: [0/20], Batch Num: [466/600]
Discriminator Loss: 1.1893, Generator Loss: 0.8188
D(x): 0.5689, D(G(z)): 0.4541
Epoch: [0/20], Batch Num: [467/600]
Discriminator Loss: 1.1707, Generator Loss: 0.8093
D(x): 0.6009, D(G(z)): 0.4682
Epoch: [0/20], Batch Num: [468/600]
Discriminator Loss: 1.1170, Generator Loss: 0.7851
D(x): 0.6240, D(G(z)): 0.4671
Epoch: [0/20], Batch Num: [469/600]
Discriminator Loss: 1.1454, Generator Loss: 0.7987
D(x): 0.6105, D(G(z)): 0.4715
Epoch: [0/20], Batch Num: [470/600]
Discriminator Loss: 1.0922, Generator Loss: 0.9043
D(x): 0.6356, D(G(z)): 0.4647
Epoch: [0/20], Batch Num: [471/600]
Discriminator Loss: 1.0707, Generator Loss: 0.9480
D(x): 0.6388, D(G(z)): 0.4524
Epoch: [0/20], Batch Num: [472/600]
Discriminator Loss: 1.0608, Generator Loss: 1.0214
D(x): 0.6164, D(G(z)): 0.4144
Epoch: [0/20], Batch Num: [473/600]
Discriminator Loss: 0.9693, Generator Loss: 1.1568
D(x): 0.6144, D(G(z)): 0.3655
Epoch: [0/20], Batch Num: [474/600]
Discriminator Loss: 1.0134, Generator Loss: 1.3134
D(x): 0.5816, D(G(z)): 0.3382
Epoch: [0/20], Batch Num: [475/600]
Discriminator Loss: 0.9288, Generator Loss: 1.3948
D(x): 0.6123, D(G(z)): 0.3353
Epoch: [0/20], Batch Num: [476/600]
Discriminator Loss: 0.9284, Generator Loss: 1.4610
D(x): 0.5886, D(G(z)): 0.3014
Epoch: [0/20], Batch Num: [477/600]
Discriminator Loss: 0.9579, Generator Loss: 1.4042
D(x): 0.5876, D(G(z)): 0.3154
Epoch: [0/20], Batch Num: [478/600]
Discriminator Loss: 0.9478, Generator Loss: 1.3496
D(x): 0.6139, D(G(z)): 0.3387
Epoch: [0/20], Batch Num: [479/600]
Discriminator Loss: 0.9340, Generator Loss: 1.2818
D(x): 0.6298, D(G(z)): 0.3506
Epoch: [0/20], Batch Num: [480/600]
Discriminator Loss: 1.0639, Generator Loss: 1.1846
D(x): 0.6208, D(G(z)): 0.3998
Epoch: [0/20], Batch Num: [481/600]
Discriminator Loss: 1.0147, Generator Loss: 1.1496
D(x): 0.6759, D(G(z)): 0.4407
Epoch: [0/20], Batch Num: [482/600]
Discriminator Loss: 1.0783, Generator Loss: 1.0985
D(x): 0.6600, D(G(z)): 0.4592
Epoch: [0/20], Batch Num: [483/600]
Discriminator Loss: 1.1376, Generator Loss: 1.1901
D(x): 0.6418, D(G(z)): 0.4669
Epoch: [0/20], Batch Num: [484/600]
Discriminator Loss: 1.1094, Generator Loss: 1.2858
D(x): 0.6293, D(G(z)): 0.4457
Epoch: [0/20], Batch Num: [485/600]
Discriminator Loss: 1.0986, Generator Loss: 1.3110
D(x): 0.6175, D(G(z)): 0.4179
Epoch: [0/20], Batch Num: [486/600]
Discriminator Loss: 1.0792, Generator Loss: 1.3372
D(x): 0.5772, D(G(z)): 0.3579
Epoch: [0/20], Batch Num: [487/600]
Discriminator Loss: 1.1029, Generator Loss: 1.2867
D(x): 0.6104, D(G(z)): 0.4011
Epoch: [0/20], Batch Num: [488/600]
Discriminator Loss: 0.9504, Generator Loss: 1.2427
D(x): 0.6405, D(G(z)): 0.3600
Epoch: [0/20], Batch Num: [489/600]
Discriminator Loss: 0.9137, Generator Loss: 1.2832
D(x): 0.6912, D(G(z)): 0.3831
Epoch: [0/20], Batch Num: [490/600]
Discriminator Loss: 1.0033, Generator Loss: 1.4633
D(x): 0.6876, D(G(z)): 0.4231
Epoch: [0/20], Batch Num: [491/600]
Discriminator Loss: 1.1423, Generator Loss: 1.6607
D(x): 0.6611, D(G(z)): 0.4593
Epoch: [0/20], Batch Num: [492/600]
Discriminator Loss: 1.3096, Generator Loss: 1.5127
D(x): 0.6177, D(G(z)): 0.4860
Epoch: [0/20], Batch Num: [493/600]
Discriminator Loss: 1.5487, Generator Loss: 1.2265
D(x): 0.5769, D(G(z)): 0.5465
Epoch: [0/20], Batch Num: [494/600]
Discriminator Loss: 1.9133, Generator Loss: 0.7441
D(x): 0.5141, D(G(z)): 0.6274
Epoch: [0/20], Batch Num: [495/600]
Discriminator Loss: 2.2534, Generator Loss: 0.4057
D(x): 0.4911, D(G(z)): 0.7207
Epoch: [0/20], Batch Num: [496/600]
Discriminator Loss: 2.2768, Generator Loss: 0.3147
D(x): 0.5284, D(G(z)): 0.7586
Epoch: [0/20], Batch Num: [497/600]
Discriminator Loss: 2.0257, Generator Loss: 0.2993
D(x): 0.6234, D(G(z)): 0.7694
Epoch: [0/20], Batch Num: [498/600]
Discriminator Loss: 1.8243, Generator Loss: 0.3429
D(x): 0.7017, D(G(z)): 0.7570
Epoch: [0/20], Batch Num: [499/600]
Discriminator Loss: 1.5677, Generator Loss: 0.5353
D(x): 0.7418, D(G(z)): 0.7064
Epoch: 0, Batch Num: [500/600]

Epoch: [0/20], Batch Num: [500/600]
Discriminator Loss: 1.2793, Generator Loss: 1.4929
D(x): 0.7467, D(G(z)): 0.6055
Epoch: [0/20], Batch Num: [501/600]
Discriminator Loss: 1.0480, Generator Loss: 3.2172
D(x): 0.7159, D(G(z)): 0.4722
Epoch: [0/20], Batch Num: [502/600]
Discriminator Loss: 0.6565, Generator Loss: 4.7493
D(x): 0.7027, D(G(z)): 0.2333
Epoch: [0/20], Batch Num: [503/600]
Discriminator Loss: 0.5683, Generator Loss: 6.1685
D(x): 0.6740, D(G(z)): 0.1390
Epoch: [0/20], Batch Num: [504/600]
Discriminator Loss: 0.5298, Generator Loss: 7.8163
D(x): 0.6362, D(G(z)): 0.0579
Epoch: [0/20], Batch Num: [505/600]
Discriminator Loss: 0.5798, Generator Loss: 8.1771
D(x): 0.5876, D(G(z)): 0.0301
Epoch: [0/20], Batch Num: [506/600]
Discriminator Loss: 0.7035, Generator Loss: 7.7513
D(x): 0.5457, D(G(z)): 0.0592
Epoch: [0/20], Batch Num: [507/600]
Discriminator Loss: 0.8863, Generator Loss: 5.1463
D(x): 0.5007, D(G(z)): 0.0934
Epoch: [0/20], Batch Num: [508/600]
Discriminator Loss: 1.1779, Generator Loss: 3.0169
D(x): 0.4853, D(G(z)): 0.1962
Epoch: [0/20], Batch Num: [509/600]
Discriminator Loss: 1.1522, Generator Loss: 1.3227
D(x): 0.5784, D(G(z)): 0.3952
Epoch: [0/20], Batch Num: [510/600]
Discriminator Loss: 1.3270, Generator Loss: 0.7961
D(x): 0.5995, D(G(z)): 0.5210
Epoch: [0/20], Batch Num: [511/600]
Discriminator Loss: 1.4325, Generator Loss: 0.5789
D(x): 0.6314, D(G(z)): 0.6003
Epoch: [0/20], Batch Num: [512/600]
Discriminator Loss: 1.4720, Generator Loss: 0.4752
D(x): 0.6679, D(G(z)): 0.6441
Epoch: [0/20], Batch Num: [513/600]
Discriminator Loss: 1.4190, Generator Loss: 0.4607
D(x): 0.7034, D(G(z)): 0.6475
Epoch: [0/20], Batch Num: [514/600]
Discriminator Loss: 1.4773, Generator Loss: 0.4723
D(x): 0.6997, D(G(z)): 0.6657
Epoch: [0/20], Batch Num: [515/600]
Discriminator Loss: 1.4308, Generator Loss: 0.4849
D(x): 0.6952, D(G(z)): 0.6467
Epoch: [0/20], Batch Num: [516/600]
Discriminator Loss: 1.3861, Generator Loss: 0.5145
D(x): 0.6968, D(G(z)): 0.6351
Epoch: [0/20], Batch Num: [517/600]
Discriminator Loss: 1.3651, Generator Loss: 0.5403
D(x): 0.6827, D(G(z)): 0.6172
Epoch: [0/20], Batch Num: [518/600]
Discriminator Loss: 1.3264, Generator Loss: 0.5971
D(x): 0.6706, D(G(z)): 0.5962
Epoch: [0/20], Batch Num: [519/600]
Discriminator Loss: 1.2548, Generator Loss: 0.6513
D(x): 0.6599, D(G(z)): 0.5602
Epoch: [0/20], Batch Num: [520/600]
Discriminator Loss: 1.2243, Generator Loss: 0.7207
D(x): 0.6444, D(G(z)): 0.5362
Epoch: [0/20], Batch Num: [521/600]
Discriminator Loss: 1.1959, Generator Loss: 0.8279
D(x): 0.6422, D(G(z)): 0.5211
Epoch: [0/20], Batch Num: [522/600]
Discriminator Loss: 1.1132, Generator Loss: 0.9050
D(x): 0.6178, D(G(z)): 0.4556
Epoch: [0/20], Batch Num: [523/600]
Discriminator Loss: 1.1174, Generator Loss: 1.0518
D(x): 0.6034, D(G(z)): 0.4463
Epoch: [0/20], Batch Num: [524/600]
Discriminator Loss: 1.0950, Generator Loss: 1.2249
D(x): 0.5833, D(G(z)): 0.4100
Epoch: [0/20], Batch Num: [525/600]
Discriminator Loss: 1.0648, Generator Loss: 1.4039
D(x): 0.5662, D(G(z)): 0.3648
Epoch: [0/20], Batch Num: [526/600]
Discriminator Loss: 1.0323, Generator Loss: 1.3902
D(x): 0.5565, D(G(z)): 0.3358
Epoch: [0/20], Batch Num: [527/600]
Discriminator Loss: 1.0399, Generator Loss: 1.4118
D(x): 0.5718, D(G(z)): 0.3455
Epoch: [0/20], Batch Num: [528/600]
Discriminator Loss: 1.0252, Generator Loss: 1.4250
D(x): 0.5838, D(G(z)): 0.3643
Epoch: [0/20], Batch Num: [529/600]
Discriminator Loss: 1.0294, Generator Loss: 1.3783
D(x): 0.6026, D(G(z)): 0.3801
Epoch: [0/20], Batch Num: [530/600]
Discriminator Loss: 1.1210, Generator Loss: 0.9469
D(x): 0.6044, D(G(z)): 0.4333
Epoch: [0/20], Batch Num: [531/600]
Discriminator Loss: 1.1565, Generator Loss: 0.8788
D(x): 0.6314, D(G(z)): 0.4843
Epoch: [0/20], Batch Num: [532/600]
Discriminator Loss: 1.3270, Generator Loss: 0.7121
D(x): 0.6100, D(G(z)): 0.5437
Epoch: [0/20], Batch Num: [533/600]
Discriminator Loss: 1.2950, Generator Loss: 0.5836
D(x): 0.6738, D(G(z)): 0.5832
Epoch: [0/20], Batch Num: [534/600]
Discriminator Loss: 1.4353, Generator Loss: 0.5390
D(x): 0.6311, D(G(z)): 0.6061
Epoch: [0/20], Batch Num: [535/600]
Discriminator Loss: 1.5094, Generator Loss: 0.4804
D(x): 0.6604, D(G(z)): 0.6515
Epoch: [0/20], Batch Num: [536/600]
Discriminator Loss: 1.4509, Generator Loss: 0.4596
D(x): 0.6765, D(G(z)): 0.6419
Epoch: [0/20], Batch Num: [537/600]
Discriminator Loss: 1.4499, Generator Loss: 0.4401
D(x): 0.6945, D(G(z)): 0.6524
Epoch: [0/20], Batch Num: [538/600]
Discriminator Loss: 1.4911, Generator Loss: 0.4549
D(x): 0.6972, D(G(z)): 0.6671
Epoch: [0/20], Batch Num: [539/600]
Discriminator Loss: 1.5097, Generator Loss: 0.4594
D(x): 0.6883, D(G(z)): 0.6685
Epoch: [0/20], Batch Num: [540/600]
Discriminator Loss: 1.4516, Generator Loss: 0.4782
D(x): 0.6919, D(G(z)): 0.6529
Epoch: [0/20], Batch Num: [541/600]
Discriminator Loss: 1.4684, Generator Loss: 0.5081
D(x): 0.6562, D(G(z)): 0.6380
Epoch: [0/20], Batch Num: [542/600]
Discriminator Loss: 1.3940, Generator Loss: 0.5446
D(x): 0.6618, D(G(z)): 0.6149
Epoch: [0/20], Batch Num: [543/600]
Discriminator Loss: 1.3598, Generator Loss: 0.5782
D(x): 0.6413, D(G(z)): 0.5886
Epoch: [0/20], Batch Num: [544/600]
Discriminator Loss: 1.3604, Generator Loss: 0.6002
D(x): 0.6473, D(G(z)): 0.5928
Epoch: [0/20], Batch Num: [545/600]
Discriminator Loss: 1.4142, Generator Loss: 0.6092
D(x): 0.5882, D(G(z)): 0.5682
Epoch: [0/20], Batch Num: [546/600]
Discriminator Loss: 1.2658, Generator Loss: 0.6269
D(x): 0.6158, D(G(z)): 0.5328
Epoch: [0/20], Batch Num: [547/600]
Discriminator Loss: 1.2629, Generator Loss: 0.6631
D(x): 0.6141, D(G(z)): 0.5308
Epoch: [0/20], Batch Num: [548/600]
Discriminator Loss: 1.2586, Generator Loss: 0.6988
D(x): 0.6009, D(G(z)): 0.5187
Epoch: [0/20], Batch Num: [549/600]
Discriminator Loss: 1.2561, Generator Loss: 0.7655
D(x): 0.5979, D(G(z)): 0.5154
Epoch: [0/20], Batch Num: [550/600]
Discriminator Loss: 1.2464, Generator Loss: 0.7612
D(x): 0.5892, D(G(z)): 0.5019
Epoch: [0/20], Batch Num: [551/600]
Discriminator Loss: 1.2191, Generator Loss: 0.7410
D(x): 0.5851, D(G(z)): 0.4859
Epoch: [0/20], Batch Num: [552/600]
Discriminator Loss: 1.1918, Generator Loss: 0.7875
D(x): 0.5827, D(G(z)): 0.4692
Epoch: [0/20], Batch Num: [553/600]
Discriminator Loss: 1.2067, Generator Loss: 0.8476
D(x): 0.5894, D(G(z)): 0.4815
Epoch: [0/20], Batch Num: [554/600]
Discriminator Loss: 1.1893, Generator Loss: 0.7849
D(x): 0.5856, D(G(z)): 0.4595
Epoch: [0/20], Batch Num: [555/600]
Discriminator Loss: 1.1549, Generator Loss: 0.8224
D(x): 0.6065, D(G(z)): 0.4666
Epoch: [0/20], Batch Num: [556/600]
Discriminator Loss: 1.1522, Generator Loss: 0.8294
D(x): 0.5985, D(G(z)): 0.4588
Epoch: [0/20], Batch Num: [557/600]
Discriminator Loss: 1.0762, Generator Loss: 0.9237
D(x): 0.6134, D(G(z)): 0.4291
Epoch: [0/20], Batch Num: [558/600]
Discriminator Loss: 1.1696, Generator Loss: 0.9288
D(x): 0.5948, D(G(z)): 0.4574
Epoch: [0/20], Batch Num: [559/600]
Discriminator Loss: 1.1231, Generator Loss: 0.9538
D(x): 0.6043, D(G(z)): 0.4472
Epoch: [0/20], Batch Num: [560/600]
Discriminator Loss: 1.1591, Generator Loss: 0.9679
D(x): 0.5951, D(G(z)): 0.4467
Epoch: [0/20], Batch Num: [561/600]
Discriminator Loss: 1.1428, Generator Loss: 0.9257
D(x): 0.6086, D(G(z)): 0.4495
Epoch: [0/20], Batch Num: [562/600]
Discriminator Loss: 0.9943, Generator Loss: 1.0142
D(x): 0.6377, D(G(z)): 0.3999
Epoch: [0/20], Batch Num: [563/600]
Discriminator Loss: 1.0881, Generator Loss: 1.0055
D(x): 0.6315, D(G(z)): 0.4449
Epoch: [0/20], Batch Num: [564/600]
Discriminator Loss: 1.0645, Generator Loss: 1.0331
D(x): 0.6345, D(G(z)): 0.4292
Epoch: [0/20], Batch Num: [565/600]
Discriminator Loss: 1.1160, Generator Loss: 1.1204
D(x): 0.6080, D(G(z)): 0.4340
Epoch: [0/20], Batch Num: [566/600]
Discriminator Loss: 1.0942, Generator Loss: 1.0848
D(x): 0.6126, D(G(z)): 0.4132
Epoch: [0/20], Batch Num: [567/600]
Discriminator Loss: 1.0479, Generator Loss: 1.1650
D(x): 0.6317, D(G(z)): 0.4138
Epoch: [0/20], Batch Num: [568/600]
Discriminator Loss: 1.1241, Generator Loss: 1.1133
D(x): 0.5868, D(G(z)): 0.3963
Epoch: [0/20], Batch Num: [569/600]
Discriminator Loss: 1.2152, Generator Loss: 0.9942
D(x): 0.5888, D(G(z)): 0.4418
Epoch: [0/20], Batch Num: [570/600]
Discriminator Loss: 1.2518, Generator Loss: 0.7934
D(x): 0.6302, D(G(z)): 0.4758
Epoch: [0/20], Batch Num: [571/600]
Discriminator Loss: 1.2667, Generator Loss: 0.8806
D(x): 0.6622, D(G(z)): 0.5397
Epoch: [0/20], Batch Num: [572/600]
Discriminator Loss: 1.2452, Generator Loss: 0.8889
D(x): 0.6773, D(G(z)): 0.5419
Epoch: [0/20], Batch Num: [573/600]
Discriminator Loss: 1.3073, Generator Loss: 0.8598
D(x): 0.6581, D(G(z)): 0.5588
Epoch: [0/20], Batch Num: [574/600]
Discriminator Loss: 1.3463, Generator Loss: 0.9907
D(x): 0.6154, D(G(z)): 0.5246
Epoch: [0/20], Batch Num: [575/600]
Discriminator Loss: 1.2217, Generator Loss: 0.9946
D(x): 0.6380, D(G(z)): 0.4988
Epoch: [0/20], Batch Num: [576/600]
Discriminator Loss: 1.2903, Generator Loss: 0.8921
D(x): 0.5952, D(G(z)): 0.4910
Epoch: [0/20], Batch Num: [577/600]
Discriminator Loss: 1.3676, Generator Loss: 0.8966
D(x): 0.5915, D(G(z)): 0.5095
Epoch: [0/20], Batch Num: [578/600]
Discriminator Loss: 1.3087, Generator Loss: 0.8960
D(x): 0.5852, D(G(z)): 0.4899
Epoch: [0/20], Batch Num: [579/600]
Discriminator Loss: 1.3688, Generator Loss: 0.8410
D(x): 0.5873, D(G(z)): 0.5100
Epoch: [0/20], Batch Num: [580/600]
Discriminator Loss: 1.2834, Generator Loss: 0.8794
D(x): 0.6406, D(G(z)): 0.5395
Epoch: [0/20], Batch Num: [581/600]
Discriminator Loss: 1.2745, Generator Loss: 0.8826
D(x): 0.6330, D(G(z)): 0.5236
Epoch: [0/20], Batch Num: [582/600]
Discriminator Loss: 1.3258, Generator Loss: 0.9011
D(x): 0.5764, D(G(z)): 0.5013
Epoch: [0/20], Batch Num: [583/600]
Discriminator Loss: 1.2215, Generator Loss: 0.9795
D(x): 0.5960, D(G(z)): 0.4712
Epoch: [0/20], Batch Num: [584/600]
Discriminator Loss: 1.2937, Generator Loss: 1.0437
D(x): 0.5462, D(G(z)): 0.4529
Epoch: [0/20], Batch Num: [585/600]
Discriminator Loss: 1.2064, Generator Loss: 1.1578
D(x): 0.5761, D(G(z)): 0.4375
Epoch: [0/20], Batch Num: [586/600]
Discriminator Loss: 1.2008, Generator Loss: 1.0913
D(x): 0.5587, D(G(z)): 0.4125
Epoch: [0/20], Batch Num: [587/600]
Discriminator Loss: 1.1162, Generator Loss: 1.2551
D(x): 0.5957, D(G(z)): 0.4070
Epoch: [0/20], Batch Num: [588/600]
Discriminator Loss: 0.9638, Generator Loss: 1.2607
D(x): 0.6516, D(G(z)): 0.3794
Epoch: [0/20], Batch Num: [589/600]
Discriminator Loss: 0.9937, Generator Loss: 1.3894
D(x): 0.6172, D(G(z)): 0.3589
Epoch: [0/20], Batch Num: [590/600]
Discriminator Loss: 0.8836, Generator Loss: 1.5058
D(x): 0.6537, D(G(z)): 0.3375
Epoch: [0/20], Batch Num: [591/600]
Discriminator Loss: 0.7659, Generator Loss: 1.8083
D(x): 0.6752, D(G(z)): 0.2879
Epoch: [0/20], Batch Num: [592/600]
Discriminator Loss: 0.7129, Generator Loss: 2.0388
D(x): 0.6614, D(G(z)): 0.2241
Epoch: [0/20], Batch Num: [593/600]
Discriminator Loss: 0.6161, Generator Loss: 2.3831
D(x): 0.6847, D(G(z)): 0.1835
Epoch: [0/20], Batch Num: [594/600]
Discriminator Loss: 0.6159, Generator Loss: 2.8345
D(x): 0.6827, D(G(z)): 0.1756
Epoch: [0/20], Batch Num: [595/600]
Discriminator Loss: 0.5369, Generator Loss: 3.0440
D(x): 0.7340, D(G(z)): 0.1715
Epoch: [0/20], Batch Num: [596/600]
Discriminator Loss: 0.4819, Generator Loss: 2.9098
D(x): 0.7618, D(G(z)): 0.1689
Epoch: [0/20], Batch Num: [597/600]
Discriminator Loss: 0.4655, Generator Loss: 3.6687
D(x): 0.7608, D(G(z)): 0.1533
Epoch: [0/20], Batch Num: [598/600]
Discriminator Loss: 0.4523, Generator Loss: 3.5715
D(x): 0.7576, D(G(z)): 0.1301
Epoch: [0/20], Batch Num: [599/600]
Discriminator Loss: 0.3859, Generator Loss: 4.1045
D(x): 0.7923, D(G(z)): 0.1262
Epoch: 1, Batch Num: [0/600]

Epoch: [1/20], Batch Num: [0/600]
Discriminator Loss: 0.5202, Generator Loss: 4.0752
D(x): 0.7597, D(G(z)): 0.1811
Epoch: [1/20], Batch Num: [1/600]
Discriminator Loss: 0.5835, Generator Loss: 4.1026
D(x): 0.7446, D(G(z)): 0.1850
Epoch: [1/20], Batch Num: [2/600]
Discriminator Loss: 0.6986, Generator Loss: 3.1937
D(x): 0.6775, D(G(z)): 0.1940
Epoch: [1/20], Batch Num: [3/600]
Discriminator Loss: 0.9460, Generator Loss: 2.7345
D(x): 0.6192, D(G(z)): 0.2588
Epoch: [1/20], Batch Num: [4/600]
Discriminator Loss: 1.2503, Generator Loss: 1.9828
D(x): 0.6789, D(G(z)): 0.4707
Epoch: [1/20], Batch Num: [5/600]
Discriminator Loss: 1.7229, Generator Loss: 1.3584
D(x): 0.6017, D(G(z)): 0.5766
Epoch: [1/20], Batch Num: [6/600]
Discriminator Loss: 1.9445, Generator Loss: 0.7158
D(x): 0.5811, D(G(z)): 0.6461
Epoch: [1/20], Batch Num: [7/600]
Discriminator Loss: 1.8127, Generator Loss: 0.7706
D(x): 0.6881, D(G(z)): 0.7034
Epoch: [1/20], Batch Num: [8/600]
Discriminator Loss: 1.7337, Generator Loss: 0.6013
D(x): 0.6748, D(G(z)): 0.6827
Epoch: [1/20], Batch Num: [9/600]
Discriminator Loss: 1.7597, Generator Loss: 0.8823
D(x): 0.6844, D(G(z)): 0.6763
Epoch: [1/20], Batch Num: [10/600]
Discriminator Loss: 1.4669, Generator Loss: 1.2611
D(x): 0.7432, D(G(z)): 0.6355
Epoch: [1/20], Batch Num: [11/600]
Discriminator Loss: 1.3161, Generator Loss: 1.7202
D(x): 0.7341, D(G(z)): 0.5414
Epoch: [1/20], Batch Num: [12/600]
Discriminator Loss: 1.3231, Generator Loss: 2.0086
D(x): 0.6429, D(G(z)): 0.4615
Epoch: [1/20], Batch Num: [13/600]
Discriminator Loss: 1.4075, Generator Loss: 2.0898
D(x): 0.6203, D(G(z)): 0.4373
Epoch: [1/20], Batch Num: [14/600]
Discriminator Loss: 1.2873, Generator Loss: 1.7170
D(x): 0.6721, D(G(z)): 0.4369
Epoch: [1/20], Batch Num: [15/600]
Discriminator Loss: 1.2319, Generator Loss: 1.7165
D(x): 0.6899, D(G(z)): 0.4640
Epoch: [1/20], Batch Num: [16/600]
Discriminator Loss: 1.0518, Generator Loss: 2.3877
D(x): 0.7247, D(G(z)): 0.4387
Epoch: [1/20], Batch Num: [17/600]
Discriminator Loss: 1.0331, Generator Loss: 2.4319
D(x): 0.7140, D(G(z)): 0.4036
Epoch: [1/20], Batch Num: [18/600]
Discriminator Loss: 0.9002, Generator Loss: 2.9957
D(x): 0.7166, D(G(z)): 0.3490
Epoch: [1/20], Batch Num: [19/600]
Discriminator Loss: 0.9703, Generator Loss: 2.8555
D(x): 0.6764, D(G(z)): 0.3222
Epoch: [1/20], Batch Num: [20/600]
Discriminator Loss: 0.9891, Generator Loss: 3.1089
D(x): 0.6197, D(G(z)): 0.2850
Epoch: [1/20], Batch Num: [21/600]
Discriminator Loss: 0.9314, Generator Loss: 2.9224
D(x): 0.6416, D(G(z)): 0.2627
Epoch: [1/20], Batch Num: [22/600]
Discriminator Loss: 1.1196, Generator Loss: 2.6932
D(x): 0.6314, D(G(z)): 0.3501
Epoch: [1/20], Batch Num: [23/600]
Discriminator Loss: 1.2226, Generator Loss: 1.6825
D(x): 0.6279, D(G(z)): 0.3938
Epoch: [1/20], Batch Num: [24/600]
Discriminator Loss: 1.1206, Generator Loss: 1.6601
D(x): 0.6634, D(G(z)): 0.4191
Epoch: [1/20], Batch Num: [25/600]
Discriminator Loss: 1.2520, Generator Loss: 1.5955
D(x): 0.6796, D(G(z)): 0.5049
Epoch: [1/20], Batch Num: [26/600]
Discriminator Loss: 1.3500, Generator Loss: 1.5838
D(x): 0.6370, D(G(z)): 0.4963
Epoch: [1/20], Batch Num: [27/600]
Discriminator Loss: 1.2013, Generator Loss: 1.9252
D(x): 0.6596, D(G(z)): 0.4681
Epoch: [1/20], Batch Num: [28/600]
Discriminator Loss: 1.2736, Generator Loss: 2.6324
D(x): 0.6054, D(G(z)): 0.4439
Epoch: [1/20], Batch Num: [29/600]
Discriminator Loss: 1.1796, Generator Loss: 2.8806
D(x): 0.5967, D(G(z)): 0.3633
Epoch: [1/20], Batch Num: [30/600]
Discriminator Loss: 0.9137, Generator Loss: 3.3054
D(x): 0.6389, D(G(z)): 0.2888
Epoch: [1/20], Batch Num: [31/600]
Discriminator Loss: 0.8566, Generator Loss: 3.6358
D(x): 0.6220, D(G(z)): 0.2421
Epoch: [1/20], Batch Num: [32/600]
Discriminator Loss: 0.7948, Generator Loss: 5.1350
D(x): 0.6418, D(G(z)): 0.2046
Epoch: [1/20], Batch Num: [33/600]
Discriminator Loss: 0.7489, Generator Loss: 4.9568
D(x): 0.6584, D(G(z)): 0.2106
Epoch: [1/20], Batch Num: [34/600]
Discriminator Loss: 0.6576, Generator Loss: 5.0740
D(x): 0.6745, D(G(z)): 0.1687
Epoch: [1/20], Batch Num: [35/600]
Discriminator Loss: 0.6565, Generator Loss: 5.0232
D(x): 0.6920, D(G(z)): 0.2013
Epoch: [1/20], Batch Num: [36/600]
Discriminator Loss: 0.6218, Generator Loss: 5.2012
D(x): 0.7254, D(G(z)): 0.2099
Epoch: [1/20], Batch Num: [37/600]
Discriminator Loss: 0.5728, Generator Loss: 5.7543
D(x): 0.7267, D(G(z)): 0.1826
Epoch: [1/20], Batch Num: [38/600]
Discriminator Loss: 0.6491, Generator Loss: 5.4521
D(x): 0.6872, D(G(z)): 0.1760
Epoch: [1/20], Batch Num: [39/600]
Discriminator Loss: 0.7195, Generator Loss: 4.7701
D(x): 0.6819, D(G(z)): 0.1971
Epoch: [1/20], Batch Num: [40/600]
Discriminator Loss: 0.9755, Generator Loss: 3.4073
D(x): 0.6637, D(G(z)): 0.2708
Epoch: [1/20], Batch Num: [41/600]
Discriminator Loss: 1.2665, Generator Loss: 2.0785
D(x): 0.6326, D(G(z)): 0.3429
Epoch: [1/20], Batch Num: [42/600]
Discriminator Loss: 1.4815, Generator Loss: 1.3432
D(x): 0.6633, D(G(z)): 0.5174
Epoch: [1/20], Batch Num: [43/600]
Discriminator Loss: 1.6972, Generator Loss: 0.7350
D(x): 0.6853, D(G(z)): 0.6258
Epoch: [1/20], Batch Num: [44/600]
Discriminator Loss: 1.8736, Generator Loss: 0.3980
D(x): 0.6664, D(G(z)): 0.6930
Epoch: [1/20], Batch Num: [45/600]
Discriminator Loss: 1.7926, Generator Loss: 0.3035
D(x): 0.7193, D(G(z)): 0.7272
Epoch: [1/20], Batch Num: [46/600]
Discriminator Loss: 1.8389, Generator Loss: 0.2684
D(x): 0.7919, D(G(z)): 0.7705
Epoch: [1/20], Batch Num: [47/600]
Discriminator Loss: 1.8936, Generator Loss: 0.2442
D(x): 0.8211, D(G(z)): 0.7930
Epoch: [1/20], Batch Num: [48/600]
Discriminator Loss: 1.8088, Generator Loss: 0.2539
D(x): 0.8613, D(G(z)): 0.8016
Epoch: [1/20], Batch Num: [49/600]
Discriminator Loss: 1.7578, Generator Loss: 0.2450
D(x): 0.8582, D(G(z)): 0.7914
Epoch: [1/20], Batch Num: [50/600]
Discriminator Loss: 1.7549, Generator Loss: 0.2689
D(x): 0.8555, D(G(z)): 0.7892
Epoch: [1/20], Batch Num: [51/600]
Discriminator Loss: 1.7663, Generator Loss: 0.2998
D(x): 0.8150, D(G(z)): 0.7693
Epoch: [1/20], Batch Num: [52/600]
Discriminator Loss: 1.6604, Generator Loss: 0.3107
D(x): 0.8221, D(G(z)): 0.7626
Epoch: [1/20], Batch Num: [53/600]
Discriminator Loss: 1.5759, Generator Loss: 0.3461
D(x): 0.8041, D(G(z)): 0.7357
Epoch: [1/20], Batch Num: [54/600]
Discriminator Loss: 1.5736, Generator Loss: 0.3840
D(x): 0.7670, D(G(z)): 0.7217
Epoch: [1/20], Batch Num: [55/600]
Discriminator Loss: 1.5024, Generator Loss: 0.4235
D(x): 0.7361, D(G(z)): 0.6909
Epoch: [1/20], Batch Num: [56/600]
Discriminator Loss: 1.5094, Generator Loss: 0.4458
D(x): 0.7081, D(G(z)): 0.6672
Epoch: [1/20], Batch Num: [57/600]
Discriminator Loss: 1.4675, Generator Loss: 0.4759
D(x): 0.6728, D(G(z)): 0.6501
Epoch: [1/20], Batch Num: [58/600]
Discriminator Loss: 1.4830, Generator Loss: 0.4909
D(x): 0.6405, D(G(z)): 0.6325
Epoch: [1/20], Batch Num: [59/600]
Discriminator Loss: 1.4441, Generator Loss: 0.5135
D(x): 0.6289, D(G(z)): 0.6194
Epoch: [1/20], Batch Num: [60/600]
Discriminator Loss: 1.4176, Generator Loss: 0.5328
D(x): 0.6229, D(G(z)): 0.6022
Epoch: [1/20], Batch Num: [61/600]
Discriminator Loss: 1.3845, Generator Loss: 0.5401
D(x): 0.6277, D(G(z)): 0.5968
Epoch: [1/20], Batch Num: [62/600]
Discriminator Loss: 1.3892, Generator Loss: 0.5613
D(x): 0.6101, D(G(z)): 0.5869
Epoch: [1/20], Batch Num: [63/600]
Discriminator Loss: 1.4288, Generator Loss: 0.5664
D(x): 0.5938, D(G(z)): 0.5765
Epoch: [1/20], Batch Num: [64/600]
Discriminator Loss: 1.3700, Generator Loss: 0.5876
D(x): 0.5901, D(G(z)): 0.5660
Epoch: [1/20], Batch Num: [65/600]
Discriminator Loss: 1.3624, Generator Loss: 0.6034
D(x): 0.5870, D(G(z)): 0.5587
Epoch: [1/20], Batch Num: [66/600]
Discriminator Loss: 1.3594, Generator Loss: 0.5974
D(x): 0.5784, D(G(z)): 0.5532
Epoch: [1/20], Batch Num: [67/600]
Discriminator Loss: 1.3697, Generator Loss: 0.6154
D(x): 0.5760, D(G(z)): 0.5565
Epoch: [1/20], Batch Num: [68/600]
Discriminator Loss: 1.3400, Generator Loss: 0.6231
D(x): 0.5777, D(G(z)): 0.5447
Epoch: [1/20], Batch Num: [69/600]
Discriminator Loss: 1.3529, Generator Loss: 0.6278
D(x): 0.5682, D(G(z)): 0.5419
Epoch: [1/20], Batch Num: [70/600]
Discriminator Loss: 1.3350, Generator Loss: 0.6336
D(x): 0.5644, D(G(z)): 0.5325
Epoch: [1/20], Batch Num: [71/600]
Discriminator Loss: 1.3427, Generator Loss: 0.6345
D(x): 0.5602, D(G(z)): 0.5323
Epoch: [1/20], Batch Num: [72/600]
Discriminator Loss: 1.3511, Generator Loss: 0.6483
D(x): 0.5534, D(G(z)): 0.5308
Epoch: [1/20], Batch Num: [73/600]
Discriminator Loss: 1.3353, Generator Loss: 0.6467
D(x): 0.5511, D(G(z)): 0.5211
Epoch: [1/20], Batch Num: [74/600]
Discriminator Loss: 1.3486, Generator Loss: 0.6592
D(x): 0.5482, D(G(z)): 0.5250
Epoch: [1/20], Batch Num: [75/600]
Discriminator Loss: 1.3330, Generator Loss: 0.6556
D(x): 0.5502, D(G(z)): 0.5194
Epoch: [1/20], Batch Num: [76/600]
Discriminator Loss: 1.3314, Generator Loss: 0.6572
D(x): 0.5492, D(G(z)): 0.5176
Epoch: [1/20], Batch Num: [77/600]
Discriminator Loss: 1.3295, Generator Loss: 0.6648
D(x): 0.5487, D(G(z)): 0.5165
Epoch: [1/20], Batch Num: [78/600]
Discriminator Loss: 1.3317, Generator Loss: 0.6587
D(x): 0.5440, D(G(z)): 0.5135
Epoch: [1/20], Batch Num: [79/600]
Discriminator Loss: 1.3227, Generator Loss: 0.6648
D(x): 0.5488, D(G(z)): 0.5133
Epoch: [1/20], Batch Num: [80/600]
Discriminator Loss: 1.3210, Generator Loss: 0.6692
D(x): 0.5484, D(G(z)): 0.5122
Epoch: [1/20], Batch Num: [81/600]
Discriminator Loss: 1.3409, Generator Loss: 0.6735
D(x): 0.5415, D(G(z)): 0.5157
Epoch: [1/20], Batch Num: [82/600]
Discriminator Loss: 1.3305, Generator Loss: 0.6761
D(x): 0.5418, D(G(z)): 0.5106
Epoch: [1/20], Batch Num: [83/600]
Discriminator Loss: 1.3376, Generator Loss: 0.6739
D(x): 0.5420, D(G(z)): 0.5144
Epoch: [1/20], Batch Num: [84/600]
Discriminator Loss: 1.3212, Generator Loss: 0.6795
D(x): 0.5440, D(G(z)): 0.5082
Epoch: [1/20], Batch Num: [85/600]
Discriminator Loss: 1.3222, Generator Loss: 0.6800
D(x): 0.5441, D(G(z)): 0.5090
Epoch: [1/20], Batch Num: [86/600]
Discriminator Loss: 1.3183, Generator Loss: 0.6817
D(x): 0.5444, D(G(z)): 0.5072
Epoch: [1/20], Batch Num: [87/600]
Discriminator Loss: 1.3160, Generator Loss: 0.6792
D(x): 0.5491, D(G(z)): 0.5105
Epoch: [1/20], Batch Num: [88/600]
Discriminator Loss: 1.3116, Generator Loss: 0.6754
D(x): 0.5502, D(G(z)): 0.5091
Epoch: [1/20], Batch Num: [89/600]
Discriminator Loss: 1.3108, Generator Loss: 0.6804
D(x): 0.5491, D(G(z)): 0.5075
Epoch: [1/20], Batch Num: [90/600]
Discriminator Loss: 1.3012, Generator Loss: 0.6803
D(x): 0.5532, D(G(z)): 0.5067
Epoch: [1/20], Batch Num: [91/600]
Discriminator Loss: 1.2992, Generator Loss: 0.6821
D(x): 0.5527, D(G(z)): 0.5051
Epoch: [1/20], Batch Num: [92/600]
Discriminator Loss: 1.3106, Generator Loss: 0.6830
D(x): 0.5492, D(G(z)): 0.5073
Epoch: [1/20], Batch Num: [93/600]
Discriminator Loss: 1.2951, Generator Loss: 0.6993
D(x): 0.5512, D(G(z)): 0.5014
Epoch: [1/20], Batch Num: [94/600]
Discriminator Loss: 1.2865, Generator Loss: 0.6918
D(x): 0.5553, D(G(z)): 0.5008
Epoch: [1/20], Batch Num: [95/600]
Discriminator Loss: 1.2884, Generator Loss: 0.7035
D(x): 0.5586, D(G(z)): 0.5042
Epoch: [1/20], Batch Num: [96/600]
Discriminator Loss: 1.2736, Generator Loss: 0.7191
D(x): 0.5592, D(G(z)): 0.4974
Epoch: [1/20], Batch Num: [97/600]
Discriminator Loss: 1.2548, Generator Loss: 0.7233
D(x): 0.5605, D(G(z)): 0.4892
Epoch: [1/20], Batch Num: [98/600]
Discriminator Loss: 1.2556, Generator Loss: 0.7573
D(x): 0.5535, D(G(z)): 0.4828
Epoch: [1/20], Batch Num: [99/600]
Discriminator Loss: 1.2242, Generator Loss: 0.7525
D(x): 0.5645, D(G(z)): 0.4755
Epoch: 1, Batch Num: [100/600]

Epoch: [1/20], Batch Num: [100/600]
Discriminator Loss: 1.2127, Generator Loss: 0.7949
D(x): 0.5596, D(G(z)): 0.4638
Epoch: [1/20], Batch Num: [101/600]
Discriminator Loss: 1.2433, Generator Loss: 0.7931
D(x): 0.5500, D(G(z)): 0.4712
Epoch: [1/20], Batch Num: [102/600]
Discriminator Loss: 1.2014, Generator Loss: 0.8524
D(x): 0.5515, D(G(z)): 0.4491
Epoch: [1/20], Batch Num: [103/600]
Discriminator Loss: 1.1858, Generator Loss: 0.9535
D(x): 0.5590, D(G(z)): 0.4459
Epoch: [1/20], Batch Num: [104/600]
Discriminator Loss: 1.1612, Generator Loss: 1.0995
D(x): 0.5397, D(G(z)): 0.4081
Epoch: [1/20], Batch Num: [105/600]
Discriminator Loss: 1.1587, Generator Loss: 1.2319
D(x): 0.5274, D(G(z)): 0.3851
Epoch: [1/20], Batch Num: [106/600]
Discriminator Loss: 1.1223, Generator Loss: 1.3173
D(x): 0.5332, D(G(z)): 0.3725
Epoch: [1/20], Batch Num: [107/600]
Discriminator Loss: 1.0942, Generator Loss: 1.5326
D(x): 0.5243, D(G(z)): 0.3395
Epoch: [1/20], Batch Num: [108/600]
Discriminator Loss: 1.0918, Generator Loss: 1.6980
D(x): 0.4965, D(G(z)): 0.2891
Epoch: [1/20], Batch Num: [109/600]
Discriminator Loss: 1.1004, Generator Loss: 1.3550
D(x): 0.5096, D(G(z)): 0.3198
Epoch: [1/20], Batch Num: [110/600]
Discriminator Loss: 1.1559, Generator Loss: 1.2692
D(x): 0.4979, D(G(z)): 0.3340
Epoch: [1/20], Batch Num: [111/600]
Discriminator Loss: 1.0940, Generator Loss: 1.1842
D(x): 0.5279, D(G(z)): 0.3345
Epoch: [1/20], Batch Num: [112/600]
Discriminator Loss: 1.0604, Generator Loss: 1.0497
D(x): 0.5566, D(G(z)): 0.3563
Epoch: [1/20], Batch Num: [113/600]
Discriminator Loss: 1.1005, Generator Loss: 0.9857
D(x): 0.5648, D(G(z)): 0.3807
Epoch: [1/20], Batch Num: [114/600]
Discriminator Loss: 1.0810, Generator Loss: 0.9391
D(x): 0.5902, D(G(z)): 0.4056
Epoch: [1/20], Batch Num: [115/600]
Discriminator Loss: 1.0503, Generator Loss: 0.9387
D(x): 0.6195, D(G(z)): 0.4217
Epoch: [1/20], Batch Num: [116/600]
Discriminator Loss: 1.0605, Generator Loss: 0.9683
D(x): 0.6214, D(G(z)): 0.4293
Epoch: [1/20], Batch Num: [117/600]
Discriminator Loss: 1.0472, Generator Loss: 1.1055
D(x): 0.6317, D(G(z)): 0.4304
Epoch: [1/20], Batch Num: [118/600]
Discriminator Loss: 0.9682, Generator Loss: 1.0705
D(x): 0.6209, D(G(z)): 0.3751
Epoch: [1/20], Batch Num: [119/600]
Discriminator Loss: 1.0113, Generator Loss: 1.2978
D(x): 0.5937, D(G(z)): 0.3602
Epoch: [1/20], Batch Num: [120/600]
Discriminator Loss: 0.9846, Generator Loss: 1.2431
D(x): 0.6039, D(G(z)): 0.3660
Epoch: [1/20], Batch Num: [121/600]
Discriminator Loss: 0.9513, Generator Loss: 1.2718
D(x): 0.6083, D(G(z)): 0.3437
Epoch: [1/20], Batch Num: [122/600]
Discriminator Loss: 0.9604, Generator Loss: 1.1973
D(x): 0.6141, D(G(z)): 0.3483
Epoch: [1/20], Batch Num: [123/600]
Discriminator Loss: 0.9191, Generator Loss: 1.1974
D(x): 0.6278, D(G(z)): 0.3446
Epoch: [1/20], Batch Num: [124/600]
Discriminator Loss: 0.9607, Generator Loss: 1.0769
D(x): 0.6469, D(G(z)): 0.3862
Epoch: [1/20], Batch Num: [125/600]
Discriminator Loss: 0.9933, Generator Loss: 1.0249
D(x): 0.6554, D(G(z)): 0.4191
Epoch: [1/20], Batch Num: [126/600]
Discriminator Loss: 1.0287, Generator Loss: 0.9983
D(x): 0.6652, D(G(z)): 0.4464
Epoch: [1/20], Batch Num: [127/600]
Discriminator Loss: 1.1149, Generator Loss: 0.9319
D(x): 0.6492, D(G(z)): 0.4727
Epoch: [1/20], Batch Num: [128/600]
Discriminator Loss: 1.1933, Generator Loss: 0.9156
D(x): 0.6469, D(G(z)): 0.5107
Epoch: [1/20], Batch Num: [129/600]
Discriminator Loss: 1.3053, Generator Loss: 0.7473
D(x): 0.6058, D(G(z)): 0.5225
Epoch: [1/20], Batch Num: [130/600]
Discriminator Loss: 1.3507, Generator Loss: 0.6419
D(x): 0.6358, D(G(z)): 0.5727
Epoch: [1/20], Batch Num: [131/600]
Discriminator Loss: 1.4902, Generator Loss: 0.5417
D(x): 0.6058, D(G(z)): 0.6017
Epoch: [1/20], Batch Num: [132/600]
Discriminator Loss: 1.5908, Generator Loss: 0.4204
D(x): 0.6240, D(G(z)): 0.6533
Epoch: [1/20], Batch Num: [133/600]
Discriminator Loss: 1.5894, Generator Loss: 0.3748
D(x): 0.6713, D(G(z)): 0.6848
Epoch: [1/20], Batch Num: [134/600]
Discriminator Loss: 1.6468, Generator Loss: 0.3430
D(x): 0.6864, D(G(z)): 0.7101
Epoch: [1/20], Batch Num: [135/600]
Discriminator Loss: 1.6953, Generator Loss: 0.3263
D(x): 0.7154, D(G(z)): 0.7321
Epoch: [1/20], Batch Num: [136/600]
Discriminator Loss: 1.6102, Generator Loss: 0.3242
D(x): 0.7690, D(G(z)): 0.7357
Epoch: [1/20], Batch Num: [137/600]
Discriminator Loss: 1.6625, Generator Loss: 0.3205
D(x): 0.7629, D(G(z)): 0.7469
Epoch: [1/20], Batch Num: [138/600]
Discriminator Loss: 1.5921, Generator Loss: 0.3375
D(x): 0.7770, D(G(z)): 0.7329
Epoch: [1/20], Batch Num: [139/600]
Discriminator Loss: 1.5574, Generator Loss: 0.3281
D(x): 0.7880, D(G(z)): 0.7294
Epoch: [1/20], Batch Num: [140/600]
Discriminator Loss: 1.5168, Generator Loss: 0.3658
D(x): 0.7803, D(G(z)): 0.7156
Epoch: [1/20], Batch Num: [141/600]
Discriminator Loss: 1.5134, Generator Loss: 0.3897
D(x): 0.7638, D(G(z)): 0.7082
Epoch: [1/20], Batch Num: [142/600]
Discriminator Loss: 1.4316, Generator Loss: 0.4188
D(x): 0.7575, D(G(z)): 0.6819
Epoch: [1/20], Batch Num: [143/600]
Discriminator Loss: 1.4038, Generator Loss: 0.4469
D(x): 0.7390, D(G(z)): 0.6643
Epoch: [1/20], Batch Num: [144/600]
Discriminator Loss: 1.3339, Generator Loss: 0.4979
D(x): 0.7285, D(G(z)): 0.6358
Epoch: [1/20], Batch Num: [145/600]
Discriminator Loss: 1.2989, Generator Loss: 0.5376
D(x): 0.7137, D(G(z)): 0.6153
Epoch: [1/20], Batch Num: [146/600]
Discriminator Loss: 1.2895, Generator Loss: 0.6357
D(x): 0.6964, D(G(z)): 0.6008
Epoch: [1/20], Batch Num: [147/600]
Discriminator Loss: 1.2083, Generator Loss: 0.7566
D(x): 0.6842, D(G(z)): 0.5580
Epoch: [1/20], Batch Num: [148/600]
Discriminator Loss: 1.1345, Generator Loss: 0.8850
D(x): 0.6711, D(G(z)): 0.5149
Epoch: [1/20], Batch Num: [149/600]
Discriminator Loss: 1.1136, Generator Loss: 1.2621
D(x): 0.6500, D(G(z)): 0.4862
Epoch: [1/20], Batch Num: [150/600]
Discriminator Loss: 1.0501, Generator Loss: 1.6938
D(x): 0.6329, D(G(z)): 0.4363
Epoch: [1/20], Batch Num: [151/600]
Discriminator Loss: 0.9636, Generator Loss: 2.0644
D(x): 0.6139, D(G(z)): 0.3624
Epoch: [1/20], Batch Num: [152/600]
Discriminator Loss: 0.9651, Generator Loss: 2.4625
D(x): 0.5749, D(G(z)): 0.3184
Epoch: [1/20], Batch Num: [153/600]
Discriminator Loss: 1.0070, Generator Loss: 3.0981
D(x): 0.4949, D(G(z)): 0.2276
Epoch: [1/20], Batch Num: [154/600]
Discriminator Loss: 1.1956, Generator Loss: 2.7561
D(x): 0.4174, D(G(z)): 0.2172
Epoch: [1/20], Batch Num: [155/600]
Discriminator Loss: 1.4286, Generator Loss: 1.5570
D(x): 0.3860, D(G(z)): 0.2606
Epoch: [1/20], Batch Num: [156/600]
Discriminator Loss: 1.4919, Generator Loss: 0.9784
D(x): 0.3950, D(G(z)): 0.3596
Epoch: [1/20], Batch Num: [157/600]
Discriminator Loss: 1.5951, Generator Loss: 0.6597
D(x): 0.4271, D(G(z)): 0.4803
Epoch: [1/20], Batch Num: [158/600]
Discriminator Loss: 1.5612, Generator Loss: 0.5554
D(x): 0.4853, D(G(z)): 0.5489
Epoch: [1/20], Batch Num: [159/600]
Discriminator Loss: 1.5450, Generator Loss: 0.4967
D(x): 0.5423, D(G(z)): 0.5970
Epoch: [1/20], Batch Num: [160/600]
Discriminator Loss: 1.4986, Generator Loss: 0.4961
D(x): 0.5918, D(G(z)): 0.6185
Epoch: [1/20], Batch Num: [161/600]
Discriminator Loss: 1.4715, Generator Loss: 0.4598
D(x): 0.6202, D(G(z)): 0.6266
Epoch: [1/20], Batch Num: [162/600]
Discriminator Loss: 1.4793, Generator Loss: 0.4776
D(x): 0.6338, D(G(z)): 0.6372
Epoch: [1/20], Batch Num: [163/600]
Discriminator Loss: 1.4486, Generator Loss: 0.4994
D(x): 0.6432, D(G(z)): 0.6300
Epoch: [1/20], Batch Num: [164/600]
Discriminator Loss: 1.4156, Generator Loss: 0.5764
D(x): 0.6366, D(G(z)): 0.6123
Epoch: [1/20], Batch Num: [165/600]
Discriminator Loss: 1.3755, Generator Loss: 0.6332
D(x): 0.6383, D(G(z)): 0.5993
Epoch: [1/20], Batch Num: [166/600]
Discriminator Loss: 1.2853, Generator Loss: 0.8119
D(x): 0.6309, D(G(z)): 0.5541
Epoch: [1/20], Batch Num: [167/600]
Discriminator Loss: 1.1430, Generator Loss: 1.0548
D(x): 0.6125, D(G(z)): 0.4661
Epoch: [1/20], Batch Num: [168/600]
Discriminator Loss: 1.0235, Generator Loss: 1.4837
D(x): 0.5902, D(G(z)): 0.3768
Epoch: [1/20], Batch Num: [169/600]
Discriminator Loss: 0.9339, Generator Loss: 2.0363
D(x): 0.5479, D(G(z)): 0.2626
Epoch: [1/20], Batch Num: [170/600]
Discriminator Loss: 0.9436, Generator Loss: 2.4489
D(x): 0.4933, D(G(z)): 0.1767
Epoch: [1/20], Batch Num: [171/600]
Discriminator Loss: 0.9290, Generator Loss: 2.5117
D(x): 0.4821, D(G(z)): 0.1434
Epoch: [1/20], Batch Num: [172/600]
Discriminator Loss: 1.0127, Generator Loss: 2.5089
D(x): 0.4370, D(G(z)): 0.1233
Epoch: [1/20], Batch Num: [173/600]
Discriminator Loss: 1.0585, Generator Loss: 2.5250
D(x): 0.4206, D(G(z)): 0.1183
Epoch: [1/20], Batch Num: [174/600]
Discriminator Loss: 0.9467, Generator Loss: 2.0686
D(x): 0.4705, D(G(z)): 0.1324
Epoch: [1/20], Batch Num: [175/600]
Discriminator Loss: 0.9256, Generator Loss: 1.9222
D(x): 0.5186, D(G(z)): 0.2022
Epoch: [1/20], Batch Num: [176/600]
Discriminator Loss: 0.8703, Generator Loss: 1.4527
D(x): 0.5684, D(G(z)): 0.2412
Epoch: [1/20], Batch Num: [177/600]
Discriminator Loss: 0.9224, Generator Loss: 1.2455
D(x): 0.6021, D(G(z)): 0.3164
Epoch: [1/20], Batch Num: [178/600]
Discriminator Loss: 0.9973, Generator Loss: 1.2469
D(x): 0.6047, D(G(z)): 0.3672
Epoch: [1/20], Batch Num: [179/600]
Discriminator Loss: 1.0308, Generator Loss: 1.2991
D(x): 0.6232, D(G(z)): 0.4054
Epoch: [1/20], Batch Num: [180/600]
Discriminator Loss: 1.0033, Generator Loss: 1.3497
D(x): 0.6185, D(G(z)): 0.3795
Epoch: [1/20], Batch Num: [181/600]
Discriminator Loss: 1.0039, Generator Loss: 1.7160
D(x): 0.5975, D(G(z)): 0.3602
Epoch: [1/20], Batch Num: [182/600]
Discriminator Loss: 0.9508, Generator Loss: 1.9729
D(x): 0.5744, D(G(z)): 0.2937
Epoch: [1/20], Batch Num: [183/600]
Discriminator Loss: 1.0776, Generator Loss: 2.1702
D(x): 0.5161, D(G(z)): 0.2854
Epoch: [1/20], Batch Num: [184/600]
Discriminator Loss: 1.1253, Generator Loss: 2.0718
D(x): 0.4746, D(G(z)): 0.2549
Epoch: [1/20], Batch Num: [185/600]
Discriminator Loss: 1.2499, Generator Loss: 1.6579
D(x): 0.4332, D(G(z)): 0.2472
Epoch: [1/20], Batch Num: [186/600]
Discriminator Loss: 1.3662, Generator Loss: 1.3006
D(x): 0.4389, D(G(z)): 0.3301
Epoch: [1/20], Batch Num: [187/600]
Discriminator Loss: 1.3480, Generator Loss: 0.9564
D(x): 0.5004, D(G(z)): 0.4245
Epoch: [1/20], Batch Num: [188/600]
Discriminator Loss: 1.4118, Generator Loss: 0.7060
D(x): 0.5398, D(G(z)): 0.4938
Epoch: [1/20], Batch Num: [189/600]
Discriminator Loss: 1.4669, Generator Loss: 0.5853
D(x): 0.5878, D(G(z)): 0.5858
Epoch: [1/20], Batch Num: [190/600]
Discriminator Loss: 1.4867, Generator Loss: 0.5272
D(x): 0.6235, D(G(z)): 0.6236
Epoch: [1/20], Batch Num: [191/600]
Discriminator Loss: 1.5006, Generator Loss: 0.4910
D(x): 0.6274, D(G(z)): 0.6317
Epoch: [1/20], Batch Num: [192/600]
Discriminator Loss: 1.5030, Generator Loss: 0.4936
D(x): 0.6319, D(G(z)): 0.6396
Epoch: [1/20], Batch Num: [193/600]
Discriminator Loss: 1.5034, Generator Loss: 0.5069
D(x): 0.6363, D(G(z)): 0.6396
Epoch: [1/20], Batch Num: [194/600]
Discriminator Loss: 1.4401, Generator Loss: 0.5166
D(x): 0.6435, D(G(z)): 0.6221
Epoch: [1/20], Batch Num: [195/600]
Discriminator Loss: 1.4373, Generator Loss: 0.5587
D(x): 0.6338, D(G(z)): 0.6125
Epoch: [1/20], Batch Num: [196/600]
Discriminator Loss: 1.3813, Generator Loss: 0.6202
D(x): 0.6384, D(G(z)): 0.5957
Epoch: [1/20], Batch Num: [197/600]
Discriminator Loss: 1.3150, Generator Loss: 0.6650
D(x): 0.6456, D(G(z)): 0.5778
Epoch: [1/20], Batch Num: [198/600]
Discriminator Loss: 1.3272, Generator Loss: 0.7199
D(x): 0.6101, D(G(z)): 0.5494
Epoch: [1/20], Batch Num: [199/600]
Discriminator Loss: 1.3272, Generator Loss: 0.8165
D(x): 0.5812, D(G(z)): 0.5202
Epoch: 1, Batch Num: [200/600]

Epoch: [1/20], Batch Num: [200/600]
Discriminator Loss: 1.2657, Generator Loss: 0.9199
D(x): 0.5667, D(G(z)): 0.4802
Epoch: [1/20], Batch Num: [201/600]
Discriminator Loss: 1.2972, Generator Loss: 0.9938
D(x): 0.5336, D(G(z)): 0.4603
Epoch: [1/20], Batch Num: [202/600]
Discriminator Loss: 1.1520, Generator Loss: 1.0635
D(x): 0.5433, D(G(z)): 0.3924
Epoch: [1/20], Batch Num: [203/600]
Discriminator Loss: 1.1581, Generator Loss: 1.1073
D(x): 0.5279, D(G(z)): 0.3707
Epoch: [1/20], Batch Num: [204/600]
Discriminator Loss: 1.1245, Generator Loss: 1.0642
D(x): 0.5521, D(G(z)): 0.3881
Epoch: [1/20], Batch Num: [205/600]
Discriminator Loss: 1.0734, Generator Loss: 1.0393
D(x): 0.5720, D(G(z)): 0.3822
Epoch: [1/20], Batch Num: [206/600]
Discriminator Loss: 1.0457, Generator Loss: 1.0726
D(x): 0.5747, D(G(z)): 0.3663
Epoch: [1/20], Batch Num: [207/600]
Discriminator Loss: 0.9892, Generator Loss: 1.0875
D(x): 0.5966, D(G(z)): 0.3631
Epoch: [1/20], Batch Num: [208/600]
Discriminator Loss: 0.9731, Generator Loss: 1.1189
D(x): 0.6077, D(G(z)): 0.3652
Epoch: [1/20], Batch Num: [209/600]
Discriminator Loss: 0.9658, Generator Loss: 1.2410
D(x): 0.6129, D(G(z)): 0.3611
Epoch: [1/20], Batch Num: [210/600]
Discriminator Loss: 0.9540, Generator Loss: 1.2404
D(x): 0.6027, D(G(z)): 0.3461
Epoch: [1/20], Batch Num: [211/600]
Discriminator Loss: 0.9353, Generator Loss: 1.3282
D(x): 0.6108, D(G(z)): 0.3354
Epoch: [1/20], Batch Num: [212/600]
Discriminator Loss: 0.8973, Generator Loss: 1.3318
D(x): 0.6162, D(G(z)): 0.3207
Epoch: [1/20], Batch Num: [213/600]
Discriminator Loss: 0.8540, Generator Loss: 1.3760
D(x): 0.6311, D(G(z)): 0.3070
Epoch: [1/20], Batch Num: [214/600]
Discriminator Loss: 0.8643, Generator Loss: 1.3023
D(x): 0.6469, D(G(z)): 0.3268
Epoch: [1/20], Batch Num: [215/600]
Discriminator Loss: 0.8145, Generator Loss: 1.2581
D(x): 0.6743, D(G(z)): 0.3283
Epoch: [1/20], Batch Num: [216/600]
Discriminator Loss: 0.9078, Generator Loss: 1.1437
D(x): 0.6447, D(G(z)): 0.3485
Epoch: [1/20], Batch Num: [217/600]
Discriminator Loss: 0.9643, Generator Loss: 1.1265
D(x): 0.6638, D(G(z)): 0.3912
Epoch: [1/20], Batch Num: [218/600]
Discriminator Loss: 1.0627, Generator Loss: 0.9930
D(x): 0.6530, D(G(z)): 0.4251
Epoch: [1/20], Batch Num: [219/600]
Discriminator Loss: 1.0570, Generator Loss: 0.8468
D(x): 0.6734, D(G(z)): 0.4554
Epoch: [1/20], Batch Num: [220/600]
Discriminator Loss: 1.0901, Generator Loss: 0.7998
D(x): 0.6922, D(G(z)): 0.4929
Epoch: [1/20], Batch Num: [221/600]
Discriminator Loss: 1.2089, Generator Loss: 0.7008
D(x): 0.6784, D(G(z)): 0.5288
Epoch: [1/20], Batch Num: [222/600]
Discriminator Loss: 1.1619, Generator Loss: 0.7053
D(x): 0.7029, D(G(z)): 0.5306
Epoch: [1/20], Batch Num: [223/600]
Discriminator Loss: 1.1734, Generator Loss: 0.7215
D(x): 0.6892, D(G(z)): 0.5269
Epoch: [1/20], Batch Num: [224/600]
Discriminator Loss: 1.2345, Generator Loss: 0.7271
D(x): 0.6769, D(G(z)): 0.5322
Epoch: [1/20], Batch Num: [225/600]
Discriminator Loss: 1.2815, Generator Loss: 0.6866
D(x): 0.6736, D(G(z)): 0.5476
Epoch: [1/20], Batch Num: [226/600]
Discriminator Loss: 1.2978, Generator Loss: 0.6424
D(x): 0.6733, D(G(z)): 0.5488
Epoch: [1/20], Batch Num: [227/600]
Discriminator Loss: 1.2926, Generator Loss: 0.6543
D(x): 0.6853, D(G(z)): 0.5609
Epoch: [1/20], Batch Num: [228/600]
Discriminator Loss: 1.2008, Generator Loss: 0.6765
D(x): 0.6841, D(G(z)): 0.5104
Epoch: [1/20], Batch Num: [229/600]
Discriminator Loss: 1.2448, Generator Loss: 0.6960
D(x): 0.7098, D(G(z)): 0.5645
Epoch: [1/20], Batch Num: [230/600]
Discriminator Loss: 1.1810, Generator Loss: 0.8411
D(x): 0.6953, D(G(z)): 0.5206
Epoch: [1/20], Batch Num: [231/600]
Discriminator Loss: 1.1980, Generator Loss: 0.9774
D(x): 0.6444, D(G(z)): 0.4877
Epoch: [1/20], Batch Num: [232/600]
Discriminator Loss: 1.1168, Generator Loss: 1.1022
D(x): 0.6376, D(G(z)): 0.4412
Epoch: [1/20], Batch Num: [233/600]
Discriminator Loss: 1.2522, Generator Loss: 0.9918
D(x): 0.5694, D(G(z)): 0.4351
Epoch: [1/20], Batch Num: [234/600]
Discriminator Loss: 1.0986, Generator Loss: 0.9638
D(x): 0.6612, D(G(z)): 0.4551
Epoch: [1/20], Batch Num: [235/600]
Discriminator Loss: 1.0983, Generator Loss: 0.9549
D(x): 0.6785, D(G(z)): 0.4605
Epoch: [1/20], Batch Num: [236/600]
Discriminator Loss: 1.0898, Generator Loss: 1.0483
D(x): 0.6833, D(G(z)): 0.4698
Epoch: [1/20], Batch Num: [237/600]
Discriminator Loss: 1.0379, Generator Loss: 1.3223
D(x): 0.6797, D(G(z)): 0.4373
Epoch: [1/20], Batch Num: [238/600]
Discriminator Loss: 1.0651, Generator Loss: 1.3179
D(x): 0.6201, D(G(z)): 0.3874
Epoch: [1/20], Batch Num: [239/600]
Discriminator Loss: 1.0222, Generator Loss: 1.1994
D(x): 0.6158, D(G(z)): 0.3633
Epoch: [1/20], Batch Num: [240/600]
Discriminator Loss: 0.9695, Generator Loss: 1.0511
D(x): 0.6395, D(G(z)): 0.3649
Epoch: [1/20], Batch Num: [241/600]
Discriminator Loss: 1.0274, Generator Loss: 0.9670
D(x): 0.6948, D(G(z)): 0.4435
Epoch: [1/20], Batch Num: [242/600]
Discriminator Loss: 0.9566, Generator Loss: 1.1470
D(x): 0.7039, D(G(z)): 0.4154
Epoch: [1/20], Batch Num: [243/600]
Discriminator Loss: 1.0208, Generator Loss: 1.2228
D(x): 0.6530, D(G(z)): 0.3958
Epoch: [1/20], Batch Num: [244/600]
Discriminator Loss: 1.0801, Generator Loss: 1.3187
D(x): 0.6361, D(G(z)): 0.4140
Epoch: [1/20], Batch Num: [245/600]
Discriminator Loss: 0.9987, Generator Loss: 1.2469
D(x): 0.6619, D(G(z)): 0.3920
Epoch: [1/20], Batch Num: [246/600]
Discriminator Loss: 0.9490, Generator Loss: 1.2624
D(x): 0.6889, D(G(z)): 0.3914
Epoch: [1/20], Batch Num: [247/600]
Discriminator Loss: 1.0646, Generator Loss: 1.1396
D(x): 0.6256, D(G(z)): 0.3803
Epoch: [1/20], Batch Num: [248/600]
Discriminator Loss: 0.9897, Generator Loss: 1.2099
D(x): 0.6992, D(G(z)): 0.4213
Epoch: [1/20], Batch Num: [249/600]
Discriminator Loss: 1.0967, Generator Loss: 1.1478
D(x): 0.6585, D(G(z)): 0.4380
Epoch: [1/20], Batch Num: [250/600]
Discriminator Loss: 1.0130, Generator Loss: 1.2354
D(x): 0.7068, D(G(z)): 0.4329
Epoch: [1/20], Batch Num: [251/600]
Discriminator Loss: 0.9812, Generator Loss: 1.3668
D(x): 0.6919, D(G(z)): 0.4029
Epoch: [1/20], Batch Num: [252/600]
Discriminator Loss: 0.8765, Generator Loss: 1.4844
D(x): 0.7372, D(G(z)): 0.3912
Epoch: [1/20], Batch Num: [253/600]
Discriminator Loss: 0.8685, Generator Loss: 1.6243
D(x): 0.6826, D(G(z)): 0.3140
Epoch: [1/20], Batch Num: [254/600]
Discriminator Loss: 1.0906, Generator Loss: 1.3823
D(x): 0.6238, D(G(z)): 0.3278
Epoch: [1/20], Batch Num: [255/600]
Discriminator Loss: 0.8677, Generator Loss: 1.5151
D(x): 0.7589, D(G(z)): 0.4026
Epoch: [1/20], Batch Num: [256/600]
Discriminator Loss: 0.7735, Generator Loss: 1.7677
D(x): 0.7788, D(G(z)): 0.3692
Epoch: [1/20], Batch Num: [257/600]
Discriminator Loss: 0.7601, Generator Loss: 2.1521
D(x): 0.7238, D(G(z)): 0.2869
Epoch: [1/20], Batch Num: [258/600]
Discriminator Loss: 0.8405, Generator Loss: 2.0535
D(x): 0.6754, D(G(z)): 0.2290
Epoch: [1/20], Batch Num: [259/600]
Discriminator Loss: 0.6788, Generator Loss: 1.8268
D(x): 0.7181, D(G(z)): 0.2236
Epoch: [1/20], Batch Num: [260/600]
Discriminator Loss: 0.6549, Generator Loss: 1.6944
D(x): 0.8097, D(G(z)): 0.3227
Epoch: [1/20], Batch Num: [261/600]
Discriminator Loss: 0.6078, Generator Loss: 2.0194
D(x): 0.8331, D(G(z)): 0.3182
Epoch: [1/20], Batch Num: [262/600]
Discriminator Loss: 0.4949, Generator Loss: 2.3889
D(x): 0.8361, D(G(z)): 0.2453
Epoch: [1/20], Batch Num: [263/600]
Discriminator Loss: 0.5350, Generator Loss: 2.5188
D(x): 0.7766, D(G(z)): 0.1713
Epoch: [1/20], Batch Num: [264/600]
Discriminator Loss: 0.3506, Generator Loss: 2.6415
D(x): 0.8372, D(G(z)): 0.1317
Epoch: [1/20], Batch Num: [265/600]
Discriminator Loss: 0.2995, Generator Loss: 2.7922
D(x): 0.8696, D(G(z)): 0.1247
Epoch: [1/20], Batch Num: [266/600]
Discriminator Loss: 0.3353, Generator Loss: 2.6894
D(x): 0.8782, D(G(z)): 0.1482
Epoch: [1/20], Batch Num: [267/600]
Discriminator Loss: 0.2618, Generator Loss: 2.5906
D(x): 0.9168, D(G(z)): 0.1463
Epoch: [1/20], Batch Num: [268/600]
Discriminator Loss: 0.2657, Generator Loss: 3.3239
D(x): 0.9418, D(G(z)): 0.1684
Epoch: [1/20], Batch Num: [269/600]
Discriminator Loss: 0.2066, Generator Loss: 3.6477
D(x): 0.9245, D(G(z)): 0.1101
Epoch: [1/20], Batch Num: [270/600]
Discriminator Loss: 0.2224, Generator Loss: 3.6712
D(x): 0.8899, D(G(z)): 0.0797
Epoch: [1/20], Batch Num: [271/600]
Discriminator Loss: 0.2132, Generator Loss: 3.6768
D(x): 0.9187, D(G(z)): 0.1030
Epoch: [1/20], Batch Num: [272/600]
Discriminator Loss: 0.2600, Generator Loss: 3.7040
D(x): 0.9274, D(G(z)): 0.1477
Epoch: [1/20], Batch Num: [273/600]
Discriminator Loss: 0.3722, Generator Loss: 3.9102
D(x): 0.9100, D(G(z)): 0.2002
Epoch: [1/20], Batch Num: [274/600]
Discriminator Loss: 0.4801, Generator Loss: 3.5706
D(x): 0.8538, D(G(z)): 0.2078
Epoch: [1/20], Batch Num: [275/600]
Discriminator Loss: 0.6245, Generator Loss: 3.0540
D(x): 0.8050, D(G(z)): 0.2304
Epoch: [1/20], Batch Num: [276/600]
Discriminator Loss: 0.8077, Generator Loss: 2.4884
D(x): 0.8531, D(G(z)): 0.3594
Epoch: [1/20], Batch Num: [277/600]
Discriminator Loss: 1.0671, Generator Loss: 2.3911
D(x): 0.8296, D(G(z)): 0.4997
Epoch: [1/20], Batch Num: [278/600]
Discriminator Loss: 1.1866, Generator Loss: 2.5445
D(x): 0.8245, D(G(z)): 0.5305
Epoch: [1/20], Batch Num: [279/600]
Discriminator Loss: 1.2398, Generator Loss: 3.1605
D(x): 0.7914, D(G(z)): 0.5256
Epoch: [1/20], Batch Num: [280/600]
Discriminator Loss: 1.3269, Generator Loss: 3.3551
D(x): 0.7612, D(G(z)): 0.5033
Epoch: [1/20], Batch Num: [281/600]
Discriminator Loss: 1.0807, Generator Loss: 3.8384
D(x): 0.7532, D(G(z)): 0.3977
Epoch: [1/20], Batch Num: [282/600]
Discriminator Loss: 1.2515, Generator Loss: 3.9048
D(x): 0.7168, D(G(z)): 0.4205
Epoch: [1/20], Batch Num: [283/600]
Discriminator Loss: 1.3069, Generator Loss: 4.6962
D(x): 0.7815, D(G(z)): 0.4802
Epoch: [1/20], Batch Num: [284/600]
Discriminator Loss: 1.1262, Generator Loss: 6.3817
D(x): 0.7899, D(G(z)): 0.3945
Epoch: [1/20], Batch Num: [285/600]
Discriminator Loss: 0.9135, Generator Loss: 6.6890
D(x): 0.7486, D(G(z)): 0.2500
Epoch: [1/20], Batch Num: [286/600]
Discriminator Loss: 1.1410, Generator Loss: 7.7410
D(x): 0.6843, D(G(z)): 0.2088
Epoch: [1/20], Batch Num: [287/600]
Discriminator Loss: 1.0326, Generator Loss: 6.6451
D(x): 0.6863, D(G(z)): 0.2197
Epoch: [1/20], Batch Num: [288/600]
Discriminator Loss: 1.3118, Generator Loss: 3.4448
D(x): 0.6443, D(G(z)): 0.2590
Epoch: [1/20], Batch Num: [289/600]
Discriminator Loss: 1.9575, Generator Loss: 2.4339
D(x): 0.6350, D(G(z)): 0.4708
Epoch: [1/20], Batch Num: [290/600]
Discriminator Loss: 2.0204, Generator Loss: 2.0913
D(x): 0.7945, D(G(z)): 0.6998
Epoch: [1/20], Batch Num: [291/600]
Discriminator Loss: 2.4954, Generator Loss: 2.7800
D(x): 0.6733, D(G(z)): 0.7301
Epoch: [1/20], Batch Num: [292/600]
Discriminator Loss: 3.0558, Generator Loss: 1.6742
D(x): 0.4259, D(G(z)): 0.5813
Epoch: [1/20], Batch Num: [293/600]
Discriminator Loss: 2.4081, Generator Loss: 1.1915
D(x): 0.4918, D(G(z)): 0.5584
Epoch: [1/20], Batch Num: [294/600]
Discriminator Loss: 2.1651, Generator Loss: 0.8055
D(x): 0.6146, D(G(z)): 0.6472
Epoch: [1/20], Batch Num: [295/600]
Discriminator Loss: 1.7864, Generator Loss: 1.0328
D(x): 0.6996, D(G(z)): 0.6619
Epoch: [1/20], Batch Num: [296/600]
Discriminator Loss: 1.3613, Generator Loss: 1.3560
D(x): 0.6860, D(G(z)): 0.5246
Epoch: [1/20], Batch Num: [297/600]
Discriminator Loss: 1.1738, Generator Loss: 1.7661
D(x): 0.6027, D(G(z)): 0.3682
Epoch: [1/20], Batch Num: [298/600]
Discriminator Loss: 1.1314, Generator Loss: 1.7094
D(x): 0.5612, D(G(z)): 0.2907
Epoch: [1/20], Batch Num: [299/600]
Discriminator Loss: 0.9483, Generator Loss: 1.5732
D(x): 0.5983, D(G(z)): 0.2728
Epoch: 1, Batch Num: [300/600]

Epoch: [1/20], Batch Num: [300/600]
Discriminator Loss: 0.9845, Generator Loss: 1.4027
D(x): 0.6229, D(G(z)): 0.2852
Epoch: [1/20], Batch Num: [301/600]
Discriminator Loss: 0.8060, Generator Loss: 1.2329
D(x): 0.7367, D(G(z)): 0.3468
Epoch: [1/20], Batch Num: [302/600]
Discriminator Loss: 0.7723, Generator Loss: 1.1407
D(x): 0.7889, D(G(z)): 0.3664
Epoch: [1/20], Batch Num: [303/600]
Discriminator Loss: 0.7332, Generator Loss: 1.1277
D(x): 0.8398, D(G(z)): 0.3992
Epoch: [1/20], Batch Num: [304/600]
Discriminator Loss: 0.8023, Generator Loss: 1.2286
D(x): 0.8239, D(G(z)): 0.4173
Epoch: [1/20], Batch Num: [305/600]
Discriminator Loss: 0.6520, Generator Loss: 1.3443
D(x): 0.8588, D(G(z)): 0.3619
Epoch: [1/20], Batch Num: [306/600]
Discriminator Loss: 0.6940, Generator Loss: 1.3691
D(x): 0.8269, D(G(z)): 0.3638
Epoch: [1/20], Batch Num: [307/600]
Discriminator Loss: 0.5981, Generator Loss: 1.4443
D(x): 0.8376, D(G(z)): 0.3153
Epoch: [1/20], Batch Num: [308/600]
Discriminator Loss: 0.6275, Generator Loss: 1.5170
D(x): 0.8138, D(G(z)): 0.3060
Epoch: [1/20], Batch Num: [309/600]
Discriminator Loss: 0.7068, Generator Loss: 1.5392
D(x): 0.7341, D(G(z)): 0.2687
Epoch: [1/20], Batch Num: [310/600]
Discriminator Loss: 0.7499, Generator Loss: 1.3021
D(x): 0.7390, D(G(z)): 0.2866
Epoch: [1/20], Batch Num: [311/600]
Discriminator Loss: 0.7966, Generator Loss: 0.9991
D(x): 0.8339, D(G(z)): 0.4083
Epoch: [1/20], Batch Num: [312/600]
Discriminator Loss: 0.8260, Generator Loss: 0.9349
D(x): 0.8549, D(G(z)): 0.4411
Epoch: [1/20], Batch Num: [313/600]
Discriminator Loss: 0.9856, Generator Loss: 1.0540
D(x): 0.8476, D(G(z)): 0.5054
Epoch: [1/20], Batch Num: [314/600]
Discriminator Loss: 1.1174, Generator Loss: 1.0001
D(x): 0.8360, D(G(z)): 0.5535
Epoch: [1/20], Batch Num: [315/600]
Discriminator Loss: 1.1299, Generator Loss: 0.9539
D(x): 0.7633, D(G(z)): 0.5000
Epoch: [1/20], Batch Num: [316/600]
Discriminator Loss: 1.4070, Generator Loss: 0.9826
D(x): 0.7052, D(G(z)): 0.5298
Epoch: [1/20], Batch Num: [317/600]
Discriminator Loss: 1.3759, Generator Loss: 0.7463
D(x): 0.6774, D(G(z)): 0.5245
Epoch: [1/20], Batch Num: [318/600]
Discriminator Loss: 1.3286, Generator Loss: 0.7324
D(x): 0.7508, D(G(z)): 0.5803
Epoch: [1/20], Batch Num: [319/600]
Discriminator Loss: 1.4075, Generator Loss: 0.6556
D(x): 0.7549, D(G(z)): 0.5940
Epoch: [1/20], Batch Num: [320/600]
Discriminator Loss: 1.3988, Generator Loss: 0.7881
D(x): 0.7706, D(G(z)): 0.6368
Epoch: [1/20], Batch Num: [321/600]
Discriminator Loss: 1.3641, Generator Loss: 0.8590
D(x): 0.6991, D(G(z)): 0.5533
Epoch: [1/20], Batch Num: [322/600]
Discriminator Loss: 1.2613, Generator Loss: 0.9876
D(x): 0.7252, D(G(z)): 0.5388
Epoch: [1/20], Batch Num: [323/600]
Discriminator Loss: 1.2581, Generator Loss: 0.9818
D(x): 0.6624, D(G(z)): 0.4916
Epoch: [1/20], Batch Num: [324/600]
Discriminator Loss: 1.0897, Generator Loss: 1.0453
D(x): 0.7296, D(G(z)): 0.4769
Epoch: [1/20], Batch Num: [325/600]
Discriminator Loss: 0.9736, Generator Loss: 1.1159
D(x): 0.7592, D(G(z)): 0.4481
Epoch: [1/20], Batch Num: [326/600]
Discriminator Loss: 0.9501, Generator Loss: 1.1244
D(x): 0.7151, D(G(z)): 0.4097
Epoch: [1/20], Batch Num: [327/600]
Discriminator Loss: 0.9520, Generator Loss: 1.1154
D(x): 0.7182, D(G(z)): 0.3938
Epoch: [1/20], Batch Num: [328/600]
Discriminator Loss: 1.0003, Generator Loss: 1.3197
D(x): 0.6878, D(G(z)): 0.4018
Epoch: [1/20], Batch Num: [329/600]
Discriminator Loss: 1.0562, Generator Loss: 1.1408
D(x): 0.6778, D(G(z)): 0.4079
Epoch: [1/20], Batch Num: [330/600]
Discriminator Loss: 1.0261, Generator Loss: 1.0849
D(x): 0.7380, D(G(z)): 0.4530
Epoch: [1/20], Batch Num: [331/600]
Discriminator Loss: 1.0489, Generator Loss: 1.1409
D(x): 0.7372, D(G(z)): 0.4593
Epoch: [1/20], Batch Num: [332/600]
Discriminator Loss: 1.1460, Generator Loss: 1.1996
D(x): 0.6793, D(G(z)): 0.4586
Epoch: [1/20], Batch Num: [333/600]
Discriminator Loss: 1.3522, Generator Loss: 1.1272
D(x): 0.5984, D(G(z)): 0.4540
Epoch: [1/20], Batch Num: [334/600]
Discriminator Loss: 1.3175, Generator Loss: 1.0791
D(x): 0.5976, D(G(z)): 0.4604
Epoch: [1/20], Batch Num: [335/600]
Discriminator Loss: 1.5319, Generator Loss: 0.8957
D(x): 0.5975, D(G(z)): 0.5407
Epoch: [1/20], Batch Num: [336/600]
Discriminator Loss: 1.7016, Generator Loss: 0.8773
D(x): 0.5931, D(G(z)): 0.6090
Epoch: [1/20], Batch Num: [337/600]
Discriminator Loss: 2.1145, Generator Loss: 0.7668
D(x): 0.4583, D(G(z)): 0.6380
Epoch: [1/20], Batch Num: [338/600]
Discriminator Loss: 2.1552, Generator Loss: 0.6174
D(x): 0.4432, D(G(z)): 0.6543
Epoch: [1/20], Batch Num: [339/600]
Discriminator Loss: 2.1549, Generator Loss: 0.5380
D(x): 0.4674, D(G(z)): 0.6842
Epoch: [1/20], Batch Num: [340/600]
Discriminator Loss: 2.6102, Generator Loss: 0.4173
D(x): 0.3918, D(G(z)): 0.7422
Epoch: [1/20], Batch Num: [341/600]
Discriminator Loss: 2.3299, Generator Loss: 0.4014
D(x): 0.4848, D(G(z)): 0.7552
Epoch: [1/20], Batch Num: [342/600]
Discriminator Loss: 2.3302, Generator Loss: 0.3887
D(x): 0.4345, D(G(z)): 0.7303
Epoch: [1/20], Batch Num: [343/600]
Discriminator Loss: 2.2319, Generator Loss: 0.4158
D(x): 0.4529, D(G(z)): 0.7338
Epoch: [1/20], Batch Num: [344/600]
Discriminator Loss: 2.0545, Generator Loss: 0.4705
D(x): 0.4718, D(G(z)): 0.7029
Epoch: [1/20], Batch Num: [345/600]
Discriminator Loss: 1.8705, Generator Loss: 0.5353
D(x): 0.4752, D(G(z)): 0.6574
Epoch: [1/20], Batch Num: [346/600]
Discriminator Loss: 1.7614, Generator Loss: 0.6037
D(x): 0.4632, D(G(z)): 0.6100
Epoch: [1/20], Batch Num: [347/600]
Discriminator Loss: 1.6668, Generator Loss: 0.6634
D(x): 0.4607, D(G(z)): 0.5754
Epoch: [1/20], Batch Num: [348/600]
Discriminator Loss: 1.5257, Generator Loss: 0.6762
D(x): 0.4901, D(G(z)): 0.5470
Epoch: [1/20], Batch Num: [349/600]
Discriminator Loss: 1.4403, Generator Loss: 0.7107
D(x): 0.4973, D(G(z)): 0.5155
Epoch: [1/20], Batch Num: [350/600]
Discriminator Loss: 1.3704, Generator Loss: 0.7146
D(x): 0.5146, D(G(z)): 0.4996
Epoch: [1/20], Batch Num: [351/600]
Discriminator Loss: 1.2755, Generator Loss: 0.7527
D(x): 0.5509, D(G(z)): 0.4883
Epoch: [1/20], Batch Num: [352/600]
Discriminator Loss: 1.1633, Generator Loss: 0.8938
D(x): 0.5938, D(G(z)): 0.4659
Epoch: [1/20], Batch Num: [353/600]
Discriminator Loss: 1.0891, Generator Loss: 1.1814
D(x): 0.6018, D(G(z)): 0.4325
Epoch: [1/20], Batch Num: [354/600]
Discriminator Loss: 0.9958, Generator Loss: 1.4902
D(x): 0.6389, D(G(z)): 0.4099
Epoch: [1/20], Batch Num: [355/600]
Discriminator Loss: 0.9175, Generator Loss: 2.0094
D(x): 0.6594, D(G(z)): 0.3796
Epoch: [1/20], Batch Num: [356/600]
Discriminator Loss: 0.8303, Generator Loss: 2.6586
D(x): 0.6688, D(G(z)): 0.3297
Epoch: [1/20], Batch Num: [357/600]
Discriminator Loss: 0.6888, Generator Loss: 3.5252
D(x): 0.6924, D(G(z)): 0.2530
Epoch: [1/20], Batch Num: [358/600]
Discriminator Loss: 0.6490, Generator Loss: 4.7828
D(x): 0.6940, D(G(z)): 0.2231
Epoch: [1/20], Batch Num: [359/600]
Discriminator Loss: 0.6020, Generator Loss: 6.8767
D(x): 0.6749, D(G(z)): 0.1616
Epoch: [1/20], Batch Num: [360/600]
Discriminator Loss: 0.5558, Generator Loss: 7.2687
D(x): 0.6405, D(G(z)): 0.0829
Epoch: [1/20], Batch Num: [361/600]
Discriminator Loss: 0.6251, Generator Loss: 7.9414
D(x): 0.6007, D(G(z)): 0.0735
Epoch: [1/20], Batch Num: [362/600]
Discriminator Loss: 0.7357, Generator Loss: 6.9619
D(x): 0.5434, D(G(z)): 0.0507
Epoch: [1/20], Batch Num: [363/600]
Discriminator Loss: 1.0041, Generator Loss: 4.2641
D(x): 0.5191, D(G(z)): 0.1208
Epoch: [1/20], Batch Num: [364/600]
Discriminator Loss: 0.8811, Generator Loss: 2.3094
D(x): 0.6430, D(G(z)): 0.2631
Epoch: [1/20], Batch Num: [365/600]
Discriminator Loss: 1.0260, Generator Loss: 1.4768
D(x): 0.6757, D(G(z)): 0.3916
Epoch: [1/20], Batch Num: [366/600]
Discriminator Loss: 1.2698, Generator Loss: 1.0260
D(x): 0.6663, D(G(z)): 0.5051
Epoch: [1/20], Batch Num: [367/600]
Discriminator Loss: 1.2993, Generator Loss: 1.2714
D(x): 0.6857, D(G(z)): 0.5330
Epoch: [1/20], Batch Num: [368/600]
Discriminator Loss: 1.5233, Generator Loss: 1.6297
D(x): 0.6240, D(G(z)): 0.5628
Epoch: [1/20], Batch Num: [369/600]
Discriminator Loss: 1.3916, Generator Loss: 1.8663
D(x): 0.5968, D(G(z)): 0.4894
Epoch: [1/20], Batch Num: [370/600]
Discriminator Loss: 1.8521, Generator Loss: 1.2579
D(x): 0.4671, D(G(z)): 0.4638
Epoch: [1/20], Batch Num: [371/600]
Discriminator Loss: 1.5719, Generator Loss: 0.9667
D(x): 0.5739, D(G(z)): 0.5069
Epoch: [1/20], Batch Num: [372/600]
Discriminator Loss: 1.7113, Generator Loss: 0.6284
D(x): 0.6072, D(G(z)): 0.6089
Epoch: [1/20], Batch Num: [373/600]
Discriminator Loss: 1.5111, Generator Loss: 0.7441
D(x): 0.6932, D(G(z)): 0.6234
Epoch: [1/20], Batch Num: [374/600]
Discriminator Loss: 1.5119, Generator Loss: 1.0832
D(x): 0.6727, D(G(z)): 0.6141
Epoch: [1/20], Batch Num: [375/600]
Discriminator Loss: 1.4434, Generator Loss: 1.1326
D(x): 0.6310, D(G(z)): 0.5433
Epoch: [1/20], Batch Num: [376/600]
Discriminator Loss: 1.4228, Generator Loss: 1.3796
D(x): 0.6013, D(G(z)): 0.4797
Epoch: [1/20], Batch Num: [377/600]
Discriminator Loss: 1.4831, Generator Loss: 1.2624
D(x): 0.5772, D(G(z)): 0.5249
Epoch: [1/20], Batch Num: [378/600]
Discriminator Loss: 1.4997, Generator Loss: 1.4580
D(x): 0.5588, D(G(z)): 0.4774
Epoch: [1/20], Batch Num: [379/600]
Discriminator Loss: 1.3290, Generator Loss: 1.4256
D(x): 0.5692, D(G(z)): 0.4065
Epoch: [1/20], Batch Num: [380/600]
Discriminator Loss: 1.1608, Generator Loss: 1.4232
D(x): 0.5928, D(G(z)): 0.3968
Epoch: [1/20], Batch Num: [381/600]
Discriminator Loss: 1.2241, Generator Loss: 1.2325
D(x): 0.5921, D(G(z)): 0.4390
Epoch: [1/20], Batch Num: [382/600]
Discriminator Loss: 1.2079, Generator Loss: 1.2108
D(x): 0.6187, D(G(z)): 0.4541
Epoch: [1/20], Batch Num: [383/600]
Discriminator Loss: 1.1552, Generator Loss: 1.2963
D(x): 0.6448, D(G(z)): 0.4502
Epoch: [1/20], Batch Num: [384/600]
Discriminator Loss: 1.2208, Generator Loss: 1.6714
D(x): 0.6181, D(G(z)): 0.4551
Epoch: [1/20], Batch Num: [385/600]
Discriminator Loss: 1.1586, Generator Loss: 1.3539
D(x): 0.6468, D(G(z)): 0.4630
Epoch: [1/20], Batch Num: [386/600]
Discriminator Loss: 1.0373, Generator Loss: 1.4064
D(x): 0.6230, D(G(z)): 0.3767
Epoch: [1/20], Batch Num: [387/600]
Discriminator Loss: 1.2379, Generator Loss: 1.6102
D(x): 0.5586, D(G(z)): 0.3873
Epoch: [1/20], Batch Num: [388/600]
Discriminator Loss: 1.1778, Generator Loss: 1.4576
D(x): 0.5608, D(G(z)): 0.3518
Epoch: [1/20], Batch Num: [389/600]
Discriminator Loss: 1.1357, Generator Loss: 1.0752
D(x): 0.5917, D(G(z)): 0.3881
Epoch: [1/20], Batch Num: [390/600]
Discriminator Loss: 1.1620, Generator Loss: 1.0857
D(x): 0.6272, D(G(z)): 0.4619
Epoch: [1/20], Batch Num: [391/600]
Discriminator Loss: 1.3163, Generator Loss: 1.1582
D(x): 0.5983, D(G(z)): 0.5048
Epoch: [1/20], Batch Num: [392/600]
Discriminator Loss: 1.1879, Generator Loss: 0.9340
D(x): 0.6157, D(G(z)): 0.4586
Epoch: [1/20], Batch Num: [393/600]
Discriminator Loss: 1.2460, Generator Loss: 0.9350
D(x): 0.5950, D(G(z)): 0.4726
Epoch: [1/20], Batch Num: [394/600]
Discriminator Loss: 1.1858, Generator Loss: 0.8934
D(x): 0.6195, D(G(z)): 0.4667
Epoch: [1/20], Batch Num: [395/600]
Discriminator Loss: 1.2797, Generator Loss: 0.9056
D(x): 0.6118, D(G(z)): 0.5030
Epoch: [1/20], Batch Num: [396/600]
Discriminator Loss: 1.3097, Generator Loss: 0.7790
D(x): 0.5850, D(G(z)): 0.4948
Epoch: [1/20], Batch Num: [397/600]
Discriminator Loss: 1.3507, Generator Loss: 0.8060
D(x): 0.5888, D(G(z)): 0.5295
Epoch: [1/20], Batch Num: [398/600]
Discriminator Loss: 1.3276, Generator Loss: 0.7505
D(x): 0.6013, D(G(z)): 0.5200
Epoch: [1/20], Batch Num: [399/600]
Discriminator Loss: 1.2232, Generator Loss: 0.8078
D(x): 0.6138, D(G(z)): 0.4958
Epoch: 1, Batch Num: [400/600]

Epoch: [1/20], Batch Num: [400/600]
Discriminator Loss: 1.2960, Generator Loss: 0.8277
D(x): 0.6102, D(G(z)): 0.5299
Epoch: [1/20], Batch Num: [401/600]
Discriminator Loss: 1.3824, Generator Loss: 0.8350
D(x): 0.5795, D(G(z)): 0.5365
Epoch: [1/20], Batch Num: [402/600]
Discriminator Loss: 1.3611, Generator Loss: 0.6972
D(x): 0.5897, D(G(z)): 0.5318
Epoch: [1/20], Batch Num: [403/600]
Discriminator Loss: 1.2887, Generator Loss: 0.7094
D(x): 0.6037, D(G(z)): 0.5158
Epoch: [1/20], Batch Num: [404/600]
Discriminator Loss: 1.3842, Generator Loss: 0.6554
D(x): 0.5806, D(G(z)): 0.5360
Epoch: [1/20], Batch Num: [405/600]
Discriminator Loss: 1.3286, Generator Loss: 0.6575
D(x): 0.5838, D(G(z)): 0.5101
Epoch: [1/20], Batch Num: [406/600]
Discriminator Loss: 1.3552, Generator Loss: 0.6725
D(x): 0.5840, D(G(z)): 0.5395
Epoch: [1/20], Batch Num: [407/600]
Discriminator Loss: 1.3539, Generator Loss: 0.6668
D(x): 0.5874, D(G(z)): 0.5418
Epoch: [1/20], Batch Num: [408/600]
Discriminator Loss: 1.3928, Generator Loss: 0.6369
D(x): 0.5893, D(G(z)): 0.5506
Epoch: [1/20], Batch Num: [409/600]
Discriminator Loss: 1.3829, Generator Loss: 0.6662
D(x): 0.5884, D(G(z)): 0.5605
Epoch: [1/20], Batch Num: [410/600]
Discriminator Loss: 1.3741, Generator Loss: 0.6351
D(x): 0.5897, D(G(z)): 0.5466
Epoch: [1/20], Batch Num: [411/600]
Discriminator Loss: 1.4053, Generator Loss: 0.6848
D(x): 0.5745, D(G(z)): 0.5513
Epoch: [1/20], Batch Num: [412/600]
Discriminator Loss: 1.3837, Generator Loss: 0.6841
D(x): 0.5890, D(G(z)): 0.5466
Epoch: [1/20], Batch Num: [413/600]
Discriminator Loss: 1.4073, Generator Loss: 0.7211
D(x): 0.5574, D(G(z)): 0.5380
Epoch: [1/20], Batch Num: [414/600]
Discriminator Loss: 1.3020, Generator Loss: 0.7121
D(x): 0.5862, D(G(z)): 0.5163
Epoch: [1/20], Batch Num: [415/600]
Discriminator Loss: 1.3149, Generator Loss: 0.7085
D(x): 0.5844, D(G(z)): 0.5066
Epoch: [1/20], Batch Num: [416/600]
Discriminator Loss: 1.2617, Generator Loss: 0.7466
D(x): 0.5846, D(G(z)): 0.4985
Epoch: [1/20], Batch Num: [417/600]
Discriminator Loss: 1.2592, Generator Loss: 0.7857
D(x): 0.5808, D(G(z)): 0.4950
Epoch: [1/20], Batch Num: [418/600]
Discriminator Loss: 1.2544, Generator Loss: 0.8473
D(x): 0.5824, D(G(z)): 0.4880
Epoch: [1/20], Batch Num: [419/600]
Discriminator Loss: 1.2467, Generator Loss: 0.8211
D(x): 0.5770, D(G(z)): 0.4828
Epoch: [1/20], Batch Num: [420/600]
Discriminator Loss: 1.1818, Generator Loss: 0.9559
D(x): 0.5764, D(G(z)): 0.4489
Epoch: [1/20], Batch Num: [421/600]
Discriminator Loss: 1.2150, Generator Loss: 0.9345
D(x): 0.5491, D(G(z)): 0.4345
Epoch: [1/20], Batch Num: [422/600]
Discriminator Loss: 1.1852, Generator Loss: 0.9425
D(x): 0.5580, D(G(z)): 0.4244
Epoch: [1/20], Batch Num: [423/600]
Discriminator Loss: 1.1981, Generator Loss: 0.9906
D(x): 0.5446, D(G(z)): 0.4214
Epoch: [1/20], Batch Num: [424/600]
Discriminator Loss: 1.1383, Generator Loss: 1.0644
D(x): 0.5535, D(G(z)): 0.3904
Epoch: [1/20], Batch Num: [425/600]
Discriminator Loss: 1.1281, Generator Loss: 1.0709
D(x): 0.5500, D(G(z)): 0.3855
Epoch: [1/20], Batch Num: [426/600]
Discriminator Loss: 1.0933, Generator Loss: 1.0133
D(x): 0.5673, D(G(z)): 0.3882
Epoch: [1/20], Batch Num: [427/600]
Discriminator Loss: 1.1271, Generator Loss: 1.0629
D(x): 0.5532, D(G(z)): 0.3754
Epoch: [1/20], Batch Num: [428/600]
Discriminator Loss: 1.0619, Generator Loss: 1.0846
D(x): 0.5760, D(G(z)): 0.3711
Epoch: [1/20], Batch Num: [429/600]
Discriminator Loss: 1.0547, Generator Loss: 1.0907
D(x): 0.6027, D(G(z)): 0.3896
Epoch: [1/20], Batch Num: [430/600]
Discriminator Loss: 1.0141, Generator Loss: 1.1360
D(x): 0.6124, D(G(z)): 0.3859
Epoch: [1/20], Batch Num: [431/600]
Discriminator Loss: 0.9982, Generator Loss: 1.1367
D(x): 0.6163, D(G(z)): 0.3730
Epoch: [1/20], Batch Num: [432/600]
Discriminator Loss: 0.9246, Generator Loss: 1.2240
D(x): 0.6452, D(G(z)): 0.3625
Epoch: [1/20], Batch Num: [433/600]
Discriminator Loss: 0.8614, Generator Loss: 1.1703
D(x): 0.6631, D(G(z)): 0.3389
Epoch: [1/20], Batch Num: [434/600]
Discriminator Loss: 0.8743, Generator Loss: 1.1841
D(x): 0.6716, D(G(z)): 0.3581
Epoch: [1/20], Batch Num: [435/600]
Discriminator Loss: 0.7857, Generator Loss: 1.3489
D(x): 0.6996, D(G(z)): 0.3326
Epoch: [1/20], Batch Num: [436/600]
Discriminator Loss: 0.7197, Generator Loss: 1.3878
D(x): 0.7115, D(G(z)): 0.2957
Epoch: [1/20], Batch Num: [437/600]
Discriminator Loss: 0.6899, Generator Loss: 1.5726
D(x): 0.7047, D(G(z)): 0.2648
Epoch: [1/20], Batch Num: [438/600]
Discriminator Loss: 0.7119, Generator Loss: 1.6469
D(x): 0.7035, D(G(z)): 0.2806
Epoch: [1/20], Batch Num: [439/600]
Discriminator Loss: 0.6523, Generator Loss: 1.6496
D(x): 0.7388, D(G(z)): 0.2763
Epoch: [1/20], Batch Num: [440/600]
Discriminator Loss: 0.6447, Generator Loss: 1.6546
D(x): 0.7441, D(G(z)): 0.2693
Epoch: [1/20], Batch Num: [441/600]
Discriminator Loss: 0.6933, Generator Loss: 1.6015
D(x): 0.7436, D(G(z)): 0.3065
Epoch: [1/20], Batch Num: [442/600]
Discriminator Loss: 0.6710, Generator Loss: 1.4932
D(x): 0.7696, D(G(z)): 0.3158
Epoch: [1/20], Batch Num: [443/600]
Discriminator Loss: 0.6809, Generator Loss: 1.3384
D(x): 0.7859, D(G(z)): 0.3387
Epoch: [1/20], Batch Num: [444/600]
Discriminator Loss: 0.7326, Generator Loss: 1.1286
D(x): 0.7746, D(G(z)): 0.3608
Epoch: [1/20], Batch Num: [445/600]
Discriminator Loss: 0.8028, Generator Loss: 1.1939
D(x): 0.7788, D(G(z)): 0.3970
Epoch: [1/20], Batch Num: [446/600]
Discriminator Loss: 0.7497, Generator Loss: 1.0513
D(x): 0.8065, D(G(z)): 0.3949
Epoch: [1/20], Batch Num: [447/600]
Discriminator Loss: 0.8164, Generator Loss: 0.9387
D(x): 0.8088, D(G(z)): 0.4286
Epoch: [1/20], Batch Num: [448/600]
Discriminator Loss: 0.8126, Generator Loss: 0.9688
D(x): 0.8084, D(G(z)): 0.4317
Epoch: [1/20], Batch Num: [449/600]
Discriminator Loss: 0.8238, Generator Loss: 0.9273
D(x): 0.8121, D(G(z)): 0.4328
Epoch: [1/20], Batch Num: [450/600]
Discriminator Loss: 0.7839, Generator Loss: 0.9802
D(x): 0.8357, D(G(z)): 0.4359
Epoch: [1/20], Batch Num: [451/600]
Discriminator Loss: 0.9192, Generator Loss: 0.9935
D(x): 0.7865, D(G(z)): 0.4400
Epoch: [1/20], Batch Num: [452/600]
Discriminator Loss: 0.8361, Generator Loss: 1.0530
D(x): 0.8222, D(G(z)): 0.4429
Epoch: [1/20], Batch Num: [453/600]
Discriminator Loss: 0.7548, Generator Loss: 1.0981
D(x): 0.8531, D(G(z)): 0.4303
Epoch: [1/20], Batch Num: [454/600]
Discriminator Loss: 0.7553, Generator Loss: 1.2100
D(x): 0.8182, D(G(z)): 0.3866
Epoch: [1/20], Batch Num: [455/600]
Discriminator Loss: 0.6326, Generator Loss: 1.4650
D(x): 0.8528, D(G(z)): 0.3510
Epoch: [1/20], Batch Num: [456/600]
Discriminator Loss: 0.6420, Generator Loss: 1.6678
D(x): 0.8369, D(G(z)): 0.3198
Epoch: [1/20], Batch Num: [457/600]
Discriminator Loss: 0.5515, Generator Loss: 1.8972
D(x): 0.8368, D(G(z)): 0.2498
Epoch: [1/20], Batch Num: [458/600]
Discriminator Loss: 0.5158, Generator Loss: 1.9659
D(x): 0.8396, D(G(z)): 0.2493
Epoch: [1/20], Batch Num: [459/600]
Discriminator Loss: 0.5020, Generator Loss: 2.1869
D(x): 0.8676, D(G(z)): 0.2386
Epoch: [1/20], Batch Num: [460/600]
Discriminator Loss: 0.4381, Generator Loss: 2.3808
D(x): 0.8622, D(G(z)): 0.1990
Epoch: [1/20], Batch Num: [461/600]
Discriminator Loss: 0.4649, Generator Loss: 2.4491
D(x): 0.8793, D(G(z)): 0.2290
Epoch: [1/20], Batch Num: [462/600]
Discriminator Loss: 0.3792, Generator Loss: 3.0717
D(x): 0.9373, D(G(z)): 0.2353
Epoch: [1/20], Batch Num: [463/600]
Discriminator Loss: 0.4563, Generator Loss: 2.9641
D(x): 0.8789, D(G(z)): 0.2093
Epoch: [1/20], Batch Num: [464/600]
Discriminator Loss: 0.3726, Generator Loss: 3.3696
D(x): 0.8780, D(G(z)): 0.1486
Epoch: [1/20], Batch Num: [465/600]
Discriminator Loss: 0.5436, Generator Loss: 3.6107
D(x): 0.8403, D(G(z)): 0.1770
Epoch: [1/20], Batch Num: [466/600]
Discriminator Loss: 0.7009, Generator Loss: 2.9398
D(x): 0.8127, D(G(z)): 0.2456
Epoch: [1/20], Batch Num: [467/600]
Discriminator Loss: 0.6341, Generator Loss: 2.6836
D(x): 0.8613, D(G(z)): 0.2379
Epoch: [1/20], Batch Num: [468/600]
Discriminator Loss: 0.7963, Generator Loss: 2.6437
D(x): 0.8334, D(G(z)): 0.3286
Epoch: [1/20], Batch Num: [469/600]
Discriminator Loss: 1.1465, Generator Loss: 2.2374
D(x): 0.7646, D(G(z)): 0.3903
Epoch: [1/20], Batch Num: [470/600]
Discriminator Loss: 1.3735, Generator Loss: 1.7113
D(x): 0.6766, D(G(z)): 0.3979
Epoch: [1/20], Batch Num: [471/600]
Discriminator Loss: 1.6047, Generator Loss: 1.4584
D(x): 0.7702, D(G(z)): 0.5576
Epoch: [1/20], Batch Num: [472/600]
Discriminator Loss: 1.9355, Generator Loss: 1.1107
D(x): 0.6219, D(G(z)): 0.5863
Epoch: [1/20], Batch Num: [473/600]
Discriminator Loss: 2.2801, Generator Loss: 0.7256
D(x): 0.5719, D(G(z)): 0.6395
Epoch: [1/20], Batch Num: [474/600]
Discriminator Loss: 2.4973, Generator Loss: 0.5283
D(x): 0.6348, D(G(z)): 0.7600
Epoch: [1/20], Batch Num: [475/600]
Discriminator Loss: 2.6726, Generator Loss: 0.4414
D(x): 0.5695, D(G(z)): 0.7550
Epoch: [1/20], Batch Num: [476/600]
Discriminator Loss: 2.2299, Generator Loss: 0.5442
D(x): 0.6004, D(G(z)): 0.7082
Epoch: [1/20], Batch Num: [477/600]
Discriminator Loss: 1.7872, Generator Loss: 1.0829
D(x): 0.6659, D(G(z)): 0.6281
Epoch: [1/20], Batch Num: [478/600]
Discriminator Loss: 1.3511, Generator Loss: 1.6837
D(x): 0.6459, D(G(z)): 0.4676
Epoch: [1/20], Batch Num: [479/600]
Discriminator Loss: 1.1958, Generator Loss: 1.8532
D(x): 0.5859, D(G(z)): 0.3259
Epoch: [1/20], Batch Num: [480/600]
Discriminator Loss: 1.0631, Generator Loss: 1.9824
D(x): 0.5872, D(G(z)): 0.2464
Epoch: [1/20], Batch Num: [481/600]
Discriminator Loss: 0.9821, Generator Loss: 1.8023
D(x): 0.6605, D(G(z)): 0.3193
Epoch: [1/20], Batch Num: [482/600]
Discriminator Loss: 0.8244, Generator Loss: 1.9807
D(x): 0.7171, D(G(z)): 0.2995
Epoch: [1/20], Batch Num: [483/600]
Discriminator Loss: 0.9527, Generator Loss: 1.5921
D(x): 0.6691, D(G(z)): 0.3223
Epoch: [1/20], Batch Num: [484/600]
Discriminator Loss: 0.9946, Generator Loss: 1.7110
D(x): 0.7039, D(G(z)): 0.3826
Epoch: [1/20], Batch Num: [485/600]
Discriminator Loss: 0.9396, Generator Loss: 1.8088
D(x): 0.6754, D(G(z)): 0.3421
Epoch: [1/20], Batch Num: [486/600]
Discriminator Loss: 0.9075, Generator Loss: 1.7866
D(x): 0.6739, D(G(z)): 0.3191
Epoch: [1/20], Batch Num: [487/600]
Discriminator Loss: 0.9838, Generator Loss: 1.6867
D(x): 0.6137, D(G(z)): 0.2770
Epoch: [1/20], Batch Num: [488/600]
Discriminator Loss: 0.8696, Generator Loss: 1.7544
D(x): 0.6638, D(G(z)): 0.2828
Epoch: [1/20], Batch Num: [489/600]
Discriminator Loss: 0.9358, Generator Loss: 1.5716
D(x): 0.6307, D(G(z)): 0.3040
Epoch: [1/20], Batch Num: [490/600]
Discriminator Loss: 0.7898, Generator Loss: 1.5926
D(x): 0.7476, D(G(z)): 0.3402
Epoch: [1/20], Batch Num: [491/600]
Discriminator Loss: 0.7673, Generator Loss: 1.7468
D(x): 0.7479, D(G(z)): 0.3213
Epoch: [1/20], Batch Num: [492/600]
Discriminator Loss: 0.8093, Generator Loss: 1.8916
D(x): 0.7510, D(G(z)): 0.3592
Epoch: [1/20], Batch Num: [493/600]
Discriminator Loss: 0.6252, Generator Loss: 2.2637
D(x): 0.7675, D(G(z)): 0.2613
Epoch: [1/20], Batch Num: [494/600]
Discriminator Loss: 0.6156, Generator Loss: 2.5885
D(x): 0.7628, D(G(z)): 0.2417
Epoch: [1/20], Batch Num: [495/600]
Discriminator Loss: 0.6653, Generator Loss: 2.3540
D(x): 0.7204, D(G(z)): 0.2138
Epoch: [1/20], Batch Num: [496/600]
Discriminator Loss: 0.7166, Generator Loss: 2.6636
D(x): 0.6959, D(G(z)): 0.2311
Epoch: [1/20], Batch Num: [497/600]
Discriminator Loss: 0.6578, Generator Loss: 2.3183
D(x): 0.7325, D(G(z)): 0.2252
Epoch: [1/20], Batch Num: [498/600]
Discriminator Loss: 0.8107, Generator Loss: 2.1832
D(x): 0.7010, D(G(z)): 0.2643
Epoch: [1/20], Batch Num: [499/600]
Discriminator Loss: 0.7243, Generator Loss: 1.8306
D(x): 0.7521, D(G(z)): 0.2730
Epoch: 1, Batch Num: [500/600]

Epoch: [1/20], Batch Num: [500/600]
Discriminator Loss: 0.8847, Generator Loss: 1.5490
D(x): 0.7315, D(G(z)): 0.3343
Epoch: [1/20], Batch Num: [501/600]
Discriminator Loss: 0.9817, Generator Loss: 1.6268
D(x): 0.7897, D(G(z)): 0.4579
Epoch: [1/20], Batch Num: [502/600]
Discriminator Loss: 0.9240, Generator Loss: 1.6969
D(x): 0.7370, D(G(z)): 0.3553
Epoch: [1/20], Batch Num: [503/600]
Discriminator Loss: 0.9282, Generator Loss: 2.0692
D(x): 0.7322, D(G(z)): 0.3635
Epoch: [1/20], Batch Num: [504/600]
Discriminator Loss: 1.1017, Generator Loss: 2.0699
D(x): 0.6843, D(G(z)): 0.3745
Epoch: [1/20], Batch Num: [505/600]
Discriminator Loss: 1.2934, Generator Loss: 1.5448
D(x): 0.6181, D(G(z)): 0.4039
Epoch: [1/20], Batch Num: [506/600]
Discriminator Loss: 1.0177, Generator Loss: 1.3812
D(x): 0.7263, D(G(z)): 0.4010
Epoch: [1/20], Batch Num: [507/600]
Discriminator Loss: 1.2561, Generator Loss: 1.1738
D(x): 0.7459, D(G(z)): 0.5061
Epoch: [1/20], Batch Num: [508/600]
Discriminator Loss: 1.2813, Generator Loss: 1.3362
D(x): 0.7317, D(G(z)): 0.5335
Epoch: [1/20], Batch Num: [509/600]
Discriminator Loss: 1.2482, Generator Loss: 1.3705
D(x): 0.7025, D(G(z)): 0.4902
Epoch: [1/20], Batch Num: [510/600]
Discriminator Loss: 1.3666, Generator Loss: 1.2643
D(x): 0.6472, D(G(z)): 0.4929
Epoch: [1/20], Batch Num: [511/600]
Discriminator Loss: 1.2931, Generator Loss: 1.3696
D(x): 0.6602, D(G(z)): 0.4694
Epoch: [1/20], Batch Num: [512/600]
Discriminator Loss: 1.1847, Generator Loss: 1.5043
D(x): 0.7129, D(G(z)): 0.4950
Epoch: [1/20], Batch Num: [513/600]
Discriminator Loss: 1.2161, Generator Loss: 1.5656
D(x): 0.6736, D(G(z)): 0.4712
Epoch: [1/20], Batch Num: [514/600]
Discriminator Loss: 1.1984, Generator Loss: 1.3411
D(x): 0.6482, D(G(z)): 0.4579
Epoch: [1/20], Batch Num: [515/600]
Discriminator Loss: 1.0429, Generator Loss: 1.4100
D(x): 0.7014, D(G(z)): 0.4313
Epoch: [1/20], Batch Num: [516/600]
Discriminator Loss: 1.2905, Generator Loss: 1.3083
D(x): 0.6026, D(G(z)): 0.4353
Epoch: [1/20], Batch Num: [517/600]
Discriminator Loss: 1.0806, Generator Loss: 1.4267
D(x): 0.6324, D(G(z)): 0.4013
Epoch: [1/20], Batch Num: [518/600]
Discriminator Loss: 1.2158, Generator Loss: 1.3356
D(x): 0.5825, D(G(z)): 0.3839
Epoch: [1/20], Batch Num: [519/600]
Discriminator Loss: 1.0907, Generator Loss: 1.2844
D(x): 0.6388, D(G(z)): 0.3806
Epoch: [1/20], Batch Num: [520/600]
Discriminator Loss: 0.8852, Generator Loss: 1.3418
D(x): 0.7298, D(G(z)): 0.3954
Epoch: [1/20], Batch Num: [521/600]
Discriminator Loss: 0.8945, Generator Loss: 1.5466
D(x): 0.6871, D(G(z)): 0.3593
Epoch: [1/20], Batch Num: [522/600]
Discriminator Loss: 0.7884, Generator Loss: 1.8791
D(x): 0.7227, D(G(z)): 0.3305
Epoch: [1/20], Batch Num: [523/600]
Discriminator Loss: 0.7454, Generator Loss: 1.8490
D(x): 0.6833, D(G(z)): 0.2587
Epoch: [1/20], Batch Num: [524/600]
Discriminator Loss: 0.7882, Generator Loss: 2.0158
D(x): 0.6554, D(G(z)): 0.2276
Epoch: [1/20], Batch Num: [525/600]
Discriminator Loss: 0.6866, Generator Loss: 2.0584
D(x): 0.6948, D(G(z)): 0.2209
Epoch: [1/20], Batch Num: [526/600]
Discriminator Loss: 0.6985, Generator Loss: 1.7937
D(x): 0.7239, D(G(z)): 0.2638
Epoch: [1/20], Batch Num: [527/600]
Discriminator Loss: 0.6897, Generator Loss: 1.8320
D(x): 0.7400, D(G(z)): 0.2788
Epoch: [1/20], Batch Num: [528/600]
Discriminator Loss: 0.7675, Generator Loss: 1.9842
D(x): 0.7562, D(G(z)): 0.3195
Epoch: [1/20], Batch Num: [529/600]
Discriminator Loss: 0.8018, Generator Loss: 2.2542
D(x): 0.7239, D(G(z)): 0.3030
Epoch: [1/20], Batch Num: [530/600]
Discriminator Loss: 0.9145, Generator Loss: 2.0802
D(x): 0.6948, D(G(z)): 0.2901
Epoch: [1/20], Batch Num: [531/600]
Discriminator Loss: 1.0531, Generator Loss: 1.6809
D(x): 0.6049, D(G(z)): 0.2799
Epoch: [1/20], Batch Num: [532/600]
Discriminator Loss: 0.7729, Generator Loss: 1.8392
D(x): 0.7756, D(G(z)): 0.3463
Epoch: [1/20], Batch Num: [533/600]
Discriminator Loss: 0.9039, Generator Loss: 2.0496
D(x): 0.7631, D(G(z)): 0.4002
Epoch: [1/20], Batch Num: [534/600]
Discriminator Loss: 1.0711, Generator Loss: 2.1733
D(x): 0.6522, D(G(z)): 0.3330
Epoch: [1/20], Batch Num: [535/600]
Discriminator Loss: 1.3109, Generator Loss: 1.7968
D(x): 0.5634, D(G(z)): 0.3334
Epoch: [1/20], Batch Num: [536/600]
Discriminator Loss: 1.4365, Generator Loss: 1.0809
D(x): 0.5609, D(G(z)): 0.4112
Epoch: [1/20], Batch Num: [537/600]
Discriminator Loss: 1.7577, Generator Loss: 1.0441
D(x): 0.6356, D(G(z)): 0.6160
Epoch: [1/20], Batch Num: [538/600]
Discriminator Loss: 1.7935, Generator Loss: 1.0546
D(x): 0.6265, D(G(z)): 0.6332
Epoch: [1/20], Batch Num: [539/600]
Discriminator Loss: 1.9421, Generator Loss: 1.3907
D(x): 0.5293, D(G(z)): 0.5826
Epoch: [1/20], Batch Num: [540/600]
Discriminator Loss: 1.9784, Generator Loss: 1.5049
D(x): 0.4148, D(G(z)): 0.4833
Epoch: [1/20], Batch Num: [541/600]
Discriminator Loss: 2.0425, Generator Loss: 0.8590
D(x): 0.3987, D(G(z)): 0.4879
Epoch: [1/20], Batch Num: [542/600]
Discriminator Loss: 2.0124, Generator Loss: 0.8357
D(x): 0.4869, D(G(z)): 0.5712
Epoch: [1/20], Batch Num: [543/600]
Discriminator Loss: 1.9955, Generator Loss: 0.7472
D(x): 0.4765, D(G(z)): 0.5926
Epoch: [1/20], Batch Num: [544/600]
Discriminator Loss: 1.8543, Generator Loss: 0.8641
D(x): 0.5258, D(G(z)): 0.6148
Epoch: [1/20], Batch Num: [545/600]
Discriminator Loss: 1.7138, Generator Loss: 1.0896
D(x): 0.5127, D(G(z)): 0.5629
Epoch: [1/20], Batch Num: [546/600]
Discriminator Loss: 1.4568, Generator Loss: 1.5084
D(x): 0.5099, D(G(z)): 0.4513
Epoch: [1/20], Batch Num: [547/600]
Discriminator Loss: 1.2522, Generator Loss: 1.4768
D(x): 0.5173, D(G(z)): 0.3424
Epoch: [1/20], Batch Num: [548/600]
Discriminator Loss: 1.1291, Generator Loss: 1.5224
D(x): 0.5128, D(G(z)): 0.2812
Epoch: [1/20], Batch Num: [549/600]
Discriminator Loss: 1.0365, Generator Loss: 1.5199
D(x): 0.5657, D(G(z)): 0.3085
Epoch: [1/20], Batch Num: [550/600]
Discriminator Loss: 0.9048, Generator Loss: 1.5436
D(x): 0.5827, D(G(z)): 0.2427
Epoch: [1/20], Batch Num: [551/600]
Discriminator Loss: 0.8677, Generator Loss: 1.7414
D(x): 0.6442, D(G(z)): 0.2968
Epoch: [1/20], Batch Num: [552/600]
Discriminator Loss: 0.9256, Generator Loss: 1.7904
D(x): 0.6536, D(G(z)): 0.3430
Epoch: [1/20], Batch Num: [553/600]
Discriminator Loss: 0.7091, Generator Loss: 2.1661
D(x): 0.6979, D(G(z)): 0.2520
Epoch: [1/20], Batch Num: [554/600]
Discriminator Loss: 0.7820, Generator Loss: 2.3006
D(x): 0.7065, D(G(z)): 0.3000
Epoch: [1/20], Batch Num: [555/600]
Discriminator Loss: 0.7472, Generator Loss: 2.6905
D(x): 0.6970, D(G(z)): 0.2580
Epoch: [1/20], Batch Num: [556/600]
Discriminator Loss: 0.6234, Generator Loss: 3.0739
D(x): 0.7224, D(G(z)): 0.2067
Epoch: [1/20], Batch Num: [557/600]
Discriminator Loss: 0.6881, Generator Loss: 3.3001
D(x): 0.6902, D(G(z)): 0.1764
Epoch: [1/20], Batch Num: [558/600]
Discriminator Loss: 0.6320, Generator Loss: 3.1124
D(x): 0.7040, D(G(z)): 0.1738
Epoch: [1/20], Batch Num: [559/600]
Discriminator Loss: 0.5911, Generator Loss: 3.0463
D(x): 0.7569, D(G(z)): 0.1949
Epoch: [1/20], Batch Num: [560/600]
Discriminator Loss: 0.6654, Generator Loss: 3.3979
D(x): 0.7343, D(G(z)): 0.2210
Epoch: [1/20], Batch Num: [561/600]
Discriminator Loss: 0.6795, Generator Loss: 4.1509
D(x): 0.7297, D(G(z)): 0.2215
Epoch: [1/20], Batch Num: [562/600]
Discriminator Loss: 0.6263, Generator Loss: 3.8873
D(x): 0.7353, D(G(z)): 0.1734
Epoch: [1/20], Batch Num: [563/600]
Discriminator Loss: 0.4889, Generator Loss: 4.6411
D(x): 0.7834, D(G(z)): 0.1551
Epoch: [1/20], Batch Num: [564/600]
Discriminator Loss: 0.5531, Generator Loss: 4.9367
D(x): 0.7691, D(G(z)): 0.1666
Epoch: [1/20], Batch Num: [565/600]
Discriminator Loss: 0.5465, Generator Loss: 4.5496
D(x): 0.7791, D(G(z)): 0.1578
Epoch: [1/20], Batch Num: [566/600]
Discriminator Loss: 0.5018, Generator Loss: 5.1563
D(x): 0.7839, D(G(z)): 0.1401
Epoch: [1/20], Batch Num: [567/600]
Discriminator Loss: 0.3932, Generator Loss: 5.1858
D(x): 0.8128, D(G(z)): 0.1060
Epoch: [1/20], Batch Num: [568/600]
Discriminator Loss: 0.3728, Generator Loss: 6.1567
D(x): 0.8451, D(G(z)): 0.1157
Epoch: [1/20], Batch Num: [569/600]
Discriminator Loss: 0.2940, Generator Loss: 5.4881
D(x): 0.8426, D(G(z)): 0.0674
Epoch: [1/20], Batch Num: [570/600]
Discriminator Loss: 0.3360, Generator Loss: 6.5283
D(x): 0.8592, D(G(z)): 0.1164
Epoch: [1/20], Batch Num: [571/600]
Discriminator Loss: 0.2798, Generator Loss: 6.9242
D(x): 0.8950, D(G(z)): 0.0940
Epoch: [1/20], Batch Num: [572/600]
Discriminator Loss: 0.2294, Generator Loss: 7.3134
D(x): 0.8714, D(G(z)): 0.0424
Epoch: [1/20], Batch Num: [573/600]
Discriminator Loss: 0.1797, Generator Loss: 6.7237
D(x): 0.9025, D(G(z)): 0.0512
Epoch: [1/20], Batch Num: [574/600]
Discriminator Loss: 0.2594, Generator Loss: 7.5498
D(x): 0.8945, D(G(z)): 0.0882
Epoch: [1/20], Batch Num: [575/600]
Discriminator Loss: 0.2978, Generator Loss: 7.0456
D(x): 0.9084, D(G(z)): 0.1319
Epoch: [1/20], Batch Num: [576/600]
Discriminator Loss: 0.3373, Generator Loss: 6.7933
D(x): 0.9084, D(G(z)): 0.1463
Epoch: [1/20], Batch Num: [577/600]
Discriminator Loss: 0.3145, Generator Loss: 7.2811
D(x): 0.8795, D(G(z)): 0.1048
Epoch: [1/20], Batch Num: [578/600]
Discriminator Loss: 0.3887, Generator Loss: 6.1874
D(x): 0.8457, D(G(z)): 0.1185
Epoch: [1/20], Batch Num: [579/600]
Discriminator Loss: 0.5126, Generator Loss: 5.9549
D(x): 0.7882, D(G(z)): 0.1430
Epoch: [1/20], Batch Num: [580/600]
Discriminator Loss: 0.7321, Generator Loss: 4.4613
D(x): 0.7566, D(G(z)): 0.2159
Epoch: [1/20], Batch Num: [581/600]
Discriminator Loss: 0.8346, Generator Loss: 3.1574
D(x): 0.7507, D(G(z)): 0.2521
Epoch: [1/20], Batch Num: [582/600]
Discriminator Loss: 1.3541, Generator Loss: 2.6909
D(x): 0.7438, D(G(z)): 0.4834
Epoch: [1/20], Batch Num: [583/600]
Discriminator Loss: 1.6264, Generator Loss: 2.0849
D(x): 0.6663, D(G(z)): 0.5400
Epoch: [1/20], Batch Num: [584/600]
Discriminator Loss: 2.2454, Generator Loss: 1.4400
D(x): 0.5067, D(G(z)): 0.5795
Epoch: [1/20], Batch Num: [585/600]
Discriminator Loss: 2.1866, Generator Loss: 1.0235
D(x): 0.5388, D(G(z)): 0.6149
Epoch: [1/20], Batch Num: [586/600]
Discriminator Loss: 2.1303, Generator Loss: 0.9382
D(x): 0.5340, D(G(z)): 0.6085
Epoch: [1/20], Batch Num: [587/600]
Discriminator Loss: 1.8317, Generator Loss: 0.8946
D(x): 0.5986, D(G(z)): 0.6029
Epoch: [1/20], Batch Num: [588/600]
Discriminator Loss: 1.3463, Generator Loss: 0.9755
D(x): 0.7077, D(G(z)): 0.5727
Epoch: [1/20], Batch Num: [589/600]
Discriminator Loss: 1.1750, Generator Loss: 1.2001
D(x): 0.6663, D(G(z)): 0.4506
Epoch: [1/20], Batch Num: [590/600]
Discriminator Loss: 1.0667, Generator Loss: 1.2103
D(x): 0.6260, D(G(z)): 0.3739
Epoch: [1/20], Batch Num: [591/600]
Discriminator Loss: 1.0735, Generator Loss: 1.1796
D(x): 0.6180, D(G(z)): 0.3790
Epoch: [1/20], Batch Num: [592/600]
Discriminator Loss: 0.9087, Generator Loss: 1.1807
D(x): 0.7007, D(G(z)): 0.3601
Epoch: [1/20], Batch Num: [593/600]
Discriminator Loss: 0.7572, Generator Loss: 1.1321
D(x): 0.8036, D(G(z)): 0.3779
Epoch: [1/20], Batch Num: [594/600]
Discriminator Loss: 0.8128, Generator Loss: 1.0493
D(x): 0.8183, D(G(z)): 0.4224
Epoch: [1/20], Batch Num: [595/600]
Discriminator Loss: 0.7681, Generator Loss: 1.0935
D(x): 0.8501, D(G(z)): 0.4304
Epoch: [1/20], Batch Num: [596/600]
Discriminator Loss: 0.7986, Generator Loss: 1.0743
D(x): 0.8359, D(G(z)): 0.4357
Epoch: [1/20], Batch Num: [597/600]
Discriminator Loss: 0.8724, Generator Loss: 1.0958
D(x): 0.8113, D(G(z)): 0.4405
Epoch: [1/20], Batch Num: [598/600]
Discriminator Loss: 0.8755, Generator Loss: 1.1316
D(x): 0.8033, D(G(z)): 0.4387
Epoch: [1/20], Batch Num: [599/600]
Discriminator Loss: 0.8456, Generator Loss: 1.0794
D(x): 0.7952, D(G(z)): 0.4166
Epoch: 2, Batch Num: [0/600]

Epoch: [2/20], Batch Num: [0/600]
Discriminator Loss: 1.0240, Generator Loss: 1.0688
D(x): 0.6854, D(G(z)): 0.4050
Epoch: [2/20], Batch Num: [1/600]
Discriminator Loss: 0.8763, Generator Loss: 1.0513
D(x): 0.7550, D(G(z)): 0.3923
Epoch: [2/20], Batch Num: [2/600]
Discriminator Loss: 1.0262, Generator Loss: 1.0592
D(x): 0.7182, D(G(z)): 0.4298
Epoch: [2/20], Batch Num: [3/600]
Discriminator Loss: 0.9234, Generator Loss: 1.0159
D(x): 0.7818, D(G(z)): 0.4178
Epoch: [2/20], Batch Num: [4/600]
Discriminator Loss: 0.8967, Generator Loss: 0.9759
D(x): 0.7867, D(G(z)): 0.4378
Epoch: [2/20], Batch Num: [5/600]
Discriminator Loss: 0.8720, Generator Loss: 1.0680
D(x): 0.8048, D(G(z)): 0.4265
Epoch: [2/20], Batch Num: [6/600]
Discriminator Loss: 0.8679, Generator Loss: 1.1260
D(x): 0.7768, D(G(z)): 0.4013
Epoch: [2/20], Batch Num: [7/600]
Discriminator Loss: 0.8070, Generator Loss: 1.4186
D(x): 0.8120, D(G(z)): 0.3970
Epoch: [2/20], Batch Num: [8/600]
Discriminator Loss: 0.7428, Generator Loss: 1.4844
D(x): 0.7668, D(G(z)): 0.3054
Epoch: [2/20], Batch Num: [9/600]
Discriminator Loss: 0.8298, Generator Loss: 1.4786
D(x): 0.7170, D(G(z)): 0.3013
Epoch: [2/20], Batch Num: [10/600]
Discriminator Loss: 0.6409, Generator Loss: 1.6399
D(x): 0.7962, D(G(z)): 0.2849
Epoch: [2/20], Batch Num: [11/600]
Discriminator Loss: 0.7103, Generator Loss: 1.6174
D(x): 0.7622, D(G(z)): 0.2735
Epoch: [2/20], Batch Num: [12/600]
Discriminator Loss: 0.6936, Generator Loss: 1.7003
D(x): 0.8225, D(G(z)): 0.3264
Epoch: [2/20], Batch Num: [13/600]
Discriminator Loss: 0.7050, Generator Loss: 1.7615
D(x): 0.8212, D(G(z)): 0.3163
Epoch: [2/20], Batch Num: [14/600]
Discriminator Loss: 0.7210, Generator Loss: 1.9694
D(x): 0.8172, D(G(z)): 0.3294
Epoch: [2/20], Batch Num: [15/600]
Discriminator Loss: 0.7028, Generator Loss: 2.2003
D(x): 0.8261, D(G(z)): 0.3256
Epoch: [2/20], Batch Num: [16/600]
Discriminator Loss: 0.6290, Generator Loss: 2.1478
D(x): 0.7869, D(G(z)): 0.2597
Epoch: [2/20], Batch Num: [17/600]
Discriminator Loss: 0.7483, Generator Loss: 1.9565
D(x): 0.7228, D(G(z)): 0.2370
Epoch: [2/20], Batch Num: [18/600]
Discriminator Loss: 0.7872, Generator Loss: 2.0596
D(x): 0.7808, D(G(z)): 0.3267
Epoch: [2/20], Batch Num: [19/600]
Discriminator Loss: 0.8098, Generator Loss: 1.6071
D(x): 0.7765, D(G(z)): 0.3364
Epoch: [2/20], Batch Num: [20/600]
Discriminator Loss: 0.8758, Generator Loss: 1.7123
D(x): 0.7721, D(G(z)): 0.3719
Epoch: [2/20], Batch Num: [21/600]
Discriminator Loss: 1.0188, Generator Loss: 1.6495
D(x): 0.7028, D(G(z)): 0.3717
Epoch: [2/20], Batch Num: [22/600]
Discriminator Loss: 1.0771, Generator Loss: 1.3324
D(x): 0.6610, D(G(z)): 0.3564
Epoch: [2/20], Batch Num: [23/600]
Discriminator Loss: 1.2665, Generator Loss: 1.1642
D(x): 0.6549, D(G(z)): 0.4167
Epoch: [2/20], Batch Num: [24/600]
Discriminator Loss: 1.5265, Generator Loss: 0.9623
D(x): 0.6423, D(G(z)): 0.5250
Epoch: [2/20], Batch Num: [25/600]
Discriminator Loss: 1.5092, Generator Loss: 0.9650
D(x): 0.7173, D(G(z)): 0.6008
Epoch: [2/20], Batch Num: [26/600]
Discriminator Loss: 1.3945, Generator Loss: 1.3128
D(x): 0.6825, D(G(z)): 0.5423
Epoch: [2/20], Batch Num: [27/600]
Discriminator Loss: 1.6109, Generator Loss: 1.3689
D(x): 0.5593, D(G(z)): 0.4522
Epoch: [2/20], Batch Num: [28/600]
Discriminator Loss: 1.6061, Generator Loss: 1.1743
D(x): 0.5417, D(G(z)): 0.4576
Epoch: [2/20], Batch Num: [29/600]
Discriminator Loss: 1.4175, Generator Loss: 1.0491
D(x): 0.6477, D(G(z)): 0.5125
Epoch: [2/20], Batch Num: [30/600]
Discriminator Loss: 1.2434, Generator Loss: 1.0866
D(x): 0.6954, D(G(z)): 0.4983
Epoch: [2/20], Batch Num: [31/600]
Discriminator Loss: 1.3273, Generator Loss: 1.2763
D(x): 0.6436, D(G(z)): 0.4799
Epoch: [2/20], Batch Num: [32/600]
Discriminator Loss: 1.1276, Generator Loss: 1.5204
D(x): 0.6881, D(G(z)): 0.4209
Epoch: [2/20], Batch Num: [33/600]
Discriminator Loss: 1.1096, Generator Loss: 1.6044
D(x): 0.6443, D(G(z)): 0.3650
Epoch: [2/20], Batch Num: [34/600]
Discriminator Loss: 0.9780, Generator Loss: 1.5817
D(x): 0.6988, D(G(z)): 0.3408
Epoch: [2/20], Batch Num: [35/600]
Discriminator Loss: 0.8751, Generator Loss: 1.5658
D(x): 0.6925, D(G(z)): 0.3248
Epoch: [2/20], Batch Num: [36/600]
Discriminator Loss: 0.8661, Generator Loss: 1.9324
D(x): 0.6845, D(G(z)): 0.3148
Epoch: [2/20], Batch Num: [37/600]
Discriminator Loss: 1.0013, Generator Loss: 1.9651
D(x): 0.6657, D(G(z)): 0.3366
Epoch: [2/20], Batch Num: [38/600]
Discriminator Loss: 1.0019, Generator Loss: 1.8424
D(x): 0.6425, D(G(z)): 0.3127
Epoch: [2/20], Batch Num: [39/600]
Discriminator Loss: 1.1565, Generator Loss: 1.7767
D(x): 0.5986, D(G(z)): 0.3226
Epoch: [2/20], Batch Num: [40/600]
Discriminator Loss: 1.1569, Generator Loss: 1.4072
D(x): 0.6260, D(G(z)): 0.3754
Epoch: [2/20], Batch Num: [41/600]
Discriminator Loss: 1.2530, Generator Loss: 1.4840
D(x): 0.6532, D(G(z)): 0.4305
Epoch: [2/20], Batch Num: [42/600]
Discriminator Loss: 1.1595, Generator Loss: 1.6840
D(x): 0.6350, D(G(z)): 0.4121
Epoch: [2/20], Batch Num: [43/600]
Discriminator Loss: 1.1885, Generator Loss: 1.8866
D(x): 0.6175, D(G(z)): 0.3699
Epoch: [2/20], Batch Num: [44/600]
Discriminator Loss: 1.1511, Generator Loss: 2.1285
D(x): 0.5671, D(G(z)): 0.2979
Epoch: [2/20], Batch Num: [45/600]
Discriminator Loss: 1.1056, Generator Loss: 2.3483
D(x): 0.5738, D(G(z)): 0.2898
Epoch: [2/20], Batch Num: [46/600]
Discriminator Loss: 1.2425, Generator Loss: 1.9560
D(x): 0.5739, D(G(z)): 0.3169
Epoch: [2/20], Batch Num: [47/600]
Discriminator Loss: 1.0935, Generator Loss: 2.0297
D(x): 0.6139, D(G(z)): 0.3258
Epoch: [2/20], Batch Num: [48/600]
Discriminator Loss: 1.1904, Generator Loss: 1.9707
D(x): 0.6568, D(G(z)): 0.4077
Epoch: [2/20], Batch Num: [49/600]
Discriminator Loss: 1.1141, Generator Loss: 1.9736
D(x): 0.6808, D(G(z)): 0.4044
Epoch: [2/20], Batch Num: [50/600]
Discriminator Loss: 1.3883, Generator Loss: 2.1530
D(x): 0.5973, D(G(z)): 0.4516
Epoch: [2/20], Batch Num: [51/600]
Discriminator Loss: 1.3708, Generator Loss: 1.7636
D(x): 0.6008, D(G(z)): 0.4517
Epoch: [2/20], Batch Num: [52/600]
Discriminator Loss: 1.3551, Generator Loss: 2.0723
D(x): 0.6170, D(G(z)): 0.4568
Epoch: [2/20], Batch Num: [53/600]
Discriminator Loss: 1.4392, Generator Loss: 1.8336
D(x): 0.5740, D(G(z)): 0.4709
Epoch: [2/20], Batch Num: [54/600]
Discriminator Loss: 1.5818, Generator Loss: 1.5177
D(x): 0.5539, D(G(z)): 0.5073
Epoch: [2/20], Batch Num: [55/600]
Discriminator Loss: 1.7818, Generator Loss: 1.1531
D(x): 0.5384, D(G(z)): 0.5769
Epoch: [2/20], Batch Num: [56/600]
Discriminator Loss: 1.9141, Generator Loss: 1.0352
D(x): 0.4922, D(G(z)): 0.5655
Epoch: [2/20], Batch Num: [57/600]
Discriminator Loss: 2.1137, Generator Loss: 0.7768
D(x): 0.5271, D(G(z)): 0.6556
Epoch: [2/20], Batch Num: [58/600]
Discriminator Loss: 2.2849, Generator Loss: 0.6060
D(x): 0.5448, D(G(z)): 0.7076
Epoch: [2/20], Batch Num: [59/600]
Discriminator Loss: 1.7242, Generator Loss: 0.7029
D(x): 0.6118, D(G(z)): 0.6370
Epoch: [2/20], Batch Num: [60/600]
Discriminator Loss: 1.7646, Generator Loss: 0.8943
D(x): 0.6129, D(G(z)): 0.6268
Epoch: [2/20], Batch Num: [61/600]
Discriminator Loss: 1.4393, Generator Loss: 1.0023
D(x): 0.6038, D(G(z)): 0.5466
Epoch: [2/20], Batch Num: [62/600]
Discriminator Loss: 1.3470, Generator Loss: 1.4008
D(x): 0.6031, D(G(z)): 0.4889
Epoch: [2/20], Batch Num: [63/600]
Discriminator Loss: 1.2851, Generator Loss: 1.7936
D(x): 0.5925, D(G(z)): 0.4144
Epoch: [2/20], Batch Num: [64/600]
Discriminator Loss: 0.9000, Generator Loss: 2.1793
D(x): 0.6714, D(G(z)): 0.3378
Epoch: [2/20], Batch Num: [65/600]
Discriminator Loss: 0.7247, Generator Loss: 3.5202
D(x): 0.6912, D(G(z)): 0.2545
Epoch: [2/20], Batch Num: [66/600]
Discriminator Loss: 0.7025, Generator Loss: 3.4735
D(x): 0.6645, D(G(z)): 0.1878
Epoch: [2/20], Batch Num: [67/600]
Discriminator Loss: 0.6521, Generator Loss: 4.3486
D(x): 0.7000, D(G(z)): 0.2129
Epoch: [2/20], Batch Num: [68/600]
Discriminator Loss: 0.5654, Generator Loss: 3.9689
D(x): 0.6922, D(G(z)): 0.1420
Epoch: [2/20], Batch Num: [69/600]
Discriminator Loss: 0.6501, Generator Loss: 4.3305
D(x): 0.6838, D(G(z)): 0.1908
Epoch: [2/20], Batch Num: [70/600]
Discriminator Loss: 0.5740, Generator Loss: 4.6725
D(x): 0.7212, D(G(z)): 0.1728
Epoch: [2/20], Batch Num: [71/600]
Discriminator Loss: 0.5425, Generator Loss: 4.2481
D(x): 0.7095, D(G(z)): 0.1407
Epoch: [2/20], Batch Num: [72/600]
Discriminator Loss: 0.6461, Generator Loss: 3.8396
D(x): 0.7011, D(G(z)): 0.1859
Epoch: [2/20], Batch Num: [73/600]
Discriminator Loss: 0.6709, Generator Loss: 3.8049
D(x): 0.6923, D(G(z)): 0.2070
Epoch: [2/20], Batch Num: [74/600]
Discriminator Loss: 0.8011, Generator Loss: 3.0118
D(x): 0.6943, D(G(z)): 0.2825
Epoch: [2/20], Batch Num: [75/600]
Discriminator Loss: 0.8511, Generator Loss: 2.8632
D(x): 0.6709, D(G(z)): 0.2725
Epoch: [2/20], Batch Num: [76/600]
Discriminator Loss: 0.9176, Generator Loss: 2.5440
D(x): 0.7119, D(G(z)): 0.3690
Epoch: [2/20], Batch Num: [77/600]
Discriminator Loss: 1.0962, Generator Loss: 2.0821
D(x): 0.6202, D(G(z)): 0.3430
Epoch: [2/20], Batch Num: [78/600]
Discriminator Loss: 1.1464, Generator Loss: 1.6457
D(x): 0.6251, D(G(z)): 0.3783
Epoch: [2/20], Batch Num: [79/600]
Discriminator Loss: 1.4565, Generator Loss: 1.2371
D(x): 0.6297, D(G(z)): 0.5007
Epoch: [2/20], Batch Num: [80/600]
Discriminator Loss: 1.5061, Generator Loss: 0.8715
D(x): 0.6446, D(G(z)): 0.5715
Epoch: [2/20], Batch Num: [81/600]
Discriminator Loss: 1.5925, Generator Loss: 0.6923
D(x): 0.6256, D(G(z)): 0.5961
Epoch: [2/20], Batch Num: [82/600]
Discriminator Loss: 1.5971, Generator Loss: 0.7409
D(x): 0.6708, D(G(z)): 0.6486
Epoch: [2/20], Batch Num: [83/600]
Discriminator Loss: 1.6176, Generator Loss: 0.6715
D(x): 0.6256, D(G(z)): 0.5955
Epoch: [2/20], Batch Num: [84/600]
Discriminator Loss: 1.7087, Generator Loss: 0.5648
D(x): 0.5772, D(G(z)): 0.6117
Epoch: [2/20], Batch Num: [85/600]
Discriminator Loss: 1.7493, Generator Loss: 0.5444
D(x): 0.6173, D(G(z)): 0.6451
Epoch: [2/20], Batch Num: [86/600]
Discriminator Loss: 1.6112, Generator Loss: 0.5554
D(x): 0.6377, D(G(z)): 0.6444
Epoch: [2/20], Batch Num: [87/600]
Discriminator Loss: 1.7207, Generator Loss: 0.5346
D(x): 0.5921, D(G(z)): 0.6450
Epoch: [2/20], Batch Num: [88/600]
Discriminator Loss: 1.4851, Generator Loss: 0.5552
D(x): 0.6349, D(G(z)): 0.5948
Epoch: [2/20], Batch Num: [89/600]
Discriminator Loss: 1.4719, Generator Loss: 0.6409
D(x): 0.6416, D(G(z)): 0.6083
Epoch: [2/20], Batch Num: [90/600]
Discriminator Loss: 1.3410, Generator Loss: 0.7152
D(x): 0.6091, D(G(z)): 0.5444
Epoch: [2/20], Batch Num: [91/600]
Discriminator Loss: 1.3934, Generator Loss: 0.7282
D(x): 0.5655, D(G(z)): 0.5342
Epoch: [2/20], Batch Num: [92/600]
Discriminator Loss: 1.2941, Generator Loss: 0.7305
D(x): 0.5898, D(G(z)): 0.5075
Epoch: [2/20], Batch Num: [93/600]
Discriminator Loss: 1.3444, Generator Loss: 0.7406
D(x): 0.5656, D(G(z)): 0.5093
Epoch: [2/20], Batch Num: [94/600]
Discriminator Loss: 1.3486, Generator Loss: 0.7245
D(x): 0.5710, D(G(z)): 0.5228
Epoch: [2/20], Batch Num: [95/600]
Discriminator Loss: 1.2929, Generator Loss: 0.7231
D(x): 0.5824, D(G(z)): 0.5055
Epoch: [2/20], Batch Num: [96/600]
Discriminator Loss: 1.2931, Generator Loss: 0.7235
D(x): 0.5832, D(G(z)): 0.5133
Epoch: [2/20], Batch Num: [97/600]
Discriminator Loss: 1.2129, Generator Loss: 0.7086
D(x): 0.6140, D(G(z)): 0.5019
Epoch: [2/20], Batch Num: [98/600]
Discriminator Loss: 1.2439, Generator Loss: 0.6945
D(x): 0.5964, D(G(z)): 0.5031
Epoch: [2/20], Batch Num: [99/600]
Discriminator Loss: 1.2120, Generator Loss: 0.6890
D(x): 0.6289, D(G(z)): 0.5148
Epoch: 2, Batch Num: [100/600]

Epoch: [2/20], Batch Num: [100/600]
Discriminator Loss: 1.2216, Generator Loss: 0.7269
D(x): 0.6161, D(G(z)): 0.5112
Epoch: [2/20], Batch Num: [101/600]
Discriminator Loss: 1.1936, Generator Loss: 0.6971
D(x): 0.6218, D(G(z)): 0.5005
Epoch: [2/20], Batch Num: [102/600]
Discriminator Loss: 1.1821, Generator Loss: 0.7190
D(x): 0.6268, D(G(z)): 0.5007
Epoch: [2/20], Batch Num: [103/600]
Discriminator Loss: 1.1475, Generator Loss: 0.7350
D(x): 0.6499, D(G(z)): 0.5012
Epoch: [2/20], Batch Num: [104/600]
Discriminator Loss: 1.1152, Generator Loss: 0.7673
D(x): 0.6555, D(G(z)): 0.4876
Epoch: [2/20], Batch Num: [105/600]
Discriminator Loss: 1.0933, Generator Loss: 0.7797
D(x): 0.6421, D(G(z)): 0.4649
Epoch: [2/20], Batch Num: [106/600]
Discriminator Loss: 1.1252, Generator Loss: 0.7872
D(x): 0.6222, D(G(z)): 0.4650
Epoch: [2/20], Batch Num: [107/600]
Discriminator Loss: 1.1267, Generator Loss: 0.8079
D(x): 0.6178, D(G(z)): 0.4607
Epoch: [2/20], Batch Num: [108/600]
Discriminator Loss: 1.0905, Generator Loss: 0.8119
D(x): 0.6292, D(G(z)): 0.4531
Epoch: [2/20], Batch Num: [109/600]
Discriminator Loss: 1.0438, Generator Loss: 0.8336
D(x): 0.6608, D(G(z)): 0.4536
Epoch: [2/20], Batch Num: [110/600]
Discriminator Loss: 1.0993, Generator Loss: 0.8189
D(x): 0.6252, D(G(z)): 0.4499
Epoch: [2/20], Batch Num: [111/600]
Discriminator Loss: 1.0249, Generator Loss: 0.8323
D(x): 0.6741, D(G(z)): 0.4519
Epoch: [2/20], Batch Num: [112/600]
Discriminator Loss: 1.0226, Generator Loss: 0.8442
D(x): 0.6766, D(G(z)): 0.4534
Epoch: [2/20], Batch Num: [113/600]
Discriminator Loss: 1.0241, Generator Loss: 0.8658
D(x): 0.6780, D(G(z)): 0.4513
Epoch: [2/20], Batch Num: [114/600]
Discriminator Loss: 0.9950, Generator Loss: 0.8617
D(x): 0.6984, D(G(z)): 0.4503
Epoch: [2/20], Batch Num: [115/600]
Discriminator Loss: 1.0134, Generator Loss: 0.9329
D(x): 0.6616, D(G(z)): 0.4308
Epoch: [2/20], Batch Num: [116/600]
Discriminator Loss: 0.9741, Generator Loss: 1.0410
D(x): 0.6818, D(G(z)): 0.4195
Epoch: [2/20], Batch Num: [117/600]
Discriminator Loss: 0.9088, Generator Loss: 1.0435
D(x): 0.7197, D(G(z)): 0.4114
Epoch: [2/20], Batch Num: [118/600]
Discriminator Loss: 0.9226, Generator Loss: 1.1435
D(x): 0.6619, D(G(z)): 0.3688
Epoch: [2/20], Batch Num: [119/600]
Discriminator Loss: 0.9316, Generator Loss: 1.1057
D(x): 0.6660, D(G(z)): 0.3727
Epoch: [2/20], Batch Num: [120/600]
Discriminator Loss: 0.8651, Generator Loss: 1.0988
D(x): 0.6917, D(G(z)): 0.3575
Epoch: [2/20], Batch Num: [121/600]
Discriminator Loss: 0.7850, Generator Loss: 1.1665
D(x): 0.7322, D(G(z)): 0.3457
Epoch: [2/20], Batch Num: [122/600]
Discriminator Loss: 0.8334, Generator Loss: 1.1551
D(x): 0.7234, D(G(z)): 0.3633
Epoch: [2/20], Batch Num: [123/600]
Discriminator Loss: 0.8414, Generator Loss: 1.1993
D(x): 0.7244, D(G(z)): 0.3612
Epoch: [2/20], Batch Num: [124/600]
Discriminator Loss: 0.8522, Generator Loss: 1.2546
D(x): 0.7364, D(G(z)): 0.3783
Epoch: [2/20], Batch Num: [125/600]
Discriminator Loss: 0.7556, Generator Loss: 1.4038
D(x): 0.7629, D(G(z)): 0.3371
Epoch: [2/20], Batch Num: [126/600]
Discriminator Loss: 0.8448, Generator Loss: 1.3384
D(x): 0.7077, D(G(z)): 0.3239
Epoch: [2/20], Batch Num: [127/600]
Discriminator Loss: 0.7672, Generator Loss: 1.3446
D(x): 0.7252, D(G(z)): 0.3079
Epoch: [2/20], Batch Num: [128/600]
Discriminator Loss: 0.7995, Generator Loss: 1.4763
D(x): 0.7458, D(G(z)): 0.3334
Epoch: [2/20], Batch Num: [129/600]
Discriminator Loss: 0.8442, Generator Loss: 1.4104
D(x): 0.7233, D(G(z)): 0.3294
Epoch: [2/20], Batch Num: [130/600]
Discriminator Loss: 0.8505, Generator Loss: 1.4422
D(x): 0.7264, D(G(z)): 0.3527
Epoch: [2/20], Batch Num: [131/600]
Discriminator Loss: 0.8613, Generator Loss: 1.4177
D(x): 0.7306, D(G(z)): 0.3348
Epoch: [2/20], Batch Num: [132/600]
Discriminator Loss: 0.8717, Generator Loss: 1.4433
D(x): 0.7103, D(G(z)): 0.3350
Epoch: [2/20], Batch Num: [133/600]
Discriminator Loss: 0.8954, Generator Loss: 1.4931
D(x): 0.7155, D(G(z)): 0.3292
Epoch: [2/20], Batch Num: [134/600]
Discriminator Loss: 0.9327, Generator Loss: 1.3545
D(x): 0.6772, D(G(z)): 0.3170
Epoch: [2/20], Batch Num: [135/600]
Discriminator Loss: 0.8546, Generator Loss: 1.3787
D(x): 0.7652, D(G(z)): 0.3505
Epoch: [2/20], Batch Num: [136/600]
Discriminator Loss: 0.9654, Generator Loss: 1.6467
D(x): 0.7071, D(G(z)): 0.3652
Epoch: [2/20], Batch Num: [137/600]
Discriminator Loss: 0.7767, Generator Loss: 1.5903
D(x): 0.7240, D(G(z)): 0.2663
Epoch: [2/20], Batch Num: [138/600]
Discriminator Loss: 0.8592, Generator Loss: 1.5923
D(x): 0.7213, D(G(z)): 0.3092
Epoch: [2/20], Batch Num: [139/600]
Discriminator Loss: 0.8784, Generator Loss: 1.7793
D(x): 0.7155, D(G(z)): 0.3210
Epoch: [2/20], Batch Num: [140/600]
Discriminator Loss: 0.7744, Generator Loss: 1.6723
D(x): 0.7245, D(G(z)): 0.2797
Epoch: [2/20], Batch Num: [141/600]
Discriminator Loss: 0.8930, Generator Loss: 1.7122
D(x): 0.7295, D(G(z)): 0.3175
Epoch: [2/20], Batch Num: [142/600]
Discriminator Loss: 0.7124, Generator Loss: 1.8779
D(x): 0.7856, D(G(z)): 0.2990
Epoch: [2/20], Batch Num: [143/600]
Discriminator Loss: 0.7246, Generator Loss: 1.9706
D(x): 0.7581, D(G(z)): 0.2330
Epoch: [2/20], Batch Num: [144/600]
Discriminator Loss: 0.7074, Generator Loss: 2.0144
D(x): 0.7202, D(G(z)): 0.2061
Epoch: [2/20], Batch Num: [145/600]
Discriminator Loss: 0.7008, Generator Loss: 1.6846
D(x): 0.7262, D(G(z)): 0.2028
Epoch: [2/20], Batch Num: [146/600]
Discriminator Loss: 0.6445, Generator Loss: 1.8232
D(x): 0.8138, D(G(z)): 0.2815
Epoch: [2/20], Batch Num: [147/600]
Discriminator Loss: 0.5758, Generator Loss: 2.3862
D(x): 0.8616, D(G(z)): 0.2985
Epoch: [2/20], Batch Num: [148/600]
Discriminator Loss: 0.4371, Generator Loss: 2.6734
D(x): 0.8297, D(G(z)): 0.1621
Epoch: [2/20], Batch Num: [149/600]
Discriminator Loss: 0.3831, Generator Loss: 2.8323
D(x): 0.8212, D(G(z)): 0.1121
Epoch: [2/20], Batch Num: [150/600]
Discriminator Loss: 0.3439, Generator Loss: 2.8658
D(x): 0.8519, D(G(z)): 0.1180
Epoch: [2/20], Batch Num: [151/600]
Discriminator Loss: 0.3785, Generator Loss: 2.7806
D(x): 0.8337, D(G(z)): 0.1067
Epoch: [2/20], Batch Num: [152/600]
Discriminator Loss: 0.3305, Generator Loss: 2.3434
D(x): 0.8749, D(G(z)): 0.1321
Epoch: [2/20], Batch Num: [153/600]
Discriminator Loss: 0.2640, Generator Loss: 2.3467
D(x): 0.9471, D(G(z)): 0.1690
Epoch: [2/20], Batch Num: [154/600]
Discriminator Loss: 0.2821, Generator Loss: 2.7527
D(x): 0.9233, D(G(z)): 0.1429
Epoch: [2/20], Batch Num: [155/600]
Discriminator Loss: 0.2577, Generator Loss: 3.3981
D(x): 0.9155, D(G(z)): 0.1285
Epoch: [2/20], Batch Num: [156/600]
Discriminator Loss: 0.2602, Generator Loss: 3.4536
D(x): 0.8885, D(G(z)): 0.0795
Epoch: [2/20], Batch Num: [157/600]
Discriminator Loss: 0.2851, Generator Loss: 3.6702
D(x): 0.8805, D(G(z)): 0.0726
Epoch: [2/20], Batch Num: [158/600]
Discriminator Loss: 0.2417, Generator Loss: 3.5472
D(x): 0.8872, D(G(z)): 0.0638
Epoch: [2/20], Batch Num: [159/600]
Discriminator Loss: 0.1936, Generator Loss: 3.2389
D(x): 0.9278, D(G(z)): 0.0856
Epoch: [2/20], Batch Num: [160/600]
Discriminator Loss: 0.2009, Generator Loss: 2.8930
D(x): 0.9443, D(G(z)): 0.1137
Epoch: [2/20], Batch Num: [161/600]
Discriminator Loss: 0.2378, Generator Loss: 3.2490
D(x): 0.9457, D(G(z)): 0.1344
Epoch: [2/20], Batch Num: [162/600]
Discriminator Loss: 0.2831, Generator Loss: 3.2153
D(x): 0.8856, D(G(z)): 0.1090
Epoch: [2/20], Batch Num: [163/600]
Discriminator Loss: 0.2765, Generator Loss: 3.1942
D(x): 0.9093, D(G(z)): 0.1041
Epoch: [2/20], Batch Num: [164/600]
Discriminator Loss: 0.2416, Generator Loss: 3.4386
D(x): 0.9233, D(G(z)): 0.0984
Epoch: [2/20], Batch Num: [165/600]
Discriminator Loss: 0.3815, Generator Loss: 3.0889
D(x): 0.8588, D(G(z)): 0.1055
Epoch: [2/20], Batch Num: [166/600]
Discriminator Loss: 0.2821, Generator Loss: 3.1096
D(x): 0.9390, D(G(z)): 0.1648
Epoch: [2/20], Batch Num: [167/600]
Discriminator Loss: 0.3089, Generator Loss: 3.4440
D(x): 0.9184, D(G(z)): 0.1620
Epoch: [2/20], Batch Num: [168/600]
Discriminator Loss: 0.4145, Generator Loss: 3.0830
D(x): 0.8444, D(G(z)): 0.1167
Epoch: [2/20], Batch Num: [169/600]
Discriminator Loss: 0.3937, Generator Loss: 2.6504
D(x): 0.8651, D(G(z)): 0.1134
Epoch: [2/20], Batch Num: [170/600]
Discriminator Loss: 0.4067, Generator Loss: 2.3166
D(x): 0.8609, D(G(z)): 0.1607
Epoch: [2/20], Batch Num: [171/600]
Discriminator Loss: 0.3961, Generator Loss: 2.3707
D(x): 0.9167, D(G(z)): 0.2199
Epoch: [2/20], Batch Num: [172/600]
Discriminator Loss: 0.4846, Generator Loss: 2.4148
D(x): 0.8544, D(G(z)): 0.2116
Epoch: [2/20], Batch Num: [173/600]
Discriminator Loss: 0.4911, Generator Loss: 2.2355
D(x): 0.8697, D(G(z)): 0.2311
Epoch: [2/20], Batch Num: [174/600]
Discriminator Loss: 0.5836, Generator Loss: 2.2701
D(x): 0.8338, D(G(z)): 0.2206
Epoch: [2/20], Batch Num: [175/600]
Discriminator Loss: 0.6273, Generator Loss: 2.0311
D(x): 0.8305, D(G(z)): 0.2299
Epoch: [2/20], Batch Num: [176/600]
Discriminator Loss: 0.6566, Generator Loss: 1.7431
D(x): 0.7990, D(G(z)): 0.2241
Epoch: [2/20], Batch Num: [177/600]
Discriminator Loss: 0.5562, Generator Loss: 1.8414
D(x): 0.9034, D(G(z)): 0.3170
Epoch: [2/20], Batch Num: [178/600]
Discriminator Loss: 0.6213, Generator Loss: 2.0356
D(x): 0.8502, D(G(z)): 0.2980
Epoch: [2/20], Batch Num: [179/600]
Discriminator Loss: 0.7616, Generator Loss: 2.0139
D(x): 0.7860, D(G(z)): 0.2564
Epoch: [2/20], Batch Num: [180/600]
Discriminator Loss: 0.9603, Generator Loss: 1.4052
D(x): 0.6923, D(G(z)): 0.2320
Epoch: [2/20], Batch Num: [181/600]
Discriminator Loss: 0.9537, Generator Loss: 1.0084
D(x): 0.8060, D(G(z)): 0.3825
Epoch: [2/20], Batch Num: [182/600]
Discriminator Loss: 0.9977, Generator Loss: 1.4183
D(x): 0.8548, D(G(z)): 0.4859
Epoch: [2/20], Batch Num: [183/600]
Discriminator Loss: 0.8942, Generator Loss: 1.6151
D(x): 0.8363, D(G(z)): 0.3990
Epoch: [2/20], Batch Num: [184/600]
Discriminator Loss: 0.8580, Generator Loss: 1.7019
D(x): 0.7283, D(G(z)): 0.2600
Epoch: [2/20], Batch Num: [185/600]
Discriminator Loss: 1.0662, Generator Loss: 1.4666
D(x): 0.6641, D(G(z)): 0.2512
Epoch: [2/20], Batch Num: [186/600]
Discriminator Loss: 0.7843, Generator Loss: 1.3124
D(x): 0.8222, D(G(z)): 0.3407
Epoch: [2/20], Batch Num: [187/600]
Discriminator Loss: 0.7525, Generator Loss: 1.2557
D(x): 0.8733, D(G(z)): 0.4135
Epoch: [2/20], Batch Num: [188/600]
Discriminator Loss: 0.8128, Generator Loss: 1.4593
D(x): 0.8329, D(G(z)): 0.3787
Epoch: [2/20], Batch Num: [189/600]
Discriminator Loss: 0.5217, Generator Loss: 1.6942
D(x): 0.8733, D(G(z)): 0.2617
Epoch: [2/20], Batch Num: [190/600]
Discriminator Loss: 0.6199, Generator Loss: 1.8003
D(x): 0.8189, D(G(z)): 0.2408
Epoch: [2/20], Batch Num: [191/600]
Discriminator Loss: 0.5184, Generator Loss: 1.8276
D(x): 0.8504, D(G(z)): 0.2262
Epoch: [2/20], Batch Num: [192/600]
Discriminator Loss: 0.5503, Generator Loss: 1.7021
D(x): 0.8313, D(G(z)): 0.2289
Epoch: [2/20], Batch Num: [193/600]
Discriminator Loss: 0.4570, Generator Loss: 1.6527
D(x): 0.8858, D(G(z)): 0.2276
Epoch: [2/20], Batch Num: [194/600]
Discriminator Loss: 0.4335, Generator Loss: 1.8009
D(x): 0.9103, D(G(z)): 0.2512
Epoch: [2/20], Batch Num: [195/600]
Discriminator Loss: 0.4418, Generator Loss: 1.8827
D(x): 0.9067, D(G(z)): 0.2394
Epoch: [2/20], Batch Num: [196/600]
Discriminator Loss: 0.3577, Generator Loss: 2.2083
D(x): 0.9343, D(G(z)): 0.2121
Epoch: [2/20], Batch Num: [197/600]
Discriminator Loss: 0.3532, Generator Loss: 2.2960
D(x): 0.8930, D(G(z)): 0.1618
Epoch: [2/20], Batch Num: [198/600]
Discriminator Loss: 0.3038, Generator Loss: 2.4703
D(x): 0.9049, D(G(z)): 0.1360
Epoch: [2/20], Batch Num: [199/600]
Discriminator Loss: 0.2994, Generator Loss: 2.4640
D(x): 0.9175, D(G(z)): 0.1213
Epoch: 2, Batch Num: [200/600]

Epoch: [2/20], Batch Num: [200/600]
Discriminator Loss: 0.1966, Generator Loss: 2.5392
D(x): 0.9532, D(G(z)): 0.1199
Epoch: [2/20], Batch Num: [201/600]
Discriminator Loss: 0.2374, Generator Loss: 2.5643
D(x): 0.9336, D(G(z)): 0.1113
Epoch: [2/20], Batch Num: [202/600]
Discriminator Loss: 0.2348, Generator Loss: 2.6261
D(x): 0.9582, D(G(z)): 0.1305
Epoch: [2/20], Batch Num: [203/600]
Discriminator Loss: 0.1315, Generator Loss: 2.6071
D(x): 0.9754, D(G(z)): 0.0970
Epoch: [2/20], Batch Num: [204/600]
Discriminator Loss: 0.1988, Generator Loss: 2.8531
D(x): 0.9478, D(G(z)): 0.0997
Epoch: [2/20], Batch Num: [205/600]
Discriminator Loss: 0.2003, Generator Loss: 2.9138
D(x): 0.9605, D(G(z)): 0.1184
Epoch: [2/20], Batch Num: [206/600]
Discriminator Loss: 0.2128, Generator Loss: 3.0314
D(x): 0.9402, D(G(z)): 0.0850
Epoch: [2/20], Batch Num: [207/600]
Discriminator Loss: 0.1497, Generator Loss: 3.1428
D(x): 0.9634, D(G(z)): 0.0855
Epoch: [2/20], Batch Num: [208/600]
Discriminator Loss: 0.2075, Generator Loss: 3.2338
D(x): 0.9627, D(G(z)): 0.0927
Epoch: [2/20], Batch Num: [209/600]
Discriminator Loss: 0.3507, Generator Loss: 2.8292
D(x): 0.8789, D(G(z)): 0.0721
Epoch: [2/20], Batch Num: [210/600]
Discriminator Loss: 0.2419, Generator Loss: 2.8379
D(x): 0.9437, D(G(z)): 0.1209
Epoch: [2/20], Batch Num: [211/600]
Discriminator Loss: 0.4057, Generator Loss: 2.4527
D(x): 0.9158, D(G(z)): 0.1510
Epoch: [2/20], Batch Num: [212/600]
Discriminator Loss: 0.3752, Generator Loss: 2.8444
D(x): 0.9506, D(G(z)): 0.1986
Epoch: [2/20], Batch Num: [213/600]
Discriminator Loss: 0.3685, Generator Loss: 3.0581
D(x): 0.9120, D(G(z)): 0.1355
Epoch: [2/20], Batch Num: [214/600]
Discriminator Loss: 0.3537, Generator Loss: 3.1022
D(x): 0.9145, D(G(z)): 0.1213
Epoch: [2/20], Batch Num: [215/600]
Discriminator Loss: 0.3431, Generator Loss: 3.1960
D(x): 0.9184, D(G(z)): 0.1347
Epoch: [2/20], Batch Num: [216/600]
Discriminator Loss: 0.4126, Generator Loss: 2.9471
D(x): 0.8732, D(G(z)): 0.1034
Epoch: [2/20], Batch Num: [217/600]
Discriminator Loss: 0.2373, Generator Loss: 3.1037
D(x): 0.9446, D(G(z)): 0.1308
Epoch: [2/20], Batch Num: [218/600]
Discriminator Loss: 0.3489, Generator Loss: 3.1286
D(x): 0.9340, D(G(z)): 0.1660
Epoch: [2/20], Batch Num: [219/600]
Discriminator Loss: 0.4888, Generator Loss: 3.4540
D(x): 0.8839, D(G(z)): 0.1425
Epoch: [2/20], Batch Num: [220/600]
Discriminator Loss: 0.3311, Generator Loss: 3.0338
D(x): 0.8868, D(G(z)): 0.0892
Epoch: [2/20], Batch Num: [221/600]
Discriminator Loss: 0.3246, Generator Loss: 3.0436
D(x): 0.9306, D(G(z)): 0.1344
Epoch: [2/20], Batch Num: [222/600]
Discriminator Loss: 0.3966, Generator Loss: 3.2423
D(x): 0.9002, D(G(z)): 0.1455
Epoch: [2/20], Batch Num: [223/600]
Discriminator Loss: 0.4510, Generator Loss: 3.3934
D(x): 0.8764, D(G(z)): 0.1342
Epoch: [2/20], Batch Num: [224/600]
Discriminator Loss: 0.6325, Generator Loss: 3.3811
D(x): 0.8651, D(G(z)): 0.1905
Epoch: [2/20], Batch Num: [225/600]
Discriminator Loss: 0.7398, Generator Loss: 3.3653
D(x): 0.8903, D(G(z)): 0.2500
Epoch: [2/20], Batch Num: [226/600]
Discriminator Loss: 0.9046, Generator Loss: 4.2680
D(x): 0.8742, D(G(z)): 0.2994
Epoch: [2/20], Batch Num: [227/600]
Discriminator Loss: 1.3982, Generator Loss: 3.4123
D(x): 0.6796, D(G(z)): 0.1927
Epoch: [2/20], Batch Num: [228/600]
Discriminator Loss: 1.5795, Generator Loss: 1.9823
D(x): 0.7014, D(G(z)): 0.2888
Epoch: [2/20], Batch Num: [229/600]
Discriminator Loss: 2.3389, Generator Loss: 1.5948
D(x): 0.7364, D(G(z)): 0.6237
Epoch: [2/20], Batch Num: [230/600]
Discriminator Loss: 3.0946, Generator Loss: 2.0579
D(x): 0.7588, D(G(z)): 0.7958
Epoch: [2/20], Batch Num: [231/600]
Discriminator Loss: 4.0759, Generator Loss: 2.0412
D(x): 0.4372, D(G(z)): 0.7109
Epoch: [2/20], Batch Num: [232/600]
Discriminator Loss: 3.9451, Generator Loss: 2.5325
D(x): 0.4120, D(G(z)): 0.5843
Epoch: [2/20], Batch Num: [233/600]
Discriminator Loss: 3.1698, Generator Loss: 4.2848
D(x): 0.3922, D(G(z)): 0.4359
Epoch: [2/20], Batch Num: [234/600]
Discriminator Loss: 2.2166, Generator Loss: 3.6660
D(x): 0.4254, D(G(z)): 0.3113
Epoch: [2/20], Batch Num: [235/600]
Discriminator Loss: 1.2165, Generator Loss: 4.8817
D(x): 0.6187, D(G(z)): 0.2423
Epoch: [2/20], Batch Num: [236/600]
Discriminator Loss: 0.8926, Generator Loss: 4.4287
D(x): 0.7323, D(G(z)): 0.2743
Epoch: [2/20], Batch Num: [237/600]
Discriminator Loss: 0.7124, Generator Loss: 4.7720
D(x): 0.8268, D(G(z)): 0.2834
Epoch: [2/20], Batch Num: [238/600]
Discriminator Loss: 0.4692, Generator Loss: 6.2570
D(x): 0.8452, D(G(z)): 0.1981
Epoch: [2/20], Batch Num: [239/600]
Discriminator Loss: 0.4025, Generator Loss: 7.2809
D(x): 0.8238, D(G(z)): 0.1310
Epoch: [2/20], Batch Num: [240/600]
Discriminator Loss: 0.3531, Generator Loss: 7.8365
D(x): 0.8136, D(G(z)): 0.0882
Epoch: [2/20], Batch Num: [241/600]
Discriminator Loss: 0.4581, Generator Loss: 8.6382
D(x): 0.7606, D(G(z)): 0.0870
Epoch: [2/20], Batch Num: [242/600]
Discriminator Loss: 0.5483, Generator Loss: 7.1468
D(x): 0.7066, D(G(z)): 0.0516
Epoch: [2/20], Batch Num: [243/600]
Discriminator Loss: 0.7615, Generator Loss: 5.9177
D(x): 0.6544, D(G(z)): 0.1129
Epoch: [2/20], Batch Num: [244/600]
Discriminator Loss: 0.8805, Generator Loss: 4.8607
D(x): 0.7313, D(G(z)): 0.2245
Epoch: [2/20], Batch Num: [245/600]
Discriminator Loss: 0.9160, Generator Loss: 4.0113
D(x): 0.7501, D(G(z)): 0.3070
Epoch: [2/20], Batch Num: [246/600]
Discriminator Loss: 1.0085, Generator Loss: 4.6245
D(x): 0.6730, D(G(z)): 0.2389
Epoch: [2/20], Batch Num: [247/600]
Discriminator Loss: 0.9361, Generator Loss: 4.2224
D(x): 0.6682, D(G(z)): 0.1970
Epoch: [2/20], Batch Num: [248/600]
Discriminator Loss: 0.9090, Generator Loss: 3.4674
D(x): 0.6790, D(G(z)): 0.1769
Epoch: [2/20], Batch Num: [249/600]
Discriminator Loss: 0.8156, Generator Loss: 3.2916
D(x): 0.6600, D(G(z)): 0.1882
Epoch: [2/20], Batch Num: [250/600]
Discriminator Loss: 0.7969, Generator Loss: 2.7988
D(x): 0.7500, D(G(z)): 0.2723
Epoch: [2/20], Batch Num: [251/600]
Discriminator Loss: 0.6964, Generator Loss: 2.9720
D(x): 0.7935, D(G(z)): 0.2780
Epoch: [2/20], Batch Num: [252/600]
Discriminator Loss: 0.6371, Generator Loss: 4.0764
D(x): 0.7730, D(G(z)): 0.2197
Epoch: [2/20], Batch Num: [253/600]
Discriminator Loss: 0.6632, Generator Loss: 3.9257
D(x): 0.7209, D(G(z)): 0.1374
Epoch: [2/20], Batch Num: [254/600]
Discriminator Loss: 0.6331, Generator Loss: 3.6517
D(x): 0.7384, D(G(z)): 0.1713
Epoch: [2/20], Batch Num: [255/600]
Discriminator Loss: 0.6546, Generator Loss: 2.5474
D(x): 0.7412, D(G(z)): 0.2095
Epoch: [2/20], Batch Num: [256/600]
Discriminator Loss: 0.6187, Generator Loss: 2.7062
D(x): 0.7735, D(G(z)): 0.2140
Epoch: [2/20], Batch Num: [257/600]
Discriminator Loss: 0.5855, Generator Loss: 3.1919
D(x): 0.8016, D(G(z)): 0.2230
Epoch: [2/20], Batch Num: [258/600]
Discriminator Loss: 0.8087, Generator Loss: 2.6573
D(x): 0.7386, D(G(z)): 0.2450
Epoch: [2/20], Batch Num: [259/600]
Discriminator Loss: 0.6764, Generator Loss: 2.4484
D(x): 0.7440, D(G(z)): 0.2138
Epoch: [2/20], Batch Num: [260/600]
Discriminator Loss: 0.8945, Generator Loss: 2.0469
D(x): 0.7010, D(G(z)): 0.2493
Epoch: [2/20], Batch Num: [261/600]
Discriminator Loss: 0.8705, Generator Loss: 2.4741
D(x): 0.7484, D(G(z)): 0.2911
Epoch: [2/20], Batch Num: [262/600]
Discriminator Loss: 0.6924, Generator Loss: 2.7228
D(x): 0.7762, D(G(z)): 0.2550
Epoch: [2/20], Batch Num: [263/600]
Discriminator Loss: 0.6681, Generator Loss: 3.4741
D(x): 0.8062, D(G(z)): 0.2307
Epoch: [2/20], Batch Num: [264/600]
Discriminator Loss: 0.6603, Generator Loss: 3.5759
D(x): 0.7909, D(G(z)): 0.2095
Epoch: [2/20], Batch Num: [265/600]
Discriminator Loss: 0.5911, Generator Loss: 3.1890
D(x): 0.7763, D(G(z)): 0.1635
Epoch: [2/20], Batch Num: [266/600]
Discriminator Loss: 0.6026, Generator Loss: 2.8332
D(x): 0.7997, D(G(z)): 0.1853
Epoch: [2/20], Batch Num: [267/600]
Discriminator Loss: 0.5120, Generator Loss: 2.7729
D(x): 0.8559, D(G(z)): 0.2173
Epoch: [2/20], Batch Num: [268/600]
Discriminator Loss: 0.5473, Generator Loss: 3.1741
D(x): 0.8585, D(G(z)): 0.2336
Epoch: [2/20], Batch Num: [269/600]
Discriminator Loss: 0.5142, Generator Loss: 2.8032
D(x): 0.8256, D(G(z)): 0.1810
Epoch: [2/20], Batch Num: [270/600]
Discriminator Loss: 0.6834, Generator Loss: 2.1869
D(x): 0.7515, D(G(z)): 0.1819
Epoch: [2/20], Batch Num: [271/600]
Discriminator Loss: 0.5074, Generator Loss: 2.2038
D(x): 0.8922, D(G(z)): 0.2545
Epoch: [2/20], Batch Num: [272/600]
Discriminator Loss: 0.6096, Generator Loss: 2.3273
D(x): 0.8565, D(G(z)): 0.2799
Epoch: [2/20], Batch Num: [273/600]
Discriminator Loss: 0.7676, Generator Loss: 2.5513
D(x): 0.7941, D(G(z)): 0.2477
Epoch: [2/20], Batch Num: [274/600]
Discriminator Loss: 0.5270, Generator Loss: 2.3053
D(x): 0.8444, D(G(z)): 0.2267
Epoch: [2/20], Batch Num: [275/600]
Discriminator Loss: 0.6790, Generator Loss: 2.2341
D(x): 0.8132, D(G(z)): 0.2532
Epoch: [2/20], Batch Num: [276/600]
Discriminator Loss: 0.6038, Generator Loss: 2.5104
D(x): 0.8383, D(G(z)): 0.2590
Epoch: [2/20], Batch Num: [277/600]
Discriminator Loss: 0.8125, Generator Loss: 2.0005
D(x): 0.7369, D(G(z)): 0.2248
Epoch: [2/20], Batch Num: [278/600]
Discriminator Loss: 0.5907, Generator Loss: 1.8110
D(x): 0.8679, D(G(z)): 0.2823
Epoch: [2/20], Batch Num: [279/600]
Discriminator Loss: 0.8563, Generator Loss: 1.9892
D(x): 0.8373, D(G(z)): 0.3939
Epoch: [2/20], Batch Num: [280/600]
Discriminator Loss: 0.7812, Generator Loss: 2.4035
D(x): 0.8399, D(G(z)): 0.3167
Epoch: [2/20], Batch Num: [281/600]
Discriminator Loss: 0.8930, Generator Loss: 2.2704
D(x): 0.7350, D(G(z)): 0.2540
Epoch: [2/20], Batch Num: [282/600]
Discriminator Loss: 0.9244, Generator Loss: 1.8976
D(x): 0.7481, D(G(z)): 0.3245
Epoch: [2/20], Batch Num: [283/600]
Discriminator Loss: 0.9397, Generator Loss: 1.5626
D(x): 0.7823, D(G(z)): 0.3690
Epoch: [2/20], Batch Num: [284/600]
Discriminator Loss: 1.1227, Generator Loss: 1.5499
D(x): 0.7755, D(G(z)): 0.4360
Epoch: [2/20], Batch Num: [285/600]
Discriminator Loss: 1.2688, Generator Loss: 1.7622
D(x): 0.7452, D(G(z)): 0.4482
Epoch: [2/20], Batch Num: [286/600]
Discriminator Loss: 1.2031, Generator Loss: 1.5334
D(x): 0.7048, D(G(z)): 0.3939
Epoch: [2/20], Batch Num: [287/600]
Discriminator Loss: 1.3510, Generator Loss: 1.4211
D(x): 0.6960, D(G(z)): 0.4389
Epoch: [2/20], Batch Num: [288/600]
Discriminator Loss: 1.2711, Generator Loss: 1.5439
D(x): 0.7112, D(G(z)): 0.4442
Epoch: [2/20], Batch Num: [289/600]
Discriminator Loss: 1.4759, Generator Loss: 1.2785
D(x): 0.6875, D(G(z)): 0.4475
Epoch: [2/20], Batch Num: [290/600]
Discriminator Loss: 1.4272, Generator Loss: 1.1597
D(x): 0.6437, D(G(z)): 0.4698
Epoch: [2/20], Batch Num: [291/600]
Discriminator Loss: 1.5918, Generator Loss: 0.9736
D(x): 0.6618, D(G(z)): 0.5123
Epoch: [2/20], Batch Num: [292/600]
Discriminator Loss: 1.5854, Generator Loss: 1.1409
D(x): 0.6081, D(G(z)): 0.5367
Epoch: [2/20], Batch Num: [293/600]
Discriminator Loss: 1.4588, Generator Loss: 1.0359
D(x): 0.6276, D(G(z)): 0.4903
Epoch: [2/20], Batch Num: [294/600]
Discriminator Loss: 1.4301, Generator Loss: 1.0644
D(x): 0.6017, D(G(z)): 0.4764
Epoch: [2/20], Batch Num: [295/600]
Discriminator Loss: 1.2677, Generator Loss: 1.0736
D(x): 0.6752, D(G(z)): 0.4935
Epoch: [2/20], Batch Num: [296/600]
Discriminator Loss: 1.2858, Generator Loss: 1.1075
D(x): 0.5944, D(G(z)): 0.4294
Epoch: [2/20], Batch Num: [297/600]
Discriminator Loss: 1.2168, Generator Loss: 1.0986
D(x): 0.6211, D(G(z)): 0.4309
Epoch: [2/20], Batch Num: [298/600]
Discriminator Loss: 1.1458, Generator Loss: 1.0245
D(x): 0.6283, D(G(z)): 0.4135
Epoch: [2/20], Batch Num: [299/600]
Discriminator Loss: 1.1468, Generator Loss: 0.9937
D(x): 0.6433, D(G(z)): 0.4370
Epoch: 2, Batch Num: [300/600]

Epoch: [2/20], Batch Num: [300/600]
Discriminator Loss: 1.0567, Generator Loss: 0.9578
D(x): 0.6908, D(G(z)): 0.4397
Epoch: [2/20], Batch Num: [301/600]
Discriminator Loss: 1.0512, Generator Loss: 1.0031
D(x): 0.6910, D(G(z)): 0.4374
Epoch: [2/20], Batch Num: [302/600]
Discriminator Loss: 0.9977, Generator Loss: 0.9954
D(x): 0.7004, D(G(z)): 0.4311
Epoch: [2/20], Batch Num: [303/600]
Discriminator Loss: 0.9576, Generator Loss: 1.0091
D(x): 0.7451, D(G(z)): 0.4498
Epoch: [2/20], Batch Num: [304/600]
Discriminator Loss: 1.0066, Generator Loss: 1.0242
D(x): 0.6915, D(G(z)): 0.4240
Epoch: [2/20], Batch Num: [305/600]
Discriminator Loss: 0.9938, Generator Loss: 1.0587
D(x): 0.6586, D(G(z)): 0.3889
Epoch: [2/20], Batch Num: [306/600]
Discriminator Loss: 0.9736, Generator Loss: 1.1313
D(x): 0.6880, D(G(z)): 0.3930
Epoch: [2/20], Batch Num: [307/600]
Discriminator Loss: 0.9586, Generator Loss: 1.1593
D(x): 0.6945, D(G(z)): 0.3963
Epoch: [2/20], Batch Num: [308/600]
Discriminator Loss: 0.9230, Generator Loss: 1.1899
D(x): 0.6917, D(G(z)): 0.3701
Epoch: [2/20], Batch Num: [309/600]
Discriminator Loss: 0.8684, Generator Loss: 1.2617
D(x): 0.7051, D(G(z)): 0.3477
Epoch: [2/20], Batch Num: [310/600]
Discriminator Loss: 0.9120, Generator Loss: 1.2183
D(x): 0.6981, D(G(z)): 0.3664
Epoch: [2/20], Batch Num: [311/600]
Discriminator Loss: 0.9908, Generator Loss: 1.2407
D(x): 0.6792, D(G(z)): 0.3841
Epoch: [2/20], Batch Num: [312/600]
Discriminator Loss: 0.9503, Generator Loss: 1.3097
D(x): 0.7384, D(G(z)): 0.4020
Epoch: [2/20], Batch Num: [313/600]
Discriminator Loss: 0.8806, Generator Loss: 1.4785
D(x): 0.7176, D(G(z)): 0.3613
Epoch: [2/20], Batch Num: [314/600]
Discriminator Loss: 0.9685, Generator Loss: 1.4071
D(x): 0.6977, D(G(z)): 0.3809
Epoch: [2/20], Batch Num: [315/600]
Discriminator Loss: 0.9304, Generator Loss: 1.4115
D(x): 0.6458, D(G(z)): 0.3134
Epoch: [2/20], Batch Num: [316/600]
Discriminator Loss: 0.7850, Generator Loss: 1.4478
D(x): 0.7144, D(G(z)): 0.2903
Epoch: [2/20], Batch Num: [317/600]
Discriminator Loss: 0.7944, Generator Loss: 1.6323
D(x): 0.7336, D(G(z)): 0.3183
Epoch: [2/20], Batch Num: [318/600]
Discriminator Loss: 0.7391, Generator Loss: 1.5790
D(x): 0.7736, D(G(z)): 0.3168
Epoch: [2/20], Batch Num: [319/600]
Discriminator Loss: 0.7212, Generator Loss: 1.5747
D(x): 0.7612, D(G(z)): 0.2902
Epoch: [2/20], Batch Num: [320/600]
Discriminator Loss: 0.8145, Generator Loss: 1.8822
D(x): 0.7279, D(G(z)): 0.3037
Epoch: [2/20], Batch Num: [321/600]
Discriminator Loss: 0.6245, Generator Loss: 2.0327
D(x): 0.7651, D(G(z)): 0.2327
Epoch: [2/20], Batch Num: [322/600]
Discriminator Loss: 0.6227, Generator Loss: 2.2540
D(x): 0.7660, D(G(z)): 0.2143
Epoch: [2/20], Batch Num: [323/600]
Discriminator Loss: 0.5413, Generator Loss: 2.1911
D(x): 0.7866, D(G(z)): 0.1985
Epoch: [2/20], Batch Num: [324/600]
Discriminator Loss: 0.5519, Generator Loss: 2.1145
D(x): 0.8341, D(G(z)): 0.2465
Epoch: [2/20], Batch Num: [325/600]
Discriminator Loss: 0.6038, Generator Loss: 2.5374
D(x): 0.7736, D(G(z)): 0.2142
Epoch: [2/20], Batch Num: [326/600]
Discriminator Loss: 0.5389, Generator Loss: 2.5130
D(x): 0.8416, D(G(z)): 0.2427
Epoch: [2/20], Batch Num: [327/600]
Discriminator Loss: 0.4334, Generator Loss: 2.9547
D(x): 0.8492, D(G(z)): 0.1835
Epoch: [2/20], Batch Num: [328/600]
Discriminator Loss: 0.3759, Generator Loss: 3.4240
D(x): 0.8603, D(G(z)): 0.1540
Epoch: [2/20], Batch Num: [329/600]
Discriminator Loss: 0.3554, Generator Loss: 3.7256
D(x): 0.8549, D(G(z)): 0.1290
Epoch: [2/20], Batch Num: [330/600]
Discriminator Loss: 0.3398, Generator Loss: 3.5663
D(x): 0.8708, D(G(z)): 0.1273
Epoch: [2/20], Batch Num: [331/600]
Discriminator Loss: 0.3394, Generator Loss: 3.9599
D(x): 0.8752, D(G(z)): 0.1342
Epoch: [2/20], Batch Num: [332/600]
Discriminator Loss: 0.4018, Generator Loss: 3.4140
D(x): 0.8301, D(G(z)): 0.1209
Epoch: [2/20], Batch Num: [333/600]
Discriminator Loss: 0.6029, Generator Loss: 3.3662
D(x): 0.7860, D(G(z)): 0.1663
Epoch: [2/20], Batch Num: [334/600]
Discriminator Loss: 0.3930, Generator Loss: 3.1205
D(x): 0.8833, D(G(z)): 0.1741
Epoch: [2/20], Batch Num: [335/600]
Discriminator Loss: 0.4727, Generator Loss: 3.2295
D(x): 0.8898, D(G(z)): 0.2327
Epoch: [2/20], Batch Num: [336/600]
Discriminator Loss: 0.5214, Generator Loss: 3.0566
D(x): 0.8512, D(G(z)): 0.2093
Epoch: [2/20], Batch Num: [337/600]
Discriminator Loss: 0.6377, Generator Loss: 3.0273
D(x): 0.8442, D(G(z)): 0.2680
Epoch: [2/20], Batch Num: [338/600]
Discriminator Loss: 0.5392, Generator Loss: 3.2758
D(x): 0.8066, D(G(z)): 0.1635
Epoch: [2/20], Batch Num: [339/600]
Discriminator Loss: 0.5141, Generator Loss: 3.2806
D(x): 0.8311, D(G(z)): 0.1878
Epoch: [2/20], Batch Num: [340/600]
Discriminator Loss: 0.5553, Generator Loss: 3.6376
D(x): 0.8092, D(G(z)): 0.1769
Epoch: [2/20], Batch Num: [341/600]
Discriminator Loss: 0.5829, Generator Loss: 3.3564
D(x): 0.7769, D(G(z)): 0.1808
Epoch: [2/20], Batch Num: [342/600]
Discriminator Loss: 0.5075, Generator Loss: 3.0666
D(x): 0.8095, D(G(z)): 0.1680
Epoch: [2/20], Batch Num: [343/600]
Discriminator Loss: 0.5697, Generator Loss: 2.7632
D(x): 0.8150, D(G(z)): 0.2027
Epoch: [2/20], Batch Num: [344/600]
Discriminator Loss: 0.6537, Generator Loss: 2.6159
D(x): 0.8209, D(G(z)): 0.2583
Epoch: [2/20], Batch Num: [345/600]
Discriminator Loss: 0.7501, Generator Loss: 2.9296
D(x): 0.7866, D(G(z)): 0.2808
Epoch: [2/20], Batch Num: [346/600]
Discriminator Loss: 0.5853, Generator Loss: 3.2945
D(x): 0.8432, D(G(z)): 0.2554
Epoch: [2/20], Batch Num: [347/600]
Discriminator Loss: 0.5283, Generator Loss: 3.4034
D(x): 0.8132, D(G(z)): 0.1936
Epoch: [2/20], Batch Num: [348/600]
Discriminator Loss: 0.4703, Generator Loss: 3.7964
D(x): 0.7955, D(G(z)): 0.1238
Epoch: [2/20], Batch Num: [349/600]
Discriminator Loss: 0.8883, Generator Loss: 3.1566
D(x): 0.7256, D(G(z)): 0.1798
Epoch: [2/20], Batch Num: [350/600]
Discriminator Loss: 0.7122, Generator Loss: 2.3841
D(x): 0.7703, D(G(z)): 0.1883
Epoch: [2/20], Batch Num: [351/600]
Discriminator Loss: 0.8566, Generator Loss: 2.2246
D(x): 0.7801, D(G(z)): 0.2966
Epoch: [2/20], Batch Num: [352/600]
Discriminator Loss: 0.9554, Generator Loss: 1.8300
D(x): 0.7604, D(G(z)): 0.3165
Epoch: [2/20], Batch Num: [353/600]
Discriminator Loss: 0.8756, Generator Loss: 2.1016
D(x): 0.7653, D(G(z)): 0.3067
Epoch: [2/20], Batch Num: [354/600]
Discriminator Loss: 0.9446, Generator Loss: 2.0095
D(x): 0.7458, D(G(z)): 0.3174
Epoch: [2/20], Batch Num: [355/600]
Discriminator Loss: 0.9266, Generator Loss: 1.9333
D(x): 0.7526, D(G(z)): 0.3398
Epoch: [2/20], Batch Num: [356/600]
Discriminator Loss: 0.9797, Generator Loss: 1.7456
D(x): 0.7202, D(G(z)): 0.3140
Epoch: [2/20], Batch Num: [357/600]
Discriminator Loss: 0.9235, Generator Loss: 1.5439
D(x): 0.7406, D(G(z)): 0.3292
Epoch: [2/20], Batch Num: [358/600]
Discriminator Loss: 1.1168, Generator Loss: 1.3020
D(x): 0.6780, D(G(z)): 0.3713
Epoch: [2/20], Batch Num: [359/600]
Discriminator Loss: 1.2368, Generator Loss: 1.2939
D(x): 0.6708, D(G(z)): 0.4201
Epoch: [2/20], Batch Num: [360/600]
Discriminator Loss: 1.2819, Generator Loss: 1.0516
D(x): 0.6863, D(G(z)): 0.4668
Epoch: [2/20], Batch Num: [361/600]
Discriminator Loss: 1.3485, Generator Loss: 1.0065
D(x): 0.6693, D(G(z)): 0.4622
Epoch: [2/20], Batch Num: [362/600]
Discriminator Loss: 1.3692, Generator Loss: 0.9342
D(x): 0.6520, D(G(z)): 0.4328
Epoch: [2/20], Batch Num: [363/600]
Discriminator Loss: 1.1573, Generator Loss: 0.8806
D(x): 0.7399, D(G(z)): 0.4784
Epoch: [2/20], Batch Num: [364/600]
Discriminator Loss: 0.9351, Generator Loss: 0.9142
D(x): 0.7994, D(G(z)): 0.4570
Epoch: [2/20], Batch Num: [365/600]
Discriminator Loss: 0.9302, Generator Loss: 0.8872
D(x): 0.7734, D(G(z)): 0.4229
Epoch: [2/20], Batch Num: [366/600]
Discriminator Loss: 0.8973, Generator Loss: 0.9312
D(x): 0.7953, D(G(z)): 0.4209
Epoch: [2/20], Batch Num: [367/600]
Discriminator Loss: 0.8513, Generator Loss: 0.9471
D(x): 0.7948, D(G(z)): 0.4100
Epoch: [2/20], Batch Num: [368/600]
Discriminator Loss: 0.8911, Generator Loss: 0.9374
D(x): 0.7838, D(G(z)): 0.4249
Epoch: [2/20], Batch Num: [369/600]
Discriminator Loss: 0.8445, Generator Loss: 0.9330
D(x): 0.8047, D(G(z)): 0.4175
Epoch: [2/20], Batch Num: [370/600]
Discriminator Loss: 0.7713, Generator Loss: 0.9550
D(x): 0.8443, D(G(z)): 0.4181
Epoch: [2/20], Batch Num: [371/600]
Discriminator Loss: 0.7105, Generator Loss: 0.9557
D(x): 0.8430, D(G(z)): 0.3882
Epoch: [2/20], Batch Num: [372/600]
Discriminator Loss: 0.7257, Generator Loss: 1.0453
D(x): 0.8288, D(G(z)): 0.3795
Epoch: [2/20], Batch Num: [373/600]
Discriminator Loss: 0.6296, Generator Loss: 1.0572
D(x): 0.8826, D(G(z)): 0.3737
Epoch: [2/20], Batch Num: [374/600]
Discriminator Loss: 0.6678, Generator Loss: 1.1358
D(x): 0.8362, D(G(z)): 0.3513
Epoch: [2/20], Batch Num: [375/600]
Discriminator Loss: 0.6253, Generator Loss: 1.1354
D(x): 0.8385, D(G(z)): 0.3307
Epoch: [2/20], Batch Num: [376/600]
Discriminator Loss: 0.7209, Generator Loss: 1.1617
D(x): 0.8244, D(G(z)): 0.3658
Epoch: [2/20], Batch Num: [377/600]
Discriminator Loss: 0.5981, Generator Loss: 1.2127
D(x): 0.8807, D(G(z)): 0.3517
Epoch: [2/20], Batch Num: [378/600]
Discriminator Loss: 0.6075, Generator Loss: 1.2220
D(x): 0.8774, D(G(z)): 0.3481
Epoch: [2/20], Batch Num: [379/600]
Discriminator Loss: 0.5462, Generator Loss: 1.2448
D(x): 0.8802, D(G(z)): 0.3063
Epoch: [2/20], Batch Num: [380/600]
Discriminator Loss: 0.5356, Generator Loss: 1.3867
D(x): 0.8767, D(G(z)): 0.2931
Epoch: [2/20], Batch Num: [381/600]
Discriminator Loss: 0.5490, Generator Loss: 1.4367
D(x): 0.8640, D(G(z)): 0.2971
Epoch: [2/20], Batch Num: [382/600]
Discriminator Loss: 0.4586, Generator Loss: 1.4474
D(x): 0.8909, D(G(z)): 0.2580
Epoch: [2/20], Batch Num: [383/600]
Discriminator Loss: 0.3824, Generator Loss: 1.6278
D(x): 0.9246, D(G(z)): 0.2452
Epoch: [2/20], Batch Num: [384/600]
Discriminator Loss: 0.4622, Generator Loss: 1.8171
D(x): 0.8632, D(G(z)): 0.2318
Epoch: [2/20], Batch Num: [385/600]
Discriminator Loss: 0.4306, Generator Loss: 1.8673
D(x): 0.8653, D(G(z)): 0.2060
Epoch: [2/20], Batch Num: [386/600]
Discriminator Loss: 0.3572, Generator Loss: 1.9743
D(x): 0.9100, D(G(z)): 0.1856
Epoch: [2/20], Batch Num: [387/600]
Discriminator Loss: 0.4001, Generator Loss: 1.9962
D(x): 0.8764, D(G(z)): 0.1846
Epoch: [2/20], Batch Num: [388/600]
Discriminator Loss: 0.3196, Generator Loss: 2.3083
D(x): 0.9191, D(G(z)): 0.1705
Epoch: [2/20], Batch Num: [389/600]
Discriminator Loss: 0.3370, Generator Loss: 2.2812
D(x): 0.9028, D(G(z)): 0.1718
Epoch: [2/20], Batch Num: [390/600]
Discriminator Loss: 0.3373, Generator Loss: 2.4044
D(x): 0.9012, D(G(z)): 0.1634
Epoch: [2/20], Batch Num: [391/600]
Discriminator Loss: 0.3272, Generator Loss: 2.4166
D(x): 0.8974, D(G(z)): 0.1556
Epoch: [2/20], Batch Num: [392/600]
Discriminator Loss: 0.2181, Generator Loss: 2.6426
D(x): 0.9557, D(G(z)): 0.1411
Epoch: [2/20], Batch Num: [393/600]
Discriminator Loss: 0.3140, Generator Loss: 2.8693
D(x): 0.8984, D(G(z)): 0.1491
Epoch: [2/20], Batch Num: [394/600]
Discriminator Loss: 0.2852, Generator Loss: 2.9645
D(x): 0.8946, D(G(z)): 0.1160
Epoch: [2/20], Batch Num: [395/600]
Discriminator Loss: 0.2875, Generator Loss: 2.7555
D(x): 0.9335, D(G(z)): 0.1660
Epoch: [2/20], Batch Num: [396/600]
Discriminator Loss: 0.2950, Generator Loss: 2.8652
D(x): 0.9125, D(G(z)): 0.1421
Epoch: [2/20], Batch Num: [397/600]
Discriminator Loss: 0.2988, Generator Loss: 2.9173
D(x): 0.8995, D(G(z)): 0.1202
Epoch: [2/20], Batch Num: [398/600]
Discriminator Loss: 0.2143, Generator Loss: 3.3528
D(x): 0.9378, D(G(z)): 0.1145
Epoch: [2/20], Batch Num: [399/600]
Discriminator Loss: 0.2044, Generator Loss: 3.2585
D(x): 0.9410, D(G(z)): 0.1053
Epoch: 2, Batch Num: [400/600]

Epoch: [2/20], Batch Num: [400/600]
Discriminator Loss: 0.2745, Generator Loss: 3.5276
D(x): 0.9114, D(G(z)): 0.1193
Epoch: [2/20], Batch Num: [401/600]
Discriminator Loss: 0.3204, Generator Loss: 3.3905
D(x): 0.8996, D(G(z)): 0.1278
Epoch: [2/20], Batch Num: [402/600]
Discriminator Loss: 0.5178, Generator Loss: 3.0527
D(x): 0.8512, D(G(z)): 0.1514
Epoch: [2/20], Batch Num: [403/600]
Discriminator Loss: 0.4249, Generator Loss: 2.3728
D(x): 0.9021, D(G(z)): 0.1925
Epoch: [2/20], Batch Num: [404/600]
Discriminator Loss: 0.4569, Generator Loss: 2.5446
D(x): 0.8873, D(G(z)): 0.1860
Epoch: [2/20], Batch Num: [405/600]
Discriminator Loss: 0.6384, Generator Loss: 2.4561
D(x): 0.8484, D(G(z)): 0.2373
Epoch: [2/20], Batch Num: [406/600]
Discriminator Loss: 0.6375, Generator Loss: 2.2214
D(x): 0.8245, D(G(z)): 0.2323
Epoch: [2/20], Batch Num: [407/600]
Discriminator Loss: 0.6409, Generator Loss: 2.0040
D(x): 0.8564, D(G(z)): 0.2882
Epoch: [2/20], Batch Num: [408/600]
Discriminator Loss: 0.6918, Generator Loss: 2.0669
D(x): 0.8337, D(G(z)): 0.2745
Epoch: [2/20], Batch Num: [409/600]
Discriminator Loss: 0.6648, Generator Loss: 2.3280
D(x): 0.8685, D(G(z)): 0.2900
Epoch: [2/20], Batch Num: [410/600]
Discriminator Loss: 0.7923, Generator Loss: 2.3476
D(x): 0.7935, D(G(z)): 0.2406
Epoch: [2/20], Batch Num: [411/600]
Discriminator Loss: 0.9216, Generator Loss: 2.0103
D(x): 0.7820, D(G(z)): 0.2554
Epoch: [2/20], Batch Num: [412/600]
Discriminator Loss: 0.8199, Generator Loss: 1.8127
D(x): 0.8170, D(G(z)): 0.2865
Epoch: [2/20], Batch Num: [413/600]
Discriminator Loss: 1.0407, Generator Loss: 1.7485
D(x): 0.7791, D(G(z)): 0.3252
Epoch: [2/20], Batch Num: [414/600]
Discriminator Loss: 0.9186, Generator Loss: 1.5330
D(x): 0.8246, D(G(z)): 0.3607
Epoch: [2/20], Batch Num: [415/600]
Discriminator Loss: 0.9723, Generator Loss: 1.6970
D(x): 0.7384, D(G(z)): 0.3086
Epoch: [2/20], Batch Num: [416/600]
Discriminator Loss: 0.7287, Generator Loss: 1.7410
D(x): 0.8131, D(G(z)): 0.2981
Epoch: [2/20], Batch Num: [417/600]
Discriminator Loss: 0.7887, Generator Loss: 1.7738
D(x): 0.8092, D(G(z)): 0.3046
Epoch: [2/20], Batch Num: [418/600]
Discriminator Loss: 0.8488, Generator Loss: 1.7216
D(x): 0.7780, D(G(z)): 0.2843
Epoch: [2/20], Batch Num: [419/600]
Discriminator Loss: 0.7017, Generator Loss: 1.7921
D(x): 0.8047, D(G(z)): 0.2528
Epoch: [2/20], Batch Num: [420/600]
Discriminator Loss: 0.5650, Generator Loss: 1.9751
D(x): 0.8710, D(G(z)): 0.2500
Epoch: [2/20], Batch Num: [421/600]
Discriminator Loss: 0.5945, Generator Loss: 2.0637
D(x): 0.8311, D(G(z)): 0.2327
Epoch: [2/20], Batch Num: [422/600]
Discriminator Loss: 0.4296, Generator Loss: 2.4088
D(x): 0.8818, D(G(z)): 0.1592
Epoch: [2/20], Batch Num: [423/600]
Discriminator Loss: 0.4433, Generator Loss: 2.7440
D(x): 0.8392, D(G(z)): 0.1475
Epoch: [2/20], Batch Num: [424/600]
Discriminator Loss: 0.4738, Generator Loss: 2.5962
D(x): 0.8459, D(G(z)): 0.1379
Epoch: [2/20], Batch Num: [425/600]
Discriminator Loss: 0.3484, Generator Loss: 2.7546
D(x): 0.8766, D(G(z)): 0.1364
Epoch: [2/20], Batch Num: [426/600]
Discriminator Loss: 0.2977, Generator Loss: 2.9396
D(x): 0.9047, D(G(z)): 0.1257
Epoch: [2/20], Batch Num: [427/600]
Discriminator Loss: 0.3023, Generator Loss: 3.0054
D(x): 0.9082, D(G(z)): 0.1496
Epoch: [2/20], Batch Num: [428/600]
Discriminator Loss: 0.2720, Generator Loss: 3.7799
D(x): 0.9286, D(G(z)): 0.1323
Epoch: [2/20], Batch Num: [429/600]
Discriminator Loss: 0.2703, Generator Loss: 3.6790
D(x): 0.9007, D(G(z)): 0.0817
Epoch: [2/20], Batch Num: [430/600]
Discriminator Loss: 0.2057, Generator Loss: 4.0350
D(x): 0.9219, D(G(z)): 0.0736
Epoch: [2/20], Batch Num: [431/600]
Discriminator Loss: 0.2822, Generator Loss: 4.1748
D(x): 0.8906, D(G(z)): 0.0686
Epoch: [2/20], Batch Num: [432/600]
Discriminator Loss: 0.2269, Generator Loss: 4.1825
D(x): 0.9138, D(G(z)): 0.0791
Epoch: [2/20], Batch Num: [433/600]
Discriminator Loss: 0.2580, Generator Loss: 4.1548
D(x): 0.9043, D(G(z)): 0.0723
Epoch: [2/20], Batch Num: [434/600]
Discriminator Loss: 0.3496, Generator Loss: 3.7479
D(x): 0.8884, D(G(z)): 0.1083
Epoch: [2/20], Batch Num: [435/600]
Discriminator Loss: 0.3469, Generator Loss: 3.8927
D(x): 0.9149, D(G(z)): 0.1508
Epoch: [2/20], Batch Num: [436/600]
Discriminator Loss: 0.4160, Generator Loss: 4.0126
D(x): 0.8647, D(G(z)): 0.1251
Epoch: [2/20], Batch Num: [437/600]
Discriminator Loss: 0.3025, Generator Loss: 3.6865
D(x): 0.8940, D(G(z)): 0.1053
Epoch: [2/20], Batch Num: [438/600]
Discriminator Loss: 0.3433, Generator Loss: 3.4583
D(x): 0.9144, D(G(z)): 0.1415
Epoch: [2/20], Batch Num: [439/600]
Discriminator Loss: 0.3446, Generator Loss: 3.4902
D(x): 0.8940, D(G(z)): 0.1186
Epoch: [2/20], Batch Num: [440/600]
Discriminator Loss: 0.3959, Generator Loss: 3.3043
D(x): 0.8891, D(G(z)): 0.1329
Epoch: [2/20], Batch Num: [441/600]
Discriminator Loss: 0.5427, Generator Loss: 2.6103
D(x): 0.8331, D(G(z)): 0.1192
Epoch: [2/20], Batch Num: [442/600]
Discriminator Loss: 0.5560, Generator Loss: 2.4245
D(x): 0.8886, D(G(z)): 0.2203
Epoch: [2/20], Batch Num: [443/600]
Discriminator Loss: 0.5837, Generator Loss: 2.5715
D(x): 0.8712, D(G(z)): 0.2275
Epoch: [2/20], Batch Num: [444/600]
Discriminator Loss: 0.5535, Generator Loss: 2.8628
D(x): 0.8755, D(G(z)): 0.2157
Epoch: [2/20], Batch Num: [445/600]
Discriminator Loss: 0.6432, Generator Loss: 3.0493
D(x): 0.8263, D(G(z)): 0.1648
Epoch: [2/20], Batch Num: [446/600]
Discriminator Loss: 0.6699, Generator Loss: 2.9913
D(x): 0.8142, D(G(z)): 0.1734
Epoch: [2/20], Batch Num: [447/600]
Discriminator Loss: 0.6816, Generator Loss: 2.5295
D(x): 0.8253, D(G(z)): 0.1597
Epoch: [2/20], Batch Num: [448/600]
Discriminator Loss: 0.7495, Generator Loss: 2.2059
D(x): 0.8122, D(G(z)): 0.2041
Epoch: [2/20], Batch Num: [449/600]
Discriminator Loss: 0.8510, Generator Loss: 2.0334
D(x): 0.8064, D(G(z)): 0.2469
Epoch: [2/20], Batch Num: [450/600]
Discriminator Loss: 0.7071, Generator Loss: 1.9393
D(x): 0.8582, D(G(z)): 0.2745
Epoch: [2/20], Batch Num: [451/600]
Discriminator Loss: 0.7735, Generator Loss: 1.9692
D(x): 0.8555, D(G(z)): 0.2940
Epoch: [2/20], Batch Num: [452/600]
Discriminator Loss: 0.6170, Generator Loss: 2.3472
D(x): 0.8742, D(G(z)): 0.2587
Epoch: [2/20], Batch Num: [453/600]
Discriminator Loss: 0.5012, Generator Loss: 2.6153
D(x): 0.8634, D(G(z)): 0.1874
Epoch: [2/20], Batch Num: [454/600]
Discriminator Loss: 0.5718, Generator Loss: 2.6426
D(x): 0.8519, D(G(z)): 0.1927
Epoch: [2/20], Batch Num: [455/600]
Discriminator Loss: 0.5511, Generator Loss: 2.6613
D(x): 0.8834, D(G(z)): 0.1863
Epoch: [2/20], Batch Num: [456/600]
Discriminator Loss: 0.5833, Generator Loss: 2.3886
D(x): 0.8274, D(G(z)): 0.1661
Epoch: [2/20], Batch Num: [457/600]
Discriminator Loss: 0.4469, Generator Loss: 2.4618
D(x): 0.8855, D(G(z)): 0.1778
Epoch: [2/20], Batch Num: [458/600]
Discriminator Loss: 0.5454, Generator Loss: 2.3137
D(x): 0.8564, D(G(z)): 0.2063
Epoch: [2/20], Batch Num: [459/600]
Discriminator Loss: 0.5122, Generator Loss: 2.2254
D(x): 0.8612, D(G(z)): 0.1866
Epoch: [2/20], Batch Num: [460/600]
Discriminator Loss: 0.4347, Generator Loss: 2.3141
D(x): 0.8822, D(G(z)): 0.1798
Epoch: [2/20], Batch Num: [461/600]
Discriminator Loss: 0.3830, Generator Loss: 2.5606
D(x): 0.9346, D(G(z)): 0.2049
Epoch: [2/20], Batch Num: [462/600]
Discriminator Loss: 0.4267, Generator Loss: 2.6615
D(x): 0.9052, D(G(z)): 0.1967
Epoch: [2/20], Batch Num: [463/600]
Discriminator Loss: 0.3264, Generator Loss: 2.8548
D(x): 0.9294, D(G(z)): 0.1532
Epoch: [2/20], Batch Num: [464/600]
Discriminator Loss: 0.2993, Generator Loss: 3.2597
D(x): 0.9203, D(G(z)): 0.1194
Epoch: [2/20], Batch Num: [465/600]
Discriminator Loss: 0.2845, Generator Loss: 3.4642
D(x): 0.9149, D(G(z)): 0.1258
Epoch: [2/20], Batch Num: [466/600]
Discriminator Loss: 0.2151, Generator Loss: 3.4062
D(x): 0.9168, D(G(z)): 0.0707
Epoch: [2/20], Batch Num: [467/600]
Discriminator Loss: 0.1855, Generator Loss: 3.3853
D(x): 0.9397, D(G(z)): 0.0831
Epoch: [2/20], Batch Num: [468/600]
Discriminator Loss: 0.2478, Generator Loss: 3.2761
D(x): 0.9154, D(G(z)): 0.0882
Epoch: [2/20], Batch Num: [469/600]
Discriminator Loss: 0.2758, Generator Loss: 3.0355
D(x): 0.9170, D(G(z)): 0.1078
Epoch: [2/20], Batch Num: [470/600]
Discriminator Loss: 0.2224, Generator Loss: 2.7507
D(x): 0.9489, D(G(z)): 0.1262
Epoch: [2/20], Batch Num: [471/600]
Discriminator Loss: 0.2787, Generator Loss: 3.2291
D(x): 0.9649, D(G(z)): 0.1545
Epoch: [2/20], Batch Num: [472/600]
Discriminator Loss: 0.1915, Generator Loss: 3.6381
D(x): 0.9532, D(G(z)): 0.0963
Epoch: [2/20], Batch Num: [473/600]
Discriminator Loss: 0.2094, Generator Loss: 3.9777
D(x): 0.9381, D(G(z)): 0.0972
Epoch: [2/20], Batch Num: [474/600]
Discriminator Loss: 0.3151, Generator Loss: 4.3085
D(x): 0.9149, D(G(z)): 0.0855
Epoch: [2/20], Batch Num: [475/600]
Discriminator Loss: 0.3432, Generator Loss: 3.8488
D(x): 0.8808, D(G(z)): 0.0834
Epoch: [2/20], Batch Num: [476/600]
Discriminator Loss: 0.2883, Generator Loss: 3.8112
D(x): 0.9173, D(G(z)): 0.0977
Epoch: [2/20], Batch Num: [477/600]
Discriminator Loss: 0.2179, Generator Loss: 3.8800
D(x): 0.9454, D(G(z)): 0.1125
Epoch: [2/20], Batch Num: [478/600]
Discriminator Loss: 0.2282, Generator Loss: 4.1743
D(x): 0.9507, D(G(z)): 0.1280
Epoch: [2/20], Batch Num: [479/600]
Discriminator Loss: 0.3396, Generator Loss: 4.0208
D(x): 0.9042, D(G(z)): 0.1220
Epoch: [2/20], Batch Num: [480/600]
Discriminator Loss: 0.3884, Generator Loss: 4.3220
D(x): 0.8866, D(G(z)): 0.1051
Epoch: [2/20], Batch Num: [481/600]
Discriminator Loss: 0.4102, Generator Loss: 4.0973
D(x): 0.8899, D(G(z)): 0.0890
Epoch: [2/20], Batch Num: [482/600]
Discriminator Loss: 0.3637, Generator Loss: 3.3373
D(x): 0.8786, D(G(z)): 0.0859
Epoch: [2/20], Batch Num: [483/600]
Discriminator Loss: 0.4131, Generator Loss: 3.4526
D(x): 0.9027, D(G(z)): 0.1551
Epoch: [2/20], Batch Num: [484/600]
Discriminator Loss: 0.4898, Generator Loss: 3.1357
D(x): 0.8961, D(G(z)): 0.1636
Epoch: [2/20], Batch Num: [485/600]
Discriminator Loss: 0.5895, Generator Loss: 3.5426
D(x): 0.8688, D(G(z)): 0.1843
Epoch: [2/20], Batch Num: [486/600]
Discriminator Loss: 0.4594, Generator Loss: 3.5920
D(x): 0.8806, D(G(z)): 0.1480
Epoch: [2/20], Batch Num: [487/600]
Discriminator Loss: 0.5292, Generator Loss: 3.5926
D(x): 0.8749, D(G(z)): 0.1484
Epoch: [2/20], Batch Num: [488/600]
Discriminator Loss: 0.6545, Generator Loss: 2.9984
D(x): 0.8305, D(G(z)): 0.1365
Epoch: [2/20], Batch Num: [489/600]
Discriminator Loss: 0.6481, Generator Loss: 2.7483
D(x): 0.8374, D(G(z)): 0.1723
Epoch: [2/20], Batch Num: [490/600]
Discriminator Loss: 0.6186, Generator Loss: 1.9750
D(x): 0.8096, D(G(z)): 0.1556
Epoch: [2/20], Batch Num: [491/600]
Discriminator Loss: 0.6628, Generator Loss: 2.0127
D(x): 0.8571, D(G(z)): 0.2682
Epoch: [2/20], Batch Num: [492/600]
Discriminator Loss: 0.5876, Generator Loss: 2.3191
D(x): 0.8893, D(G(z)): 0.2564
Epoch: [2/20], Batch Num: [493/600]
Discriminator Loss: 0.5343, Generator Loss: 2.9938
D(x): 0.8580, D(G(z)): 0.2106
Epoch: [2/20], Batch Num: [494/600]
Discriminator Loss: 0.7279, Generator Loss: 2.8302
D(x): 0.8069, D(G(z)): 0.1431
Epoch: [2/20], Batch Num: [495/600]
Discriminator Loss: 0.4339, Generator Loss: 3.0634
D(x): 0.8484, D(G(z)): 0.1268
Epoch: [2/20], Batch Num: [496/600]
Discriminator Loss: 0.5605, Generator Loss: 2.9121
D(x): 0.8513, D(G(z)): 0.1616
Epoch: [2/20], Batch Num: [497/600]
Discriminator Loss: 0.4371, Generator Loss: 2.5759
D(x): 0.8708, D(G(z)): 0.1573
Epoch: [2/20], Batch Num: [498/600]
Discriminator Loss: 0.3628, Generator Loss: 2.8961
D(x): 0.9287, D(G(z)): 0.1851
Epoch: [2/20], Batch Num: [499/600]
Discriminator Loss: 0.3382, Generator Loss: 3.3413
D(x): 0.9136, D(G(z)): 0.1471
Epoch: 2, Batch Num: [500/600]

Epoch: [2/20], Batch Num: [500/600]
Discriminator Loss: 0.3280, Generator Loss: 3.5138
D(x): 0.8833, D(G(z)): 0.1026
Epoch: [2/20], Batch Num: [501/600]
Discriminator Loss: 0.3592, Generator Loss: 3.5746
D(x): 0.8742, D(G(z)): 0.0982
Epoch: [2/20], Batch Num: [502/600]
Discriminator Loss: 0.2388, Generator Loss: 3.3838
D(x): 0.9162, D(G(z)): 0.0946
Epoch: [2/20], Batch Num: [503/600]
Discriminator Loss: 0.2142, Generator Loss: 3.8340
D(x): 0.9285, D(G(z)): 0.0847
Epoch: [2/20], Batch Num: [504/600]
Discriminator Loss: 0.5557, Generator Loss: 3.3326
D(x): 0.8176, D(G(z)): 0.0983
Epoch: [2/20], Batch Num: [505/600]
Discriminator Loss: 0.3239, Generator Loss: 2.9646
D(x): 0.9270, D(G(z)): 0.1338
Epoch: [2/20], Batch Num: [506/600]
Discriminator Loss: 0.4123, Generator Loss: 3.1733
D(x): 0.9252, D(G(z)): 0.1817
Epoch: [2/20], Batch Num: [507/600]
Discriminator Loss: 0.3160, Generator Loss: 3.6987
D(x): 0.9045, D(G(z)): 0.1223
Epoch: [2/20], Batch Num: [508/600]
Discriminator Loss: 0.3227, Generator Loss: 3.5277
D(x): 0.8840, D(G(z)): 0.1054
Epoch: [2/20], Batch Num: [509/600]
Discriminator Loss: 0.2917, Generator Loss: 3.5108
D(x): 0.8957, D(G(z)): 0.0996
Epoch: [2/20], Batch Num: [510/600]
Discriminator Loss: 0.4209, Generator Loss: 3.7104
D(x): 0.8800, D(G(z)): 0.0975
Epoch: [2/20], Batch Num: [511/600]
Discriminator Loss: 0.3071, Generator Loss: 3.4448
D(x): 0.8773, D(G(z)): 0.0921
Epoch: [2/20], Batch Num: [512/600]
Discriminator Loss: 0.3566, Generator Loss: 2.8003
D(x): 0.9086, D(G(z)): 0.1400
Epoch: [2/20], Batch Num: [513/600]
Discriminator Loss: 0.3298, Generator Loss: 3.3152
D(x): 0.9238, D(G(z)): 0.1657
Epoch: [2/20], Batch Num: [514/600]
Discriminator Loss: 0.3061, Generator Loss: 3.0098
D(x): 0.9119, D(G(z)): 0.1314
Epoch: [2/20], Batch Num: [515/600]
Discriminator Loss: 0.3857, Generator Loss: 3.4769
D(x): 0.8724, D(G(z)): 0.1262
Epoch: [2/20], Batch Num: [516/600]
Discriminator Loss: 0.3805, Generator Loss: 3.2151
D(x): 0.8817, D(G(z)): 0.1168
Epoch: [2/20], Batch Num: [517/600]
Discriminator Loss: 0.2494, Generator Loss: 3.2816
D(x): 0.9254, D(G(z)): 0.1093
Epoch: [2/20], Batch Num: [518/600]
Discriminator Loss: 0.3378, Generator Loss: 3.4366
D(x): 0.9104, D(G(z)): 0.1427
Epoch: [2/20], Batch Num: [519/600]
Discriminator Loss: 0.3430, Generator Loss: 3.2848
D(x): 0.8943, D(G(z)): 0.1266
Epoch: [2/20], Batch Num: [520/600]
Discriminator Loss: 0.3168, Generator Loss: 3.9282
D(x): 0.9189, D(G(z)): 0.1051
Epoch: [2/20], Batch Num: [521/600]
Discriminator Loss: 0.3326, Generator Loss: 4.0431
D(x): 0.9157, D(G(z)): 0.1112
Epoch: [2/20], Batch Num: [522/600]
Discriminator Loss: 0.5829, Generator Loss: 3.2619
D(x): 0.8144, D(G(z)): 0.0955
Epoch: [2/20], Batch Num: [523/600]
Discriminator Loss: 0.5408, Generator Loss: 3.2318
D(x): 0.8455, D(G(z)): 0.1323
Epoch: [2/20], Batch Num: [524/600]
Discriminator Loss: 0.4846, Generator Loss: 2.5967
D(x): 0.8480, D(G(z)): 0.1365
Epoch: [2/20], Batch Num: [525/600]
Discriminator Loss: 0.4630, Generator Loss: 2.7210
D(x): 0.9284, D(G(z)): 0.2446
Epoch: [2/20], Batch Num: [526/600]
Discriminator Loss: 0.4760, Generator Loss: 3.4729
D(x): 0.8852, D(G(z)): 0.1866
Epoch: [2/20], Batch Num: [527/600]
Discriminator Loss: 0.3595, Generator Loss: 3.7769
D(x): 0.8745, D(G(z)): 0.1102
Epoch: [2/20], Batch Num: [528/600]
Discriminator Loss: 0.6139, Generator Loss: 2.9864
D(x): 0.7943, D(G(z)): 0.1106
Epoch: [2/20], Batch Num: [529/600]
Discriminator Loss: 0.5276, Generator Loss: 2.6043
D(x): 0.8398, D(G(z)): 0.1444
Epoch: [2/20], Batch Num: [530/600]
Discriminator Loss: 0.4922, Generator Loss: 1.9068
D(x): 0.8877, D(G(z)): 0.2195
Epoch: [2/20], Batch Num: [531/600]
Discriminator Loss: 0.4768, Generator Loss: 2.1967
D(x): 0.9059, D(G(z)): 0.2310
Epoch: [2/20], Batch Num: [532/600]
Discriminator Loss: 0.6611, Generator Loss: 2.6549
D(x): 0.8219, D(G(z)): 0.2043
Epoch: [2/20], Batch Num: [533/600]
Discriminator Loss: 0.4753, Generator Loss: 2.7623
D(x): 0.8868, D(G(z)): 0.1713
Epoch: [2/20], Batch Num: [534/600]
Discriminator Loss: 0.6415, Generator Loss: 2.7720
D(x): 0.7914, D(G(z)): 0.1465
Epoch: [2/20], Batch Num: [535/600]
Discriminator Loss: 0.5787, Generator Loss: 2.1198
D(x): 0.8083, D(G(z)): 0.1394
Epoch: [2/20], Batch Num: [536/600]
Discriminator Loss: 0.6452, Generator Loss: 2.2294
D(x): 0.8307, D(G(z)): 0.1867
Epoch: [2/20], Batch Num: [537/600]
Discriminator Loss: 0.5680, Generator Loss: 2.5459
D(x): 0.8655, D(G(z)): 0.2117
Epoch: [2/20], Batch Num: [538/600]
Discriminator Loss: 0.5586, Generator Loss: 3.1105
D(x): 0.8503, D(G(z)): 0.1875
Epoch: [2/20], Batch Num: [539/600]
Discriminator Loss: 0.4299, Generator Loss: 3.0394
D(x): 0.8722, D(G(z)): 0.1293
Epoch: [2/20], Batch Num: [540/600]
Discriminator Loss: 0.7002, Generator Loss: 2.9521
D(x): 0.7989, D(G(z)): 0.1350
Epoch: [2/20], Batch Num: [541/600]
Discriminator Loss: 0.5738, Generator Loss: 2.5684
D(x): 0.8291, D(G(z)): 0.1331
Epoch: [2/20], Batch Num: [542/600]
Discriminator Loss: 0.5801, Generator Loss: 2.2791
D(x): 0.8404, D(G(z)): 0.1854
Epoch: [2/20], Batch Num: [543/600]
Discriminator Loss: 0.6087, Generator Loss: 2.5898
D(x): 0.8369, D(G(z)): 0.1873
Epoch: [2/20], Batch Num: [544/600]
Discriminator Loss: 0.6396, Generator Loss: 2.3771
D(x): 0.8585, D(G(z)): 0.2372
Epoch: [2/20], Batch Num: [545/600]
Discriminator Loss: 0.5555, Generator Loss: 2.7561
D(x): 0.8592, D(G(z)): 0.2024
Epoch: [2/20], Batch Num: [546/600]
Discriminator Loss: 0.5553, Generator Loss: 2.9965
D(x): 0.8929, D(G(z)): 0.2307
Epoch: [2/20], Batch Num: [547/600]
Discriminator Loss: 0.5368, Generator Loss: 3.3924
D(x): 0.8460, D(G(z)): 0.1711
Epoch: [2/20], Batch Num: [548/600]
Discriminator Loss: 0.6376, Generator Loss: 3.0162
D(x): 0.7824, D(G(z)): 0.1350
Epoch: [2/20], Batch Num: [549/600]
Discriminator Loss: 0.5440, Generator Loss: 3.0545
D(x): 0.8086, D(G(z)): 0.0958
Epoch: [2/20], Batch Num: [550/600]
Discriminator Loss: 0.4516, Generator Loss: 2.5883
D(x): 0.8426, D(G(z)): 0.1250
Epoch: [2/20], Batch Num: [551/600]
Discriminator Loss: 0.4362, Generator Loss: 2.2810
D(x): 0.8881, D(G(z)): 0.1830
Epoch: [2/20], Batch Num: [552/600]
Discriminator Loss: 0.4817, Generator Loss: 2.2490
D(x): 0.8478, D(G(z)): 0.1525
Epoch: [2/20], Batch Num: [553/600]
Discriminator Loss: 0.4413, Generator Loss: 2.2241
D(x): 0.9011, D(G(z)): 0.2069
Epoch: [2/20], Batch Num: [554/600]
Discriminator Loss: 0.5147, Generator Loss: 2.0441
D(x): 0.8487, D(G(z)): 0.1875
Epoch: [2/20], Batch Num: [555/600]
Discriminator Loss: 0.5012, Generator Loss: 1.9946
D(x): 0.8450, D(G(z)): 0.2105
Epoch: [2/20], Batch Num: [556/600]
Discriminator Loss: 0.5286, Generator Loss: 2.0413
D(x): 0.8613, D(G(z)): 0.2114
Epoch: [2/20], Batch Num: [557/600]
Discriminator Loss: 0.4146, Generator Loss: 2.5323
D(x): 0.8805, D(G(z)): 0.1837
Epoch: [2/20], Batch Num: [558/600]
Discriminator Loss: 0.5613, Generator Loss: 2.4303
D(x): 0.8454, D(G(z)): 0.2051
Epoch: [2/20], Batch Num: [559/600]
Discriminator Loss: 0.4537, Generator Loss: 2.4315
D(x): 0.8343, D(G(z)): 0.1549
Epoch: [2/20], Batch Num: [560/600]
Discriminator Loss: 0.5165, Generator Loss: 2.2935
D(x): 0.8397, D(G(z)): 0.1524
Epoch: [2/20], Batch Num: [561/600]
Discriminator Loss: 0.4419, Generator Loss: 2.3356
D(x): 0.8601, D(G(z)): 0.1703
Epoch: [2/20], Batch Num: [562/600]
Discriminator Loss: 0.4702, Generator Loss: 2.1895
D(x): 0.8549, D(G(z)): 0.1849
Epoch: [2/20], Batch Num: [563/600]
Discriminator Loss: 0.4353, Generator Loss: 2.3790
D(x): 0.8705, D(G(z)): 0.1904
Epoch: [2/20], Batch Num: [564/600]
Discriminator Loss: 0.6189, Generator Loss: 2.4683
D(x): 0.7985, D(G(z)): 0.1925
Epoch: [2/20], Batch Num: [565/600]
Discriminator Loss: 0.6424, Generator Loss: 2.2647
D(x): 0.8378, D(G(z)): 0.2264
Epoch: [2/20], Batch Num: [566/600]
Discriminator Loss: 0.5839, Generator Loss: 2.3010
D(x): 0.8535, D(G(z)): 0.2016
Epoch: [2/20], Batch Num: [567/600]
Discriminator Loss: 0.6366, Generator Loss: 2.2389
D(x): 0.7911, D(G(z)): 0.1904
Epoch: [2/20], Batch Num: [568/600]
Discriminator Loss: 0.5808, Generator Loss: 2.3189
D(x): 0.8298, D(G(z)): 0.2223
Epoch: [2/20], Batch Num: [569/600]
Discriminator Loss: 0.6957, Generator Loss: 2.4354
D(x): 0.7666, D(G(z)): 0.2139
Epoch: [2/20], Batch Num: [570/600]
Discriminator Loss: 0.5452, Generator Loss: 2.6839
D(x): 0.8782, D(G(z)): 0.2317
Epoch: [2/20], Batch Num: [571/600]
Discriminator Loss: 0.6377, Generator Loss: 2.9216
D(x): 0.8109, D(G(z)): 0.1684
Epoch: [2/20], Batch Num: [572/600]
Discriminator Loss: 0.5700, Generator Loss: 2.7795
D(x): 0.8136, D(G(z)): 0.1421
Epoch: [2/20], Batch Num: [573/600]
Discriminator Loss: 0.6299, Generator Loss: 2.5451
D(x): 0.7773, D(G(z)): 0.1606
Epoch: [2/20], Batch Num: [574/600]
Discriminator Loss: 0.7134, Generator Loss: 2.3985
D(x): 0.8287, D(G(z)): 0.1901
Epoch: [2/20], Batch Num: [575/600]
Discriminator Loss: 0.8514, Generator Loss: 2.3777
D(x): 0.8184, D(G(z)): 0.2653
Epoch: [2/20], Batch Num: [576/600]
Discriminator Loss: 0.7823, Generator Loss: 2.5128
D(x): 0.8205, D(G(z)): 0.2516
Epoch: [2/20], Batch Num: [577/600]
Discriminator Loss: 0.6397, Generator Loss: 3.0134
D(x): 0.8297, D(G(z)): 0.1860
Epoch: [2/20], Batch Num: [578/600]
Discriminator Loss: 0.5856, Generator Loss: 3.1050
D(x): 0.8485, D(G(z)): 0.1838
Epoch: [2/20], Batch Num: [579/600]
Discriminator Loss: 0.7295, Generator Loss: 3.1830
D(x): 0.7949, D(G(z)): 0.1851
Epoch: [2/20], Batch Num: [580/600]
Discriminator Loss: 0.5727, Generator Loss: 2.8754
D(x): 0.8319, D(G(z)): 0.1845
Epoch: [2/20], Batch Num: [581/600]
Discriminator Loss: 0.6010, Generator Loss: 2.9561
D(x): 0.8483, D(G(z)): 0.1687
Epoch: [2/20], Batch Num: [582/600]
Discriminator Loss: 0.6010, Generator Loss: 3.3821
D(x): 0.8452, D(G(z)): 0.1799
Epoch: [2/20], Batch Num: [583/600]
Discriminator Loss: 0.4814, Generator Loss: 3.8126
D(x): 0.8789, D(G(z)): 0.1532
Epoch: [2/20], Batch Num: [584/600]
Discriminator Loss: 0.4436, Generator Loss: 3.8514
D(x): 0.8659, D(G(z)): 0.1289
Epoch: [2/20], Batch Num: [585/600]
Discriminator Loss: 0.3764, Generator Loss: 4.1429
D(x): 0.8784, D(G(z)): 0.0898
Epoch: [2/20], Batch Num: [586/600]
Discriminator Loss: 0.3435, Generator Loss: 3.6664
D(x): 0.8634, D(G(z)): 0.0809
Epoch: [2/20], Batch Num: [587/600]
Discriminator Loss: 0.2946, Generator Loss: 3.3778
D(x): 0.9128, D(G(z)): 0.1116
Epoch: [2/20], Batch Num: [588/600]
Discriminator Loss: 0.3056, Generator Loss: 3.8511
D(x): 0.9287, D(G(z)): 0.1496
Epoch: [2/20], Batch Num: [589/600]
Discriminator Loss: 0.2916, Generator Loss: 4.5541
D(x): 0.9632, D(G(z)): 0.1417
Epoch: [2/20], Batch Num: [590/600]
Discriminator Loss: 0.2233, Generator Loss: 5.6558
D(x): 0.9111, D(G(z)): 0.0685
Epoch: [2/20], Batch Num: [591/600]
Discriminator Loss: 0.2316, Generator Loss: 6.0755
D(x): 0.9092, D(G(z)): 0.0528
Epoch: [2/20], Batch Num: [592/600]
Discriminator Loss: 0.3043, Generator Loss: 5.7643
D(x): 0.9000, D(G(z)): 0.0589
Epoch: [2/20], Batch Num: [593/600]
Discriminator Loss: 0.1839, Generator Loss: 5.5321
D(x): 0.9292, D(G(z)): 0.0517
Epoch: [2/20], Batch Num: [594/600]
Discriminator Loss: 0.3034, Generator Loss: 4.8261
D(x): 0.8907, D(G(z)): 0.0544
Epoch: [2/20], Batch Num: [595/600]
Discriminator Loss: 0.1963, Generator Loss: 4.3770
D(x): 0.9441, D(G(z)): 0.0900
Epoch: [2/20], Batch Num: [596/600]
Discriminator Loss: 0.3671, Generator Loss: 4.7107
D(x): 0.9218, D(G(z)): 0.1598
Epoch: [2/20], Batch Num: [597/600]
Discriminator Loss: 0.2457, Generator Loss: 5.1484
D(x): 0.9385, D(G(z)): 0.1212
Epoch: [2/20], Batch Num: [598/600]
Discriminator Loss: 0.3434, Generator Loss: 4.9325
D(x): 0.9160, D(G(z)): 0.1101
Epoch: [2/20], Batch Num: [599/600]
Discriminator Loss: 0.3542, Generator Loss: 5.1652
D(x): 0.8854, D(G(z)): 0.0799
Epoch: 3, Batch Num: [0/600]

Epoch: [3/20], Batch Num: [0/600]
Discriminator Loss: 0.2803, Generator Loss: 4.6414
D(x): 0.8805, D(G(z)): 0.0668
Epoch: [3/20], Batch Num: [1/600]
Discriminator Loss: 0.5252, Generator Loss: 3.4366
D(x): 0.8180, D(G(z)): 0.1060
Epoch: [3/20], Batch Num: [2/600]
Discriminator Loss: 0.4270, Generator Loss: 2.7340
D(x): 0.8839, D(G(z)): 0.1439
Epoch: [3/20], Batch Num: [3/600]
Discriminator Loss: 0.5769, Generator Loss: 2.5804
D(x): 0.8975, D(G(z)): 0.2647
Epoch: [3/20], Batch Num: [4/600]
Discriminator Loss: 0.7294, Generator Loss: 3.2428
D(x): 0.8473, D(G(z)): 0.2689
Epoch: [3/20], Batch Num: [5/600]
Discriminator Loss: 0.5935, Generator Loss: 3.3538
D(x): 0.8202, D(G(z)): 0.1132
Epoch: [3/20], Batch Num: [6/600]
Discriminator Loss: 0.6471, Generator Loss: 3.1355
D(x): 0.7632, D(G(z)): 0.1024
Epoch: [3/20], Batch Num: [7/600]
Discriminator Loss: 0.5833, Generator Loss: 2.5193
D(x): 0.8336, D(G(z)): 0.1623
Epoch: [3/20], Batch Num: [8/600]
Discriminator Loss: 0.7906, Generator Loss: 2.2499
D(x): 0.7884, D(G(z)): 0.1979
Epoch: [3/20], Batch Num: [9/600]
Discriminator Loss: 0.7086, Generator Loss: 2.1264
D(x): 0.8079, D(G(z)): 0.2020
Epoch: [3/20], Batch Num: [10/600]
Discriminator Loss: 0.6892, Generator Loss: 2.0445
D(x): 0.7967, D(G(z)): 0.2079
Epoch: [3/20], Batch Num: [11/600]
Discriminator Loss: 0.6036, Generator Loss: 1.9682
D(x): 0.8458, D(G(z)): 0.2311
Epoch: [3/20], Batch Num: [12/600]
Discriminator Loss: 0.5267, Generator Loss: 2.1940
D(x): 0.8711, D(G(z)): 0.2322
Epoch: [3/20], Batch Num: [13/600]
Discriminator Loss: 0.5221, Generator Loss: 2.3340
D(x): 0.8212, D(G(z)): 0.1687
Epoch: [3/20], Batch Num: [14/600]
Discriminator Loss: 0.6611, Generator Loss: 2.1094
D(x): 0.7706, D(G(z)): 0.1662
Epoch: [3/20], Batch Num: [15/600]
Discriminator Loss: 0.5351, Generator Loss: 2.3262
D(x): 0.8346, D(G(z)): 0.1940
Epoch: [3/20], Batch Num: [16/600]
Discriminator Loss: 0.4095, Generator Loss: 2.1235
D(x): 0.8817, D(G(z)): 0.1786
Epoch: [3/20], Batch Num: [17/600]
Discriminator Loss: 0.5104, Generator Loss: 2.0567
D(x): 0.8444, D(G(z)): 0.1936
Epoch: [3/20], Batch Num: [18/600]
Discriminator Loss: 0.5000, Generator Loss: 2.1121
D(x): 0.8345, D(G(z)): 0.1721
Epoch: [3/20], Batch Num: [19/600]
Discriminator Loss: 0.3622, Generator Loss: 2.4158
D(x): 0.9006, D(G(z)): 0.1783
Epoch: [3/20], Batch Num: [20/600]
Discriminator Loss: 0.4071, Generator Loss: 2.4992
D(x): 0.8647, D(G(z)): 0.1451
Epoch: [3/20], Batch Num: [21/600]
Discriminator Loss: 0.4324, Generator Loss: 2.6467
D(x): 0.8727, D(G(z)): 0.1808
Epoch: [3/20], Batch Num: [22/600]
Discriminator Loss: 0.3289, Generator Loss: 2.6265
D(x): 0.8949, D(G(z)): 0.1438
Epoch: [3/20], Batch Num: [23/600]
Discriminator Loss: 0.3931, Generator Loss: 2.8208
D(x): 0.8764, D(G(z)): 0.1473
Epoch: [3/20], Batch Num: [24/600]
Discriminator Loss: 0.3953, Generator Loss: 2.7778
D(x): 0.8661, D(G(z)): 0.1398
Epoch: [3/20], Batch Num: [25/600]
Discriminator Loss: 0.4617, Generator Loss: 2.7617
D(x): 0.8773, D(G(z)): 0.1833
Epoch: [3/20], Batch Num: [26/600]
Discriminator Loss: 0.3485, Generator Loss: 3.0695
D(x): 0.8841, D(G(z)): 0.1348
Epoch: [3/20], Batch Num: [27/600]
Discriminator Loss: 0.4292, Generator Loss: 2.4642
D(x): 0.8560, D(G(z)): 0.1404
Epoch: [3/20], Batch Num: [28/600]
Discriminator Loss: 0.3944, Generator Loss: 2.4170
D(x): 0.8791, D(G(z)): 0.1576
Epoch: [3/20], Batch Num: [29/600]
Discriminator Loss: 0.4000, Generator Loss: 2.5855
D(x): 0.8966, D(G(z)): 0.1709
Epoch: [3/20], Batch Num: [30/600]
Discriminator Loss: 0.4017, Generator Loss: 2.3666
D(x): 0.8876, D(G(z)): 0.1631
Epoch: [3/20], Batch Num: [31/600]
Discriminator Loss: 0.3921, Generator Loss: 2.8048
D(x): 0.8751, D(G(z)): 0.1447
Epoch: [3/20], Batch Num: [32/600]
Discriminator Loss: 0.3727, Generator Loss: 2.6628
D(x): 0.8917, D(G(z)): 0.1721
Epoch: [3/20], Batch Num: [33/600]
Discriminator Loss: 0.3934, Generator Loss: 2.4001
D(x): 0.8708, D(G(z)): 0.1221
Epoch: [3/20], Batch Num: [34/600]
Discriminator Loss: 0.3839, Generator Loss: 2.5294
D(x): 0.8921, D(G(z)): 0.1818
Epoch: [3/20], Batch Num: [35/600]
Discriminator Loss: 0.4118, Generator Loss: 2.3544
D(x): 0.8780, D(G(z)): 0.1725
Epoch: [3/20], Batch Num: [36/600]
Discriminator Loss: 0.5539, Generator Loss: 2.7144
D(x): 0.8452, D(G(z)): 0.1939
Epoch: [3/20], Batch Num: [37/600]
Discriminator Loss: 0.4037, Generator Loss: 2.6046
D(x): 0.8819, D(G(z)): 0.1620
Epoch: [3/20], Batch Num: [38/600]
Discriminator Loss: 0.4329, Generator Loss: 2.6971
D(x): 0.8806, D(G(z)): 0.1683
Epoch: [3/20], Batch Num: [39/600]
Discriminator Loss: 0.4165, Generator Loss: 2.6403
D(x): 0.8641, D(G(z)): 0.1504
Epoch: [3/20], Batch Num: [40/600]
Discriminator Loss: 0.4019, Generator Loss: 2.3765
D(x): 0.8845, D(G(z)): 0.1494
Epoch: [3/20], Batch Num: [41/600]
Discriminator Loss: 0.3654, Generator Loss: 2.3894
D(x): 0.9150, D(G(z)): 0.1674
Epoch: [3/20], Batch Num: [42/600]
Discriminator Loss: 0.2733, Generator Loss: 2.2136
D(x): 0.9281, D(G(z)): 0.1376
Epoch: [3/20], Batch Num: [43/600]
Discriminator Loss: 0.3727, Generator Loss: 2.6807
D(x): 0.8942, D(G(z)): 0.1473
Epoch: [3/20], Batch Num: [44/600]
Discriminator Loss: 0.3038, Generator Loss: 2.9641
D(x): 0.9176, D(G(z)): 0.1407
Epoch: [3/20], Batch Num: [45/600]
Discriminator Loss: 0.3362, Generator Loss: 2.7564
D(x): 0.8751, D(G(z)): 0.1133
Epoch: [3/20], Batch Num: [46/600]
Discriminator Loss: 0.3941, Generator Loss: 2.4695
D(x): 0.9085, D(G(z)): 0.1681
Epoch: [3/20], Batch Num: [47/600]
Discriminator Loss: 0.3034, Generator Loss: 2.7549
D(x): 0.9308, D(G(z)): 0.1421
Epoch: [3/20], Batch Num: [48/600]
Discriminator Loss: 0.2814, Generator Loss: 3.2147
D(x): 0.9405, D(G(z)): 0.1379
Epoch: [3/20], Batch Num: [49/600]
Discriminator Loss: 0.3089, Generator Loss: 3.5201
D(x): 0.8737, D(G(z)): 0.0900
Epoch: [3/20], Batch Num: [50/600]
Discriminator Loss: 0.2846, Generator Loss: 3.1533
D(x): 0.8900, D(G(z)): 0.0838
Epoch: [3/20], Batch Num: [51/600]
Discriminator Loss: 0.3889, Generator Loss: 3.0328
D(x): 0.8697, D(G(z)): 0.1133
Epoch: [3/20], Batch Num: [52/600]
Discriminator Loss: 0.3940, Generator Loss: 2.8693
D(x): 0.9481, D(G(z)): 0.1933
Epoch: [3/20], Batch Num: [53/600]
Discriminator Loss: 0.3384, Generator Loss: 3.8388
D(x): 0.9549, D(G(z)): 0.1683
Epoch: [3/20], Batch Num: [54/600]
Discriminator Loss: 0.2292, Generator Loss: 4.4998
D(x): 0.9481, D(G(z)): 0.1091
Epoch: [3/20], Batch Num: [55/600]
Discriminator Loss: 0.2422, Generator Loss: 4.5663
D(x): 0.9304, D(G(z)): 0.0957
Epoch: [3/20], Batch Num: [56/600]
Discriminator Loss: 0.1823, Generator Loss: 4.5039
D(x): 0.9295, D(G(z)): 0.0559
Epoch: [3/20], Batch Num: [57/600]
Discriminator Loss: 0.4241, Generator Loss: 4.0898
D(x): 0.8496, D(G(z)): 0.0591
Epoch: [3/20], Batch Num: [58/600]
Discriminator Loss: 0.1871, Generator Loss: 3.4584
D(x): 0.9390, D(G(z)): 0.0713
Epoch: [3/20], Batch Num: [59/600]
Discriminator Loss: 0.4928, Generator Loss: 2.8912
D(x): 0.9260, D(G(z)): 0.1713
Epoch: [3/20], Batch Num: [60/600]
Discriminator Loss: 0.3439, Generator Loss: 3.1216
D(x): 0.9721, D(G(z)): 0.1683
Epoch: [3/20], Batch Num: [61/600]
Discriminator Loss: 0.4135, Generator Loss: 3.8479
D(x): 0.8973, D(G(z)): 0.1350
Epoch: [3/20], Batch Num: [62/600]
Discriminator Loss: 0.4387, Generator Loss: 3.8701
D(x): 0.8818, D(G(z)): 0.1277
Epoch: [3/20], Batch Num: [63/600]
Discriminator Loss: 0.4445, Generator Loss: 3.2708
D(x): 0.8562, D(G(z)): 0.1207
Epoch: [3/20], Batch Num: [64/600]
Discriminator Loss: 0.4182, Generator Loss: 3.1337
D(x): 0.9082, D(G(z)): 0.1624
Epoch: [3/20], Batch Num: [65/600]
Discriminator Loss: 0.3115, Generator Loss: 3.0938
D(x): 0.9496, D(G(z)): 0.1714
Epoch: [3/20], Batch Num: [66/600]
Discriminator Loss: 0.3526, Generator Loss: 3.3179
D(x): 0.9106, D(G(z)): 0.1533
Epoch: [3/20], Batch Num: [67/600]
Discriminator Loss: 0.3377, Generator Loss: 3.6486
D(x): 0.9228, D(G(z)): 0.1474
Epoch: [3/20], Batch Num: [68/600]
Discriminator Loss: 0.4394, Generator Loss: 3.5376
D(x): 0.9028, D(G(z)): 0.1394
Epoch: [3/20], Batch Num: [69/600]
Discriminator Loss: 0.3886, Generator Loss: 3.0050
D(x): 0.8993, D(G(z)): 0.1294
Epoch: [3/20], Batch Num: [70/600]
Discriminator Loss: 0.5216, Generator Loss: 3.1296
D(x): 0.8907, D(G(z)): 0.1889
Epoch: [3/20], Batch Num: [71/600]
Discriminator Loss: 0.4368, Generator Loss: 3.5754
D(x): 0.9191, D(G(z)): 0.1881
Epoch: [3/20], Batch Num: [72/600]
Discriminator Loss: 0.5471, Generator Loss: 3.6357
D(x): 0.8844, D(G(z)): 0.1788
Epoch: [3/20], Batch Num: [73/600]
Discriminator Loss: 0.4143, Generator Loss: 3.4073
D(x): 0.8806, D(G(z)): 0.1271
Epoch: [3/20], Batch Num: [74/600]
Discriminator Loss: 0.4043, Generator Loss: 2.9204
D(x): 0.9292, D(G(z)): 0.1486
Epoch: [3/20], Batch Num: [75/600]
Discriminator Loss: 0.3772, Generator Loss: 3.4457
D(x): 0.9166, D(G(z)): 0.1734
Epoch: [3/20], Batch Num: [76/600]
Discriminator Loss: 0.7441, Generator Loss: 3.8181
D(x): 0.8819, D(G(z)): 0.2531
Epoch: [3/20], Batch Num: [77/600]
Discriminator Loss: 0.5182, Generator Loss: 3.4760
D(x): 0.8666, D(G(z)): 0.1375
Epoch: [3/20], Batch Num: [78/600]
Discriminator Loss: 0.3911, Generator Loss: 3.7167
D(x): 0.8821, D(G(z)): 0.1438
Epoch: [3/20], Batch Num: [79/600]
Discriminator Loss: 0.6571, Generator Loss: 3.4813
D(x): 0.8004, D(G(z)): 0.1424
Epoch: [3/20], Batch Num: [80/600]
Discriminator Loss: 0.5561, Generator Loss: 2.9209
D(x): 0.8411, D(G(z)): 0.1456
Epoch: [3/20], Batch Num: [81/600]
Discriminator Loss: 0.6611, Generator Loss: 2.4294
D(x): 0.8710, D(G(z)): 0.2487
Epoch: [3/20], Batch Num: [82/600]
Discriminator Loss: 0.8119, Generator Loss: 3.2956
D(x): 0.8909, D(G(z)): 0.3511
Epoch: [3/20], Batch Num: [83/600]
Discriminator Loss: 0.6165, Generator Loss: 3.4864
D(x): 0.8185, D(G(z)): 0.1656
Epoch: [3/20], Batch Num: [84/600]
Discriminator Loss: 0.6442, Generator Loss: 3.8543
D(x): 0.8097, D(G(z)): 0.1531
Epoch: [3/20], Batch Num: [85/600]
Discriminator Loss: 0.8026, Generator Loss: 3.7600
D(x): 0.7806, D(G(z)): 0.1783
Epoch: [3/20], Batch Num: [86/600]
Discriminator Loss: 0.7045, Generator Loss: 3.5956
D(x): 0.8259, D(G(z)): 0.1713
Epoch: [3/20], Batch Num: [87/600]
Discriminator Loss: 0.8003, Generator Loss: 3.2359
D(x): 0.8252, D(G(z)): 0.2092
Epoch: [3/20], Batch Num: [88/600]
Discriminator Loss: 0.5251, Generator Loss: 3.3844
D(x): 0.8896, D(G(z)): 0.2391
Epoch: [3/20], Batch Num: [89/600]
Discriminator Loss: 0.7595, Generator Loss: 4.1413
D(x): 0.8268, D(G(z)): 0.2401
Epoch: [3/20], Batch Num: [90/600]
Discriminator Loss: 0.5190, Generator Loss: 5.1972
D(x): 0.8390, D(G(z)): 0.1702
Epoch: [3/20], Batch Num: [91/600]
Discriminator Loss: 0.3513, Generator Loss: 4.9275
D(x): 0.8672, D(G(z)): 0.1095
Epoch: [3/20], Batch Num: [92/600]
Discriminator Loss: 0.8190, Generator Loss: 3.9976
D(x): 0.7384, D(G(z)): 0.0978
Epoch: [3/20], Batch Num: [93/600]
Discriminator Loss: 0.5710, Generator Loss: 4.0246
D(x): 0.8503, D(G(z)): 0.1810
Epoch: [3/20], Batch Num: [94/600]
Discriminator Loss: 0.4590, Generator Loss: 4.6406
D(x): 0.9437, D(G(z)): 0.2552
Epoch: [3/20], Batch Num: [95/600]
Discriminator Loss: 0.7565, Generator Loss: 4.8868
D(x): 0.8351, D(G(z)): 0.2109
Epoch: [3/20], Batch Num: [96/600]
Discriminator Loss: 0.5329, Generator Loss: 5.9336
D(x): 0.8568, D(G(z)): 0.1723
Epoch: [3/20], Batch Num: [97/600]
Discriminator Loss: 0.4561, Generator Loss: 5.6637
D(x): 0.8258, D(G(z)): 0.0913
Epoch: [3/20], Batch Num: [98/600]
Discriminator Loss: 0.4467, Generator Loss: 5.0490
D(x): 0.8087, D(G(z)): 0.0822
Epoch: [3/20], Batch Num: [99/600]
Discriminator Loss: 0.4941, Generator Loss: 4.1341
D(x): 0.8442, D(G(z)): 0.1427
Epoch: 3, Batch Num: [100/600]

Epoch: [3/20], Batch Num: [100/600]
Discriminator Loss: 0.6674, Generator Loss: 4.4320
D(x): 0.8805, D(G(z)): 0.2533
Epoch: [3/20], Batch Num: [101/600]
Discriminator Loss: 0.5297, Generator Loss: 4.9036
D(x): 0.8862, D(G(z)): 0.2220
Epoch: [3/20], Batch Num: [102/600]
Discriminator Loss: 0.4371, Generator Loss: 5.8543
D(x): 0.8681, D(G(z)): 0.1415
Epoch: [3/20], Batch Num: [103/600]
Discriminator Loss: 0.4149, Generator Loss: 5.7760
D(x): 0.8237, D(G(z)): 0.0932
Epoch: [3/20], Batch Num: [104/600]
Discriminator Loss: 0.5407, Generator Loss: 5.1412
D(x): 0.7944, D(G(z)): 0.1151
Epoch: [3/20], Batch Num: [105/600]
Discriminator Loss: 0.6781, Generator Loss: 4.2498
D(x): 0.8103, D(G(z)): 0.1850
Epoch: [3/20], Batch Num: [106/600]
Discriminator Loss: 0.6758, Generator Loss: 4.3425
D(x): 0.8725, D(G(z)): 0.2503
Epoch: [3/20], Batch Num: [107/600]
Discriminator Loss: 0.7079, Generator Loss: 4.5658
D(x): 0.7682, D(G(z)): 0.1623
Epoch: [3/20], Batch Num: [108/600]
Discriminator Loss: 1.1526, Generator Loss: 4.4287
D(x): 0.7017, D(G(z)): 0.2734
Epoch: [3/20], Batch Num: [109/600]
Discriminator Loss: 0.6600, Generator Loss: 3.4691
D(x): 0.7653, D(G(z)): 0.1331
Epoch: [3/20], Batch Num: [110/600]
Discriminator Loss: 0.9111, Generator Loss: 2.6549
D(x): 0.7399, D(G(z)): 0.2423
Epoch: [3/20], Batch Num: [111/600]
Discriminator Loss: 0.9113, Generator Loss: 2.7409
D(x): 0.7970, D(G(z)): 0.3192
Epoch: [3/20], Batch Num: [112/600]
Discriminator Loss: 0.7449, Generator Loss: 3.7599
D(x): 0.8067, D(G(z)): 0.2427
Epoch: [3/20], Batch Num: [113/600]
Discriminator Loss: 0.7515, Generator Loss: 3.5121
D(x): 0.7247, D(G(z)): 0.1397
Epoch: [3/20], Batch Num: [114/600]
Discriminator Loss: 0.8410, Generator Loss: 3.4111
D(x): 0.7310, D(G(z)): 0.2317
Epoch: [3/20], Batch Num: [115/600]
Discriminator Loss: 0.5877, Generator Loss: 3.2018
D(x): 0.7874, D(G(z)): 0.1828
Epoch: [3/20], Batch Num: [116/600]
Discriminator Loss: 0.5716, Generator Loss: 3.4074
D(x): 0.8138, D(G(z)): 0.1882
Epoch: [3/20], Batch Num: [117/600]
Discriminator Loss: 0.4919, Generator Loss: 3.3198
D(x): 0.8370, D(G(z)): 0.1943
Epoch: [3/20], Batch Num: [118/600]
Discriminator Loss: 0.4838, Generator Loss: 3.2900
D(x): 0.8413, D(G(z)): 0.1861
Epoch: [3/20], Batch Num: [119/600]
Discriminator Loss: 0.5038, Generator Loss: 4.0856
D(x): 0.8341, D(G(z)): 0.1891
Epoch: [3/20], Batch Num: [120/600]
Discriminator Loss: 0.4993, Generator Loss: 4.0682
D(x): 0.7912, D(G(z)): 0.1355
Epoch: [3/20], Batch Num: [121/600]
Discriminator Loss: 0.5505, Generator Loss: 3.3495
D(x): 0.7644, D(G(z)): 0.1294
Epoch: [3/20], Batch Num: [122/600]
Discriminator Loss: 0.5929, Generator Loss: 3.1933
D(x): 0.8253, D(G(z)): 0.2019
Epoch: [3/20], Batch Num: [123/600]
Discriminator Loss: 0.5031, Generator Loss: 3.6100
D(x): 0.8553, D(G(z)): 0.1934
Epoch: [3/20], Batch Num: [124/600]
Discriminator Loss: 0.5466, Generator Loss: 4.0162
D(x): 0.8423, D(G(z)): 0.1985
Epoch: [3/20], Batch Num: [125/600]
Discriminator Loss: 0.5621, Generator Loss: 4.7576
D(x): 0.8526, D(G(z)): 0.2052
Epoch: [3/20], Batch Num: [126/600]
Discriminator Loss: 0.6189, Generator Loss: 3.9730
D(x): 0.7423, D(G(z)): 0.0961
Epoch: [3/20], Batch Num: [127/600]
Discriminator Loss: 0.6416, Generator Loss: 3.8987
D(x): 0.7698, D(G(z)): 0.1378
Epoch: [3/20], Batch Num: [128/600]
Discriminator Loss: 0.6512, Generator Loss: 2.8783
D(x): 0.8310, D(G(z)): 0.2297
Epoch: [3/20], Batch Num: [129/600]
Discriminator Loss: 0.6681, Generator Loss: 2.7682
D(x): 0.8468, D(G(z)): 0.2443
Epoch: [3/20], Batch Num: [130/600]
Discriminator Loss: 0.8001, Generator Loss: 4.0513
D(x): 0.8020, D(G(z)): 0.2689
Epoch: [3/20], Batch Num: [131/600]
Discriminator Loss: 0.6123, Generator Loss: 4.1237
D(x): 0.7802, D(G(z)): 0.1466
Epoch: [3/20], Batch Num: [132/600]
Discriminator Loss: 0.6949, Generator Loss: 3.2453
D(x): 0.7543, D(G(z)): 0.1088
Epoch: [3/20], Batch Num: [133/600]
Discriminator Loss: 0.6335, Generator Loss: 3.0134
D(x): 0.8314, D(G(z)): 0.2143
Epoch: [3/20], Batch Num: [134/600]
Discriminator Loss: 0.5985, Generator Loss: 3.2447
D(x): 0.8828, D(G(z)): 0.2552
Epoch: [3/20], Batch Num: [135/600]
Discriminator Loss: 0.6188, Generator Loss: 3.4409
D(x): 0.8360, D(G(z)): 0.1973
Epoch: [3/20], Batch Num: [136/600]
Discriminator Loss: 0.5079, Generator Loss: 3.5692
D(x): 0.8478, D(G(z)): 0.1547
Epoch: [3/20], Batch Num: [137/600]
Discriminator Loss: 0.6636, Generator Loss: 3.4818
D(x): 0.7702, D(G(z)): 0.1060
Epoch: [3/20], Batch Num: [138/600]
Discriminator Loss: 0.6928, Generator Loss: 2.8284
D(x): 0.7595, D(G(z)): 0.1395
Epoch: [3/20], Batch Num: [139/600]
Discriminator Loss: 0.6536, Generator Loss: 2.3380
D(x): 0.8039, D(G(z)): 0.1841
Epoch: [3/20], Batch Num: [140/600]
Discriminator Loss: 0.6263, Generator Loss: 2.4725
D(x): 0.8788, D(G(z)): 0.2782
Epoch: [3/20], Batch Num: [141/600]
Discriminator Loss: 0.4969, Generator Loss: 2.5413
D(x): 0.8755, D(G(z)): 0.2214
Epoch: [3/20], Batch Num: [142/600]
Discriminator Loss: 0.4076, Generator Loss: 3.5683
D(x): 0.9012, D(G(z)): 0.1892
Epoch: [3/20], Batch Num: [143/600]
Discriminator Loss: 0.3525, Generator Loss: 4.4821
D(x): 0.9007, D(G(z)): 0.1506
Epoch: [3/20], Batch Num: [144/600]
Discriminator Loss: 0.3438, Generator Loss: 4.4414
D(x): 0.8800, D(G(z)): 0.1016
Epoch: [3/20], Batch Num: [145/600]
Discriminator Loss: 0.4972, Generator Loss: 4.3704
D(x): 0.8090, D(G(z)): 0.0817
Epoch: [3/20], Batch Num: [146/600]
Discriminator Loss: 0.5154, Generator Loss: 3.8427
D(x): 0.8126, D(G(z)): 0.0734
Epoch: [3/20], Batch Num: [147/600]
Discriminator Loss: 0.5522, Generator Loss: 2.7418
D(x): 0.8126, D(G(z)): 0.1184
Epoch: [3/20], Batch Num: [148/600]
Discriminator Loss: 0.4377, Generator Loss: 2.1112
D(x): 0.9215, D(G(z)): 0.2286
Epoch: [3/20], Batch Num: [149/600]
Discriminator Loss: 0.5200, Generator Loss: 2.0913
D(x): 0.9196, D(G(z)): 0.2770
Epoch: [3/20], Batch Num: [150/600]
Discriminator Loss: 0.4515, Generator Loss: 2.6459
D(x): 0.9335, D(G(z)): 0.2522
Epoch: [3/20], Batch Num: [151/600]
Discriminator Loss: 0.3479, Generator Loss: 3.1265
D(x): 0.9210, D(G(z)): 0.1858
Epoch: [3/20], Batch Num: [152/600]
Discriminator Loss: 0.4069, Generator Loss: 2.8907
D(x): 0.8894, D(G(z)): 0.1696
Epoch: [3/20], Batch Num: [153/600]
Discriminator Loss: 0.2421, Generator Loss: 3.2735
D(x): 0.9378, D(G(z)): 0.1355
Epoch: [3/20], Batch Num: [154/600]
Discriminator Loss: 0.4977, Generator Loss: 3.4185
D(x): 0.8161, D(G(z)): 0.1078
Epoch: [3/20], Batch Num: [155/600]
Discriminator Loss: 0.4338, Generator Loss: 2.8930
D(x): 0.8374, D(G(z)): 0.1024
Epoch: [3/20], Batch Num: [156/600]
Discriminator Loss: 0.3173, Generator Loss: 2.4036
D(x): 0.8994, D(G(z)): 0.1269
Epoch: [3/20], Batch Num: [157/600]
Discriminator Loss: 0.3522, Generator Loss: 2.3934
D(x): 0.9177, D(G(z)): 0.1607
Epoch: [3/20], Batch Num: [158/600]
Discriminator Loss: 0.3169, Generator Loss: 2.1136
D(x): 0.9347, D(G(z)): 0.1767
Epoch: [3/20], Batch Num: [159/600]
Discriminator Loss: 0.3711, Generator Loss: 2.0274
D(x): 0.9411, D(G(z)): 0.2120
Epoch: [3/20], Batch Num: [160/600]
Discriminator Loss: 0.3992, Generator Loss: 2.2576
D(x): 0.9319, D(G(z)): 0.2314
Epoch: [3/20], Batch Num: [161/600]
Discriminator Loss: 0.3221, Generator Loss: 2.1921
D(x): 0.9319, D(G(z)): 0.1667
Epoch: [3/20], Batch Num: [162/600]
Discriminator Loss: 0.3348, Generator Loss: 2.5532
D(x): 0.9271, D(G(z)): 0.1529
Epoch: [3/20], Batch Num: [163/600]
Discriminator Loss: 0.3316, Generator Loss: 2.6082
D(x): 0.9055, D(G(z)): 0.1496
Epoch: [3/20], Batch Num: [164/600]
Discriminator Loss: 0.3558, Generator Loss: 2.5630
D(x): 0.8824, D(G(z)): 0.1368
Epoch: [3/20], Batch Num: [165/600]
Discriminator Loss: 0.3772, Generator Loss: 2.2649
D(x): 0.8783, D(G(z)): 0.1409
Epoch: [3/20], Batch Num: [166/600]
Discriminator Loss: 0.3189, Generator Loss: 2.1891
D(x): 0.9271, D(G(z)): 0.1763
Epoch: [3/20], Batch Num: [167/600]
Discriminator Loss: 0.2982, Generator Loss: 1.9847
D(x): 0.9288, D(G(z)): 0.1539
Epoch: [3/20], Batch Num: [168/600]
Discriminator Loss: 0.3022, Generator Loss: 2.0354
D(x): 0.9517, D(G(z)): 0.1895
Epoch: [3/20], Batch Num: [169/600]
Discriminator Loss: 0.3094, Generator Loss: 1.9416
D(x): 0.9565, D(G(z)): 0.2017
Epoch: [3/20], Batch Num: [170/600]
Discriminator Loss: 0.4033, Generator Loss: 2.2200
D(x): 0.9294, D(G(z)): 0.1836
Epoch: [3/20], Batch Num: [171/600]
Discriminator Loss: 0.3608, Generator Loss: 2.3482
D(x): 0.9251, D(G(z)): 0.1770
Epoch: [3/20], Batch Num: [172/600]
Discriminator Loss: 0.4392, Generator Loss: 2.3070
D(x): 0.8819, D(G(z)): 0.1710
Epoch: [3/20], Batch Num: [173/600]
Discriminator Loss: 0.3923, Generator Loss: 2.2878
D(x): 0.8995, D(G(z)): 0.1547
Epoch: [3/20], Batch Num: [174/600]
Discriminator Loss: 0.2603, Generator Loss: 2.2627
D(x): 0.9524, D(G(z)): 0.1582
Epoch: [3/20], Batch Num: [175/600]
Discriminator Loss: 0.4151, Generator Loss: 2.0882
D(x): 0.9049, D(G(z)): 0.1918
Epoch: [3/20], Batch Num: [176/600]
Discriminator Loss: 0.4639, Generator Loss: 2.0359
D(x): 0.8942, D(G(z)): 0.1762
Epoch: [3/20], Batch Num: [177/600]
Discriminator Loss: 0.3791, Generator Loss: 2.0015
D(x): 0.9435, D(G(z)): 0.2020
Epoch: [3/20], Batch Num: [178/600]
Discriminator Loss: 0.3972, Generator Loss: 1.9219
D(x): 0.9325, D(G(z)): 0.2233
Epoch: [3/20], Batch Num: [179/600]
Discriminator Loss: 0.6440, Generator Loss: 2.0223
D(x): 0.8775, D(G(z)): 0.2214
Epoch: [3/20], Batch Num: [180/600]
Discriminator Loss: 0.4678, Generator Loss: 2.2763
D(x): 0.9212, D(G(z)): 0.2162
Epoch: [3/20], Batch Num: [181/600]
Discriminator Loss: 0.4992, Generator Loss: 2.2462
D(x): 0.8896, D(G(z)): 0.1516
Epoch: [3/20], Batch Num: [182/600]
Discriminator Loss: 0.6291, Generator Loss: 2.2669
D(x): 0.8429, D(G(z)): 0.1539
Epoch: [3/20], Batch Num: [183/600]
Discriminator Loss: 0.4135, Generator Loss: 2.0453
D(x): 0.8943, D(G(z)): 0.1593
Epoch: [3/20], Batch Num: [184/600]
Discriminator Loss: 0.4195, Generator Loss: 2.0278
D(x): 0.9168, D(G(z)): 0.1901
Epoch: [3/20], Batch Num: [185/600]
Discriminator Loss: 0.3995, Generator Loss: 2.0500
D(x): 0.9252, D(G(z)): 0.1923
Epoch: [3/20], Batch Num: [186/600]
Discriminator Loss: 0.5066, Generator Loss: 2.1401
D(x): 0.9206, D(G(z)): 0.2061
Epoch: [3/20], Batch Num: [187/600]
Discriminator Loss: 0.5017, Generator Loss: 2.3928
D(x): 0.8982, D(G(z)): 0.1768
Epoch: [3/20], Batch Num: [188/600]
Discriminator Loss: 0.3446, Generator Loss: 2.3754
D(x): 0.9336, D(G(z)): 0.1447
Epoch: [3/20], Batch Num: [189/600]
Discriminator Loss: 0.4219, Generator Loss: 2.7620
D(x): 0.9152, D(G(z)): 0.1314
Epoch: [3/20], Batch Num: [190/600]
Discriminator Loss: 0.3107, Generator Loss: 2.9532
D(x): 0.9399, D(G(z)): 0.1366
Epoch: [3/20], Batch Num: [191/600]
Discriminator Loss: 0.1951, Generator Loss: 3.1432
D(x): 0.9644, D(G(z)): 0.0835
Epoch: [3/20], Batch Num: [192/600]
Discriminator Loss: 0.1682, Generator Loss: 3.4565
D(x): 0.9659, D(G(z)): 0.0595
Epoch: [3/20], Batch Num: [193/600]
Discriminator Loss: 0.3232, Generator Loss: 3.5414
D(x): 0.9131, D(G(z)): 0.0590
Epoch: [3/20], Batch Num: [194/600]
Discriminator Loss: 0.2394, Generator Loss: 3.5277
D(x): 0.9329, D(G(z)): 0.0644
Epoch: [3/20], Batch Num: [195/600]
Discriminator Loss: 0.2493, Generator Loss: 3.5817
D(x): 0.9328, D(G(z)): 0.0589
Epoch: [3/20], Batch Num: [196/600]
Discriminator Loss: 0.3338, Generator Loss: 3.6358
D(x): 0.9354, D(G(z)): 0.0705
Epoch: [3/20], Batch Num: [197/600]
Discriminator Loss: 0.2047, Generator Loss: 3.6545
D(x): 0.9564, D(G(z)): 0.0979
Epoch: [3/20], Batch Num: [198/600]
Discriminator Loss: 0.1988, Generator Loss: 3.4510
D(x): 0.9570, D(G(z)): 0.0919
Epoch: [3/20], Batch Num: [199/600]
Discriminator Loss: 0.2534, Generator Loss: 4.1448
D(x): 0.9699, D(G(z)): 0.1030
Epoch: 3, Batch Num: [200/600]

Epoch: [3/20], Batch Num: [200/600]
Discriminator Loss: 0.3682, Generator Loss: 4.1992
D(x): 0.9406, D(G(z)): 0.0870
Epoch: [3/20], Batch Num: [201/600]
Discriminator Loss: 0.2618, Generator Loss: 4.3445
D(x): 0.9370, D(G(z)): 0.0493
Epoch: [3/20], Batch Num: [202/600]
Discriminator Loss: 0.3363, Generator Loss: 4.5155
D(x): 0.9078, D(G(z)): 0.0611
Epoch: [3/20], Batch Num: [203/600]
Discriminator Loss: 0.2005, Generator Loss: 4.3117
D(x): 0.9273, D(G(z)): 0.0359
Epoch: [3/20], Batch Num: [204/600]
Discriminator Loss: 0.2979, Generator Loss: 4.1807
D(x): 0.9285, D(G(z)): 0.0587
Epoch: [3/20], Batch Num: [205/600]
Discriminator Loss: 0.2636, Generator Loss: 3.9513
D(x): 0.9480, D(G(z)): 0.0937
Epoch: [3/20], Batch Num: [206/600]
Discriminator Loss: 0.1965, Generator Loss: 3.7793
D(x): 0.9726, D(G(z)): 0.0796
Epoch: [3/20], Batch Num: [207/600]
Discriminator Loss: 0.2491, Generator Loss: 4.1090
D(x): 0.9519, D(G(z)): 0.0912
Epoch: [3/20], Batch Num: [208/600]
Discriminator Loss: 0.1731, Generator Loss: 4.2706
D(x): 0.9694, D(G(z)): 0.0769
Epoch: [3/20], Batch Num: [209/600]
Discriminator Loss: 0.2682, Generator Loss: 4.4743
D(x): 0.9607, D(G(z)): 0.0891
Epoch: [3/20], Batch Num: [210/600]
Discriminator Loss: 0.4188, Generator Loss: 4.5707
D(x): 0.9206, D(G(z)): 0.0734
Epoch: [3/20], Batch Num: [211/600]
Discriminator Loss: 0.0757, Generator Loss: 4.3377
D(x): 0.9796, D(G(z)): 0.0439
Epoch: [3/20], Batch Num: [212/600]
Discriminator Loss: 0.2518, Generator Loss: 4.3431
D(x): 0.9459, D(G(z)): 0.0589
Epoch: [3/20], Batch Num: [213/600]
Discriminator Loss: 0.2213, Generator Loss: 3.9368
D(x): 0.9508, D(G(z)): 0.0681
Epoch: [3/20], Batch Num: [214/600]
Discriminator Loss: 0.4077, Generator Loss: 3.8235
D(x): 0.9233, D(G(z)): 0.0664
Epoch: [3/20], Batch Num: [215/600]
Discriminator Loss: 0.3139, Generator Loss: 3.3027
D(x): 0.9532, D(G(z)): 0.1318
Epoch: [3/20], Batch Num: [216/600]
Discriminator Loss: 0.3134, Generator Loss: 3.7103
D(x): 0.9513, D(G(z)): 0.1180
Epoch: [3/20], Batch Num: [217/600]
Discriminator Loss: 0.6114, Generator Loss: 3.5533
D(x): 0.8608, D(G(z)): 0.1359
Epoch: [3/20], Batch Num: [218/600]
Discriminator Loss: 0.7391, Generator Loss: 3.0347
D(x): 0.8855, D(G(z)): 0.2216
Epoch: [3/20], Batch Num: [219/600]
Discriminator Loss: 0.5394, Generator Loss: 3.0010
D(x): 0.8993, D(G(z)): 0.1672
Epoch: [3/20], Batch Num: [220/600]
Discriminator Loss: 0.6753, Generator Loss: 2.9527
D(x): 0.8626, D(G(z)): 0.1792
Epoch: [3/20], Batch Num: [221/600]
Discriminator Loss: 0.8414, Generator Loss: 2.7139
D(x): 0.8703, D(G(z)): 0.2272
Epoch: [3/20], Batch Num: [222/600]
Discriminator Loss: 0.5302, Generator Loss: 2.9246
D(x): 0.8934, D(G(z)): 0.1702
Epoch: [3/20], Batch Num: [223/600]
Discriminator Loss: 0.6151, Generator Loss: 2.9639
D(x): 0.8673, D(G(z)): 0.1827
Epoch: [3/20], Batch Num: [224/600]
Discriminator Loss: 1.0494, Generator Loss: 2.4187
D(x): 0.7514, D(G(z)): 0.1440
Epoch: [3/20], Batch Num: [225/600]
Discriminator Loss: 0.8444, Generator Loss: 2.1595
D(x): 0.8090, D(G(z)): 0.2275
Epoch: [3/20], Batch Num: [226/600]
Discriminator Loss: 0.8341, Generator Loss: 2.0799
D(x): 0.8639, D(G(z)): 0.2917
Epoch: [3/20], Batch Num: [227/600]
Discriminator Loss: 0.7945, Generator Loss: 1.8836
D(x): 0.8550, D(G(z)): 0.2669
Epoch: [3/20], Batch Num: [228/600]
Discriminator Loss: 0.8124, Generator Loss: 1.9553
D(x): 0.8598, D(G(z)): 0.3144
Epoch: [3/20], Batch Num: [229/600]
Discriminator Loss: 0.6760, Generator Loss: 2.0186
D(x): 0.8649, D(G(z)): 0.2842
Epoch: [3/20], Batch Num: [230/600]
Discriminator Loss: 0.8087, Generator Loss: 1.9637
D(x): 0.7908, D(G(z)): 0.2563
Epoch: [3/20], Batch Num: [231/600]
Discriminator Loss: 0.7375, Generator Loss: 2.0488
D(x): 0.7940, D(G(z)): 0.2302
Epoch: [3/20], Batch Num: [232/600]
Discriminator Loss: 0.6432, Generator Loss: 1.9478
D(x): 0.8186, D(G(z)): 0.2265
Epoch: [3/20], Batch Num: [233/600]
Discriminator Loss: 0.4844, Generator Loss: 2.1048
D(x): 0.8781, D(G(z)): 0.2313
Epoch: [3/20], Batch Num: [234/600]
Discriminator Loss: 0.5834, Generator Loss: 2.1665
D(x): 0.9146, D(G(z)): 0.2842
Epoch: [3/20], Batch Num: [235/600]
Discriminator Loss: 0.4563, Generator Loss: 2.4299
D(x): 0.8880, D(G(z)): 0.2070
Epoch: [3/20], Batch Num: [236/600]
Discriminator Loss: 0.4782, Generator Loss: 2.6078
D(x): 0.8789, D(G(z)): 0.1852
Epoch: [3/20], Batch Num: [237/600]
Discriminator Loss: 0.4779, Generator Loss: 2.3691
D(x): 0.8421, D(G(z)): 0.1826
Epoch: [3/20], Batch Num: [238/600]
Discriminator Loss: 0.4021, Generator Loss: 2.8311
D(x): 0.8511, D(G(z)): 0.1599
Epoch: [3/20], Batch Num: [239/600]
Discriminator Loss: 0.4235, Generator Loss: 2.7076
D(x): 0.8537, D(G(z)): 0.1729
Epoch: [3/20], Batch Num: [240/600]
Discriminator Loss: 0.3935, Generator Loss: 2.4688
D(x): 0.8650, D(G(z)): 0.1466
Epoch: [3/20], Batch Num: [241/600]
Discriminator Loss: 0.3167, Generator Loss: 2.4936
D(x): 0.8846, D(G(z)): 0.1412
Epoch: [3/20], Batch Num: [242/600]
Discriminator Loss: 0.4250, Generator Loss: 2.8054
D(x): 0.9175, D(G(z)): 0.2188
Epoch: [3/20], Batch Num: [243/600]
Discriminator Loss: 0.3209, Generator Loss: 2.8669
D(x): 0.9088, D(G(z)): 0.1586
Epoch: [3/20], Batch Num: [244/600]
Discriminator Loss: 0.3712, Generator Loss: 2.9539
D(x): 0.8868, D(G(z)): 0.1436
Epoch: [3/20], Batch Num: [245/600]
Discriminator Loss: 0.3073, Generator Loss: 3.1538
D(x): 0.9117, D(G(z)): 0.1499
Epoch: [3/20], Batch Num: [246/600]
Discriminator Loss: 0.3337, Generator Loss: 3.1807
D(x): 0.8484, D(G(z)): 0.0916
Epoch: [3/20], Batch Num: [247/600]
Discriminator Loss: 0.2827, Generator Loss: 3.0274
D(x): 0.8961, D(G(z)): 0.1096
Epoch: [3/20], Batch Num: [248/600]
Discriminator Loss: 0.2817, Generator Loss: 3.2895
D(x): 0.8859, D(G(z)): 0.1059
Epoch: [3/20], Batch Num: [249/600]
Discriminator Loss: 0.2857, Generator Loss: 3.3615
D(x): 0.9016, D(G(z)): 0.1036
Epoch: [3/20], Batch Num: [250/600]
Discriminator Loss: 0.2503, Generator Loss: 3.0705
D(x): 0.9111, D(G(z)): 0.1141
Epoch: [3/20], Batch Num: [251/600]
Discriminator Loss: 0.2295, Generator Loss: 3.1986
D(x): 0.9385, D(G(z)): 0.1251
Epoch: [3/20], Batch Num: [252/600]
Discriminator Loss: 0.2275, Generator Loss: 3.3584
D(x): 0.9339, D(G(z)): 0.1146
Epoch: [3/20], Batch Num: [253/600]
Discriminator Loss: 0.2064, Generator Loss: 4.0934
D(x): 0.9445, D(G(z)): 0.1129
Epoch: [3/20], Batch Num: [254/600]
Discriminator Loss: 0.2289, Generator Loss: 4.1113
D(x): 0.9110, D(G(z)): 0.0763
Epoch: [3/20], Batch Num: [255/600]
Discriminator Loss: 0.3106, Generator Loss: 3.8347
D(x): 0.8638, D(G(z)): 0.0670
Epoch: [3/20], Batch Num: [256/600]
Discriminator Loss: 0.2665, Generator Loss: 3.6830
D(x): 0.8870, D(G(z)): 0.0727
Epoch: [3/20], Batch Num: [257/600]
Discriminator Loss: 0.2369, Generator Loss: 3.6001
D(x): 0.9376, D(G(z)): 0.1165
Epoch: [3/20], Batch Num: [258/600]
Discriminator Loss: 0.2372, Generator Loss: 3.9783
D(x): 0.9514, D(G(z)): 0.1233
Epoch: [3/20], Batch Num: [259/600]
Discriminator Loss: 0.2775, Generator Loss: 3.9384
D(x): 0.9256, D(G(z)): 0.1132
Epoch: [3/20], Batch Num: [260/600]
Discriminator Loss: 0.2067, Generator Loss: 4.3169
D(x): 0.9289, D(G(z)): 0.0772
Epoch: [3/20], Batch Num: [261/600]
Discriminator Loss: 0.3375, Generator Loss: 4.4638
D(x): 0.8826, D(G(z)): 0.0873
Epoch: [3/20], Batch Num: [262/600]
Discriminator Loss: 0.2344, Generator Loss: 3.7392
D(x): 0.9241, D(G(z)): 0.0794
Epoch: [3/20], Batch Num: [263/600]
Discriminator Loss: 0.2496, Generator Loss: 3.6510
D(x): 0.9141, D(G(z)): 0.0679
Epoch: [3/20], Batch Num: [264/600]
Discriminator Loss: 0.2397, Generator Loss: 3.7980
D(x): 0.9222, D(G(z)): 0.1043
Epoch: [3/20], Batch Num: [265/600]
Discriminator Loss: 0.2350, Generator Loss: 3.9991
D(x): 0.9513, D(G(z)): 0.1218
Epoch: [3/20], Batch Num: [266/600]
Discriminator Loss: 0.2518, Generator Loss: 4.0131
D(x): 0.9311, D(G(z)): 0.1171
Epoch: [3/20], Batch Num: [267/600]
Discriminator Loss: 0.3378, Generator Loss: 4.8104
D(x): 0.8704, D(G(z)): 0.0796
Epoch: [3/20], Batch Num: [268/600]
Discriminator Loss: 0.4235, Generator Loss: 3.9279
D(x): 0.8481, D(G(z)): 0.0712
Epoch: [3/20], Batch Num: [269/600]
Discriminator Loss: 0.3808, Generator Loss: 3.2301
D(x): 0.8765, D(G(z)): 0.1142
Epoch: [3/20], Batch Num: [270/600]
Discriminator Loss: 0.4624, Generator Loss: 3.4756
D(x): 0.9144, D(G(z)): 0.1959
Epoch: [3/20], Batch Num: [271/600]
Discriminator Loss: 0.5265, Generator Loss: 3.1931
D(x): 0.8498, D(G(z)): 0.1310
Epoch: [3/20], Batch Num: [272/600]
Discriminator Loss: 0.4695, Generator Loss: 3.2906
D(x): 0.9128, D(G(z)): 0.1818
Epoch: [3/20], Batch Num: [273/600]
Discriminator Loss: 0.5285, Generator Loss: 3.7930
D(x): 0.8365, D(G(z)): 0.1530
Epoch: [3/20], Batch Num: [274/600]
Discriminator Loss: 0.7807, Generator Loss: 3.3141
D(x): 0.8114, D(G(z)): 0.1590
Epoch: [3/20], Batch Num: [275/600]
Discriminator Loss: 0.5088, Generator Loss: 2.8634
D(x): 0.8455, D(G(z)): 0.1251
Epoch: [3/20], Batch Num: [276/600]
Discriminator Loss: 0.4601, Generator Loss: 2.5341
D(x): 0.8900, D(G(z)): 0.1972
Epoch: [3/20], Batch Num: [277/600]
Discriminator Loss: 0.6381, Generator Loss: 3.0845
D(x): 0.8623, D(G(z)): 0.2312
Epoch: [3/20], Batch Num: [278/600]
Discriminator Loss: 0.6475, Generator Loss: 3.1509
D(x): 0.8248, D(G(z)): 0.1812
Epoch: [3/20], Batch Num: [279/600]
Discriminator Loss: 0.5615, Generator Loss: 3.2070
D(x): 0.8077, D(G(z)): 0.1160
Epoch: [3/20], Batch Num: [280/600]
Discriminator Loss: 0.7641, Generator Loss: 2.1285
D(x): 0.7566, D(G(z)): 0.1420
Epoch: [3/20], Batch Num: [281/600]
Discriminator Loss: 0.7718, Generator Loss: 2.0824
D(x): 0.8740, D(G(z)): 0.2682
Epoch: [3/20], Batch Num: [282/600]
Discriminator Loss: 0.6160, Generator Loss: 2.3453
D(x): 0.8850, D(G(z)): 0.2611
Epoch: [3/20], Batch Num: [283/600]
Discriminator Loss: 0.7997, Generator Loss: 2.8753
D(x): 0.8451, D(G(z)): 0.2645
Epoch: [3/20], Batch Num: [284/600]
Discriminator Loss: 0.7366, Generator Loss: 3.0771
D(x): 0.7775, D(G(z)): 0.1324
Epoch: [3/20], Batch Num: [285/600]
Discriminator Loss: 0.8453, Generator Loss: 2.4100
D(x): 0.7652, D(G(z)): 0.1429
Epoch: [3/20], Batch Num: [286/600]
Discriminator Loss: 0.6422, Generator Loss: 2.2009
D(x): 0.8624, D(G(z)): 0.2541
Epoch: [3/20], Batch Num: [287/600]
Discriminator Loss: 0.5445, Generator Loss: 2.5719
D(x): 0.8384, D(G(z)): 0.1873
Epoch: [3/20], Batch Num: [288/600]
Discriminator Loss: 0.6729, Generator Loss: 2.2512
D(x): 0.7635, D(G(z)): 0.1735
Epoch: [3/20], Batch Num: [289/600]
Discriminator Loss: 0.6609, Generator Loss: 2.1726
D(x): 0.8008, D(G(z)): 0.2081
Epoch: [3/20], Batch Num: [290/600]
Discriminator Loss: 0.5207, Generator Loss: 2.3847
D(x): 0.8759, D(G(z)): 0.2047
Epoch: [3/20], Batch Num: [291/600]
Discriminator Loss: 0.5316, Generator Loss: 2.7051
D(x): 0.8618, D(G(z)): 0.1981
Epoch: [3/20], Batch Num: [292/600]
Discriminator Loss: 0.5979, Generator Loss: 2.9160
D(x): 0.7799, D(G(z)): 0.1455
Epoch: [3/20], Batch Num: [293/600]
Discriminator Loss: 0.4026, Generator Loss: 2.6699
D(x): 0.8726, D(G(z)): 0.1525
Epoch: [3/20], Batch Num: [294/600]
Discriminator Loss: 0.4921, Generator Loss: 3.0639
D(x): 0.8685, D(G(z)): 0.1537
Epoch: [3/20], Batch Num: [295/600]
Discriminator Loss: 0.4233, Generator Loss: 2.8994
D(x): 0.8487, D(G(z)): 0.1089
Epoch: [3/20], Batch Num: [296/600]
Discriminator Loss: 0.4141, Generator Loss: 3.0089
D(x): 0.8856, D(G(z)): 0.1376
Epoch: [3/20], Batch Num: [297/600]
Discriminator Loss: 0.3459, Generator Loss: 2.8101
D(x): 0.8913, D(G(z)): 0.1165
Epoch: [3/20], Batch Num: [298/600]
Discriminator Loss: 0.4371, Generator Loss: 2.5771
D(x): 0.8667, D(G(z)): 0.1212
Epoch: [3/20], Batch Num: [299/600]
Discriminator Loss: 0.2833, Generator Loss: 3.0718
D(x): 0.9292, D(G(z)): 0.1312
Epoch: 3, Batch Num: [300/600]

Epoch: [3/20], Batch Num: [300/600]
Discriminator Loss: 0.2482, Generator Loss: 3.5954
D(x): 0.9362, D(G(z)): 0.1135
Epoch: [3/20], Batch Num: [301/600]
Discriminator Loss: 0.3034, Generator Loss: 4.0051
D(x): 0.8816, D(G(z)): 0.0946
Epoch: [3/20], Batch Num: [302/600]
Discriminator Loss: 0.1851, Generator Loss: 3.9329
D(x): 0.9337, D(G(z)): 0.0694
Epoch: [3/20], Batch Num: [303/600]
Discriminator Loss: 0.3318, Generator Loss: 3.5036
D(x): 0.8605, D(G(z)): 0.0653
Epoch: [3/20], Batch Num: [304/600]
Discriminator Loss: 0.3689, Generator Loss: 2.6976
D(x): 0.8728, D(G(z)): 0.1048
Epoch: [3/20], Batch Num: [305/600]
Discriminator Loss: 0.3058, Generator Loss: 2.9521
D(x): 0.9565, D(G(z)): 0.1664
Epoch: [3/20], Batch Num: [306/600]
Discriminator Loss: 0.1900, Generator Loss: 3.5290
D(x): 0.9422, D(G(z)): 0.0934
Epoch: [3/20], Batch Num: [307/600]
Discriminator Loss: 0.2221, Generator Loss: 4.0833
D(x): 0.9303, D(G(z)): 0.0901
Epoch: [3/20], Batch Num: [308/600]
Discriminator Loss: 0.2980, Generator Loss: 4.0587
D(x): 0.8880, D(G(z)): 0.0723
Epoch: [3/20], Batch Num: [309/600]
Discriminator Loss: 0.2506, Generator Loss: 4.0598
D(x): 0.8903, D(G(z)): 0.0517
Epoch: [3/20], Batch Num: [310/600]
Discriminator Loss: 0.2151, Generator Loss: 3.1264
D(x): 0.9139, D(G(z)): 0.0686
Epoch: [3/20], Batch Num: [311/600]
Discriminator Loss: 0.2976, Generator Loss: 2.8178
D(x): 0.9196, D(G(z)): 0.1191
Epoch: [3/20], Batch Num: [312/600]
Discriminator Loss: 0.4020, Generator Loss: 3.2205
D(x): 0.8987, D(G(z)): 0.1505
Epoch: [3/20], Batch Num: [313/600]
Discriminator Loss: 0.2520, Generator Loss: 3.7606
D(x): 0.9577, D(G(z)): 0.1429
Epoch: [3/20], Batch Num: [314/600]
Discriminator Loss: 0.3087, Generator Loss: 4.4027
D(x): 0.8839, D(G(z)): 0.0781
Epoch: [3/20], Batch Num: [315/600]
Discriminator Loss: 0.2253, Generator Loss: 4.2632
D(x): 0.8925, D(G(z)): 0.0488
Epoch: [3/20], Batch Num: [316/600]
Discriminator Loss: 0.4547, Generator Loss: 3.2022
D(x): 0.8231, D(G(z)): 0.0595
Epoch: [3/20], Batch Num: [317/600]
Discriminator Loss: 0.3507, Generator Loss: 2.7669
D(x): 0.9200, D(G(z)): 0.1767
Epoch: [3/20], Batch Num: [318/600]
Discriminator Loss: 0.3907, Generator Loss: 3.5272
D(x): 0.9213, D(G(z)): 0.1778
Epoch: [3/20], Batch Num: [319/600]
Discriminator Loss: 0.2557, Generator Loss: 4.1384
D(x): 0.9138, D(G(z)): 0.1044
Epoch: [3/20], Batch Num: [320/600]
Discriminator Loss: 0.3363, Generator Loss: 3.8535
D(x): 0.8801, D(G(z)): 0.0634
Epoch: [3/20], Batch Num: [321/600]
Discriminator Loss: 0.2782, Generator Loss: 4.1872
D(x): 0.9056, D(G(z)): 0.0875
Epoch: [3/20], Batch Num: [322/600]
Discriminator Loss: 0.3440, Generator Loss: 3.3955
D(x): 0.8727, D(G(z)): 0.0618
Epoch: [3/20], Batch Num: [323/600]
Discriminator Loss: 0.4467, Generator Loss: 2.8212
D(x): 0.8479, D(G(z)): 0.1083
Epoch: [3/20], Batch Num: [324/600]
Discriminator Loss: 0.4607, Generator Loss: 3.7155
D(x): 0.9431, D(G(z)): 0.2225
Epoch: [3/20], Batch Num: [325/600]
Discriminator Loss: 0.4951, Generator Loss: 4.4152
D(x): 0.8760, D(G(z)): 0.1293
Epoch: [3/20], Batch Num: [326/600]
Discriminator Loss: 0.6056, Generator Loss: 3.8299
D(x): 0.7983, D(G(z)): 0.0872
Epoch: [3/20], Batch Num: [327/600]
Discriminator Loss: 0.3761, Generator Loss: 3.3839
D(x): 0.8689, D(G(z)): 0.0706
Epoch: [3/20], Batch Num: [328/600]
Discriminator Loss: 0.6198, Generator Loss: 2.8917
D(x): 0.8454, D(G(z)): 0.1988
Epoch: [3/20], Batch Num: [329/600]
Discriminator Loss: 0.5658, Generator Loss: 3.1835
D(x): 0.8531, D(G(z)): 0.2029
Epoch: [3/20], Batch Num: [330/600]
Discriminator Loss: 0.6184, Generator Loss: 3.2958
D(x): 0.8577, D(G(z)): 0.1745
Epoch: [3/20], Batch Num: [331/600]
Discriminator Loss: 0.6803, Generator Loss: 2.9718
D(x): 0.7829, D(G(z)): 0.1267
Epoch: [3/20], Batch Num: [332/600]
Discriminator Loss: 0.4778, Generator Loss: 2.9699
D(x): 0.8617, D(G(z)): 0.1547
Epoch: [3/20], Batch Num: [333/600]
Discriminator Loss: 0.5861, Generator Loss: 2.6847
D(x): 0.8473, D(G(z)): 0.1886
Epoch: [3/20], Batch Num: [334/600]
Discriminator Loss: 0.5853, Generator Loss: 2.5978
D(x): 0.8211, D(G(z)): 0.1691
Epoch: [3/20], Batch Num: [335/600]
Discriminator Loss: 0.5192, Generator Loss: 2.5373
D(x): 0.8504, D(G(z)): 0.1910
Epoch: [3/20], Batch Num: [336/600]
Discriminator Loss: 0.4621, Generator Loss: 2.4559
D(x): 0.8531, D(G(z)): 0.1682
Epoch: [3/20], Batch Num: [337/600]
Discriminator Loss: 0.5826, Generator Loss: 2.3659
D(x): 0.8387, D(G(z)): 0.1966
Epoch: [3/20], Batch Num: [338/600]
Discriminator Loss: 0.5796, Generator Loss: 2.5562
D(x): 0.8090, D(G(z)): 0.1561
Epoch: [3/20], Batch Num: [339/600]
Discriminator Loss: 0.5109, Generator Loss: 2.2722
D(x): 0.8136, D(G(z)): 0.1291
Epoch: [3/20], Batch Num: [340/600]
Discriminator Loss: 0.5457, Generator Loss: 2.3883
D(x): 0.8533, D(G(z)): 0.1800
Epoch: [3/20], Batch Num: [341/600]
Discriminator Loss: 0.5950, Generator Loss: 2.5169
D(x): 0.8417, D(G(z)): 0.1771
Epoch: [3/20], Batch Num: [342/600]
Discriminator Loss: 0.4344, Generator Loss: 2.8085
D(x): 0.8576, D(G(z)): 0.1367
Epoch: [3/20], Batch Num: [343/600]
Discriminator Loss: 0.4556, Generator Loss: 2.6756
D(x): 0.8645, D(G(z)): 0.1602
Epoch: [3/20], Batch Num: [344/600]
Discriminator Loss: 0.4987, Generator Loss: 2.6721
D(x): 0.8445, D(G(z)): 0.1358
Epoch: [3/20], Batch Num: [345/600]
Discriminator Loss: 0.4302, Generator Loss: 2.4499
D(x): 0.8612, D(G(z)): 0.1254
Epoch: [3/20], Batch Num: [346/600]
Discriminator Loss: 0.3931, Generator Loss: 2.4644
D(x): 0.8676, D(G(z)): 0.1200
Epoch: [3/20], Batch Num: [347/600]
Discriminator Loss: 0.4362, Generator Loss: 2.3760
D(x): 0.8936, D(G(z)): 0.1999
Epoch: [3/20], Batch Num: [348/600]
Discriminator Loss: 0.3400, Generator Loss: 2.8074
D(x): 0.9169, D(G(z)): 0.1619
Epoch: [3/20], Batch Num: [349/600]
Discriminator Loss: 0.4131, Generator Loss: 3.0643
D(x): 0.8398, D(G(z)): 0.1133
Epoch: [3/20], Batch Num: [350/600]
Discriminator Loss: 0.4471, Generator Loss: 2.9457
D(x): 0.8425, D(G(z)): 0.1202
Epoch: [3/20], Batch Num: [351/600]
Discriminator Loss: 0.3858, Generator Loss: 2.7053
D(x): 0.8900, D(G(z)): 0.1077
Epoch: [3/20], Batch Num: [352/600]
Discriminator Loss: 0.2421, Generator Loss: 2.8034
D(x): 0.9629, D(G(z)): 0.1455
Epoch: [3/20], Batch Num: [353/600]
Discriminator Loss: 0.2251, Generator Loss: 3.2224
D(x): 0.9493, D(G(z)): 0.1063
Epoch: [3/20], Batch Num: [354/600]
Discriminator Loss: 0.3887, Generator Loss: 3.5876
D(x): 0.8783, D(G(z)): 0.0740
Epoch: [3/20], Batch Num: [355/600]
Discriminator Loss: 0.2023, Generator Loss: 3.5868
D(x): 0.9167, D(G(z)): 0.0549
Epoch: [3/20], Batch Num: [356/600]
Discriminator Loss: 0.2597, Generator Loss: 3.4964
D(x): 0.8977, D(G(z)): 0.0596
Epoch: [3/20], Batch Num: [357/600]
Discriminator Loss: 0.2002, Generator Loss: 3.3760
D(x): 0.9428, D(G(z)): 0.0928
Epoch: [3/20], Batch Num: [358/600]
Discriminator Loss: 0.2736, Generator Loss: 3.4936
D(x): 0.9170, D(G(z)): 0.0936
Epoch: [3/20], Batch Num: [359/600]
Discriminator Loss: 0.2256, Generator Loss: 3.5832
D(x): 0.9295, D(G(z)): 0.0885
Epoch: [3/20], Batch Num: [360/600]
Discriminator Loss: 0.2572, Generator Loss: 3.5325
D(x): 0.9199, D(G(z)): 0.0862
Epoch: [3/20], Batch Num: [361/600]
Discriminator Loss: 0.2190, Generator Loss: 3.8812
D(x): 0.9546, D(G(z)): 0.1039
Epoch: [3/20], Batch Num: [362/600]
Discriminator Loss: 0.1612, Generator Loss: 4.3084
D(x): 0.9455, D(G(z)): 0.0462
Epoch: [3/20], Batch Num: [363/600]
Discriminator Loss: 0.1210, Generator Loss: 4.2972
D(x): 0.9531, D(G(z)): 0.0389
Epoch: [3/20], Batch Num: [364/600]
Discriminator Loss: 0.1593, Generator Loss: 4.4312
D(x): 0.9402, D(G(z)): 0.0477
Epoch: [3/20], Batch Num: [365/600]
Discriminator Loss: 0.1561, Generator Loss: 4.4121
D(x): 0.9377, D(G(z)): 0.0440
Epoch: [3/20], Batch Num: [366/600]
Discriminator Loss: 0.2134, Generator Loss: 4.2684
D(x): 0.9480, D(G(z)): 0.0826
Epoch: [3/20], Batch Num: [367/600]
Discriminator Loss: 0.2050, Generator Loss: 4.2254
D(x): 0.9466, D(G(z)): 0.0791
Epoch: [3/20], Batch Num: [368/600]
Discriminator Loss: 0.2038, Generator Loss: 4.1590
D(x): 0.9478, D(G(z)): 0.0712
Epoch: [3/20], Batch Num: [369/600]
Discriminator Loss: 0.1506, Generator Loss: 4.4684
D(x): 0.9483, D(G(z)): 0.0447
Epoch: [3/20], Batch Num: [370/600]
Discriminator Loss: 0.2122, Generator Loss: 4.1785
D(x): 0.9381, D(G(z)): 0.0613
Epoch: [3/20], Batch Num: [371/600]
Discriminator Loss: 0.2218, Generator Loss: 4.0705
D(x): 0.9491, D(G(z)): 0.0882
Epoch: [3/20], Batch Num: [372/600]
Discriminator Loss: 0.1926, Generator Loss: 4.4691
D(x): 0.9453, D(G(z)): 0.0762
Epoch: [3/20], Batch Num: [373/600]
Discriminator Loss: 0.2459, Generator Loss: 4.1535
D(x): 0.9196, D(G(z)): 0.0617
Epoch: [3/20], Batch Num: [374/600]
Discriminator Loss: 0.1982, Generator Loss: 3.8078
D(x): 0.9299, D(G(z)): 0.0673
Epoch: [3/20], Batch Num: [375/600]
Discriminator Loss: 0.5162, Generator Loss: 3.0894
D(x): 0.8460, D(G(z)): 0.0774
Epoch: [3/20], Batch Num: [376/600]
Discriminator Loss: 0.4443, Generator Loss: 2.4999
D(x): 0.8978, D(G(z)): 0.1315
Epoch: [3/20], Batch Num: [377/600]
Discriminator Loss: 0.6040, Generator Loss: 3.3890
D(x): 0.9241, D(G(z)): 0.2528
Epoch: [3/20], Batch Num: [378/600]
Discriminator Loss: 0.6489, Generator Loss: 3.8030
D(x): 0.8511, D(G(z)): 0.1361
Epoch: [3/20], Batch Num: [379/600]
Discriminator Loss: 0.7951, Generator Loss: 3.8652
D(x): 0.7919, D(G(z)): 0.0622
Epoch: [3/20], Batch Num: [380/600]
Discriminator Loss: 0.7358, Generator Loss: 3.2918
D(x): 0.7788, D(G(z)): 0.0751
Epoch: [3/20], Batch Num: [381/600]
Discriminator Loss: 0.5072, Generator Loss: 2.5926
D(x): 0.8536, D(G(z)): 0.1237
Epoch: [3/20], Batch Num: [382/600]
Discriminator Loss: 0.5013, Generator Loss: 2.2255
D(x): 0.9086, D(G(z)): 0.2019
Epoch: [3/20], Batch Num: [383/600]
Discriminator Loss: 0.3833, Generator Loss: 2.6761
D(x): 0.9504, D(G(z)): 0.2175
Epoch: [3/20], Batch Num: [384/600]
Discriminator Loss: 0.3492, Generator Loss: 3.4918
D(x): 0.9415, D(G(z)): 0.1533
Epoch: [3/20], Batch Num: [385/600]
Discriminator Loss: 0.3076, Generator Loss: 4.1630
D(x): 0.9354, D(G(z)): 0.1158
Epoch: [3/20], Batch Num: [386/600]
Discriminator Loss: 0.2764, Generator Loss: 4.6366
D(x): 0.9395, D(G(z)): 0.0818
Epoch: [3/20], Batch Num: [387/600]
Discriminator Loss: 0.3107, Generator Loss: 4.6286
D(x): 0.9001, D(G(z)): 0.0747
Epoch: [3/20], Batch Num: [388/600]
Discriminator Loss: 0.2414, Generator Loss: 5.0615
D(x): 0.9088, D(G(z)): 0.0631
Epoch: [3/20], Batch Num: [389/600]
Discriminator Loss: 0.2569, Generator Loss: 4.2267
D(x): 0.9123, D(G(z)): 0.0677
Epoch: [3/20], Batch Num: [390/600]
Discriminator Loss: 0.1798, Generator Loss: 3.9529
D(x): 0.9252, D(G(z)): 0.0604
Epoch: [3/20], Batch Num: [391/600]
Discriminator Loss: 0.1757, Generator Loss: 3.6140
D(x): 0.9561, D(G(z)): 0.0925
Epoch: [3/20], Batch Num: [392/600]
Discriminator Loss: 0.2530, Generator Loss: 3.5236
D(x): 0.9431, D(G(z)): 0.1201
Epoch: [3/20], Batch Num: [393/600]
Discriminator Loss: 0.3618, Generator Loss: 3.7532
D(x): 0.9416, D(G(z)): 0.1565
Epoch: [3/20], Batch Num: [394/600]
Discriminator Loss: 0.2169, Generator Loss: 4.1160
D(x): 0.9570, D(G(z)): 0.1160
Epoch: [3/20], Batch Num: [395/600]
Discriminator Loss: 0.2753, Generator Loss: 4.3822
D(x): 0.9227, D(G(z)): 0.1210
Epoch: [3/20], Batch Num: [396/600]
Discriminator Loss: 0.3409, Generator Loss: 5.0604
D(x): 0.8959, D(G(z)): 0.1266
Epoch: [3/20], Batch Num: [397/600]
Discriminator Loss: 0.3628, Generator Loss: 4.8191
D(x): 0.8977, D(G(z)): 0.1286
Epoch: [3/20], Batch Num: [398/600]
Discriminator Loss: 0.3966, Generator Loss: 4.9587
D(x): 0.8742, D(G(z)): 0.1098
Epoch: [3/20], Batch Num: [399/600]
Discriminator Loss: 0.4308, Generator Loss: 4.5817
D(x): 0.8652, D(G(z)): 0.1104
Epoch: 3, Batch Num: [400/600]

Epoch: [3/20], Batch Num: [400/600]
Discriminator Loss: 0.6885, Generator Loss: 3.9052
D(x): 0.8004, D(G(z)): 0.1465
Epoch: [3/20], Batch Num: [401/600]
Discriminator Loss: 0.5118, Generator Loss: 2.7711
D(x): 0.8761, D(G(z)): 0.1827
Epoch: [3/20], Batch Num: [402/600]
Discriminator Loss: 0.3831, Generator Loss: 2.9700
D(x): 0.9070, D(G(z)): 0.1670
Epoch: [3/20], Batch Num: [403/600]
Discriminator Loss: 0.7117, Generator Loss: 3.0673
D(x): 0.8617, D(G(z)): 0.2646
Epoch: [3/20], Batch Num: [404/600]
Discriminator Loss: 0.5370, Generator Loss: 3.1071
D(x): 0.8263, D(G(z)): 0.1533
Epoch: [3/20], Batch Num: [405/600]
Discriminator Loss: 0.6949, Generator Loss: 2.6662
D(x): 0.7923, D(G(z)): 0.1451
Epoch: [3/20], Batch Num: [406/600]
Discriminator Loss: 0.5157, Generator Loss: 2.5191
D(x): 0.8353, D(G(z)): 0.1572
Epoch: [3/20], Batch Num: [407/600]
Discriminator Loss: 0.6304, Generator Loss: 2.0944
D(x): 0.8493, D(G(z)): 0.2317
Epoch: [3/20], Batch Num: [408/600]
Discriminator Loss: 0.6762, Generator Loss: 2.0679
D(x): 0.8270, D(G(z)): 0.2473
Epoch: [3/20], Batch Num: [409/600]
Discriminator Loss: 0.5669, Generator Loss: 2.1122
D(x): 0.8561, D(G(z)): 0.2296
Epoch: [3/20], Batch Num: [410/600]
Discriminator Loss: 0.4502, Generator Loss: 2.8230
D(x): 0.9143, D(G(z)): 0.2211
Epoch: [3/20], Batch Num: [411/600]
Discriminator Loss: 0.4512, Generator Loss: 2.9681
D(x): 0.8745, D(G(z)): 0.1869
Epoch: [3/20], Batch Num: [412/600]
Discriminator Loss: 0.5145, Generator Loss: 3.6297
D(x): 0.8447, D(G(z)): 0.1380
Epoch: [3/20], Batch Num: [413/600]
Discriminator Loss: 0.5525, Generator Loss: 3.5937
D(x): 0.8077, D(G(z)): 0.1318
Epoch: [3/20], Batch Num: [414/600]
Discriminator Loss: 0.5862, Generator Loss: 2.8294
D(x): 0.7943, D(G(z)): 0.1351
Epoch: [3/20], Batch Num: [415/600]
Discriminator Loss: 0.6125, Generator Loss: 2.2825
D(x): 0.8429, D(G(z)): 0.1532
Epoch: [3/20], Batch Num: [416/600]
Discriminator Loss: 0.5908, Generator Loss: 2.4679
D(x): 0.8695, D(G(z)): 0.2567
Epoch: [3/20], Batch Num: [417/600]
Discriminator Loss: 0.4611, Generator Loss: 2.8970
D(x): 0.8737, D(G(z)): 0.1908
Epoch: [3/20], Batch Num: [418/600]
Discriminator Loss: 0.4712, Generator Loss: 3.1956
D(x): 0.9014, D(G(z)): 0.2022
Epoch: [3/20], Batch Num: [419/600]
Discriminator Loss: 0.4612, Generator Loss: 3.4397
D(x): 0.8340, D(G(z)): 0.1110
Epoch: [3/20], Batch Num: [420/600]
Discriminator Loss: 0.6738, Generator Loss: 3.3245
D(x): 0.7148, D(G(z)): 0.0792
Epoch: [3/20], Batch Num: [421/600]
Discriminator Loss: 0.6222, Generator Loss: 2.3634
D(x): 0.7734, D(G(z)): 0.1256
Epoch: [3/20], Batch Num: [422/600]
Discriminator Loss: 0.4871, Generator Loss: 2.1942
D(x): 0.8523, D(G(z)): 0.1879
Epoch: [3/20], Batch Num: [423/600]
Discriminator Loss: 0.5486, Generator Loss: 2.4527
D(x): 0.8590, D(G(z)): 0.2260
Epoch: [3/20], Batch Num: [424/600]
Discriminator Loss: 0.3669, Generator Loss: 3.2299
D(x): 0.9191, D(G(z)): 0.1989
Epoch: [3/20], Batch Num: [425/600]
Discriminator Loss: 0.3053, Generator Loss: 4.2166
D(x): 0.9003, D(G(z)): 0.1110
Epoch: [3/20], Batch Num: [426/600]
Discriminator Loss: 0.2630, Generator Loss: 4.3988
D(x): 0.8743, D(G(z)): 0.0530
Epoch: [3/20], Batch Num: [427/600]
Discriminator Loss: 0.4531, Generator Loss: 4.4710
D(x): 0.7954, D(G(z)): 0.0474
Epoch: [3/20], Batch Num: [428/600]
Discriminator Loss: 0.3130, Generator Loss: 3.5288
D(x): 0.8702, D(G(z)): 0.0534
Epoch: [3/20], Batch Num: [429/600]
Discriminator Loss: 0.2412, Generator Loss: 3.1840
D(x): 0.9119, D(G(z)): 0.0917
Epoch: [3/20], Batch Num: [430/600]
Discriminator Loss: 0.3057, Generator Loss: 2.5692
D(x): 0.8796, D(G(z)): 0.1047
Epoch: [3/20], Batch Num: [431/600]
Discriminator Loss: 0.4951, Generator Loss: 3.5045
D(x): 0.9329, D(G(z)): 0.2251
Epoch: [3/20], Batch Num: [432/600]
Discriminator Loss: 0.3215, Generator Loss: 4.4333
D(x): 0.9202, D(G(z)): 0.1381
Epoch: [3/20], Batch Num: [433/600]
Discriminator Loss: 0.2524, Generator Loss: 5.6012
D(x): 0.9117, D(G(z)): 0.0841
Epoch: [3/20], Batch Num: [434/600]
Discriminator Loss: 0.3146, Generator Loss: 6.1546
D(x): 0.8635, D(G(z)): 0.0312
Epoch: [3/20], Batch Num: [435/600]
Discriminator Loss: 0.2841, Generator Loss: 5.7144
D(x): 0.8898, D(G(z)): 0.0380
Epoch: [3/20], Batch Num: [436/600]
Discriminator Loss: 0.1843, Generator Loss: 5.2747
D(x): 0.9128, D(G(z)): 0.0263
Epoch: [3/20], Batch Num: [437/600]
Discriminator Loss: 0.1840, Generator Loss: 4.8654
D(x): 0.9128, D(G(z)): 0.0248
Epoch: [3/20], Batch Num: [438/600]
Discriminator Loss: 0.2340, Generator Loss: 4.1283
D(x): 0.9069, D(G(z)): 0.0583
Epoch: [3/20], Batch Num: [439/600]
Discriminator Loss: 0.1905, Generator Loss: 3.2262
D(x): 0.9422, D(G(z)): 0.0948
Epoch: [3/20], Batch Num: [440/600]
Discriminator Loss: 0.2340, Generator Loss: 3.4210
D(x): 0.9398, D(G(z)): 0.1150
Epoch: [3/20], Batch Num: [441/600]
Discriminator Loss: 0.3092, Generator Loss: 3.4077
D(x): 0.9155, D(G(z)): 0.1229
Epoch: [3/20], Batch Num: [442/600]
Discriminator Loss: 0.2654, Generator Loss: 3.8080
D(x): 0.9226, D(G(z)): 0.1234
Epoch: [3/20], Batch Num: [443/600]
Discriminator Loss: 0.1783, Generator Loss: 4.0517
D(x): 0.9525, D(G(z)): 0.0925
Epoch: [3/20], Batch Num: [444/600]
Discriminator Loss: 0.2152, Generator Loss: 4.7074
D(x): 0.9234, D(G(z)): 0.0493
Epoch: [3/20], Batch Num: [445/600]
Discriminator Loss: 0.3228, Generator Loss: 4.4494
D(x): 0.8830, D(G(z)): 0.0429
Epoch: [3/20], Batch Num: [446/600]
Discriminator Loss: 0.3038, Generator Loss: 3.6569
D(x): 0.8730, D(G(z)): 0.0699
Epoch: [3/20], Batch Num: [447/600]
Discriminator Loss: 0.3139, Generator Loss: 2.8133
D(x): 0.9008, D(G(z)): 0.1054
Epoch: [3/20], Batch Num: [448/600]
Discriminator Loss: 0.3328, Generator Loss: 2.9956
D(x): 0.9662, D(G(z)): 0.1887
Epoch: [3/20], Batch Num: [449/600]
Discriminator Loss: 0.2752, Generator Loss: 4.2083
D(x): 0.9404, D(G(z)): 0.1363
Epoch: [3/20], Batch Num: [450/600]
Discriminator Loss: 0.3059, Generator Loss: 4.4291
D(x): 0.8930, D(G(z)): 0.0731
Epoch: [3/20], Batch Num: [451/600]
Discriminator Loss: 0.4213, Generator Loss: 4.0178
D(x): 0.8534, D(G(z)): 0.0792
Epoch: [3/20], Batch Num: [452/600]
Discriminator Loss: 0.5312, Generator Loss: 2.6982
D(x): 0.8228, D(G(z)): 0.0836
Epoch: [3/20], Batch Num: [453/600]
Discriminator Loss: 0.3421, Generator Loss: 2.5202
D(x): 0.9390, D(G(z)): 0.1692
Epoch: [3/20], Batch Num: [454/600]
Discriminator Loss: 0.4454, Generator Loss: 3.1258
D(x): 0.9341, D(G(z)): 0.2070
Epoch: [3/20], Batch Num: [455/600]
Discriminator Loss: 0.4178, Generator Loss: 3.2947
D(x): 0.9106, D(G(z)): 0.1658
Epoch: [3/20], Batch Num: [456/600]
Discriminator Loss: 0.2606, Generator Loss: 4.2180
D(x): 0.9347, D(G(z)): 0.1112
Epoch: [3/20], Batch Num: [457/600]
Discriminator Loss: 0.3864, Generator Loss: 3.9907
D(x): 0.8689, D(G(z)): 0.0712
Epoch: [3/20], Batch Num: [458/600]
Discriminator Loss: 0.2956, Generator Loss: 3.6176
D(x): 0.8912, D(G(z)): 0.0565
Epoch: [3/20], Batch Num: [459/600]
Discriminator Loss: 0.3377, Generator Loss: 3.1029
D(x): 0.8835, D(G(z)): 0.0788
Epoch: [3/20], Batch Num: [460/600]
Discriminator Loss: 0.3536, Generator Loss: 2.7251
D(x): 0.9097, D(G(z)): 0.1236
Epoch: [3/20], Batch Num: [461/600]
Discriminator Loss: 0.4412, Generator Loss: 2.9507
D(x): 0.9249, D(G(z)): 0.2016
Epoch: [3/20], Batch Num: [462/600]
Discriminator Loss: 0.4935, Generator Loss: 3.4354
D(x): 0.8807, D(G(z)): 0.1556
Epoch: [3/20], Batch Num: [463/600]
Discriminator Loss: 0.3191, Generator Loss: 3.5513
D(x): 0.8928, D(G(z)): 0.0857
Epoch: [3/20], Batch Num: [464/600]
Discriminator Loss: 0.2270, Generator Loss: 3.4992
D(x): 0.9426, D(G(z)): 0.0867
Epoch: [3/20], Batch Num: [465/600]
Discriminator Loss: 0.3006, Generator Loss: 3.9763
D(x): 0.9173, D(G(z)): 0.0845
Epoch: [3/20], Batch Num: [466/600]
Discriminator Loss: 0.2745, Generator Loss: 4.0558
D(x): 0.9212, D(G(z)): 0.0376
Epoch: [3/20], Batch Num: [467/600]
Discriminator Loss: 0.1979, Generator Loss: 3.8710
D(x): 0.9317, D(G(z)): 0.0472
Epoch: [3/20], Batch Num: [468/600]
Discriminator Loss: 0.2348, Generator Loss: 3.4363
D(x): 0.9328, D(G(z)): 0.0889
Epoch: [3/20], Batch Num: [469/600]
Discriminator Loss: 0.1926, Generator Loss: 3.7265
D(x): 0.9551, D(G(z)): 0.0923
Epoch: [3/20], Batch Num: [470/600]
Discriminator Loss: 0.1332, Generator Loss: 4.1708
D(x): 0.9686, D(G(z)): 0.0673
Epoch: [3/20], Batch Num: [471/600]
Discriminator Loss: 0.1699, Generator Loss: 4.2745
D(x): 0.9720, D(G(z)): 0.0902
Epoch: [3/20], Batch Num: [472/600]
Discriminator Loss: 0.1737, Generator Loss: 4.3588
D(x): 0.9546, D(G(z)): 0.0564
Epoch: [3/20], Batch Num: [473/600]
Discriminator Loss: 0.1710, Generator Loss: 4.9999
D(x): 0.9406, D(G(z)): 0.0442
Epoch: [3/20], Batch Num: [474/600]
Discriminator Loss: 0.1373, Generator Loss: 5.1099
D(x): 0.9728, D(G(z)): 0.0513
Epoch: [3/20], Batch Num: [475/600]
Discriminator Loss: 0.1558, Generator Loss: 5.4643
D(x): 0.9466, D(G(z)): 0.0349
Epoch: [3/20], Batch Num: [476/600]
Discriminator Loss: 0.0882, Generator Loss: 5.4138
D(x): 0.9818, D(G(z)): 0.0424
Epoch: [3/20], Batch Num: [477/600]
Discriminator Loss: 0.1815, Generator Loss: 5.3728
D(x): 0.9570, D(G(z)): 0.0596
Epoch: [3/20], Batch Num: [478/600]
Discriminator Loss: 0.1636, Generator Loss: 5.0299
D(x): 0.9423, D(G(z)): 0.0453
Epoch: [3/20], Batch Num: [479/600]
Discriminator Loss: 0.1497, Generator Loss: 4.8217
D(x): 0.9784, D(G(z)): 0.0455
Epoch: [3/20], Batch Num: [480/600]
Discriminator Loss: 0.2459, Generator Loss: 4.5272
D(x): 0.9254, D(G(z)): 0.0689
Epoch: [3/20], Batch Num: [481/600]
Discriminator Loss: 0.2223, Generator Loss: 4.5785
D(x): 0.9677, D(G(z)): 0.1229
Epoch: [3/20], Batch Num: [482/600]
Discriminator Loss: 0.3008, Generator Loss: 4.3807
D(x): 0.9180, D(G(z)): 0.0797
Epoch: [3/20], Batch Num: [483/600]
Discriminator Loss: 0.3144, Generator Loss: 4.4334
D(x): 0.9347, D(G(z)): 0.1072
Epoch: [3/20], Batch Num: [484/600]
Discriminator Loss: 0.2514, Generator Loss: 4.7252
D(x): 0.9522, D(G(z)): 0.1130
Epoch: [3/20], Batch Num: [485/600]
Discriminator Loss: 0.2514, Generator Loss: 3.9578
D(x): 0.9211, D(G(z)): 0.0763
Epoch: [3/20], Batch Num: [486/600]
Discriminator Loss: 0.2672, Generator Loss: 4.2760
D(x): 0.9352, D(G(z)): 0.1107
Epoch: [3/20], Batch Num: [487/600]
Discriminator Loss: 0.2670, Generator Loss: 4.1592
D(x): 0.9450, D(G(z)): 0.1024
Epoch: [3/20], Batch Num: [488/600]
Discriminator Loss: 0.4903, Generator Loss: 3.7715
D(x): 0.9044, D(G(z)): 0.1217
Epoch: [3/20], Batch Num: [489/600]
Discriminator Loss: 0.5324, Generator Loss: 3.7273
D(x): 0.8836, D(G(z)): 0.1267
Epoch: [3/20], Batch Num: [490/600]
Discriminator Loss: 0.4036, Generator Loss: 3.3756
D(x): 0.8869, D(G(z)): 0.1006
Epoch: [3/20], Batch Num: [491/600]
Discriminator Loss: 0.5240, Generator Loss: 3.3643
D(x): 0.8870, D(G(z)): 0.1646
Epoch: [3/20], Batch Num: [492/600]
Discriminator Loss: 0.2577, Generator Loss: 3.4623
D(x): 0.9388, D(G(z)): 0.1131
Epoch: [3/20], Batch Num: [493/600]
Discriminator Loss: 0.3257, Generator Loss: 3.6265
D(x): 0.9449, D(G(z)): 0.1313
Epoch: [3/20], Batch Num: [494/600]
Discriminator Loss: 0.2999, Generator Loss: 4.2397
D(x): 0.9448, D(G(z)): 0.1332
Epoch: [3/20], Batch Num: [495/600]
Discriminator Loss: 0.2494, Generator Loss: 4.6255
D(x): 0.9319, D(G(z)): 0.0820
Epoch: [3/20], Batch Num: [496/600]
Discriminator Loss: 0.2074, Generator Loss: 4.7900
D(x): 0.9478, D(G(z)): 0.0529
Epoch: [3/20], Batch Num: [497/600]
Discriminator Loss: 0.3672, Generator Loss: 4.8335
D(x): 0.8873, D(G(z)): 0.0596
Epoch: [3/20], Batch Num: [498/600]
Discriminator Loss: 0.2239, Generator Loss: 5.0160
D(x): 0.9386, D(G(z)): 0.0439
Epoch: [3/20], Batch Num: [499/600]
Discriminator Loss: 0.1579, Generator Loss: 4.8238
D(x): 0.9642, D(G(z)): 0.0681
Epoch: 3, Batch Num: [500/600]

Epoch: [3/20], Batch Num: [500/600]
Discriminator Loss: 0.1309, Generator Loss: 4.6292
D(x): 0.9663, D(G(z)): 0.0688
Epoch: [3/20], Batch Num: [501/600]
Discriminator Loss: 0.2291, Generator Loss: 4.7240
D(x): 0.9528, D(G(z)): 0.0853
Epoch: [3/20], Batch Num: [502/600]
Discriminator Loss: 0.2264, Generator Loss: 4.6318
D(x): 0.9342, D(G(z)): 0.0718
Epoch: [3/20], Batch Num: [503/600]
Discriminator Loss: 0.1661, Generator Loss: 5.2279
D(x): 0.9592, D(G(z)): 0.0742
Epoch: [3/20], Batch Num: [504/600]
Discriminator Loss: 0.1088, Generator Loss: 5.6378
D(x): 0.9685, D(G(z)): 0.0431
Epoch: [3/20], Batch Num: [505/600]
Discriminator Loss: 0.1229, Generator Loss: 5.6673
D(x): 0.9623, D(G(z)): 0.0412
Epoch: [3/20], Batch Num: [506/600]
Discriminator Loss: 0.1554, Generator Loss: 5.9345
D(x): 0.9651, D(G(z)): 0.0668
Epoch: [3/20], Batch Num: [507/600]
Discriminator Loss: 0.1734, Generator Loss: 6.2592
D(x): 0.9486, D(G(z)): 0.0558
Epoch: [3/20], Batch Num: [508/600]
Discriminator Loss: 0.1245, Generator Loss: 6.2123
D(x): 0.9560, D(G(z)): 0.0459
Epoch: [3/20], Batch Num: [509/600]
Discriminator Loss: 0.1665, Generator Loss: 5.9161
D(x): 0.9481, D(G(z)): 0.0330
Epoch: [3/20], Batch Num: [510/600]
Discriminator Loss: 0.2047, Generator Loss: 5.7138
D(x): 0.9389, D(G(z)): 0.0569
Epoch: [3/20], Batch Num: [511/600]
Discriminator Loss: 0.2350, Generator Loss: 5.9417
D(x): 0.9399, D(G(z)): 0.0665
Epoch: [3/20], Batch Num: [512/600]
Discriminator Loss: 0.1867, Generator Loss: 5.8507
D(x): 0.9409, D(G(z)): 0.0394
Epoch: [3/20], Batch Num: [513/600]
Discriminator Loss: 0.3378, Generator Loss: 6.1427
D(x): 0.9428, D(G(z)): 0.1249
Epoch: [3/20], Batch Num: [514/600]
Discriminator Loss: 0.2694, Generator Loss: 6.6355
D(x): 0.9103, D(G(z)): 0.0550
Epoch: [3/20], Batch Num: [515/600]
Discriminator Loss: 0.6078, Generator Loss: 6.2718
D(x): 0.8155, D(G(z)): 0.0573
Epoch: [3/20], Batch Num: [516/600]
Discriminator Loss: 0.3861, Generator Loss: 4.9492
D(x): 0.8826, D(G(z)): 0.0422
Epoch: [3/20], Batch Num: [517/600]
Discriminator Loss: 0.3019, Generator Loss: 4.3938
D(x): 0.9498, D(G(z)): 0.1167
Epoch: [3/20], Batch Num: [518/600]
Discriminator Loss: 0.3563, Generator Loss: 5.1080
D(x): 0.9269, D(G(z)): 0.1356
Epoch: [3/20], Batch Num: [519/600]
Discriminator Loss: 0.2156, Generator Loss: 5.8741
D(x): 0.9357, D(G(z)): 0.0704
Epoch: [3/20], Batch Num: [520/600]
Discriminator Loss: 0.1781, Generator Loss: 5.7746
D(x): 0.9275, D(G(z)): 0.0346
Epoch: [3/20], Batch Num: [521/600]
Discriminator Loss: 0.1041, Generator Loss: 6.2661
D(x): 0.9703, D(G(z)): 0.0400
Epoch: [3/20], Batch Num: [522/600]
Discriminator Loss: 0.2121, Generator Loss: 5.7395
D(x): 0.9234, D(G(z)): 0.0467
Epoch: [3/20], Batch Num: [523/600]
Discriminator Loss: 0.2534, Generator Loss: 5.4451
D(x): 0.9118, D(G(z)): 0.0427
Epoch: [3/20], Batch Num: [524/600]
Discriminator Loss: 0.1430, Generator Loss: 5.4305
D(x): 0.9693, D(G(z)): 0.0651
Epoch: [3/20], Batch Num: [525/600]
Discriminator Loss: 0.2568, Generator Loss: 5.8840
D(x): 0.9379, D(G(z)): 0.0651
Epoch: [3/20], Batch Num: [526/600]
Discriminator Loss: 0.1881, Generator Loss: 5.4781
D(x): 0.9326, D(G(z)): 0.0455
Epoch: [3/20], Batch Num: [527/600]
Discriminator Loss: 0.2350, Generator Loss: 5.3307
D(x): 0.9308, D(G(z)): 0.0538
Epoch: [3/20], Batch Num: [528/600]
Discriminator Loss: 0.2348, Generator Loss: 5.8115
D(x): 0.9503, D(G(z)): 0.0768
Epoch: [3/20], Batch Num: [529/600]
Discriminator Loss: 0.1780, Generator Loss: 5.7098
D(x): 0.9354, D(G(z)): 0.0366
Epoch: [3/20], Batch Num: [530/600]
Discriminator Loss: 0.0969, Generator Loss: 5.4568
D(x): 0.9707, D(G(z)): 0.0343
Epoch: [3/20], Batch Num: [531/600]
Discriminator Loss: 0.1927, Generator Loss: 5.1947
D(x): 0.9778, D(G(z)): 0.0840
Epoch: [3/20], Batch Num: [532/600]
Discriminator Loss: 0.1912, Generator Loss: 6.0581
D(x): 0.9591, D(G(z)): 0.0808
Epoch: [3/20], Batch Num: [533/600]
Discriminator Loss: 0.3643, Generator Loss: 5.5317
D(x): 0.9003, D(G(z)): 0.0594
Epoch: [3/20], Batch Num: [534/600]
Discriminator Loss: 0.3533, Generator Loss: 4.8534
D(x): 0.9024, D(G(z)): 0.0595
Epoch: [3/20], Batch Num: [535/600]
Discriminator Loss: 0.3124, Generator Loss: 4.8646
D(x): 0.9265, D(G(z)): 0.1110
Epoch: [3/20], Batch Num: [536/600]
Discriminator Loss: 0.4381, Generator Loss: 4.5017
D(x): 0.8852, D(G(z)): 0.0927
Epoch: [3/20], Batch Num: [537/600]
Discriminator Loss: 0.2347, Generator Loss: 4.3872
D(x): 0.9324, D(G(z)): 0.0724
Epoch: [3/20], Batch Num: [538/600]
Discriminator Loss: 0.4702, Generator Loss: 4.1829
D(x): 0.8822, D(G(z)): 0.1035
Epoch: [3/20], Batch Num: [539/600]
Discriminator Loss: 0.3688, Generator Loss: 3.5669
D(x): 0.8672, D(G(z)): 0.0611
Epoch: [3/20], Batch Num: [540/600]
Discriminator Loss: 0.2207, Generator Loss: 3.3427
D(x): 0.9432, D(G(z)): 0.0975
Epoch: [3/20], Batch Num: [541/600]
Discriminator Loss: 0.2174, Generator Loss: 3.8222
D(x): 0.9571, D(G(z)): 0.1122
Epoch: [3/20], Batch Num: [542/600]
Discriminator Loss: 0.3254, Generator Loss: 3.8087
D(x): 0.9206, D(G(z)): 0.0689
Epoch: [3/20], Batch Num: [543/600]
Discriminator Loss: 0.2279, Generator Loss: 3.4177
D(x): 0.8968, D(G(z)): 0.0342
Epoch: [3/20], Batch Num: [544/600]
Discriminator Loss: 0.2444, Generator Loss: 3.3644
D(x): 0.9274, D(G(z)): 0.0615
Epoch: [3/20], Batch Num: [545/600]
Discriminator Loss: 0.2382, Generator Loss: 3.0686
D(x): 0.9254, D(G(z)): 0.0907
Epoch: [3/20], Batch Num: [546/600]
Discriminator Loss: 0.1678, Generator Loss: 3.3768
D(x): 0.9743, D(G(z)): 0.1090
Epoch: [3/20], Batch Num: [547/600]
Discriminator Loss: 0.1798, Generator Loss: 3.9192
D(x): 0.9692, D(G(z)): 0.0851
Epoch: [3/20], Batch Num: [548/600]
Discriminator Loss: 0.1698, Generator Loss: 4.3908
D(x): 0.9543, D(G(z)): 0.0723
Epoch: [3/20], Batch Num: [549/600]
Discriminator Loss: 0.1401, Generator Loss: 4.4507
D(x): 0.9573, D(G(z)): 0.0486
Epoch: [3/20], Batch Num: [550/600]
Discriminator Loss: 0.2610, Generator Loss: 4.3889
D(x): 0.8958, D(G(z)): 0.0338
Epoch: [3/20], Batch Num: [551/600]
Discriminator Loss: 0.2651, Generator Loss: 3.8920
D(x): 0.9065, D(G(z)): 0.0374
Epoch: [3/20], Batch Num: [552/600]
Discriminator Loss: 0.2355, Generator Loss: 3.8112
D(x): 0.9530, D(G(z)): 0.1088
Epoch: [3/20], Batch Num: [553/600]
Discriminator Loss: 0.3297, Generator Loss: 3.6284
D(x): 0.9010, D(G(z)): 0.0833
Epoch: [3/20], Batch Num: [554/600]
Discriminator Loss: 0.1521, Generator Loss: 4.0353
D(x): 0.9520, D(G(z)): 0.0666
Epoch: [3/20], Batch Num: [555/600]
Discriminator Loss: 0.1374, Generator Loss: 4.1843
D(x): 0.9655, D(G(z)): 0.0641
Epoch: [3/20], Batch Num: [556/600]
Discriminator Loss: 0.2079, Generator Loss: 4.4706
D(x): 0.9295, D(G(z)): 0.0569
Epoch: [3/20], Batch Num: [557/600]
Discriminator Loss: 0.1521, Generator Loss: 4.7666
D(x): 0.9444, D(G(z)): 0.0451
Epoch: [3/20], Batch Num: [558/600]
Discriminator Loss: 0.2069, Generator Loss: 4.3717
D(x): 0.9218, D(G(z)): 0.0397
Epoch: [3/20], Batch Num: [559/600]
Discriminator Loss: 0.1747, Generator Loss: 4.2676
D(x): 0.9398, D(G(z)): 0.0521
Epoch: [3/20], Batch Num: [560/600]
Discriminator Loss: 0.2300, Generator Loss: 4.0344
D(x): 0.9316, D(G(z)): 0.0836
Epoch: [3/20], Batch Num: [561/600]
Discriminator Loss: 0.1361, Generator Loss: 4.5009
D(x): 0.9494, D(G(z)): 0.0396
Epoch: [3/20], Batch Num: [562/600]
Discriminator Loss: 0.1444, Generator Loss: 4.0155
D(x): 0.9512, D(G(z)): 0.0461
Epoch: [3/20], Batch Num: [563/600]
Discriminator Loss: 0.1600, Generator Loss: 4.2971
D(x): 0.9381, D(G(z)): 0.0418
Epoch: [3/20], Batch Num: [564/600]
Discriminator Loss: 0.1594, Generator Loss: 4.3969
D(x): 0.9512, D(G(z)): 0.0556
Epoch: [3/20], Batch Num: [565/600]
Discriminator Loss: 0.0758, Generator Loss: 4.3297
D(x): 0.9725, D(G(z)): 0.0358
Epoch: [3/20], Batch Num: [566/600]
Discriminator Loss: 0.0617, Generator Loss: 4.4837
D(x): 0.9756, D(G(z)): 0.0281
Epoch: [3/20], Batch Num: [567/600]
Discriminator Loss: 0.0574, Generator Loss: 4.7388
D(x): 0.9842, D(G(z)): 0.0330
Epoch: [3/20], Batch Num: [568/600]
Discriminator Loss: 0.0639, Generator Loss: 4.8575
D(x): 0.9894, D(G(z)): 0.0398
Epoch: [3/20], Batch Num: [569/600]
Discriminator Loss: 0.1346, Generator Loss: 5.8641
D(x): 0.9617, D(G(z)): 0.0223
Epoch: [3/20], Batch Num: [570/600]
Discriminator Loss: 0.0260, Generator Loss: 5.9045
D(x): 0.9891, D(G(z)): 0.0124
Epoch: [3/20], Batch Num: [571/600]
Discriminator Loss: 0.0369, Generator Loss: 5.9373
D(x): 0.9861, D(G(z)): 0.0153
Epoch: [3/20], Batch Num: [572/600]
Discriminator Loss: 0.0165, Generator Loss: 5.9538
D(x): 0.9935, D(G(z)): 0.0092
Epoch: [3/20], Batch Num: [573/600]
Discriminator Loss: 0.0353, Generator Loss: 6.2329
D(x): 0.9829, D(G(z)): 0.0103
Epoch: [3/20], Batch Num: [574/600]
Discriminator Loss: 0.0140, Generator Loss: 6.1992
D(x): 0.9972, D(G(z)): 0.0107
Epoch: [3/20], Batch Num: [575/600]
Discriminator Loss: 0.0208, Generator Loss: 6.0994
D(x): 0.9954, D(G(z)): 0.0147
Epoch: [3/20], Batch Num: [576/600]
Discriminator Loss: 0.0228, Generator Loss: 6.2537
D(x): 0.9972, D(G(z)): 0.0190
Epoch: [3/20], Batch Num: [577/600]
Discriminator Loss: 0.0375, Generator Loss: 6.3344
D(x): 0.9857, D(G(z)): 0.0171
Epoch: [3/20], Batch Num: [578/600]
Discriminator Loss: 0.0441, Generator Loss: 6.5684
D(x): 0.9850, D(G(z)): 0.0177
Epoch: [3/20], Batch Num: [579/600]
Discriminator Loss: 0.0605, Generator Loss: 7.0896
D(x): 0.9821, D(G(z)): 0.0155
Epoch: [3/20], Batch Num: [580/600]
Discriminator Loss: 0.0102, Generator Loss: 7.5751
D(x): 0.9981, D(G(z)): 0.0079
Epoch: [3/20], Batch Num: [581/600]
Discriminator Loss: 0.0142, Generator Loss: 8.1902
D(x): 0.9925, D(G(z)): 0.0040
Epoch: [3/20], Batch Num: [582/600]
Discriminator Loss: 0.0174, Generator Loss: 7.9958
D(x): 0.9899, D(G(z)): 0.0044
Epoch: [3/20], Batch Num: [583/600]
Discriminator Loss: 0.0663, Generator Loss: 7.3812
D(x): 0.9731, D(G(z)): 0.0056
Epoch: [3/20], Batch Num: [584/600]
Discriminator Loss: 0.0495, Generator Loss: 6.8249
D(x): 0.9847, D(G(z)): 0.0029
Epoch: [3/20], Batch Num: [585/600]
Discriminator Loss: 0.0136, Generator Loss: 6.3376
D(x): 0.9950, D(G(z)): 0.0079
Epoch: [3/20], Batch Num: [586/600]
Discriminator Loss: 0.0115, Generator Loss: 5.8652
D(x): 0.9970, D(G(z)): 0.0082
Epoch: [3/20], Batch Num: [587/600]
Discriminator Loss: 0.0552, Generator Loss: 5.8886
D(x): 0.9919, D(G(z)): 0.0296
Epoch: [3/20], Batch Num: [588/600]
Discriminator Loss: 0.0163, Generator Loss: 6.5976
D(x): 0.9983, D(G(z)): 0.0140
Epoch: [3/20], Batch Num: [589/600]
Discriminator Loss: 0.0119, Generator Loss: 6.6057
D(x): 0.9990, D(G(z)): 0.0106
Epoch: [3/20], Batch Num: [590/600]
Discriminator Loss: 0.0171, Generator Loss: 6.8599
D(x): 0.9967, D(G(z)): 0.0131
Epoch: [3/20], Batch Num: [591/600]
Discriminator Loss: 0.0570, Generator Loss: 7.0975
D(x): 0.9832, D(G(z)): 0.0103
Epoch: [3/20], Batch Num: [592/600]
Discriminator Loss: 0.0197, Generator Loss: 7.0059
D(x): 0.9963, D(G(z)): 0.0151
Epoch: [3/20], Batch Num: [593/600]
Discriminator Loss: 0.0283, Generator Loss: 6.8731
D(x): 0.9869, D(G(z)): 0.0123
Epoch: [3/20], Batch Num: [594/600]
Discriminator Loss: 0.0473, Generator Loss: 7.2553
D(x): 0.9963, D(G(z)): 0.0344
Epoch: [3/20], Batch Num: [595/600]
Discriminator Loss: 0.0728, Generator Loss: 7.1238
D(x): 0.9707, D(G(z)): 0.0155
Epoch: [3/20], Batch Num: [596/600]
Discriminator Loss: 0.0517, Generator Loss: 7.9196
D(x): 0.9833, D(G(z)): 0.0204
Epoch: [3/20], Batch Num: [597/600]
Discriminator Loss: 0.0932, Generator Loss: 7.7314
D(x): 0.9661, D(G(z)): 0.0068
Epoch: [3/20], Batch Num: [598/600]
Discriminator Loss: 0.0437, Generator Loss: 7.1023
D(x): 0.9788, D(G(z)): 0.0105
Epoch: [3/20], Batch Num: [599/600]
Discriminator Loss: 0.1029, Generator Loss: 6.0554
D(x): 0.9644, D(G(z)): 0.0309
Epoch: 4, Batch Num: [0/600]

Epoch: [4/20], Batch Num: [0/600]
Discriminator Loss: 0.1072, Generator Loss: 6.4802
D(x): 0.9893, D(G(z)): 0.0516
Epoch: [4/20], Batch Num: [1/600]
Discriminator Loss: 0.2384, Generator Loss: 7.0929
D(x): 0.9590, D(G(z)): 0.0587
Epoch: [4/20], Batch Num: [2/600]
Discriminator Loss: 0.1146, Generator Loss: 6.8372
D(x): 0.9577, D(G(z)): 0.0357
Epoch: [4/20], Batch Num: [3/600]
Discriminator Loss: 0.2100, Generator Loss: 7.5124
D(x): 0.9640, D(G(z)): 0.0470
Epoch: [4/20], Batch Num: [4/600]
Discriminator Loss: 0.1122, Generator Loss: 7.8918
D(x): 0.9611, D(G(z)): 0.0294
Epoch: [4/20], Batch Num: [5/600]
Discriminator Loss: 0.1658, Generator Loss: 8.2442
D(x): 0.9617, D(G(z)): 0.0516
Epoch: [4/20], Batch Num: [6/600]
Discriminator Loss: 0.1128, Generator Loss: 7.6133
D(x): 0.9757, D(G(z)): 0.0313
Epoch: [4/20], Batch Num: [7/600]
Discriminator Loss: 0.1500, Generator Loss: 7.9525
D(x): 0.9612, D(G(z)): 0.0263
Epoch: [4/20], Batch Num: [8/600]
Discriminator Loss: 0.2084, Generator Loss: 7.2970
D(x): 0.9441, D(G(z)): 0.0201
Epoch: [4/20], Batch Num: [9/600]
Discriminator Loss: 0.1397, Generator Loss: 6.8978
D(x): 0.9676, D(G(z)): 0.0340
Epoch: [4/20], Batch Num: [10/600]
Discriminator Loss: 0.1795, Generator Loss: 6.9386
D(x): 0.9765, D(G(z)): 0.0701
Epoch: [4/20], Batch Num: [11/600]
Discriminator Loss: 0.1461, Generator Loss: 7.1556
D(x): 0.9810, D(G(z)): 0.0618
Epoch: [4/20], Batch Num: [12/600]
Discriminator Loss: 0.1566, Generator Loss: 8.1084
D(x): 0.9570, D(G(z)): 0.0471
Epoch: [4/20], Batch Num: [13/600]
Discriminator Loss: 0.1410, Generator Loss: 8.1784
D(x): 0.9578, D(G(z)): 0.0265
Epoch: [4/20], Batch Num: [14/600]
Discriminator Loss: 0.2130, Generator Loss: 8.2614
D(x): 0.9488, D(G(z)): 0.0339
Epoch: [4/20], Batch Num: [15/600]
Discriminator Loss: 0.2605, Generator Loss: 7.9845
D(x): 0.9615, D(G(z)): 0.0738
Epoch: [4/20], Batch Num: [16/600]
Discriminator Loss: 0.1266, Generator Loss: 7.5464
D(x): 0.9615, D(G(z)): 0.0529
Epoch: [4/20], Batch Num: [17/600]
Discriminator Loss: 0.2311, Generator Loss: 6.4602
D(x): 0.9393, D(G(z)): 0.0625
Epoch: [4/20], Batch Num: [18/600]
Discriminator Loss: 0.4871, Generator Loss: 7.7528
D(x): 0.9818, D(G(z)): 0.2078
Epoch: [4/20], Batch Num: [19/600]
Discriminator Loss: 0.4107, Generator Loss: 7.2438
D(x): 0.9386, D(G(z)): 0.1386
Epoch: [4/20], Batch Num: [20/600]
Discriminator Loss: 0.4113, Generator Loss: 8.7295
D(x): 0.9311, D(G(z)): 0.1182
Epoch: [4/20], Batch Num: [21/600]
Discriminator Loss: 0.3691, Generator Loss: 7.6844
D(x): 0.9060, D(G(z)): 0.0984
Epoch: [4/20], Batch Num: [22/600]
Discriminator Loss: 0.5503, Generator Loss: 5.8229
D(x): 0.8842, D(G(z)): 0.0930
Epoch: [4/20], Batch Num: [23/600]
Discriminator Loss: 0.5905, Generator Loss: 5.0161
D(x): 0.8121, D(G(z)): 0.1053
Epoch: [4/20], Batch Num: [24/600]
Discriminator Loss: 0.7162, Generator Loss: 3.8389
D(x): 0.8771, D(G(z)): 0.2174
Epoch: [4/20], Batch Num: [25/600]
Discriminator Loss: 0.7803, Generator Loss: 3.4952
D(x): 0.9366, D(G(z)): 0.3239
Epoch: [4/20], Batch Num: [26/600]
Discriminator Loss: 0.8598, Generator Loss: 4.9928
D(x): 0.8842, D(G(z)): 0.3226
Epoch: [4/20], Batch Num: [27/600]
Discriminator Loss: 0.5163, Generator Loss: 5.5884
D(x): 0.8832, D(G(z)): 0.1401
Epoch: [4/20], Batch Num: [28/600]
Discriminator Loss: 0.4471, Generator Loss: 6.5239
D(x): 0.8533, D(G(z)): 0.0857
Epoch: [4/20], Batch Num: [29/600]
Discriminator Loss: 0.5204, Generator Loss: 5.4166
D(x): 0.7994, D(G(z)): 0.0446
Epoch: [4/20], Batch Num: [30/600]
Discriminator Loss: 0.5045, Generator Loss: 4.8988
D(x): 0.8173, D(G(z)): 0.0573
Epoch: [4/20], Batch Num: [31/600]
Discriminator Loss: 0.5455, Generator Loss: 3.9772
D(x): 0.8591, D(G(z)): 0.1349
Epoch: [4/20], Batch Num: [32/600]
Discriminator Loss: 0.4631, Generator Loss: 3.4366
D(x): 0.9172, D(G(z)): 0.1856
Epoch: [4/20], Batch Num: [33/600]
Discriminator Loss: 0.5931, Generator Loss: 3.8007
D(x): 0.9241, D(G(z)): 0.2536
Epoch: [4/20], Batch Num: [34/600]
Discriminator Loss: 0.4734, Generator Loss: 5.2266
D(x): 0.9008, D(G(z)): 0.2055
Epoch: [4/20], Batch Num: [35/600]
Discriminator Loss: 0.5733, Generator Loss: 5.6980
D(x): 0.8841, D(G(z)): 0.1516
Epoch: [4/20], Batch Num: [36/600]
Discriminator Loss: 0.5564, Generator Loss: 6.5305
D(x): 0.7913, D(G(z)): 0.0655
Epoch: [4/20], Batch Num: [37/600]
Discriminator Loss: 0.5502, Generator Loss: 5.3786
D(x): 0.8018, D(G(z)): 0.0724
Epoch: [4/20], Batch Num: [38/600]
Discriminator Loss: 0.7638, Generator Loss: 4.5821
D(x): 0.7734, D(G(z)): 0.1036
Epoch: [4/20], Batch Num: [39/600]
Discriminator Loss: 0.8969, Generator Loss: 2.7162
D(x): 0.8201, D(G(z)): 0.2084
Epoch: [4/20], Batch Num: [40/600]
Discriminator Loss: 0.6947, Generator Loss: 2.7197
D(x): 0.8936, D(G(z)): 0.3007
Epoch: [4/20], Batch Num: [41/600]
Discriminator Loss: 0.6508, Generator Loss: 3.0914
D(x): 0.9099, D(G(z)): 0.2995
Epoch: [4/20], Batch Num: [42/600]
Discriminator Loss: 0.7317, Generator Loss: 3.8575
D(x): 0.8583, D(G(z)): 0.2413
Epoch: [4/20], Batch Num: [43/600]
Discriminator Loss: 0.6819, Generator Loss: 4.0843
D(x): 0.7591, D(G(z)): 0.1006
Epoch: [4/20], Batch Num: [44/600]
Discriminator Loss: 0.7278, Generator Loss: 4.1200
D(x): 0.7354, D(G(z)): 0.1211
Epoch: [4/20], Batch Num: [45/600]
Discriminator Loss: 1.0406, Generator Loss: 2.4562
D(x): 0.6568, D(G(z)): 0.1601
Epoch: [4/20], Batch Num: [46/600]
Discriminator Loss: 0.9040, Generator Loss: 1.9330
D(x): 0.7895, D(G(z)): 0.2800
Epoch: [4/20], Batch Num: [47/600]
Discriminator Loss: 0.9516, Generator Loss: 1.9214
D(x): 0.8246, D(G(z)): 0.3872
Epoch: [4/20], Batch Num: [48/600]
Discriminator Loss: 1.2891, Generator Loss: 2.3484
D(x): 0.6834, D(G(z)): 0.3549
Epoch: [4/20], Batch Num: [49/600]
Discriminator Loss: 1.1486, Generator Loss: 1.9339
D(x): 0.6483, D(G(z)): 0.2658
Epoch: [4/20], Batch Num: [50/600]
Discriminator Loss: 0.9530, Generator Loss: 2.0866
D(x): 0.7000, D(G(z)): 0.2466
Epoch: [4/20], Batch Num: [51/600]
Discriminator Loss: 1.1771, Generator Loss: 1.7437
D(x): 0.6149, D(G(z)): 0.2247
Epoch: [4/20], Batch Num: [52/600]
Discriminator Loss: 1.2130, Generator Loss: 1.5488
D(x): 0.6342, D(G(z)): 0.3022
Epoch: [4/20], Batch Num: [53/600]
Discriminator Loss: 0.9065, Generator Loss: 1.2553
D(x): 0.7117, D(G(z)): 0.2960
Epoch: [4/20], Batch Num: [54/600]
Discriminator Loss: 0.9645, Generator Loss: 1.4204
D(x): 0.7151, D(G(z)): 0.3247
Epoch: [4/20], Batch Num: [55/600]
Discriminator Loss: 1.0618, Generator Loss: 1.3763
D(x): 0.6508, D(G(z)): 0.3261
Epoch: [4/20], Batch Num: [56/600]
Discriminator Loss: 0.8111, Generator Loss: 1.4239
D(x): 0.7404, D(G(z)): 0.3009
Epoch: [4/20], Batch Num: [57/600]
Discriminator Loss: 0.8510, Generator Loss: 1.4571
D(x): 0.7298, D(G(z)): 0.3100
Epoch: [4/20], Batch Num: [58/600]
Discriminator Loss: 0.7297, Generator Loss: 1.5942
D(x): 0.7496, D(G(z)): 0.2647
Epoch: [4/20], Batch Num: [59/600]
Discriminator Loss: 0.6840, Generator Loss: 1.6504
D(x): 0.7703, D(G(z)): 0.2662
Epoch: [4/20], Batch Num: [60/600]
Discriminator Loss: 0.5871, Generator Loss: 1.8016
D(x): 0.8359, D(G(z)): 0.2485
Epoch: [4/20], Batch Num: [61/600]
Discriminator Loss: 0.5975, Generator Loss: 2.0051
D(x): 0.7880, D(G(z)): 0.2235
Epoch: [4/20], Batch Num: [62/600]
Discriminator Loss: 0.5758, Generator Loss: 1.8989
D(x): 0.7889, D(G(z)): 0.2117
Epoch: [4/20], Batch Num: [63/600]
Discriminator Loss: 0.4016, Generator Loss: 2.0366
D(x): 0.8536, D(G(z)): 0.1683
Epoch: [4/20], Batch Num: [64/600]
Discriminator Loss: 0.4661, Generator Loss: 1.8159
D(x): 0.8283, D(G(z)): 0.1768
Epoch: [4/20], Batch Num: [65/600]
Discriminator Loss: 0.4975, Generator Loss: 1.9863
D(x): 0.8534, D(G(z)): 0.2212
Epoch: [4/20], Batch Num: [66/600]
Discriminator Loss: 0.4247, Generator Loss: 2.0772
D(x): 0.8815, D(G(z)): 0.1886
Epoch: [4/20], Batch Num: [67/600]
Discriminator Loss: 0.3915, Generator Loss: 2.4297
D(x): 0.8785, D(G(z)): 0.1506
Epoch: [4/20], Batch Num: [68/600]
Discriminator Loss: 0.4209, Generator Loss: 2.4177
D(x): 0.8687, D(G(z)): 0.1646
Epoch: [4/20], Batch Num: [69/600]
Discriminator Loss: 0.3875, Generator Loss: 2.8695
D(x): 0.9158, D(G(z)): 0.1719
Epoch: [4/20], Batch Num: [70/600]
Discriminator Loss: 0.4407, Generator Loss: 3.2973
D(x): 0.8335, D(G(z)): 0.1178
Epoch: [4/20], Batch Num: [71/600]
Discriminator Loss: 0.3862, Generator Loss: 3.0066
D(x): 0.8743, D(G(z)): 0.1017
Epoch: [4/20], Batch Num: [72/600]
Discriminator Loss: 0.3438, Generator Loss: 2.7135
D(x): 0.8731, D(G(z)): 0.0882
Epoch: [4/20], Batch Num: [73/600]
Discriminator Loss: 0.2417, Generator Loss: 2.5084
D(x): 0.9248, D(G(z)): 0.1060
Epoch: [4/20], Batch Num: [74/600]
Discriminator Loss: 0.3436, Generator Loss: 2.3467
D(x): 0.9246, D(G(z)): 0.1646
Epoch: [4/20], Batch Num: [75/600]
Discriminator Loss: 0.3305, Generator Loss: 2.9540
D(x): 0.9420, D(G(z)): 0.1725
Epoch: [4/20], Batch Num: [76/600]
Discriminator Loss: 0.2830, Generator Loss: 3.6131
D(x): 0.9336, D(G(z)): 0.1297
Epoch: [4/20], Batch Num: [77/600]
Discriminator Loss: 0.2395, Generator Loss: 4.1846
D(x): 0.9084, D(G(z)): 0.0698
Epoch: [4/20], Batch Num: [78/600]
Discriminator Loss: 0.3204, Generator Loss: 4.0181
D(x): 0.8755, D(G(z)): 0.0451
Epoch: [4/20], Batch Num: [79/600]
Discriminator Loss: 0.2987, Generator Loss: 2.9825
D(x): 0.8742, D(G(z)): 0.0432
Epoch: [4/20], Batch Num: [80/600]
Discriminator Loss: 0.2630, Generator Loss: 2.2442
D(x): 0.9240, D(G(z)): 0.1016
Epoch: [4/20], Batch Num: [81/600]
Discriminator Loss: 0.3758, Generator Loss: 2.9371
D(x): 0.9673, D(G(z)): 0.2210
Epoch: [4/20], Batch Num: [82/600]
Discriminator Loss: 0.3227, Generator Loss: 3.2633
D(x): 0.9177, D(G(z)): 0.1345
Epoch: [4/20], Batch Num: [83/600]
Discriminator Loss: 0.3247, Generator Loss: 3.3779
D(x): 0.9017, D(G(z)): 0.0918
Epoch: [4/20], Batch Num: [84/600]
Discriminator Loss: 0.2675, Generator Loss: 3.5780
D(x): 0.9299, D(G(z)): 0.0808
Epoch: [4/20], Batch Num: [85/600]
Discriminator Loss: 0.2475, Generator Loss: 3.3650
D(x): 0.9206, D(G(z)): 0.0647
Epoch: [4/20], Batch Num: [86/600]
Discriminator Loss: 0.3723, Generator Loss: 2.9614
D(x): 0.8775, D(G(z)): 0.0892
Epoch: [4/20], Batch Num: [87/600]
Discriminator Loss: 0.4657, Generator Loss: 2.6730
D(x): 0.9153, D(G(z)): 0.1485
Epoch: [4/20], Batch Num: [88/600]
Discriminator Loss: 0.3679, Generator Loss: 2.9919
D(x): 0.9104, D(G(z)): 0.1515
Epoch: [4/20], Batch Num: [89/600]
Discriminator Loss: 0.1865, Generator Loss: 3.7474
D(x): 0.9729, D(G(z)): 0.1257
Epoch: [4/20], Batch Num: [90/600]
Discriminator Loss: 0.3649, Generator Loss: 3.7715
D(x): 0.8746, D(G(z)): 0.0831
Epoch: [4/20], Batch Num: [91/600]
Discriminator Loss: 0.4219, Generator Loss: 3.3962
D(x): 0.8752, D(G(z)): 0.0984
Epoch: [4/20], Batch Num: [92/600]
Discriminator Loss: 0.4060, Generator Loss: 3.2735
D(x): 0.8814, D(G(z)): 0.0829
Epoch: [4/20], Batch Num: [93/600]
Discriminator Loss: 0.3052, Generator Loss: 2.8474
D(x): 0.9560, D(G(z)): 0.1463
Epoch: [4/20], Batch Num: [94/600]
Discriminator Loss: 0.2495, Generator Loss: 3.3790
D(x): 0.9389, D(G(z)): 0.1215
Epoch: [4/20], Batch Num: [95/600]
Discriminator Loss: 0.1912, Generator Loss: 4.0309
D(x): 0.9604, D(G(z)): 0.0956
Epoch: [4/20], Batch Num: [96/600]
Discriminator Loss: 0.3116, Generator Loss: 3.8398
D(x): 0.9002, D(G(z)): 0.0770
Epoch: [4/20], Batch Num: [97/600]
Discriminator Loss: 0.3890, Generator Loss: 3.3222
D(x): 0.8594, D(G(z)): 0.0561
Epoch: [4/20], Batch Num: [98/600]
Discriminator Loss: 0.2836, Generator Loss: 3.2248
D(x): 0.9347, D(G(z)): 0.1180
Epoch: [4/20], Batch Num: [99/600]
Discriminator Loss: 0.3335, Generator Loss: 3.0520
D(x): 0.9099, D(G(z)): 0.0995
Epoch: 4, Batch Num: [100/600]

Epoch: [4/20], Batch Num: [100/600]
Discriminator Loss: 0.2645, Generator Loss: 2.9137
D(x): 0.9379, D(G(z)): 0.1221
Epoch: [4/20], Batch Num: [101/600]
Discriminator Loss: 0.2310, Generator Loss: 3.7265
D(x): 0.9673, D(G(z)): 0.1154
Epoch: [4/20], Batch Num: [102/600]
Discriminator Loss: 0.1261, Generator Loss: 3.8735
D(x): 0.9777, D(G(z)): 0.0779
Epoch: [4/20], Batch Num: [103/600]
Discriminator Loss: 0.1285, Generator Loss: 4.6112
D(x): 0.9525, D(G(z)): 0.0548
Epoch: [4/20], Batch Num: [104/600]
Discriminator Loss: 0.2581, Generator Loss: 4.6940
D(x): 0.8951, D(G(z)): 0.0427
Epoch: [4/20], Batch Num: [105/600]
Discriminator Loss: 0.1936, Generator Loss: 4.5081
D(x): 0.9230, D(G(z)): 0.0491
Epoch: [4/20], Batch Num: [106/600]
Discriminator Loss: 0.2277, Generator Loss: 4.3372
D(x): 0.9463, D(G(z)): 0.0587
Epoch: [4/20], Batch Num: [107/600]
Discriminator Loss: 0.1309, Generator Loss: 4.2961
D(x): 0.9631, D(G(z)): 0.0558
Epoch: [4/20], Batch Num: [108/600]
Discriminator Loss: 0.1104, Generator Loss: 3.8932
D(x): 0.9741, D(G(z)): 0.0596
Epoch: [4/20], Batch Num: [109/600]
Discriminator Loss: 0.1101, Generator Loss: 4.0616
D(x): 0.9723, D(G(z)): 0.0587
Epoch: [4/20], Batch Num: [110/600]
Discriminator Loss: 0.2671, Generator Loss: 4.6487
D(x): 0.9639, D(G(z)): 0.1140
Epoch: [4/20], Batch Num: [111/600]
Discriminator Loss: 0.1174, Generator Loss: 4.9186
D(x): 0.9777, D(G(z)): 0.0617
Epoch: [4/20], Batch Num: [112/600]
Discriminator Loss: 0.2567, Generator Loss: 5.0285
D(x): 0.9355, D(G(z)): 0.0623
Epoch: [4/20], Batch Num: [113/600]
Discriminator Loss: 0.1537, Generator Loss: 5.1422
D(x): 0.9463, D(G(z)): 0.0340
Epoch: [4/20], Batch Num: [114/600]
Discriminator Loss: 0.1270, Generator Loss: 5.0178
D(x): 0.9582, D(G(z)): 0.0513
Epoch: [4/20], Batch Num: [115/600]
Discriminator Loss: 0.1265, Generator Loss: 4.8586
D(x): 0.9495, D(G(z)): 0.0484
Epoch: [4/20], Batch Num: [116/600]
Discriminator Loss: 0.2284, Generator Loss: 4.8060
D(x): 0.9142, D(G(z)): 0.0357
Epoch: [4/20], Batch Num: [117/600]
Discriminator Loss: 0.1013, Generator Loss: 3.9753
D(x): 0.9894, D(G(z)): 0.0722
Epoch: [4/20], Batch Num: [118/600]
Discriminator Loss: 0.2563, Generator Loss: 3.6055
D(x): 0.9467, D(G(z)): 0.1080
Epoch: [4/20], Batch Num: [119/600]
Discriminator Loss: 0.1555, Generator Loss: 4.0020
D(x): 0.9878, D(G(z)): 0.1015
Epoch: [4/20], Batch Num: [120/600]
Discriminator Loss: 0.1473, Generator Loss: 4.4720
D(x): 0.9670, D(G(z)): 0.0776
Epoch: [4/20], Batch Num: [121/600]
Discriminator Loss: 0.2098, Generator Loss: 4.5879
D(x): 0.9561, D(G(z)): 0.0907
Epoch: [4/20], Batch Num: [122/600]
Discriminator Loss: 0.2847, Generator Loss: 4.8071
D(x): 0.9023, D(G(z)): 0.0449
Epoch: [4/20], Batch Num: [123/600]
Discriminator Loss: 0.2272, Generator Loss: 4.0179
D(x): 0.9327, D(G(z)): 0.0565
Epoch: [4/20], Batch Num: [124/600]
Discriminator Loss: 0.2536, Generator Loss: 3.2823
D(x): 0.9019, D(G(z)): 0.0682
Epoch: [4/20], Batch Num: [125/600]
Discriminator Loss: 0.4135, Generator Loss: 2.8633
D(x): 0.9231, D(G(z)): 0.1537
Epoch: [4/20], Batch Num: [126/600]
Discriminator Loss: 0.2918, Generator Loss: 3.1692
D(x): 0.9728, D(G(z)): 0.1644
Epoch: [4/20], Batch Num: [127/600]
Discriminator Loss: 0.3166, Generator Loss: 3.3580
D(x): 0.9442, D(G(z)): 0.1497
Epoch: [4/20], Batch Num: [128/600]
Discriminator Loss: 0.3298, Generator Loss: 3.9556
D(x): 0.9084, D(G(z)): 0.0990
Epoch: [4/20], Batch Num: [129/600]
Discriminator Loss: 0.3275, Generator Loss: 4.1296
D(x): 0.9206, D(G(z)): 0.0778
Epoch: [4/20], Batch Num: [130/600]
Discriminator Loss: 0.2099, Generator Loss: 3.6871
D(x): 0.9443, D(G(z)): 0.0749
Epoch: [4/20], Batch Num: [131/600]
Discriminator Loss: 0.3041, Generator Loss: 3.1473
D(x): 0.9028, D(G(z)): 0.0779
Epoch: [4/20], Batch Num: [132/600]
Discriminator Loss: 0.3687, Generator Loss: 2.5818
D(x): 0.9059, D(G(z)): 0.1109
Epoch: [4/20], Batch Num: [133/600]
Discriminator Loss: 0.3263, Generator Loss: 2.4848
D(x): 0.9305, D(G(z)): 0.1357
Epoch: [4/20], Batch Num: [134/600]
Discriminator Loss: 0.4183, Generator Loss: 2.6917
D(x): 0.9670, D(G(z)): 0.2142
Epoch: [4/20], Batch Num: [135/600]
Discriminator Loss: 0.3273, Generator Loss: 3.2109
D(x): 0.9439, D(G(z)): 0.1641
Epoch: [4/20], Batch Num: [136/600]
Discriminator Loss: 0.2224, Generator Loss: 3.5060
D(x): 0.9476, D(G(z)): 0.1027
Epoch: [4/20], Batch Num: [137/600]
Discriminator Loss: 0.3561, Generator Loss: 3.3047
D(x): 0.8776, D(G(z)): 0.0583
Epoch: [4/20], Batch Num: [138/600]
Discriminator Loss: 0.3901, Generator Loss: 3.3500
D(x): 0.8780, D(G(z)): 0.0705
Epoch: [4/20], Batch Num: [139/600]
Discriminator Loss: 0.2930, Generator Loss: 2.6436
D(x): 0.9043, D(G(z)): 0.0682
Epoch: [4/20], Batch Num: [140/600]
Discriminator Loss: 0.2273, Generator Loss: 2.3871
D(x): 0.9558, D(G(z)): 0.1232
Epoch: [4/20], Batch Num: [141/600]
Discriminator Loss: 0.2969, Generator Loss: 2.3676
D(x): 0.9592, D(G(z)): 0.1875
Epoch: [4/20], Batch Num: [142/600]
Discriminator Loss: 0.2448, Generator Loss: 2.1769
D(x): 0.9622, D(G(z)): 0.1617
Epoch: [4/20], Batch Num: [143/600]
Discriminator Loss: 0.3250, Generator Loss: 2.3573
D(x): 0.9559, D(G(z)): 0.1934
Epoch: [4/20], Batch Num: [144/600]
Discriminator Loss: 0.2625, Generator Loss: 2.4979
D(x): 0.9375, D(G(z)): 0.1433
Epoch: [4/20], Batch Num: [145/600]
Discriminator Loss: 0.3794, Generator Loss: 2.4836
D(x): 0.9019, D(G(z)): 0.1443
Epoch: [4/20], Batch Num: [146/600]
Discriminator Loss: 0.3475, Generator Loss: 2.7190
D(x): 0.9498, D(G(z)): 0.1802
Epoch: [4/20], Batch Num: [147/600]
Discriminator Loss: 0.3553, Generator Loss: 2.7119
D(x): 0.9195, D(G(z)): 0.1262
Epoch: [4/20], Batch Num: [148/600]
Discriminator Loss: 0.3529, Generator Loss: 2.6155
D(x): 0.9400, D(G(z)): 0.1456
Epoch: [4/20], Batch Num: [149/600]
Discriminator Loss: 0.4500, Generator Loss: 2.4114
D(x): 0.8919, D(G(z)): 0.1443
Epoch: [4/20], Batch Num: [150/600]
Discriminator Loss: 0.3934, Generator Loss: 2.1045
D(x): 0.9110, D(G(z)): 0.1448
Epoch: [4/20], Batch Num: [151/600]
Discriminator Loss: 0.3782, Generator Loss: 2.0095
D(x): 0.9441, D(G(z)): 0.2002
Epoch: [4/20], Batch Num: [152/600]
Discriminator Loss: 0.5119, Generator Loss: 2.0659
D(x): 0.9547, D(G(z)): 0.2313
Epoch: [4/20], Batch Num: [153/600]
Discriminator Loss: 0.5723, Generator Loss: 2.2646
D(x): 0.9124, D(G(z)): 0.2387
Epoch: [4/20], Batch Num: [154/600]
Discriminator Loss: 0.4838, Generator Loss: 2.4584
D(x): 0.9316, D(G(z)): 0.2241
Epoch: [4/20], Batch Num: [155/600]
Discriminator Loss: 0.5291, Generator Loss: 2.5226
D(x): 0.9474, D(G(z)): 0.2138
Epoch: [4/20], Batch Num: [156/600]
Discriminator Loss: 0.5073, Generator Loss: 2.7312
D(x): 0.8652, D(G(z)): 0.1448
Epoch: [4/20], Batch Num: [157/600]
Discriminator Loss: 0.6420, Generator Loss: 2.7070
D(x): 0.8523, D(G(z)): 0.1532
Epoch: [4/20], Batch Num: [158/600]
Discriminator Loss: 0.3318, Generator Loss: 2.4594
D(x): 0.9110, D(G(z)): 0.1448
Epoch: [4/20], Batch Num: [159/600]
Discriminator Loss: 0.5579, Generator Loss: 2.5251
D(x): 0.8948, D(G(z)): 0.1973
Epoch: [4/20], Batch Num: [160/600]
Discriminator Loss: 0.3905, Generator Loss: 2.5179
D(x): 0.9602, D(G(z)): 0.2089
Epoch: [4/20], Batch Num: [161/600]
Discriminator Loss: 0.3516, Generator Loss: 2.6561
D(x): 0.9555, D(G(z)): 0.2021
Epoch: [4/20], Batch Num: [162/600]
Discriminator Loss: 0.3343, Generator Loss: 3.0472
D(x): 0.9680, D(G(z)): 0.1707
Epoch: [4/20], Batch Num: [163/600]
Discriminator Loss: 0.2501, Generator Loss: 3.4776
D(x): 0.9414, D(G(z)): 0.1176
Epoch: [4/20], Batch Num: [164/600]
Discriminator Loss: 0.2666, Generator Loss: 3.7752
D(x): 0.9074, D(G(z)): 0.0850
Epoch: [4/20], Batch Num: [165/600]
Discriminator Loss: 0.2136, Generator Loss: 3.7241
D(x): 0.9326, D(G(z)): 0.0749
Epoch: [4/20], Batch Num: [166/600]
Discriminator Loss: 0.2790, Generator Loss: 3.9004
D(x): 0.9086, D(G(z)): 0.0711
Epoch: [4/20], Batch Num: [167/600]
Discriminator Loss: 0.2381, Generator Loss: 3.8098
D(x): 0.9201, D(G(z)): 0.0656
Epoch: [4/20], Batch Num: [168/600]
Discriminator Loss: 0.1691, Generator Loss: 3.5468
D(x): 0.9584, D(G(z)): 0.0809
Epoch: [4/20], Batch Num: [169/600]
Discriminator Loss: 0.2286, Generator Loss: 3.3353
D(x): 0.9698, D(G(z)): 0.1225
Epoch: [4/20], Batch Num: [170/600]
Discriminator Loss: 0.3044, Generator Loss: 3.5961
D(x): 0.9505, D(G(z)): 0.1300
Epoch: [4/20], Batch Num: [171/600]
Discriminator Loss: 0.1703, Generator Loss: 3.7342
D(x): 0.9594, D(G(z)): 0.0755
Epoch: [4/20], Batch Num: [172/600]
Discriminator Loss: 0.1954, Generator Loss: 3.9897
D(x): 0.9478, D(G(z)): 0.0651
Epoch: [4/20], Batch Num: [173/600]
Discriminator Loss: 0.1459, Generator Loss: 4.3666
D(x): 0.9522, D(G(z)): 0.0677
Epoch: [4/20], Batch Num: [174/600]
Discriminator Loss: 0.2646, Generator Loss: 4.4789
D(x): 0.9028, D(G(z)): 0.0501
Epoch: [4/20], Batch Num: [175/600]
Discriminator Loss: 0.1836, Generator Loss: 4.3579
D(x): 0.9222, D(G(z)): 0.0477
Epoch: [4/20], Batch Num: [176/600]
Discriminator Loss: 0.2838, Generator Loss: 4.1035
D(x): 0.9240, D(G(z)): 0.0673
Epoch: [4/20], Batch Num: [177/600]
Discriminator Loss: 0.3681, Generator Loss: 3.6322
D(x): 0.9079, D(G(z)): 0.1238
Epoch: [4/20], Batch Num: [178/600]
Discriminator Loss: 0.3030, Generator Loss: 3.7420
D(x): 0.9277, D(G(z)): 0.0967
Epoch: [4/20], Batch Num: [179/600]
Discriminator Loss: 0.3937, Generator Loss: 4.1868
D(x): 0.9167, D(G(z)): 0.1180
Epoch: [4/20], Batch Num: [180/600]
Discriminator Loss: 0.2426, Generator Loss: 4.5777
D(x): 0.9236, D(G(z)): 0.0699
Epoch: [4/20], Batch Num: [181/600]
Discriminator Loss: 0.1915, Generator Loss: 4.9134
D(x): 0.9499, D(G(z)): 0.0767
Epoch: [4/20], Batch Num: [182/600]
Discriminator Loss: 0.2676, Generator Loss: 4.6297
D(x): 0.9189, D(G(z)): 0.0455
Epoch: [4/20], Batch Num: [183/600]
Discriminator Loss: 0.2055, Generator Loss: 4.5154
D(x): 0.9359, D(G(z)): 0.0478
Epoch: [4/20], Batch Num: [184/600]
Discriminator Loss: 0.1705, Generator Loss: 4.2805
D(x): 0.9620, D(G(z)): 0.0763
Epoch: [4/20], Batch Num: [185/600]
Discriminator Loss: 0.2089, Generator Loss: 4.5304
D(x): 0.9431, D(G(z)): 0.0808
Epoch: [4/20], Batch Num: [186/600]
Discriminator Loss: 0.0812, Generator Loss: 4.7930
D(x): 0.9748, D(G(z)): 0.0437
Epoch: [4/20], Batch Num: [187/600]
Discriminator Loss: 0.2151, Generator Loss: 4.4958
D(x): 0.9378, D(G(z)): 0.0742
Epoch: [4/20], Batch Num: [188/600]
Discriminator Loss: 0.2380, Generator Loss: 4.7407
D(x): 0.9449, D(G(z)): 0.0573
Epoch: [4/20], Batch Num: [189/600]
Discriminator Loss: 0.2869, Generator Loss: 4.1497
D(x): 0.9018, D(G(z)): 0.0559
Epoch: [4/20], Batch Num: [190/600]
Discriminator Loss: 0.4008, Generator Loss: 3.7108
D(x): 0.9196, D(G(z)): 0.1239
Epoch: [4/20], Batch Num: [191/600]
Discriminator Loss: 0.3598, Generator Loss: 4.2803
D(x): 0.9552, D(G(z)): 0.1655
Epoch: [4/20], Batch Num: [192/600]
Discriminator Loss: 0.4252, Generator Loss: 4.3834
D(x): 0.8933, D(G(z)): 0.0845
Epoch: [4/20], Batch Num: [193/600]
Discriminator Loss: 0.6188, Generator Loss: 3.4054
D(x): 0.8668, D(G(z)): 0.1046
Epoch: [4/20], Batch Num: [194/600]
Discriminator Loss: 0.5420, Generator Loss: 2.9810
D(x): 0.8844, D(G(z)): 0.1411
Epoch: [4/20], Batch Num: [195/600]
Discriminator Loss: 0.6476, Generator Loss: 3.1129
D(x): 0.9059, D(G(z)): 0.2067
Epoch: [4/20], Batch Num: [196/600]
Discriminator Loss: 0.5747, Generator Loss: 3.2459
D(x): 0.8692, D(G(z)): 0.1609
Epoch: [4/20], Batch Num: [197/600]
Discriminator Loss: 0.5679, Generator Loss: 3.1749
D(x): 0.8776, D(G(z)): 0.1517
Epoch: [4/20], Batch Num: [198/600]
Discriminator Loss: 0.3698, Generator Loss: 3.5293
D(x): 0.9089, D(G(z)): 0.1304
Epoch: [4/20], Batch Num: [199/600]
Discriminator Loss: 0.4594, Generator Loss: 3.0087
D(x): 0.8877, D(G(z)): 0.1248
Epoch: 4, Batch Num: [200/600]

Epoch: [4/20], Batch Num: [200/600]
Discriminator Loss: 0.4166, Generator Loss: 3.0356
D(x): 0.8990, D(G(z)): 0.1227
Epoch: [4/20], Batch Num: [201/600]
Discriminator Loss: 0.3838, Generator Loss: 3.1462
D(x): 0.9203, D(G(z)): 0.1374
Epoch: [4/20], Batch Num: [202/600]
Discriminator Loss: 0.4935, Generator Loss: 2.9721
D(x): 0.8704, D(G(z)): 0.1235
Epoch: [4/20], Batch Num: [203/600]
Discriminator Loss: 0.3762, Generator Loss: 2.7346
D(x): 0.9029, D(G(z)): 0.1485
Epoch: [4/20], Batch Num: [204/600]
Discriminator Loss: 0.3764, Generator Loss: 2.7901
D(x): 0.9310, D(G(z)): 0.1632
Epoch: [4/20], Batch Num: [205/600]
Discriminator Loss: 0.3029, Generator Loss: 3.0026
D(x): 0.9474, D(G(z)): 0.1471
Epoch: [4/20], Batch Num: [206/600]
Discriminator Loss: 0.2662, Generator Loss: 3.6819
D(x): 0.9270, D(G(z)): 0.0981
Epoch: [4/20], Batch Num: [207/600]
Discriminator Loss: 0.2250, Generator Loss: 3.5871
D(x): 0.9310, D(G(z)): 0.0837
Epoch: [4/20], Batch Num: [208/600]
Discriminator Loss: 0.2181, Generator Loss: 3.9734
D(x): 0.9354, D(G(z)): 0.0653
Epoch: [4/20], Batch Num: [209/600]
Discriminator Loss: 0.2660, Generator Loss: 3.7994
D(x): 0.8977, D(G(z)): 0.0613
Epoch: [4/20], Batch Num: [210/600]
Discriminator Loss: 0.2692, Generator Loss: 3.7337
D(x): 0.9198, D(G(z)): 0.0868
Epoch: [4/20], Batch Num: [211/600]
Discriminator Loss: 0.2234, Generator Loss: 3.1305
D(x): 0.9193, D(G(z)): 0.0720
Epoch: [4/20], Batch Num: [212/600]
Discriminator Loss: 0.3438, Generator Loss: 3.4313
D(x): 0.9327, D(G(z)): 0.1360
Epoch: [4/20], Batch Num: [213/600]
Discriminator Loss: 0.2154, Generator Loss: 3.5311
D(x): 0.9501, D(G(z)): 0.1109
Epoch: [4/20], Batch Num: [214/600]
Discriminator Loss: 0.3836, Generator Loss: 3.6669
D(x): 0.8928, D(G(z)): 0.1166
Epoch: [4/20], Batch Num: [215/600]
Discriminator Loss: 0.3744, Generator Loss: 3.7060
D(x): 0.9043, D(G(z)): 0.1151
Epoch: [4/20], Batch Num: [216/600]
Discriminator Loss: 0.2416, Generator Loss: 4.2348
D(x): 0.9452, D(G(z)): 0.0888
Epoch: [4/20], Batch Num: [217/600]
Discriminator Loss: 0.3707, Generator Loss: 3.6854
D(x): 0.8745, D(G(z)): 0.0660
Epoch: [4/20], Batch Num: [218/600]
Discriminator Loss: 0.5033, Generator Loss: 3.2937
D(x): 0.8435, D(G(z)): 0.0741
Epoch: [4/20], Batch Num: [219/600]
Discriminator Loss: 0.3175, Generator Loss: 3.0348
D(x): 0.9152, D(G(z)): 0.1206
Epoch: [4/20], Batch Num: [220/600]
Discriminator Loss: 0.4876, Generator Loss: 3.1146
D(x): 0.8857, D(G(z)): 0.1795
Epoch: [4/20], Batch Num: [221/600]
Discriminator Loss: 0.4939, Generator Loss: 3.7188
D(x): 0.9210, D(G(z)): 0.1758
Epoch: [4/20], Batch Num: [222/600]
Discriminator Loss: 0.6843, Generator Loss: 3.6128
D(x): 0.8045, D(G(z)): 0.1052
Epoch: [4/20], Batch Num: [223/600]
Discriminator Loss: 0.5476, Generator Loss: 3.3267
D(x): 0.8504, D(G(z)): 0.1100
Epoch: [4/20], Batch Num: [224/600]
Discriminator Loss: 0.5003, Generator Loss: 2.9497
D(x): 0.8297, D(G(z)): 0.1198
Epoch: [4/20], Batch Num: [225/600]
Discriminator Loss: 0.5096, Generator Loss: 2.7785
D(x): 0.8703, D(G(z)): 0.1551
Epoch: [4/20], Batch Num: [226/600]
Discriminator Loss: 0.3784, Generator Loss: 3.0216
D(x): 0.9369, D(G(z)): 0.1866
Epoch: [4/20], Batch Num: [227/600]
Discriminator Loss: 0.6164, Generator Loss: 3.5360
D(x): 0.8334, D(G(z)): 0.1316
Epoch: [4/20], Batch Num: [228/600]
Discriminator Loss: 0.4795, Generator Loss: 3.2060
D(x): 0.8448, D(G(z)): 0.1034
Epoch: [4/20], Batch Num: [229/600]
Discriminator Loss: 0.4328, Generator Loss: 2.9559
D(x): 0.8489, D(G(z)): 0.1089
Epoch: [4/20], Batch Num: [230/600]
Discriminator Loss: 0.3478, Generator Loss: 2.7962
D(x): 0.8768, D(G(z)): 0.1022
Epoch: [4/20], Batch Num: [231/600]
Discriminator Loss: 0.3997, Generator Loss: 2.6788
D(x): 0.9075, D(G(z)): 0.1625
Epoch: [4/20], Batch Num: [232/600]
Discriminator Loss: 0.5176, Generator Loss: 2.6039
D(x): 0.8561, D(G(z)): 0.1497
Epoch: [4/20], Batch Num: [233/600]
Discriminator Loss: 0.3155, Generator Loss: 3.0601
D(x): 0.9175, D(G(z)): 0.1382
Epoch: [4/20], Batch Num: [234/600]
Discriminator Loss: 0.3688, Generator Loss: 3.1849
D(x): 0.8634, D(G(z)): 0.1008
Epoch: [4/20], Batch Num: [235/600]
Discriminator Loss: 0.3365, Generator Loss: 3.3929
D(x): 0.9107, D(G(z)): 0.1235
Epoch: [4/20], Batch Num: [236/600]
Discriminator Loss: 0.2946, Generator Loss: 3.3857
D(x): 0.8946, D(G(z)): 0.0825
Epoch: [4/20], Batch Num: [237/600]
Discriminator Loss: 0.3482, Generator Loss: 3.7244
D(x): 0.8838, D(G(z)): 0.0883
Epoch: [4/20], Batch Num: [238/600]
Discriminator Loss: 0.2576, Generator Loss: 4.0544
D(x): 0.9441, D(G(z)): 0.1111
Epoch: [4/20], Batch Num: [239/600]
Discriminator Loss: 0.2527, Generator Loss: 4.2542
D(x): 0.9073, D(G(z)): 0.0621
Epoch: [4/20], Batch Num: [240/600]
Discriminator Loss: 0.3374, Generator Loss: 4.5206
D(x): 0.9028, D(G(z)): 0.0637
Epoch: [4/20], Batch Num: [241/600]
Discriminator Loss: 0.3808, Generator Loss: 4.2332
D(x): 0.8977, D(G(z)): 0.0828
Epoch: [4/20], Batch Num: [242/600]
Discriminator Loss: 0.1588, Generator Loss: 4.4285
D(x): 0.9410, D(G(z)): 0.0679
Epoch: [4/20], Batch Num: [243/600]
Discriminator Loss: 0.3111, Generator Loss: 3.6124
D(x): 0.8868, D(G(z)): 0.0619
Epoch: [4/20], Batch Num: [244/600]
Discriminator Loss: 0.2727, Generator Loss: 3.4255
D(x): 0.9433, D(G(z)): 0.0846
Epoch: [4/20], Batch Num: [245/600]
Discriminator Loss: 0.2370, Generator Loss: 3.5152
D(x): 0.9470, D(G(z)): 0.1173
Epoch: [4/20], Batch Num: [246/600]
Discriminator Loss: 0.3192, Generator Loss: 3.6820
D(x): 0.9186, D(G(z)): 0.0934
Epoch: [4/20], Batch Num: [247/600]
Discriminator Loss: 0.2981, Generator Loss: 4.3883
D(x): 0.9433, D(G(z)): 0.1066
Epoch: [4/20], Batch Num: [248/600]
Discriminator Loss: 0.2021, Generator Loss: 4.4554
D(x): 0.9245, D(G(z)): 0.0648
Epoch: [4/20], Batch Num: [249/600]
Discriminator Loss: 0.2791, Generator Loss: 4.3082
D(x): 0.8875, D(G(z)): 0.0460
Epoch: [4/20], Batch Num: [250/600]
Discriminator Loss: 0.2778, Generator Loss: 3.6879
D(x): 0.8713, D(G(z)): 0.0464
Epoch: [4/20], Batch Num: [251/600]
Discriminator Loss: 0.2204, Generator Loss: 3.6089
D(x): 0.9412, D(G(z)): 0.0933
Epoch: [4/20], Batch Num: [252/600]
Discriminator Loss: 0.3422, Generator Loss: 3.7693
D(x): 0.9205, D(G(z)): 0.1396
Epoch: [4/20], Batch Num: [253/600]
Discriminator Loss: 0.2963, Generator Loss: 4.3126
D(x): 0.9117, D(G(z)): 0.1041
Epoch: [4/20], Batch Num: [254/600]
Discriminator Loss: 0.2012, Generator Loss: 4.5464
D(x): 0.9448, D(G(z)): 0.0870
Epoch: [4/20], Batch Num: [255/600]
Discriminator Loss: 0.2515, Generator Loss: 4.8797
D(x): 0.9172, D(G(z)): 0.0510
Epoch: [4/20], Batch Num: [256/600]
Discriminator Loss: 0.2090, Generator Loss: 4.8233
D(x): 0.9184, D(G(z)): 0.0378
Epoch: [4/20], Batch Num: [257/600]
Discriminator Loss: 0.2196, Generator Loss: 4.5407
D(x): 0.9166, D(G(z)): 0.0505
Epoch: [4/20], Batch Num: [258/600]
Discriminator Loss: 0.2021, Generator Loss: 3.9074
D(x): 0.9324, D(G(z)): 0.0509
Epoch: [4/20], Batch Num: [259/600]
Discriminator Loss: 0.3206, Generator Loss: 3.6092
D(x): 0.9180, D(G(z)): 0.1143
Epoch: [4/20], Batch Num: [260/600]
Discriminator Loss: 0.4022, Generator Loss: 3.7126
D(x): 0.9206, D(G(z)): 0.1537
Epoch: [4/20], Batch Num: [261/600]
Discriminator Loss: 0.3855, Generator Loss: 4.1152
D(x): 0.8835, D(G(z)): 0.0958
Epoch: [4/20], Batch Num: [262/600]
Discriminator Loss: 0.6275, Generator Loss: 3.6369
D(x): 0.8376, D(G(z)): 0.1060
Epoch: [4/20], Batch Num: [263/600]
Discriminator Loss: 0.4580, Generator Loss: 3.4731
D(x): 0.9077, D(G(z)): 0.1205
Epoch: [4/20], Batch Num: [264/600]
Discriminator Loss: 0.4700, Generator Loss: 3.1238
D(x): 0.8963, D(G(z)): 0.1530
Epoch: [4/20], Batch Num: [265/600]
Discriminator Loss: 0.5053, Generator Loss: 2.8452
D(x): 0.8748, D(G(z)): 0.1296
Epoch: [4/20], Batch Num: [266/600]
Discriminator Loss: 0.5661, Generator Loss: 3.2579
D(x): 0.8683, D(G(z)): 0.1751
Epoch: [4/20], Batch Num: [267/600]
Discriminator Loss: 0.6176, Generator Loss: 2.7720
D(x): 0.8112, D(G(z)): 0.1204
Epoch: [4/20], Batch Num: [268/600]
Discriminator Loss: 0.8373, Generator Loss: 2.4217
D(x): 0.7791, D(G(z)): 0.1780
Epoch: [4/20], Batch Num: [269/600]
Discriminator Loss: 0.6296, Generator Loss: 2.2065
D(x): 0.8515, D(G(z)): 0.1884
Epoch: [4/20], Batch Num: [270/600]
Discriminator Loss: 0.6328, Generator Loss: 2.4673
D(x): 0.8725, D(G(z)): 0.2331
Epoch: [4/20], Batch Num: [271/600]
Discriminator Loss: 0.5197, Generator Loss: 2.8974
D(x): 0.8673, D(G(z)): 0.1699
Epoch: [4/20], Batch Num: [272/600]
Discriminator Loss: 0.5769, Generator Loss: 2.8238
D(x): 0.8165, D(G(z)): 0.1423
Epoch: [4/20], Batch Num: [273/600]
Discriminator Loss: 0.6322, Generator Loss: 2.7777
D(x): 0.8121, D(G(z)): 0.1145
Epoch: [4/20], Batch Num: [274/600]
Discriminator Loss: 0.3770, Generator Loss: 2.3838
D(x): 0.8817, D(G(z)): 0.1107
Epoch: [4/20], Batch Num: [275/600]
Discriminator Loss: 0.6418, Generator Loss: 2.0637
D(x): 0.8150, D(G(z)): 0.1447
Epoch: [4/20], Batch Num: [276/600]
Discriminator Loss: 0.5368, Generator Loss: 1.8031
D(x): 0.8735, D(G(z)): 0.2054
Epoch: [4/20], Batch Num: [277/600]
Discriminator Loss: 0.5055, Generator Loss: 2.1526
D(x): 0.8891, D(G(z)): 0.2343
Epoch: [4/20], Batch Num: [278/600]
Discriminator Loss: 0.5010, Generator Loss: 2.2883
D(x): 0.8892, D(G(z)): 0.2019
Epoch: [4/20], Batch Num: [279/600]
Discriminator Loss: 0.3727, Generator Loss: 2.6668
D(x): 0.8683, D(G(z)): 0.1286
Epoch: [4/20], Batch Num: [280/600]
Discriminator Loss: 0.4113, Generator Loss: 2.6640
D(x): 0.8616, D(G(z)): 0.1263
Epoch: [4/20], Batch Num: [281/600]
Discriminator Loss: 0.3389, Generator Loss: 2.7015
D(x): 0.8799, D(G(z)): 0.1026
Epoch: [4/20], Batch Num: [282/600]
Discriminator Loss: 0.3246, Generator Loss: 2.6630
D(x): 0.8792, D(G(z)): 0.0939
Epoch: [4/20], Batch Num: [283/600]
Discriminator Loss: 0.2940, Generator Loss: 2.6549
D(x): 0.8938, D(G(z)): 0.1116
Epoch: [4/20], Batch Num: [284/600]
Discriminator Loss: 0.4271, Generator Loss: 2.3837
D(x): 0.8729, D(G(z)): 0.1248
Epoch: [4/20], Batch Num: [285/600]
Discriminator Loss: 0.3765, Generator Loss: 2.4338
D(x): 0.9036, D(G(z)): 0.1452
Epoch: [4/20], Batch Num: [286/600]
Discriminator Loss: 0.3231, Generator Loss: 2.7480
D(x): 0.9580, D(G(z)): 0.1793
Epoch: [4/20], Batch Num: [287/600]
Discriminator Loss: 0.2966, Generator Loss: 3.2309
D(x): 0.9338, D(G(z)): 0.1363
Epoch: [4/20], Batch Num: [288/600]
Discriminator Loss: 0.2901, Generator Loss: 3.4097
D(x): 0.8985, D(G(z)): 0.0798
Epoch: [4/20], Batch Num: [289/600]
Discriminator Loss: 0.2834, Generator Loss: 3.4746
D(x): 0.8902, D(G(z)): 0.0526
Epoch: [4/20], Batch Num: [290/600]
Discriminator Loss: 0.3032, Generator Loss: 3.3728
D(x): 0.8884, D(G(z)): 0.0532
Epoch: [4/20], Batch Num: [291/600]
Discriminator Loss: 0.2553, Generator Loss: 3.3609
D(x): 0.9129, D(G(z)): 0.0731
Epoch: [4/20], Batch Num: [292/600]
Discriminator Loss: 0.2932, Generator Loss: 3.1376
D(x): 0.8895, D(G(z)): 0.0757
Epoch: [4/20], Batch Num: [293/600]
Discriminator Loss: 0.2014, Generator Loss: 3.0585
D(x): 0.9581, D(G(z)): 0.1019
Epoch: [4/20], Batch Num: [294/600]
Discriminator Loss: 0.1891, Generator Loss: 3.1658
D(x): 0.9556, D(G(z)): 0.0889
Epoch: [4/20], Batch Num: [295/600]
Discriminator Loss: 0.2246, Generator Loss: 3.1243
D(x): 0.9297, D(G(z)): 0.0800
Epoch: [4/20], Batch Num: [296/600]
Discriminator Loss: 0.2310, Generator Loss: 3.2236
D(x): 0.9434, D(G(z)): 0.0995
Epoch: [4/20], Batch Num: [297/600]
Discriminator Loss: 0.1527, Generator Loss: 3.8842
D(x): 0.9595, D(G(z)): 0.0718
Epoch: [4/20], Batch Num: [298/600]
Discriminator Loss: 0.2365, Generator Loss: 4.0881
D(x): 0.9150, D(G(z)): 0.0586
Epoch: [4/20], Batch Num: [299/600]
Discriminator Loss: 0.2442, Generator Loss: 3.9506
D(x): 0.9113, D(G(z)): 0.0507
Epoch: 4, Batch Num: [300/600]

Epoch: [4/20], Batch Num: [300/600]
Discriminator Loss: 0.2819, Generator Loss: 3.6702
D(x): 0.9113, D(G(z)): 0.0528
Epoch: [4/20], Batch Num: [301/600]
Discriminator Loss: 0.2203, Generator Loss: 3.5173
D(x): 0.9344, D(G(z)): 0.0668
Epoch: [4/20], Batch Num: [302/600]
Discriminator Loss: 0.1333, Generator Loss: 3.5199
D(x): 0.9631, D(G(z)): 0.0519
Epoch: [4/20], Batch Num: [303/600]
Discriminator Loss: 0.1362, Generator Loss: 3.6099
D(x): 0.9692, D(G(z)): 0.0565
Epoch: [4/20], Batch Num: [304/600]
Discriminator Loss: 0.2287, Generator Loss: 3.6629
D(x): 0.9485, D(G(z)): 0.0808
Epoch: [4/20], Batch Num: [305/600]
Discriminator Loss: 0.1650, Generator Loss: 3.4217
D(x): 0.9633, D(G(z)): 0.0836
Epoch: [4/20], Batch Num: [306/600]
Discriminator Loss: 0.1643, Generator Loss: 3.9484
D(x): 0.9601, D(G(z)): 0.0674
Epoch: [4/20], Batch Num: [307/600]
Discriminator Loss: 0.1515, Generator Loss: 3.7423
D(x): 0.9396, D(G(z)): 0.0465
Epoch: [4/20], Batch Num: [308/600]
Discriminator Loss: 0.2210, Generator Loss: 3.7722
D(x): 0.9236, D(G(z)): 0.0360
Epoch: [4/20], Batch Num: [309/600]
Discriminator Loss: 0.2638, Generator Loss: 3.6172
D(x): 0.9188, D(G(z)): 0.0483
Epoch: [4/20], Batch Num: [310/600]
Discriminator Loss: 0.2644, Generator Loss: 3.2595
D(x): 0.9128, D(G(z)): 0.0740
Epoch: [4/20], Batch Num: [311/600]
Discriminator Loss: 0.1516, Generator Loss: 3.3743
D(x): 0.9755, D(G(z)): 0.0920
Epoch: [4/20], Batch Num: [312/600]
Discriminator Loss: 0.1132, Generator Loss: 3.3118
D(x): 0.9880, D(G(z)): 0.0808
Epoch: [4/20], Batch Num: [313/600]
Discriminator Loss: 0.1868, Generator Loss: 3.5908
D(x): 0.9497, D(G(z)): 0.0785
Epoch: [4/20], Batch Num: [314/600]
Discriminator Loss: 0.2758, Generator Loss: 3.5439
D(x): 0.9424, D(G(z)): 0.0826
Epoch: [4/20], Batch Num: [315/600]
Discriminator Loss: 0.3071, Generator Loss: 3.8540
D(x): 0.9272, D(G(z)): 0.0981
Epoch: [4/20], Batch Num: [316/600]
Discriminator Loss: 0.2943, Generator Loss: 3.8308
D(x): 0.9098, D(G(z)): 0.0537
Epoch: [4/20], Batch Num: [317/600]
Discriminator Loss: 0.2718, Generator Loss: 3.7211
D(x): 0.9239, D(G(z)): 0.0522
Epoch: [4/20], Batch Num: [318/600]
Discriminator Loss: 0.2510, Generator Loss: 3.4117
D(x): 0.9375, D(G(z)): 0.0726
Epoch: [4/20], Batch Num: [319/600]
Discriminator Loss: 0.2364, Generator Loss: 3.5937
D(x): 0.9632, D(G(z)): 0.1001
Epoch: [4/20], Batch Num: [320/600]
Discriminator Loss: 0.3478, Generator Loss: 3.8081
D(x): 0.9405, D(G(z)): 0.0951
Epoch: [4/20], Batch Num: [321/600]
Discriminator Loss: 0.3555, Generator Loss: 3.5333
D(x): 0.9024, D(G(z)): 0.0884
Epoch: [4/20], Batch Num: [322/600]
Discriminator Loss: 0.3421, Generator Loss: 3.5075
D(x): 0.9030, D(G(z)): 0.0927
Epoch: [4/20], Batch Num: [323/600]
Discriminator Loss: 0.2272, Generator Loss: 3.0599
D(x): 0.9414, D(G(z)): 0.0819
Epoch: [4/20], Batch Num: [324/600]
Discriminator Loss: 0.3079, Generator Loss: 3.1635
D(x): 0.9136, D(G(z)): 0.0892
Epoch: [4/20], Batch Num: [325/600]
Discriminator Loss: 0.2755, Generator Loss: 3.3414
D(x): 0.9426, D(G(z)): 0.1144
Epoch: [4/20], Batch Num: [326/600]
Discriminator Loss: 0.2767, Generator Loss: 3.3294
D(x): 0.9319, D(G(z)): 0.1051
Epoch: [4/20], Batch Num: [327/600]
Discriminator Loss: 0.2838, Generator Loss: 3.6436
D(x): 0.9403, D(G(z)): 0.1066
Epoch: [4/20], Batch Num: [328/600]
Discriminator Loss: 0.3184, Generator Loss: 4.2502
D(x): 0.9000, D(G(z)): 0.0613
Epoch: [4/20], Batch Num: [329/600]
Discriminator Loss: 0.2865, Generator Loss: 4.0895
D(x): 0.9216, D(G(z)): 0.0454
Epoch: [4/20], Batch Num: [330/600]
Discriminator Loss: 0.2590, Generator Loss: 3.9197
D(x): 0.9129, D(G(z)): 0.0602
Epoch: [4/20], Batch Num: [331/600]
Discriminator Loss: 0.1975, Generator Loss: 4.0609
D(x): 0.9364, D(G(z)): 0.0478
Epoch: [4/20], Batch Num: [332/600]
Discriminator Loss: 0.1664, Generator Loss: 3.7700
D(x): 0.9719, D(G(z)): 0.0806
Epoch: [4/20], Batch Num: [333/600]
Discriminator Loss: 0.1511, Generator Loss: 4.0684
D(x): 0.9663, D(G(z)): 0.0781
Epoch: [4/20], Batch Num: [334/600]
Discriminator Loss: 0.1137, Generator Loss: 4.1665
D(x): 0.9750, D(G(z)): 0.0548
Epoch: [4/20], Batch Num: [335/600]
Discriminator Loss: 0.2483, Generator Loss: 4.0181
D(x): 0.9031, D(G(z)): 0.0548
Epoch: [4/20], Batch Num: [336/600]
Discriminator Loss: 0.1950, Generator Loss: 4.1593
D(x): 0.9555, D(G(z)): 0.0818
Epoch: [4/20], Batch Num: [337/600]
Discriminator Loss: 0.2326, Generator Loss: 4.3918
D(x): 0.9624, D(G(z)): 0.1018
Epoch: [4/20], Batch Num: [338/600]
Discriminator Loss: 0.2190, Generator Loss: 5.1063
D(x): 0.9390, D(G(z)): 0.0653
Epoch: [4/20], Batch Num: [339/600]
Discriminator Loss: 0.3823, Generator Loss: 4.3140
D(x): 0.8912, D(G(z)): 0.0528
Epoch: [4/20], Batch Num: [340/600]
Discriminator Loss: 0.2898, Generator Loss: 4.2955
D(x): 0.9062, D(G(z)): 0.0404
Epoch: [4/20], Batch Num: [341/600]
Discriminator Loss: 0.1800, Generator Loss: 3.8015
D(x): 0.9648, D(G(z)): 0.0809
Epoch: [4/20], Batch Num: [342/600]
Discriminator Loss: 0.3101, Generator Loss: 3.4312
D(x): 0.9177, D(G(z)): 0.0916
Epoch: [4/20], Batch Num: [343/600]
Discriminator Loss: 0.3105, Generator Loss: 3.5614
D(x): 0.9435, D(G(z)): 0.1390
Epoch: [4/20], Batch Num: [344/600]
Discriminator Loss: 0.4293, Generator Loss: 3.8946
D(x): 0.8970, D(G(z)): 0.1392
Epoch: [4/20], Batch Num: [345/600]
Discriminator Loss: 0.3228, Generator Loss: 4.1591
D(x): 0.8942, D(G(z)): 0.0904
Epoch: [4/20], Batch Num: [346/600]
Discriminator Loss: 0.2450, Generator Loss: 3.9771
D(x): 0.9155, D(G(z)): 0.0742
Epoch: [4/20], Batch Num: [347/600]
Discriminator Loss: 0.4045, Generator Loss: 3.9577
D(x): 0.8773, D(G(z)): 0.0781
Epoch: [4/20], Batch Num: [348/600]
Discriminator Loss: 0.5542, Generator Loss: 3.2233
D(x): 0.8416, D(G(z)): 0.1098
Epoch: [4/20], Batch Num: [349/600]
Discriminator Loss: 0.2994, Generator Loss: 2.9320
D(x): 0.9233, D(G(z)): 0.1270
Epoch: [4/20], Batch Num: [350/600]
Discriminator Loss: 0.3129, Generator Loss: 2.9719
D(x): 0.9193, D(G(z)): 0.1416
Epoch: [4/20], Batch Num: [351/600]
Discriminator Loss: 0.4761, Generator Loss: 3.5930
D(x): 0.8770, D(G(z)): 0.1749
Epoch: [4/20], Batch Num: [352/600]
Discriminator Loss: 0.2566, Generator Loss: 3.4952
D(x): 0.9426, D(G(z)): 0.1206
Epoch: [4/20], Batch Num: [353/600]
Discriminator Loss: 0.4624, Generator Loss: 3.1995
D(x): 0.8245, D(G(z)): 0.0935
Epoch: [4/20], Batch Num: [354/600]
Discriminator Loss: 0.3895, Generator Loss: 2.5460
D(x): 0.8659, D(G(z)): 0.1126
Epoch: [4/20], Batch Num: [355/600]
Discriminator Loss: 0.5955, Generator Loss: 2.8827
D(x): 0.8818, D(G(z)): 0.2294
Epoch: [4/20], Batch Num: [356/600]
Discriminator Loss: 0.4001, Generator Loss: 2.8870
D(x): 0.8925, D(G(z)): 0.1442
Epoch: [4/20], Batch Num: [357/600]
Discriminator Loss: 0.5592, Generator Loss: 3.0866
D(x): 0.8479, D(G(z)): 0.1639
Epoch: [4/20], Batch Num: [358/600]
Discriminator Loss: 0.4188, Generator Loss: 2.9214
D(x): 0.8708, D(G(z)): 0.1181
Epoch: [4/20], Batch Num: [359/600]
Discriminator Loss: 0.6128, Generator Loss: 2.6878
D(x): 0.8076, D(G(z)): 0.1220
Epoch: [4/20], Batch Num: [360/600]
Discriminator Loss: 0.5984, Generator Loss: 2.6043
D(x): 0.8494, D(G(z)): 0.1966
Epoch: [4/20], Batch Num: [361/600]
Discriminator Loss: 0.4751, Generator Loss: 2.9873
D(x): 0.8738, D(G(z)): 0.1631
Epoch: [4/20], Batch Num: [362/600]
Discriminator Loss: 0.4982, Generator Loss: 3.1015
D(x): 0.8864, D(G(z)): 0.1434
Epoch: [4/20], Batch Num: [363/600]
Discriminator Loss: 0.4224, Generator Loss: 2.9661
D(x): 0.8530, D(G(z)): 0.0956
Epoch: [4/20], Batch Num: [364/600]
Discriminator Loss: 0.4279, Generator Loss: 2.4817
D(x): 0.8390, D(G(z)): 0.1039
Epoch: [4/20], Batch Num: [365/600]
Discriminator Loss: 0.3846, Generator Loss: 2.4134
D(x): 0.8975, D(G(z)): 0.1214
Epoch: [4/20], Batch Num: [366/600]
Discriminator Loss: 0.3032, Generator Loss: 2.6005
D(x): 0.9266, D(G(z)): 0.1538
Epoch: [4/20], Batch Num: [367/600]
Discriminator Loss: 0.3222, Generator Loss: 3.0002
D(x): 0.9113, D(G(z)): 0.1344
Epoch: [4/20], Batch Num: [368/600]
Discriminator Loss: 0.3111, Generator Loss: 3.3578
D(x): 0.9083, D(G(z)): 0.0905
Epoch: [4/20], Batch Num: [369/600]
Discriminator Loss: 0.2100, Generator Loss: 3.7698
D(x): 0.9336, D(G(z)): 0.0843
Epoch: [4/20], Batch Num: [370/600]
Discriminator Loss: 0.3587, Generator Loss: 3.4364
D(x): 0.8465, D(G(z)): 0.0593
Epoch: [4/20], Batch Num: [371/600]
Discriminator Loss: 0.2583, Generator Loss: 2.8867
D(x): 0.9166, D(G(z)): 0.0822
Epoch: [4/20], Batch Num: [372/600]
Discriminator Loss: 0.3445, Generator Loss: 2.9281
D(x): 0.9108, D(G(z)): 0.1181
Epoch: [4/20], Batch Num: [373/600]
Discriminator Loss: 0.3664, Generator Loss: 3.0303
D(x): 0.9188, D(G(z)): 0.1509
Epoch: [4/20], Batch Num: [374/600]
Discriminator Loss: 0.3043, Generator Loss: 3.6977
D(x): 0.9164, D(G(z)): 0.1096
Epoch: [4/20], Batch Num: [375/600]
Discriminator Loss: 0.4537, Generator Loss: 3.5195
D(x): 0.8305, D(G(z)): 0.0744
Epoch: [4/20], Batch Num: [376/600]
Discriminator Loss: 0.3078, Generator Loss: 3.0186
D(x): 0.8817, D(G(z)): 0.0649
Epoch: [4/20], Batch Num: [377/600]
Discriminator Loss: 0.2849, Generator Loss: 2.9700
D(x): 0.9105, D(G(z)): 0.1013
Epoch: [4/20], Batch Num: [378/600]
Discriminator Loss: 0.1767, Generator Loss: 3.2260
D(x): 0.9627, D(G(z)): 0.0899
Epoch: [4/20], Batch Num: [379/600]
Discriminator Loss: 0.2371, Generator Loss: 3.4844
D(x): 0.9433, D(G(z)): 0.1041
Epoch: [4/20], Batch Num: [380/600]
Discriminator Loss: 0.2372, Generator Loss: 3.4688
D(x): 0.9335, D(G(z)): 0.0780
Epoch: [4/20], Batch Num: [381/600]
Discriminator Loss: 0.3889, Generator Loss: 3.3723
D(x): 0.8680, D(G(z)): 0.0514
Epoch: [4/20], Batch Num: [382/600]
Discriminator Loss: 0.1910, Generator Loss: 3.2622
D(x): 0.9577, D(G(z)): 0.0961
Epoch: [4/20], Batch Num: [383/600]
Discriminator Loss: 0.3093, Generator Loss: 3.5567
D(x): 0.9103, D(G(z)): 0.1004
Epoch: [4/20], Batch Num: [384/600]
Discriminator Loss: 0.1949, Generator Loss: 3.6000
D(x): 0.9466, D(G(z)): 0.0679
Epoch: [4/20], Batch Num: [385/600]
Discriminator Loss: 0.2972, Generator Loss: 3.6592
D(x): 0.8950, D(G(z)): 0.0892
Epoch: [4/20], Batch Num: [386/600]
Discriminator Loss: 0.2660, Generator Loss: 3.4897
D(x): 0.9186, D(G(z)): 0.0711
Epoch: [4/20], Batch Num: [387/600]
Discriminator Loss: 0.3634, Generator Loss: 3.3967
D(x): 0.8785, D(G(z)): 0.0833
Epoch: [4/20], Batch Num: [388/600]
Discriminator Loss: 0.2809, Generator Loss: 3.2963
D(x): 0.9386, D(G(z)): 0.1027
Epoch: [4/20], Batch Num: [389/600]
Discriminator Loss: 0.2869, Generator Loss: 2.9591
D(x): 0.9423, D(G(z)): 0.1087
Epoch: [4/20], Batch Num: [390/600]
Discriminator Loss: 0.2733, Generator Loss: 3.4913
D(x): 0.9289, D(G(z)): 0.1028
Epoch: [4/20], Batch Num: [391/600]
Discriminator Loss: 0.3223, Generator Loss: 3.5650
D(x): 0.9063, D(G(z)): 0.0768
Epoch: [4/20], Batch Num: [392/600]
Discriminator Loss: 0.2851, Generator Loss: 3.6685
D(x): 0.8937, D(G(z)): 0.0571
Epoch: [4/20], Batch Num: [393/600]
Discriminator Loss: 0.2781, Generator Loss: 3.1081
D(x): 0.9365, D(G(z)): 0.0909
Epoch: [4/20], Batch Num: [394/600]
Discriminator Loss: 0.3424, Generator Loss: 3.0541
D(x): 0.8999, D(G(z)): 0.0831
Epoch: [4/20], Batch Num: [395/600]
Discriminator Loss: 0.3154, Generator Loss: 3.1268
D(x): 0.9213, D(G(z)): 0.1067
Epoch: [4/20], Batch Num: [396/600]
Discriminator Loss: 0.3521, Generator Loss: 3.1112
D(x): 0.9040, D(G(z)): 0.1040
Epoch: [4/20], Batch Num: [397/600]
Discriminator Loss: 0.3406, Generator Loss: 2.7848
D(x): 0.8972, D(G(z)): 0.1117
Epoch: [4/20], Batch Num: [398/600]
Discriminator Loss: 0.2505, Generator Loss: 3.4447
D(x): 0.9541, D(G(z)): 0.1313
Epoch: [4/20], Batch Num: [399/600]
Discriminator Loss: 0.2358, Generator Loss: 3.5665
D(x): 0.9305, D(G(z)): 0.0791
Epoch: 4, Batch Num: [400/600]

Epoch: [4/20], Batch Num: [400/600]
Discriminator Loss: 0.1678, Generator Loss: 3.9275
D(x): 0.9418, D(G(z)): 0.0669
Epoch: [4/20], Batch Num: [401/600]
Discriminator Loss: 0.4014, Generator Loss: 3.5121
D(x): 0.8684, D(G(z)): 0.0522
Epoch: [4/20], Batch Num: [402/600]
Discriminator Loss: 0.2752, Generator Loss: 2.8550
D(x): 0.8968, D(G(z)): 0.0645
Epoch: [4/20], Batch Num: [403/600]
Discriminator Loss: 0.2603, Generator Loss: 2.5281
D(x): 0.9410, D(G(z)): 0.1063
Epoch: [4/20], Batch Num: [404/600]
Discriminator Loss: 0.2478, Generator Loss: 3.1680
D(x): 0.9547, D(G(z)): 0.1343
Epoch: [4/20], Batch Num: [405/600]
Discriminator Loss: 0.3030, Generator Loss: 3.9050
D(x): 0.9196, D(G(z)): 0.1097
Epoch: [4/20], Batch Num: [406/600]
Discriminator Loss: 0.3422, Generator Loss: 3.4719
D(x): 0.8957, D(G(z)): 0.0664
Epoch: [4/20], Batch Num: [407/600]
Discriminator Loss: 0.3256, Generator Loss: 3.8147
D(x): 0.9090, D(G(z)): 0.0716
Epoch: [4/20], Batch Num: [408/600]
Discriminator Loss: 0.2649, Generator Loss: 3.6919
D(x): 0.9396, D(G(z)): 0.0837
Epoch: [4/20], Batch Num: [409/600]
Discriminator Loss: 0.2326, Generator Loss: 3.4104
D(x): 0.9156, D(G(z)): 0.0522
Epoch: [4/20], Batch Num: [410/600]
Discriminator Loss: 0.3130, Generator Loss: 3.2969
D(x): 0.9267, D(G(z)): 0.0889
Epoch: [4/20], Batch Num: [411/600]
Discriminator Loss: 0.2431, Generator Loss: 2.9933
D(x): 0.9285, D(G(z)): 0.0969
Epoch: [4/20], Batch Num: [412/600]
Discriminator Loss: 0.1700, Generator Loss: 3.5515
D(x): 0.9611, D(G(z)): 0.0985
Epoch: [4/20], Batch Num: [413/600]
Discriminator Loss: 0.1879, Generator Loss: 3.6452
D(x): 0.9401, D(G(z)): 0.0752
Epoch: [4/20], Batch Num: [414/600]
Discriminator Loss: 0.2552, Generator Loss: 3.8989
D(x): 0.9258, D(G(z)): 0.0730
Epoch: [4/20], Batch Num: [415/600]
Discriminator Loss: 0.2197, Generator Loss: 3.7370
D(x): 0.9423, D(G(z)): 0.0614
Epoch: [4/20], Batch Num: [416/600]
Discriminator Loss: 0.1849, Generator Loss: 4.0072
D(x): 0.9510, D(G(z)): 0.0607
Epoch: [4/20], Batch Num: [417/600]
Discriminator Loss: 0.2366, Generator Loss: 3.7698
D(x): 0.9274, D(G(z)): 0.0619
Epoch: [4/20], Batch Num: [418/600]
Discriminator Loss: 0.2463, Generator Loss: 3.4446
D(x): 0.9228, D(G(z)): 0.0716
Epoch: [4/20], Batch Num: [419/600]
Discriminator Loss: 0.1207, Generator Loss: 3.3765
D(x): 0.9711, D(G(z)): 0.0712
Epoch: [4/20], Batch Num: [420/600]
Discriminator Loss: 0.2123, Generator Loss: 4.1573
D(x): 0.9793, D(G(z)): 0.1255
Epoch: [4/20], Batch Num: [421/600]
Discriminator Loss: 0.2512, Generator Loss: 5.2640
D(x): 0.9526, D(G(z)): 0.0832
Epoch: [4/20], Batch Num: [422/600]
Discriminator Loss: 0.2390, Generator Loss: 5.3065
D(x): 0.9147, D(G(z)): 0.0248
Epoch: [4/20], Batch Num: [423/600]
Discriminator Loss: 0.2604, Generator Loss: 4.5698
D(x): 0.9004, D(G(z)): 0.0175
Epoch: [4/20], Batch Num: [424/600]
Discriminator Loss: 0.2292, Generator Loss: 3.5911
D(x): 0.9211, D(G(z)): 0.0342
Epoch: [4/20], Batch Num: [425/600]
Discriminator Loss: 0.3132, Generator Loss: 3.0457
D(x): 0.9625, D(G(z)): 0.1132
Epoch: [4/20], Batch Num: [426/600]
Discriminator Loss: 0.2149, Generator Loss: 3.8392
D(x): 0.9678, D(G(z)): 0.1162
Epoch: [4/20], Batch Num: [427/600]
Discriminator Loss: 0.3970, Generator Loss: 3.9775
D(x): 0.9005, D(G(z)): 0.0854
Epoch: [4/20], Batch Num: [428/600]
Discriminator Loss: 0.3891, Generator Loss: 4.6277
D(x): 0.9055, D(G(z)): 0.0682
Epoch: [4/20], Batch Num: [429/600]
Discriminator Loss: 0.5148, Generator Loss: 3.9176
D(x): 0.8946, D(G(z)): 0.0828
Epoch: [4/20], Batch Num: [430/600]
Discriminator Loss: 0.1484, Generator Loss: 3.7260
D(x): 0.9416, D(G(z)): 0.0343
Epoch: [4/20], Batch Num: [431/600]
Discriminator Loss: 0.2411, Generator Loss: 3.6691
D(x): 0.9475, D(G(z)): 0.1005
Epoch: [4/20], Batch Num: [432/600]
Discriminator Loss: 0.4496, Generator Loss: 3.7269
D(x): 0.9125, D(G(z)): 0.1119
Epoch: [4/20], Batch Num: [433/600]
Discriminator Loss: 0.2890, Generator Loss: 3.8615
D(x): 0.9214, D(G(z)): 0.0866
Epoch: [4/20], Batch Num: [434/600]
Discriminator Loss: 0.3468, Generator Loss: 3.6633
D(x): 0.9249, D(G(z)): 0.0826
Epoch: [4/20], Batch Num: [435/600]
Discriminator Loss: 0.3628, Generator Loss: 3.8557
D(x): 0.9155, D(G(z)): 0.1080
Epoch: [4/20], Batch Num: [436/600]
Discriminator Loss: 0.2373, Generator Loss: 3.7656
D(x): 0.9223, D(G(z)): 0.0547
Epoch: [4/20], Batch Num: [437/600]
Discriminator Loss: 0.2613, Generator Loss: 3.6853
D(x): 0.9093, D(G(z)): 0.0505
Epoch: [4/20], Batch Num: [438/600]
Discriminator Loss: 0.2278, Generator Loss: 3.5100
D(x): 0.9552, D(G(z)): 0.1001
Epoch: [4/20], Batch Num: [439/600]
Discriminator Loss: 0.3463, Generator Loss: 3.2940
D(x): 0.9020, D(G(z)): 0.0725
Epoch: [4/20], Batch Num: [440/600]
Discriminator Loss: 0.2454, Generator Loss: 3.4489
D(x): 0.9442, D(G(z)): 0.1203
Epoch: [4/20], Batch Num: [441/600]
Discriminator Loss: 0.2524, Generator Loss: 3.5176
D(x): 0.9307, D(G(z)): 0.0860
Epoch: [4/20], Batch Num: [442/600]
Discriminator Loss: 0.4181, Generator Loss: 3.2988
D(x): 0.8987, D(G(z)): 0.1085
Epoch: [4/20], Batch Num: [443/600]
Discriminator Loss: 0.3755, Generator Loss: 3.0853
D(x): 0.8813, D(G(z)): 0.0791
Epoch: [4/20], Batch Num: [444/600]
Discriminator Loss: 0.3029, Generator Loss: 3.1614
D(x): 0.9549, D(G(z)): 0.1210
Epoch: [4/20], Batch Num: [445/600]
Discriminator Loss: 0.2766, Generator Loss: 3.0360
D(x): 0.9358, D(G(z)): 0.1114
Epoch: [4/20], Batch Num: [446/600]
Discriminator Loss: 0.4092, Generator Loss: 3.0196
D(x): 0.9262, D(G(z)): 0.1210
Epoch: [4/20], Batch Num: [447/600]
Discriminator Loss: 0.1965, Generator Loss: 2.9220
D(x): 0.9479, D(G(z)): 0.0846
Epoch: [4/20], Batch Num: [448/600]
Discriminator Loss: 0.1526, Generator Loss: 3.3968
D(x): 0.9576, D(G(z)): 0.0776
Epoch: [4/20], Batch Num: [449/600]
Discriminator Loss: 0.1909, Generator Loss: 3.7358
D(x): 0.9602, D(G(z)): 0.0852
Epoch: [4/20], Batch Num: [450/600]
Discriminator Loss: 0.2524, Generator Loss: 3.4736
D(x): 0.9134, D(G(z)): 0.0616
Epoch: [4/20], Batch Num: [451/600]
Discriminator Loss: 0.2383, Generator Loss: 3.3788
D(x): 0.9278, D(G(z)): 0.0832
Epoch: [4/20], Batch Num: [452/600]
Discriminator Loss: 0.3674, Generator Loss: 3.6239
D(x): 0.9000, D(G(z)): 0.0904
Epoch: [4/20], Batch Num: [453/600]
Discriminator Loss: 0.2895, Generator Loss: 3.4087
D(x): 0.9304, D(G(z)): 0.0883
Epoch: [4/20], Batch Num: [454/600]
Discriminator Loss: 0.3642, Generator Loss: 3.4860
D(x): 0.9243, D(G(z)): 0.1109
Epoch: [4/20], Batch Num: [455/600]
Discriminator Loss: 0.2661, Generator Loss: 3.7450
D(x): 0.9245, D(G(z)): 0.1124
Epoch: [4/20], Batch Num: [456/600]
Discriminator Loss: 0.2872, Generator Loss: 3.6526
D(x): 0.9088, D(G(z)): 0.0882
Epoch: [4/20], Batch Num: [457/600]
Discriminator Loss: 0.3063, Generator Loss: 3.8181
D(x): 0.9115, D(G(z)): 0.0945
Epoch: [4/20], Batch Num: [458/600]
Discriminator Loss: 0.2851, Generator Loss: 3.6461
D(x): 0.9027, D(G(z)): 0.0693
Epoch: [4/20], Batch Num: [459/600]
Discriminator Loss: 0.3639, Generator Loss: 3.3132
D(x): 0.9044, D(G(z)): 0.1060
Epoch: [4/20], Batch Num: [460/600]
Discriminator Loss: 0.4853, Generator Loss: 3.0389
D(x): 0.8531, D(G(z)): 0.1192
Epoch: [4/20], Batch Num: [461/600]
Discriminator Loss: 0.3587, Generator Loss: 3.0995
D(x): 0.9163, D(G(z)): 0.1499
Epoch: [4/20], Batch Num: [462/600]
Discriminator Loss: 0.3716, Generator Loss: 3.5178
D(x): 0.9303, D(G(z)): 0.1395
Epoch: [4/20], Batch Num: [463/600]
Discriminator Loss: 0.2337, Generator Loss: 3.4638
D(x): 0.9191, D(G(z)): 0.0759
Epoch: [4/20], Batch Num: [464/600]
Discriminator Loss: 0.1800, Generator Loss: 3.9761
D(x): 0.9356, D(G(z)): 0.0699
Epoch: [4/20], Batch Num: [465/600]
Discriminator Loss: 0.5380, Generator Loss: 3.9270
D(x): 0.8373, D(G(z)): 0.0779
Epoch: [4/20], Batch Num: [466/600]
Discriminator Loss: 0.3238, Generator Loss: 3.7654
D(x): 0.9278, D(G(z)): 0.1036
Epoch: [4/20], Batch Num: [467/600]
Discriminator Loss: 0.3020, Generator Loss: 4.0099
D(x): 0.8998, D(G(z)): 0.0794
Epoch: [4/20], Batch Num: [468/600]
Discriminator Loss: 0.3946, Generator Loss: 3.8163
D(x): 0.8901, D(G(z)): 0.1038
Epoch: [4/20], Batch Num: [469/600]
Discriminator Loss: 0.2200, Generator Loss: 4.2495
D(x): 0.9556, D(G(z)): 0.0917
Epoch: [4/20], Batch Num: [470/600]
Discriminator Loss: 0.2882, Generator Loss: 4.0551
D(x): 0.9069, D(G(z)): 0.0699
Epoch: [4/20], Batch Num: [471/600]
Discriminator Loss: 0.5540, Generator Loss: 3.3303
D(x): 0.8441, D(G(z)): 0.0679
Epoch: [4/20], Batch Num: [472/600]
Discriminator Loss: 0.4108, Generator Loss: 3.0798
D(x): 0.8889, D(G(z)): 0.1219
Epoch: [4/20], Batch Num: [473/600]
Discriminator Loss: 0.5504, Generator Loss: 3.5292
D(x): 0.8882, D(G(z)): 0.1923
Epoch: [4/20], Batch Num: [474/600]
Discriminator Loss: 0.3564, Generator Loss: 3.9194
D(x): 0.8931, D(G(z)): 0.0909
Epoch: [4/20], Batch Num: [475/600]
Discriminator Loss: 0.3299, Generator Loss: 4.3900
D(x): 0.8955, D(G(z)): 0.0890
Epoch: [4/20], Batch Num: [476/600]
Discriminator Loss: 0.2652, Generator Loss: 4.2379
D(x): 0.8966, D(G(z)): 0.0445
Epoch: [4/20], Batch Num: [477/600]
Discriminator Loss: 0.3620, Generator Loss: 3.5153
D(x): 0.8814, D(G(z)): 0.0564
Epoch: [4/20], Batch Num: [478/600]
Discriminator Loss: 0.2583, Generator Loss: 3.1504
D(x): 0.9442, D(G(z)): 0.1019
Epoch: [4/20], Batch Num: [479/600]
Discriminator Loss: 0.3253, Generator Loss: 3.7081
D(x): 0.9307, D(G(z)): 0.1223
Epoch: [4/20], Batch Num: [480/600]
Discriminator Loss: 0.3691, Generator Loss: 3.8857
D(x): 0.9044, D(G(z)): 0.1046
Epoch: [4/20], Batch Num: [481/600]
Discriminator Loss: 0.2636, Generator Loss: 3.9980
D(x): 0.9106, D(G(z)): 0.0545
Epoch: [4/20], Batch Num: [482/600]
Discriminator Loss: 0.3020, Generator Loss: 3.6445
D(x): 0.9083, D(G(z)): 0.0636
Epoch: [4/20], Batch Num: [483/600]
Discriminator Loss: 0.2891, Generator Loss: 3.9766
D(x): 0.9298, D(G(z)): 0.1003
Epoch: [4/20], Batch Num: [484/600]
Discriminator Loss: 0.2821, Generator Loss: 4.4669
D(x): 0.9260, D(G(z)): 0.1000
Epoch: [4/20], Batch Num: [485/600]
Discriminator Loss: 0.1520, Generator Loss: 4.5709
D(x): 0.9322, D(G(z)): 0.0318
Epoch: [4/20], Batch Num: [486/600]
Discriminator Loss: 0.2296, Generator Loss: 4.1357
D(x): 0.9147, D(G(z)): 0.0457
Epoch: [4/20], Batch Num: [487/600]
Discriminator Loss: 0.3578, Generator Loss: 3.4486
D(x): 0.8959, D(G(z)): 0.0517
Epoch: [4/20], Batch Num: [488/600]
Discriminator Loss: 0.3051, Generator Loss: 3.5298
D(x): 0.9342, D(G(z)): 0.1217
Epoch: [4/20], Batch Num: [489/600]
Discriminator Loss: 0.3080, Generator Loss: 3.9110
D(x): 0.9491, D(G(z)): 0.1226
Epoch: [4/20], Batch Num: [490/600]
Discriminator Loss: 0.3051, Generator Loss: 4.4026
D(x): 0.9102, D(G(z)): 0.0734
Epoch: [4/20], Batch Num: [491/600]
Discriminator Loss: 0.2988, Generator Loss: 4.4776
D(x): 0.8753, D(G(z)): 0.0351
Epoch: [4/20], Batch Num: [492/600]
Discriminator Loss: 0.3679, Generator Loss: 3.7268
D(x): 0.8587, D(G(z)): 0.0486
Epoch: [4/20], Batch Num: [493/600]
Discriminator Loss: 0.3439, Generator Loss: 2.8347
D(x): 0.9079, D(G(z)): 0.1029
Epoch: [4/20], Batch Num: [494/600]
Discriminator Loss: 0.4547, Generator Loss: 3.7941
D(x): 0.9808, D(G(z)): 0.2378
Epoch: [4/20], Batch Num: [495/600]
Discriminator Loss: 0.3401, Generator Loss: 4.5330
D(x): 0.9025, D(G(z)): 0.0805
Epoch: [4/20], Batch Num: [496/600]
Discriminator Loss: 0.5866, Generator Loss: 4.6822
D(x): 0.8129, D(G(z)): 0.0366
Epoch: [4/20], Batch Num: [497/600]
Discriminator Loss: 0.3912, Generator Loss: 4.0006
D(x): 0.8645, D(G(z)): 0.0518
Epoch: [4/20], Batch Num: [498/600]
Discriminator Loss: 0.4952, Generator Loss: 2.8718
D(x): 0.8575, D(G(z)): 0.1009
Epoch: [4/20], Batch Num: [499/600]
Discriminator Loss: 0.7026, Generator Loss: 2.4998
D(x): 0.8550, D(G(z)): 0.1756
Epoch: 4, Batch Num: [500/600]

Epoch: [4/20], Batch Num: [500/600]
Discriminator Loss: 0.5606, Generator Loss: 3.0590
D(x): 0.8958, D(G(z)): 0.2208
Epoch: [4/20], Batch Num: [501/600]
Discriminator Loss: 0.5744, Generator Loss: 3.1576
D(x): 0.8494, D(G(z)): 0.1441
Epoch: [4/20], Batch Num: [502/600]
Discriminator Loss: 0.2738, Generator Loss: 3.3390
D(x): 0.9108, D(G(z)): 0.0947
Epoch: [4/20], Batch Num: [503/600]
Discriminator Loss: 0.3006, Generator Loss: 3.3366
D(x): 0.8850, D(G(z)): 0.0700
Epoch: [4/20], Batch Num: [504/600]
Discriminator Loss: 0.4224, Generator Loss: 3.1869
D(x): 0.8744, D(G(z)): 0.1218
Epoch: [4/20], Batch Num: [505/600]
Discriminator Loss: 0.4941, Generator Loss: 2.9791
D(x): 0.8631, D(G(z)): 0.1297
Epoch: [4/20], Batch Num: [506/600]
Discriminator Loss: 0.5751, Generator Loss: 2.7349
D(x): 0.8399, D(G(z)): 0.1409
Epoch: [4/20], Batch Num: [507/600]
Discriminator Loss: 0.6095, Generator Loss: 2.4477
D(x): 0.7822, D(G(z)): 0.1306
Epoch: [4/20], Batch Num: [508/600]
Discriminator Loss: 0.2963, Generator Loss: 2.4082
D(x): 0.9534, D(G(z)): 0.1644
Epoch: [4/20], Batch Num: [509/600]
Discriminator Loss: 0.3330, Generator Loss: 2.7215
D(x): 0.9313, D(G(z)): 0.1675
Epoch: [4/20], Batch Num: [510/600]
Discriminator Loss: 0.3497, Generator Loss: 2.9984
D(x): 0.9378, D(G(z)): 0.1567
Epoch: [4/20], Batch Num: [511/600]
Discriminator Loss: 0.2814, Generator Loss: 3.7085
D(x): 0.9452, D(G(z)): 0.1393
Epoch: [4/20], Batch Num: [512/600]
Discriminator Loss: 0.2396, Generator Loss: 3.7944
D(x): 0.9047, D(G(z)): 0.0729
Epoch: [4/20], Batch Num: [513/600]
Discriminator Loss: 0.3568, Generator Loss: 3.8890
D(x): 0.8499, D(G(z)): 0.0307
Epoch: [4/20], Batch Num: [514/600]
Discriminator Loss: 0.3630, Generator Loss: 3.7524
D(x): 0.8805, D(G(z)): 0.0831
Epoch: [4/20], Batch Num: [515/600]
Discriminator Loss: 0.2813, Generator Loss: 3.2629
D(x): 0.8751, D(G(z)): 0.0617
Epoch: [4/20], Batch Num: [516/600]
Discriminator Loss: 0.2887, Generator Loss: 2.7523
D(x): 0.9056, D(G(z)): 0.0915
Epoch: [4/20], Batch Num: [517/600]
Discriminator Loss: 0.2532, Generator Loss: 3.0460
D(x): 0.9600, D(G(z)): 0.1303
Epoch: [4/20], Batch Num: [518/600]
Discriminator Loss: 0.2263, Generator Loss: 2.9437
D(x): 0.9685, D(G(z)): 0.1514
Epoch: [4/20], Batch Num: [519/600]
Discriminator Loss: 0.4523, Generator Loss: 3.3047
D(x): 0.9032, D(G(z)): 0.1807
Epoch: [4/20], Batch Num: [520/600]
Discriminator Loss: 0.2241, Generator Loss: 3.7329
D(x): 0.9533, D(G(z)): 0.1283
Epoch: [4/20], Batch Num: [521/600]
Discriminator Loss: 0.3756, Generator Loss: 4.5789
D(x): 0.8674, D(G(z)): 0.1042
Epoch: [4/20], Batch Num: [522/600]
Discriminator Loss: 0.2825, Generator Loss: 3.9369
D(x): 0.8956, D(G(z)): 0.0696
Epoch: [4/20], Batch Num: [523/600]
Discriminator Loss: 0.3095, Generator Loss: 3.8691
D(x): 0.8639, D(G(z)): 0.0736
Epoch: [4/20], Batch Num: [524/600]
Discriminator Loss: 0.3892, Generator Loss: 3.2020
D(x): 0.8863, D(G(z)): 0.1209
Epoch: [4/20], Batch Num: [525/600]
Discriminator Loss: 0.4784, Generator Loss: 3.5256
D(x): 0.9137, D(G(z)): 0.1877
Epoch: [4/20], Batch Num: [526/600]
Discriminator Loss: 0.4668, Generator Loss: 4.1775
D(x): 0.9399, D(G(z)): 0.2000
Epoch: [4/20], Batch Num: [527/600]
Discriminator Loss: 0.2829, Generator Loss: 4.4378
D(x): 0.8794, D(G(z)): 0.0732
Epoch: [4/20], Batch Num: [528/600]
Discriminator Loss: 0.5054, Generator Loss: 4.3047
D(x): 0.8197, D(G(z)): 0.0712
Epoch: [4/20], Batch Num: [529/600]
Discriminator Loss: 0.3752, Generator Loss: 3.6055
D(x): 0.8687, D(G(z)): 0.0504
Epoch: [4/20], Batch Num: [530/600]
Discriminator Loss: 0.3193, Generator Loss: 3.2387
D(x): 0.9271, D(G(z)): 0.1242
Epoch: [4/20], Batch Num: [531/600]
Discriminator Loss: 0.3854, Generator Loss: 2.5445
D(x): 0.9116, D(G(z)): 0.1400
Epoch: [4/20], Batch Num: [532/600]
Discriminator Loss: 0.3508, Generator Loss: 3.3654
D(x): 0.9487, D(G(z)): 0.1725
Epoch: [4/20], Batch Num: [533/600]
Discriminator Loss: 0.3173, Generator Loss: 4.5937
D(x): 0.9161, D(G(z)): 0.1199
Epoch: [4/20], Batch Num: [534/600]
Discriminator Loss: 0.2908, Generator Loss: 4.4907
D(x): 0.8893, D(G(z)): 0.0653
Epoch: [4/20], Batch Num: [535/600]
Discriminator Loss: 0.3378, Generator Loss: 4.4458
D(x): 0.8569, D(G(z)): 0.0436
Epoch: [4/20], Batch Num: [536/600]
Discriminator Loss: 0.4162, Generator Loss: 3.8442
D(x): 0.8669, D(G(z)): 0.0765
Epoch: [4/20], Batch Num: [537/600]
Discriminator Loss: 0.3314, Generator Loss: 2.8671
D(x): 0.8936, D(G(z)): 0.0952
Epoch: [4/20], Batch Num: [538/600]
Discriminator Loss: 0.2444, Generator Loss: 2.7351
D(x): 0.9598, D(G(z)): 0.1535
Epoch: [4/20], Batch Num: [539/600]
Discriminator Loss: 0.3682, Generator Loss: 3.3111
D(x): 0.9431, D(G(z)): 0.1732
Epoch: [4/20], Batch Num: [540/600]
Discriminator Loss: 0.2643, Generator Loss: 4.4522
D(x): 0.9282, D(G(z)): 0.0993
Epoch: [4/20], Batch Num: [541/600]
Discriminator Loss: 0.3940, Generator Loss: 4.1538
D(x): 0.8778, D(G(z)): 0.0677
Epoch: [4/20], Batch Num: [542/600]
Discriminator Loss: 0.3047, Generator Loss: 3.7221
D(x): 0.8753, D(G(z)): 0.0497
Epoch: [4/20], Batch Num: [543/600]
Discriminator Loss: 0.4002, Generator Loss: 3.3409
D(x): 0.9090, D(G(z)): 0.1344
Epoch: [4/20], Batch Num: [544/600]
Discriminator Loss: 0.2489, Generator Loss: 3.3631
D(x): 0.9286, D(G(z)): 0.0990
Epoch: [4/20], Batch Num: [545/600]
Discriminator Loss: 0.3260, Generator Loss: 3.4647
D(x): 0.9346, D(G(z)): 0.1256
Epoch: [4/20], Batch Num: [546/600]
Discriminator Loss: 0.4551, Generator Loss: 4.2684
D(x): 0.9094, D(G(z)): 0.1353
Epoch: [4/20], Batch Num: [547/600]
Discriminator Loss: 0.4155, Generator Loss: 4.8090
D(x): 0.8822, D(G(z)): 0.0715
Epoch: [4/20], Batch Num: [548/600]
Discriminator Loss: 0.4980, Generator Loss: 3.9054
D(x): 0.8425, D(G(z)): 0.0639
Epoch: [4/20], Batch Num: [549/600]
Discriminator Loss: 0.2567, Generator Loss: 3.1799
D(x): 0.8971, D(G(z)): 0.0582
Epoch: [4/20], Batch Num: [550/600]
Discriminator Loss: 0.3745, Generator Loss: 2.7333
D(x): 0.9249, D(G(z)): 0.1496
Epoch: [4/20], Batch Num: [551/600]
Discriminator Loss: 0.4099, Generator Loss: 3.4283
D(x): 0.9408, D(G(z)): 0.1861
Epoch: [4/20], Batch Num: [552/600]
Discriminator Loss: 0.3107, Generator Loss: 4.1872
D(x): 0.9164, D(G(z)): 0.0989
Epoch: [4/20], Batch Num: [553/600]
Discriminator Loss: 0.3051, Generator Loss: 4.3131
D(x): 0.8879, D(G(z)): 0.0658
Epoch: [4/20], Batch Num: [554/600]
Discriminator Loss: 0.4125, Generator Loss: 3.6517
D(x): 0.8470, D(G(z)): 0.0501
Epoch: [4/20], Batch Num: [555/600]
Discriminator Loss: 0.3573, Generator Loss: 2.9716
D(x): 0.8686, D(G(z)): 0.0711
Epoch: [4/20], Batch Num: [556/600]
Discriminator Loss: 0.4788, Generator Loss: 2.6411
D(x): 0.9199, D(G(z)): 0.2178
Epoch: [4/20], Batch Num: [557/600]
Discriminator Loss: 0.3285, Generator Loss: 2.9458
D(x): 0.9332, D(G(z)): 0.1387
Epoch: [4/20], Batch Num: [558/600]
Discriminator Loss: 0.3749, Generator Loss: 3.4872
D(x): 0.8974, D(G(z)): 0.1165
Epoch: [4/20], Batch Num: [559/600]
Discriminator Loss: 0.5014, Generator Loss: 3.5593
D(x): 0.8527, D(G(z)): 0.0916
Epoch: [4/20], Batch Num: [560/600]
Discriminator Loss: 0.5690, Generator Loss: 3.1080
D(x): 0.8441, D(G(z)): 0.0879
Epoch: [4/20], Batch Num: [561/600]
Discriminator Loss: 0.3757, Generator Loss: 2.7368
D(x): 0.9088, D(G(z)): 0.1281
Epoch: [4/20], Batch Num: [562/600]
Discriminator Loss: 0.4195, Generator Loss: 2.8376
D(x): 0.8991, D(G(z)): 0.1601
Epoch: [4/20], Batch Num: [563/600]
Discriminator Loss: 0.3454, Generator Loss: 3.0818
D(x): 0.9159, D(G(z)): 0.1328
Epoch: [4/20], Batch Num: [564/600]
Discriminator Loss: 0.3491, Generator Loss: 3.2180
D(x): 0.8757, D(G(z)): 0.0813
Epoch: [4/20], Batch Num: [565/600]
Discriminator Loss: 0.2503, Generator Loss: 3.0089
D(x): 0.8970, D(G(z)): 0.0681
Epoch: [4/20], Batch Num: [566/600]
Discriminator Loss: 0.3933, Generator Loss: 2.6330
D(x): 0.8676, D(G(z)): 0.1043
Epoch: [4/20], Batch Num: [567/600]
Discriminator Loss: 0.3100, Generator Loss: 2.6039
D(x): 0.9099, D(G(z)): 0.1083
Epoch: [4/20], Batch Num: [568/600]
Discriminator Loss: 0.3540, Generator Loss: 2.9489
D(x): 0.9285, D(G(z)): 0.1644
Epoch: [4/20], Batch Num: [569/600]
Discriminator Loss: 0.3444, Generator Loss: 3.1927
D(x): 0.9346, D(G(z)): 0.1216
Epoch: [4/20], Batch Num: [570/600]
Discriminator Loss: 0.3125, Generator Loss: 3.5997
D(x): 0.8860, D(G(z)): 0.0670
Epoch: [4/20], Batch Num: [571/600]
Discriminator Loss: 0.3171, Generator Loss: 3.8898
D(x): 0.9053, D(G(z)): 0.0844
Epoch: [4/20], Batch Num: [572/600]
Discriminator Loss: 0.3211, Generator Loss: 3.3073
D(x): 0.9001, D(G(z)): 0.0670
Epoch: [4/20], Batch Num: [573/600]
Discriminator Loss: 0.2048, Generator Loss: 3.1513
D(x): 0.9173, D(G(z)): 0.0563
Epoch: [4/20], Batch Num: [574/600]
Discriminator Loss: 0.2456, Generator Loss: 2.6759
D(x): 0.9223, D(G(z)): 0.0862
Epoch: [4/20], Batch Num: [575/600]
Discriminator Loss: 0.2623, Generator Loss: 3.2600
D(x): 0.9736, D(G(z)): 0.1647
Epoch: [4/20], Batch Num: [576/600]
Discriminator Loss: 0.2940, Generator Loss: 4.1266
D(x): 0.9305, D(G(z)): 0.0919
Epoch: [4/20], Batch Num: [577/600]
Discriminator Loss: 0.2400, Generator Loss: 4.1856
D(x): 0.8994, D(G(z)): 0.0452
Epoch: [4/20], Batch Num: [578/600]
Discriminator Loss: 0.2588, Generator Loss: 4.3341
D(x): 0.9067, D(G(z)): 0.0477
Epoch: [4/20], Batch Num: [579/600]
Discriminator Loss: 0.2535, Generator Loss: 3.9753
D(x): 0.9126, D(G(z)): 0.0384
Epoch: [4/20], Batch Num: [580/600]
Discriminator Loss: 0.2565, Generator Loss: 3.4272
D(x): 0.9163, D(G(z)): 0.0518
Epoch: [4/20], Batch Num: [581/600]
Discriminator Loss: 0.2962, Generator Loss: 3.5031
D(x): 0.9225, D(G(z)): 0.1024
Epoch: [4/20], Batch Num: [582/600]
Discriminator Loss: 0.3017, Generator Loss: 3.2705
D(x): 0.9128, D(G(z)): 0.0844
Epoch: [4/20], Batch Num: [583/600]
Discriminator Loss: 0.2998, Generator Loss: 3.3506
D(x): 0.9460, D(G(z)): 0.1155
Epoch: [4/20], Batch Num: [584/600]
Discriminator Loss: 0.2045, Generator Loss: 4.1631
D(x): 0.9603, D(G(z)): 0.0970
Epoch: [4/20], Batch Num: [585/600]
Discriminator Loss: 0.1324, Generator Loss: 4.6403
D(x): 0.9657, D(G(z)): 0.0567
Epoch: [4/20], Batch Num: [586/600]
Discriminator Loss: 0.2618, Generator Loss: 4.8177
D(x): 0.9095, D(G(z)): 0.0637
Epoch: [4/20], Batch Num: [587/600]
Discriminator Loss: 0.3262, Generator Loss: 4.5827
D(x): 0.8823, D(G(z)): 0.0270
Epoch: [4/20], Batch Num: [588/600]
Discriminator Loss: 0.3271, Generator Loss: 3.7843
D(x): 0.8799, D(G(z)): 0.0305
Epoch: [4/20], Batch Num: [589/600]
Discriminator Loss: 0.2557, Generator Loss: 3.2072
D(x): 0.9260, D(G(z)): 0.0762
Epoch: [4/20], Batch Num: [590/600]
Discriminator Loss: 0.2773, Generator Loss: 3.7999
D(x): 0.9587, D(G(z)): 0.1217
Epoch: [4/20], Batch Num: [591/600]
Discriminator Loss: 0.2309, Generator Loss: 3.4608
D(x): 0.9387, D(G(z)): 0.0724
Epoch: [4/20], Batch Num: [592/600]
Discriminator Loss: 0.2739, Generator Loss: 3.9530
D(x): 0.9213, D(G(z)): 0.0795
Epoch: [4/20], Batch Num: [593/600]
Discriminator Loss: 0.1834, Generator Loss: 3.9317
D(x): 0.9449, D(G(z)): 0.0819
Epoch: [4/20], Batch Num: [594/600]
Discriminator Loss: 0.1674, Generator Loss: 4.2305
D(x): 0.9594, D(G(z)): 0.0638
Epoch: [4/20], Batch Num: [595/600]
Discriminator Loss: 0.0926, Generator Loss: 4.1689
D(x): 0.9664, D(G(z)): 0.0398
Epoch: [4/20], Batch Num: [596/600]
Discriminator Loss: 0.0693, Generator Loss: 4.3632
D(x): 0.9790, D(G(z)): 0.0384
Epoch: [4/20], Batch Num: [597/600]
Discriminator Loss: 0.3033, Generator Loss: 4.1278
D(x): 0.9075, D(G(z)): 0.0238
Epoch: [4/20], Batch Num: [598/600]
Discriminator Loss: 0.1370, Generator Loss: 4.0671
D(x): 0.9519, D(G(z)): 0.0326
Epoch: [4/20], Batch Num: [599/600]
Discriminator Loss: 0.1377, Generator Loss: 3.8009
D(x): 0.9727, D(G(z)): 0.0516
Epoch: 5, Batch Num: [0/600]

Epoch: [5/20], Batch Num: [0/600]
Discriminator Loss: 0.3212, Generator Loss: 3.4082
D(x): 0.9103, D(G(z)): 0.0642
Epoch: [5/20], Batch Num: [1/600]
Discriminator Loss: 0.1953, Generator Loss: 3.4145
D(x): 0.9613, D(G(z)): 0.1024
Epoch: [5/20], Batch Num: [2/600]
Discriminator Loss: 0.2166, Generator Loss: 3.5712
D(x): 0.9660, D(G(z)): 0.0949
Epoch: [5/20], Batch Num: [3/600]
Discriminator Loss: 0.2406, Generator Loss: 3.9384
D(x): 0.9409, D(G(z)): 0.0865
Epoch: [5/20], Batch Num: [4/600]
Discriminator Loss: 0.2343, Generator Loss: 3.9536
D(x): 0.9225, D(G(z)): 0.0433
Epoch: [5/20], Batch Num: [5/600]
Discriminator Loss: 0.2505, Generator Loss: 3.5946
D(x): 0.9095, D(G(z)): 0.0460
Epoch: [5/20], Batch Num: [6/600]
Discriminator Loss: 0.2312, Generator Loss: 3.3356
D(x): 0.9142, D(G(z)): 0.0510
Epoch: [5/20], Batch Num: [7/600]
Discriminator Loss: 0.2041, Generator Loss: 3.0601
D(x): 0.9433, D(G(z)): 0.0803
Epoch: [5/20], Batch Num: [8/600]
Discriminator Loss: 0.2884, Generator Loss: 3.6390
D(x): 0.9846, D(G(z)): 0.1498
Epoch: [5/20], Batch Num: [9/600]
Discriminator Loss: 0.2191, Generator Loss: 4.0197
D(x): 0.9575, D(G(z)): 0.0955
Epoch: [5/20], Batch Num: [10/600]
Discriminator Loss: 0.2114, Generator Loss: 4.4582
D(x): 0.9265, D(G(z)): 0.0458
Epoch: [5/20], Batch Num: [11/600]
Discriminator Loss: 0.2220, Generator Loss: 4.2163
D(x): 0.9118, D(G(z)): 0.0248
Epoch: [5/20], Batch Num: [12/600]
Discriminator Loss: 0.3913, Generator Loss: 3.5734
D(x): 0.8396, D(G(z)): 0.0234
Epoch: [5/20], Batch Num: [13/600]
Discriminator Loss: 0.2489, Generator Loss: 2.5417
D(x): 0.9214, D(G(z)): 0.0858
Epoch: [5/20], Batch Num: [14/600]
Discriminator Loss: 0.4178, Generator Loss: 2.9253
D(x): 0.9650, D(G(z)): 0.2309
Epoch: [5/20], Batch Num: [15/600]
Discriminator Loss: 0.2162, Generator Loss: 3.5234
D(x): 0.9731, D(G(z)): 0.1183
Epoch: [5/20], Batch Num: [16/600]
Discriminator Loss: 0.2392, Generator Loss: 3.9572
D(x): 0.9289, D(G(z)): 0.0560
Epoch: [5/20], Batch Num: [17/600]
Discriminator Loss: 0.1794, Generator Loss: 4.3472
D(x): 0.9308, D(G(z)): 0.0416
Epoch: [5/20], Batch Num: [18/600]
Discriminator Loss: 0.4257, Generator Loss: 4.0126
D(x): 0.8566, D(G(z)): 0.0339
Epoch: [5/20], Batch Num: [19/600]
Discriminator Loss: 0.1777, Generator Loss: 3.4106
D(x): 0.9308, D(G(z)): 0.0406
Epoch: [5/20], Batch Num: [20/600]
Discriminator Loss: 0.2155, Generator Loss: 3.0587
D(x): 0.9415, D(G(z)): 0.0614
Epoch: [5/20], Batch Num: [21/600]
Discriminator Loss: 0.2829, Generator Loss: 3.0382
D(x): 0.9484, D(G(z)): 0.1372
Epoch: [5/20], Batch Num: [22/600]
Discriminator Loss: 0.1913, Generator Loss: 3.0858
D(x): 0.9571, D(G(z)): 0.0912
Epoch: [5/20], Batch Num: [23/600]
Discriminator Loss: 0.2073, Generator Loss: 3.5729
D(x): 0.9488, D(G(z)): 0.0918
Epoch: [5/20], Batch Num: [24/600]
Discriminator Loss: 0.2273, Generator Loss: 4.0500
D(x): 0.9329, D(G(z)): 0.0628
Epoch: [5/20], Batch Num: [25/600]
Discriminator Loss: 0.3302, Generator Loss: 3.5655
D(x): 0.8857, D(G(z)): 0.0440
Epoch: [5/20], Batch Num: [26/600]
Discriminator Loss: 0.1901, Generator Loss: 3.7856
D(x): 0.9422, D(G(z)): 0.0531
Epoch: [5/20], Batch Num: [27/600]
Discriminator Loss: 0.1640, Generator Loss: 3.3957
D(x): 0.9496, D(G(z)): 0.0562
Epoch: [5/20], Batch Num: [28/600]
Discriminator Loss: 0.1632, Generator Loss: 3.3958
D(x): 0.9557, D(G(z)): 0.0730
Epoch: [5/20], Batch Num: [29/600]
Discriminator Loss: 0.1625, Generator Loss: 3.4757
D(x): 0.9600, D(G(z)): 0.0803
Epoch: [5/20], Batch Num: [30/600]
Discriminator Loss: 0.1727, Generator Loss: 3.7224
D(x): 0.9631, D(G(z)): 0.0854
Epoch: [5/20], Batch Num: [31/600]
Discriminator Loss: 0.2704, Generator Loss: 3.7350
D(x): 0.9164, D(G(z)): 0.0582
Epoch: [5/20], Batch Num: [32/600]
Discriminator Loss: 0.2080, Generator Loss: 3.7916
D(x): 0.9432, D(G(z)): 0.0633
Epoch: [5/20], Batch Num: [33/600]
Discriminator Loss: 0.2908, Generator Loss: 3.4949
D(x): 0.8996, D(G(z)): 0.0464
Epoch: [5/20], Batch Num: [34/600]
Discriminator Loss: 0.2194, Generator Loss: 3.5329
D(x): 0.9544, D(G(z)): 0.1016
Epoch: [5/20], Batch Num: [35/600]
Discriminator Loss: 0.2642, Generator Loss: 3.2079
D(x): 0.9401, D(G(z)): 0.0766
Epoch: [5/20], Batch Num: [36/600]
Discriminator Loss: 0.2298, Generator Loss: 3.7943
D(x): 0.9514, D(G(z)): 0.1096
Epoch: [5/20], Batch Num: [37/600]
Discriminator Loss: 0.2420, Generator Loss: 4.4665
D(x): 0.9638, D(G(z)): 0.0869
Epoch: [5/20], Batch Num: [38/600]
Discriminator Loss: 0.1836, Generator Loss: 4.8152
D(x): 0.9378, D(G(z)): 0.0242
Epoch: [5/20], Batch Num: [39/600]
Discriminator Loss: 0.2576, Generator Loss: 4.6420
D(x): 0.8988, D(G(z)): 0.0273
Epoch: [5/20], Batch Num: [40/600]
Discriminator Loss: 0.4327, Generator Loss: 3.8146
D(x): 0.8779, D(G(z)): 0.0356
Epoch: [5/20], Batch Num: [41/600]
Discriminator Loss: 0.2071, Generator Loss: 3.4284
D(x): 0.9432, D(G(z)): 0.0710
Epoch: [5/20], Batch Num: [42/600]
Discriminator Loss: 0.2670, Generator Loss: 3.3093
D(x): 0.9424, D(G(z)): 0.1042
Epoch: [5/20], Batch Num: [43/600]
Discriminator Loss: 0.1992, Generator Loss: 3.3550
D(x): 0.9745, D(G(z)): 0.1224
Epoch: [5/20], Batch Num: [44/600]
Discriminator Loss: 0.2931, Generator Loss: 3.8135
D(x): 0.9362, D(G(z)): 0.0906
Epoch: [5/20], Batch Num: [45/600]
Discriminator Loss: 0.2371, Generator Loss: 4.2820
D(x): 0.9390, D(G(z)): 0.0741
Epoch: [5/20], Batch Num: [46/600]
Discriminator Loss: 0.3057, Generator Loss: 4.1235
D(x): 0.9062, D(G(z)): 0.0380
Epoch: [5/20], Batch Num: [47/600]
Discriminator Loss: 0.3133, Generator Loss: 4.0462
D(x): 0.9067, D(G(z)): 0.0570
Epoch: [5/20], Batch Num: [48/600]
Discriminator Loss: 0.2832, Generator Loss: 3.6340
D(x): 0.9195, D(G(z)): 0.0643
Epoch: [5/20], Batch Num: [49/600]
Discriminator Loss: 0.3003, Generator Loss: 3.5192
D(x): 0.9510, D(G(z)): 0.1039
Epoch: [5/20], Batch Num: [50/600]
Discriminator Loss: 0.2451, Generator Loss: 3.6213
D(x): 0.9316, D(G(z)): 0.0918
Epoch: [5/20], Batch Num: [51/600]
Discriminator Loss: 0.3123, Generator Loss: 3.6720
D(x): 0.9366, D(G(z)): 0.0953
Epoch: [5/20], Batch Num: [52/600]
Discriminator Loss: 0.3290, Generator Loss: 3.6650
D(x): 0.9040, D(G(z)): 0.0749
Epoch: [5/20], Batch Num: [53/600]
Discriminator Loss: 0.2705, Generator Loss: 3.5752
D(x): 0.9204, D(G(z)): 0.0852
Epoch: [5/20], Batch Num: [54/600]
Discriminator Loss: 0.2360, Generator Loss: 3.6223
D(x): 0.9518, D(G(z)): 0.0904
Epoch: [5/20], Batch Num: [55/600]
Discriminator Loss: 0.2385, Generator Loss: 3.4290
D(x): 0.9245, D(G(z)): 0.0761
Epoch: [5/20], Batch Num: [56/600]
Discriminator Loss: 0.1964, Generator Loss: 3.7530
D(x): 0.9422, D(G(z)): 0.0877
Epoch: [5/20], Batch Num: [57/600]
Discriminator Loss: 0.3906, Generator Loss: 3.3035
D(x): 0.8700, D(G(z)): 0.0461
Epoch: [5/20], Batch Num: [58/600]
Discriminator Loss: 0.2578, Generator Loss: 2.9168
D(x): 0.9346, D(G(z)): 0.1009
Epoch: [5/20], Batch Num: [59/600]
Discriminator Loss: 0.2351, Generator Loss: 3.3972
D(x): 0.9486, D(G(z)): 0.1191
Epoch: [5/20], Batch Num: [60/600]
Discriminator Loss: 0.2145, Generator Loss: 3.6716
D(x): 0.9261, D(G(z)): 0.0719
Epoch: [5/20], Batch Num: [61/600]
Discriminator Loss: 0.1800, Generator Loss: 3.9390
D(x): 0.9331, D(G(z)): 0.0676
Epoch: [5/20], Batch Num: [62/600]
Discriminator Loss: 0.2928, Generator Loss: 3.4753
D(x): 0.9090, D(G(z)): 0.0749
Epoch: [5/20], Batch Num: [63/600]
Discriminator Loss: 0.2095, Generator Loss: 3.4364
D(x): 0.9458, D(G(z)): 0.0739
Epoch: [5/20], Batch Num: [64/600]
Discriminator Loss: 0.1825, Generator Loss: 3.9069
D(x): 0.9365, D(G(z)): 0.0564
Epoch: [5/20], Batch Num: [65/600]
Discriminator Loss: 0.1956, Generator Loss: 3.4644
D(x): 0.9688, D(G(z)): 0.0864
Epoch: [5/20], Batch Num: [66/600]
Discriminator Loss: 0.2188, Generator Loss: 3.8315
D(x): 0.9572, D(G(z)): 0.0734
Epoch: [5/20], Batch Num: [67/600]
Discriminator Loss: 0.1357, Generator Loss: 4.0402
D(x): 0.9572, D(G(z)): 0.0535
Epoch: [5/20], Batch Num: [68/600]
Discriminator Loss: 0.2173, Generator Loss: 4.1823
D(x): 0.9382, D(G(z)): 0.0600
Epoch: [5/20], Batch Num: [69/600]
Discriminator Loss: 0.1986, Generator Loss: 3.9880
D(x): 0.9296, D(G(z)): 0.0445
Epoch: [5/20], Batch Num: [70/600]
Discriminator Loss: 0.1480, Generator Loss: 3.4460
D(x): 0.9395, D(G(z)): 0.0366
Epoch: [5/20], Batch Num: [71/600]
Discriminator Loss: 0.1830, Generator Loss: 3.7724
D(x): 0.9712, D(G(z)): 0.0759
Epoch: [5/20], Batch Num: [72/600]
Discriminator Loss: 0.2023, Generator Loss: 3.9296
D(x): 0.9786, D(G(z)): 0.0961
Epoch: [5/20], Batch Num: [73/600]
Discriminator Loss: 0.2281, Generator Loss: 4.1926
D(x): 0.9458, D(G(z)): 0.0508
Epoch: [5/20], Batch Num: [74/600]
Discriminator Loss: 0.1336, Generator Loss: 4.3585
D(x): 0.9460, D(G(z)): 0.0395
Epoch: [5/20], Batch Num: [75/600]
Discriminator Loss: 0.1231, Generator Loss: 4.1052
D(x): 0.9596, D(G(z)): 0.0483
Epoch: [5/20], Batch Num: [76/600]
Discriminator Loss: 0.1342, Generator Loss: 4.1463
D(x): 0.9633, D(G(z)): 0.0504
Epoch: [5/20], Batch Num: [77/600]
Discriminator Loss: 0.1791, Generator Loss: 4.1438
D(x): 0.9565, D(G(z)): 0.0734
Epoch: [5/20], Batch Num: [78/600]
Discriminator Loss: 0.2119, Generator Loss: 4.0706
D(x): 0.9408, D(G(z)): 0.0714
Epoch: [5/20], Batch Num: [79/600]
Discriminator Loss: 0.2124, Generator Loss: 4.2292
D(x): 0.9396, D(G(z)): 0.0662
Epoch: [5/20], Batch Num: [80/600]
Discriminator Loss: 0.2282, Generator Loss: 4.0968
D(x): 0.9477, D(G(z)): 0.0822
Epoch: [5/20], Batch Num: [81/600]
Discriminator Loss: 0.2107, Generator Loss: 4.4508
D(x): 0.9426, D(G(z)): 0.0735
Epoch: [5/20], Batch Num: [82/600]
Discriminator Loss: 0.3505, Generator Loss: 3.9590
D(x): 0.9270, D(G(z)): 0.0773
Epoch: [5/20], Batch Num: [83/600]
Discriminator Loss: 0.2899, Generator Loss: 3.7785
D(x): 0.9148, D(G(z)): 0.0742
Epoch: [5/20], Batch Num: [84/600]
Discriminator Loss: 0.3611, Generator Loss: 3.6005
D(x): 0.9368, D(G(z)): 0.0992
Epoch: [5/20], Batch Num: [85/600]
Discriminator Loss: 0.2446, Generator Loss: 3.1346
D(x): 0.9343, D(G(z)): 0.0736
Epoch: [5/20], Batch Num: [86/600]
Discriminator Loss: 0.4480, Generator Loss: 3.5594
D(x): 0.9461, D(G(z)): 0.1587
Epoch: [5/20], Batch Num: [87/600]
Discriminator Loss: 0.2741, Generator Loss: 4.3481
D(x): 0.9489, D(G(z)): 0.1100
Epoch: [5/20], Batch Num: [88/600]
Discriminator Loss: 0.3868, Generator Loss: 4.2666
D(x): 0.8724, D(G(z)): 0.0458
Epoch: [5/20], Batch Num: [89/600]
Discriminator Loss: 0.5124, Generator Loss: 3.5610
D(x): 0.8551, D(G(z)): 0.0608
Epoch: [5/20], Batch Num: [90/600]
Discriminator Loss: 0.3789, Generator Loss: 2.4774
D(x): 0.8881, D(G(z)): 0.0816
Epoch: [5/20], Batch Num: [91/600]
Discriminator Loss: 0.6544, Generator Loss: 3.5061
D(x): 0.9334, D(G(z)): 0.2557
Epoch: [5/20], Batch Num: [92/600]
Discriminator Loss: 0.4515, Generator Loss: 4.1078
D(x): 0.8933, D(G(z)): 0.0846
Epoch: [5/20], Batch Num: [93/600]
Discriminator Loss: 0.4126, Generator Loss: 4.2083
D(x): 0.8557, D(G(z)): 0.0502
Epoch: [5/20], Batch Num: [94/600]
Discriminator Loss: 0.3174, Generator Loss: 3.9274
D(x): 0.8922, D(G(z)): 0.0607
Epoch: [5/20], Batch Num: [95/600]
Discriminator Loss: 0.3185, Generator Loss: 3.5335
D(x): 0.8820, D(G(z)): 0.0752
Epoch: [5/20], Batch Num: [96/600]
Discriminator Loss: 0.2679, Generator Loss: 3.1256
D(x): 0.9015, D(G(z)): 0.0566
Epoch: [5/20], Batch Num: [97/600]
Discriminator Loss: 0.3906, Generator Loss: 2.7738
D(x): 0.8893, D(G(z)): 0.1219
Epoch: [5/20], Batch Num: [98/600]
Discriminator Loss: 0.3642, Generator Loss: 2.6140
D(x): 0.9406, D(G(z)): 0.1658
Epoch: [5/20], Batch Num: [99/600]
Discriminator Loss: 0.2730, Generator Loss: 3.0651
D(x): 0.9569, D(G(z)): 0.1301
Epoch: 5, Batch Num: [100/600]

Epoch: [5/20], Batch Num: [100/600]
Discriminator Loss: 0.1770, Generator Loss: 3.4383
D(x): 0.9637, D(G(z)): 0.0933
Epoch: [5/20], Batch Num: [101/600]
Discriminator Loss: 0.2982, Generator Loss: 3.5569
D(x): 0.9179, D(G(z)): 0.0653
Epoch: [5/20], Batch Num: [102/600]
Discriminator Loss: 0.3353, Generator Loss: 3.9805
D(x): 0.8739, D(G(z)): 0.0682
Epoch: [5/20], Batch Num: [103/600]
Discriminator Loss: 0.2426, Generator Loss: 3.6342
D(x): 0.8874, D(G(z)): 0.0482
Epoch: [5/20], Batch Num: [104/600]
Discriminator Loss: 0.1931, Generator Loss: 3.1433
D(x): 0.9330, D(G(z)): 0.0726
Epoch: [5/20], Batch Num: [105/600]
Discriminator Loss: 0.2532, Generator Loss: 3.1092
D(x): 0.9546, D(G(z)): 0.1264
Epoch: [5/20], Batch Num: [106/600]
Discriminator Loss: 0.2179, Generator Loss: 3.4084
D(x): 0.9531, D(G(z)): 0.0882
Epoch: [5/20], Batch Num: [107/600]
Discriminator Loss: 0.2052, Generator Loss: 3.7419
D(x): 0.9242, D(G(z)): 0.0634
Epoch: [5/20], Batch Num: [108/600]
Discriminator Loss: 0.1821, Generator Loss: 3.9074
D(x): 0.9418, D(G(z)): 0.0632
Epoch: [5/20], Batch Num: [109/600]
Discriminator Loss: 0.1630, Generator Loss: 3.9424
D(x): 0.9326, D(G(z)): 0.0371
Epoch: [5/20], Batch Num: [110/600]
Discriminator Loss: 0.1942, Generator Loss: 3.7707
D(x): 0.9348, D(G(z)): 0.0501
Epoch: [5/20], Batch Num: [111/600]
Discriminator Loss: 0.0828, Generator Loss: 3.6595
D(x): 0.9715, D(G(z)): 0.0443
Epoch: [5/20], Batch Num: [112/600]
Discriminator Loss: 0.1906, Generator Loss: 3.9420
D(x): 0.9481, D(G(z)): 0.0704
Epoch: [5/20], Batch Num: [113/600]
Discriminator Loss: 0.2450, Generator Loss: 4.4690
D(x): 0.9376, D(G(z)): 0.0872
Epoch: [5/20], Batch Num: [114/600]
Discriminator Loss: 0.1418, Generator Loss: 4.8457
D(x): 0.9580, D(G(z)): 0.0507
Epoch: [5/20], Batch Num: [115/600]
Discriminator Loss: 0.2495, Generator Loss: 4.0857
D(x): 0.9039, D(G(z)): 0.0364
Epoch: [5/20], Batch Num: [116/600]
Discriminator Loss: 0.2181, Generator Loss: 4.0483
D(x): 0.9139, D(G(z)): 0.0449
Epoch: [5/20], Batch Num: [117/600]
Discriminator Loss: 0.1251, Generator Loss: 3.9614
D(x): 0.9604, D(G(z)): 0.0426
Epoch: [5/20], Batch Num: [118/600]
Discriminator Loss: 0.2468, Generator Loss: 3.6358
D(x): 0.9547, D(G(z)): 0.0896
Epoch: [5/20], Batch Num: [119/600]
Discriminator Loss: 0.1749, Generator Loss: 4.0035
D(x): 0.9577, D(G(z)): 0.0670
Epoch: [5/20], Batch Num: [120/600]
Discriminator Loss: 0.1047, Generator Loss: 4.8121
D(x): 0.9730, D(G(z)): 0.0602
Epoch: [5/20], Batch Num: [121/600]
Discriminator Loss: 0.1417, Generator Loss: 4.8563
D(x): 0.9733, D(G(z)): 0.0621
Epoch: [5/20], Batch Num: [122/600]
Discriminator Loss: 0.0912, Generator Loss: 5.0482
D(x): 0.9636, D(G(z)): 0.0228
Epoch: [5/20], Batch Num: [123/600]
Discriminator Loss: 0.1314, Generator Loss: 4.8895
D(x): 0.9344, D(G(z)): 0.0143
Epoch: [5/20], Batch Num: [124/600]
Discriminator Loss: 0.1914, Generator Loss: 4.2943
D(x): 0.9191, D(G(z)): 0.0339
Epoch: [5/20], Batch Num: [125/600]
Discriminator Loss: 0.1715, Generator Loss: 3.4205
D(x): 0.9566, D(G(z)): 0.0560
Epoch: [5/20], Batch Num: [126/600]
Discriminator Loss: 0.1168, Generator Loss: 3.6350
D(x): 0.9737, D(G(z)): 0.0692
Epoch: [5/20], Batch Num: [127/600]
Discriminator Loss: 0.2521, Generator Loss: 4.7921
D(x): 0.9611, D(G(z)): 0.1219
Epoch: [5/20], Batch Num: [128/600]
Discriminator Loss: 0.2274, Generator Loss: 4.5584
D(x): 0.9263, D(G(z)): 0.0642
Epoch: [5/20], Batch Num: [129/600]
Discriminator Loss: 0.1283, Generator Loss: 4.4926
D(x): 0.9459, D(G(z)): 0.0326
Epoch: [5/20], Batch Num: [130/600]
Discriminator Loss: 0.1862, Generator Loss: 4.0517
D(x): 0.9382, D(G(z)): 0.0395
Epoch: [5/20], Batch Num: [131/600]
Discriminator Loss: 0.2059, Generator Loss: 4.1125
D(x): 0.9461, D(G(z)): 0.0687
Epoch: [5/20], Batch Num: [132/600]
Discriminator Loss: 0.2028, Generator Loss: 3.9744
D(x): 0.9403, D(G(z)): 0.0681
Epoch: [5/20], Batch Num: [133/600]
Discriminator Loss: 0.2689, Generator Loss: 3.6498
D(x): 0.9290, D(G(z)): 0.0802
Epoch: [5/20], Batch Num: [134/600]
Discriminator Loss: 0.1993, Generator Loss: 3.9873
D(x): 0.9599, D(G(z)): 0.1064
Epoch: [5/20], Batch Num: [135/600]
Discriminator Loss: 0.2721, Generator Loss: 4.0355
D(x): 0.9292, D(G(z)): 0.0798
Epoch: [5/20], Batch Num: [136/600]
Discriminator Loss: 0.5245, Generator Loss: 3.8551
D(x): 0.8795, D(G(z)): 0.0874
Epoch: [5/20], Batch Num: [137/600]
Discriminator Loss: 0.2641, Generator Loss: 3.5374
D(x): 0.9234, D(G(z)): 0.0794
Epoch: [5/20], Batch Num: [138/600]
Discriminator Loss: 0.3485, Generator Loss: 3.8147
D(x): 0.9311, D(G(z)): 0.1344
Epoch: [5/20], Batch Num: [139/600]
Discriminator Loss: 0.2460, Generator Loss: 3.7500
D(x): 0.9481, D(G(z)): 0.1108
Epoch: [5/20], Batch Num: [140/600]
Discriminator Loss: 0.3542, Generator Loss: 3.6669
D(x): 0.8597, D(G(z)): 0.0435
Epoch: [5/20], Batch Num: [141/600]
Discriminator Loss: 0.3269, Generator Loss: 3.2525
D(x): 0.9207, D(G(z)): 0.1008
Epoch: [5/20], Batch Num: [142/600]
Discriminator Loss: 0.4946, Generator Loss: 3.0151
D(x): 0.8978, D(G(z)): 0.1621
Epoch: [5/20], Batch Num: [143/600]
Discriminator Loss: 0.3729, Generator Loss: 3.5627
D(x): 0.9259, D(G(z)): 0.1545
Epoch: [5/20], Batch Num: [144/600]
Discriminator Loss: 0.3533, Generator Loss: 4.0994
D(x): 0.9126, D(G(z)): 0.1139
Epoch: [5/20], Batch Num: [145/600]
Discriminator Loss: 0.2868, Generator Loss: 3.8084
D(x): 0.8935, D(G(z)): 0.0568
Epoch: [5/20], Batch Num: [146/600]
Discriminator Loss: 0.2408, Generator Loss: 3.8847
D(x): 0.9266, D(G(z)): 0.0803
Epoch: [5/20], Batch Num: [147/600]
Discriminator Loss: 0.3091, Generator Loss: 3.8647
D(x): 0.9121, D(G(z)): 0.0825
Epoch: [5/20], Batch Num: [148/600]
Discriminator Loss: 0.1803, Generator Loss: 3.6867
D(x): 0.9440, D(G(z)): 0.0723
Epoch: [5/20], Batch Num: [149/600]
Discriminator Loss: 0.3137, Generator Loss: 3.4844
D(x): 0.9212, D(G(z)): 0.0966
Epoch: [5/20], Batch Num: [150/600]
Discriminator Loss: 0.3394, Generator Loss: 4.6234
D(x): 0.9493, D(G(z)): 0.1588
Epoch: [5/20], Batch Num: [151/600]
Discriminator Loss: 0.3872, Generator Loss: 4.7503
D(x): 0.8714, D(G(z)): 0.0700
Epoch: [5/20], Batch Num: [152/600]
Discriminator Loss: 0.3090, Generator Loss: 4.2767
D(x): 0.9172, D(G(z)): 0.0854
Epoch: [5/20], Batch Num: [153/600]
Discriminator Loss: 0.3348, Generator Loss: 4.5064
D(x): 0.9394, D(G(z)): 0.1068
Epoch: [5/20], Batch Num: [154/600]
Discriminator Loss: 0.3533, Generator Loss: 4.4463
D(x): 0.9103, D(G(z)): 0.0749
Epoch: [5/20], Batch Num: [155/600]
Discriminator Loss: 0.4161, Generator Loss: 4.0486
D(x): 0.8811, D(G(z)): 0.0589
Epoch: [5/20], Batch Num: [156/600]
Discriminator Loss: 0.1606, Generator Loss: 3.1287
D(x): 0.9455, D(G(z)): 0.0563
Epoch: [5/20], Batch Num: [157/600]
Discriminator Loss: 0.3162, Generator Loss: 3.5282
D(x): 0.9537, D(G(z)): 0.1358
Epoch: [5/20], Batch Num: [158/600]
Discriminator Loss: 0.3201, Generator Loss: 4.3804
D(x): 0.9637, D(G(z)): 0.1467
Epoch: [5/20], Batch Num: [159/600]
Discriminator Loss: 0.2679, Generator Loss: 5.3391
D(x): 0.8915, D(G(z)): 0.0537
Epoch: [5/20], Batch Num: [160/600]
Discriminator Loss: 0.4341, Generator Loss: 4.4318
D(x): 0.8313, D(G(z)): 0.0487
Epoch: [5/20], Batch Num: [161/600]
Discriminator Loss: 0.4558, Generator Loss: 3.5464
D(x): 0.8474, D(G(z)): 0.0738
Epoch: [5/20], Batch Num: [162/600]
Discriminator Loss: 0.2471, Generator Loss: 3.1508
D(x): 0.9403, D(G(z)): 0.1022
Epoch: [5/20], Batch Num: [163/600]
Discriminator Loss: 0.3423, Generator Loss: 3.4287
D(x): 0.9570, D(G(z)): 0.1793
Epoch: [5/20], Batch Num: [164/600]
Discriminator Loss: 0.2672, Generator Loss: 4.3106
D(x): 0.9472, D(G(z)): 0.1106
Epoch: [5/20], Batch Num: [165/600]
Discriminator Loss: 0.2901, Generator Loss: 5.2639
D(x): 0.9167, D(G(z)): 0.0710
Epoch: [5/20], Batch Num: [166/600]
Discriminator Loss: 0.4520, Generator Loss: 4.4796
D(x): 0.8235, D(G(z)): 0.0169
Epoch: [5/20], Batch Num: [167/600]
Discriminator Loss: 0.4424, Generator Loss: 3.5852
D(x): 0.8793, D(G(z)): 0.0686
Epoch: [5/20], Batch Num: [168/600]
Discriminator Loss: 0.3113, Generator Loss: 3.2860
D(x): 0.9235, D(G(z)): 0.1260
Epoch: [5/20], Batch Num: [169/600]
Discriminator Loss: 0.3343, Generator Loss: 3.3738
D(x): 0.9429, D(G(z)): 0.1353
Epoch: [5/20], Batch Num: [170/600]
Discriminator Loss: 0.3459, Generator Loss: 4.5718
D(x): 0.9585, D(G(z)): 0.1772
Epoch: [5/20], Batch Num: [171/600]
Discriminator Loss: 0.2291, Generator Loss: 5.4503
D(x): 0.8966, D(G(z)): 0.0383
Epoch: [5/20], Batch Num: [172/600]
Discriminator Loss: 0.1717, Generator Loss: 5.1199
D(x): 0.9352, D(G(z)): 0.0315
Epoch: [5/20], Batch Num: [173/600]
Discriminator Loss: 0.2915, Generator Loss: 4.9336
D(x): 0.8764, D(G(z)): 0.0179
Epoch: [5/20], Batch Num: [174/600]
Discriminator Loss: 0.2216, Generator Loss: 3.8284
D(x): 0.9265, D(G(z)): 0.0510
Epoch: [5/20], Batch Num: [175/600]
Discriminator Loss: 0.2887, Generator Loss: 3.3340
D(x): 0.9308, D(G(z)): 0.0959
Epoch: [5/20], Batch Num: [176/600]
Discriminator Loss: 0.2780, Generator Loss: 3.9172
D(x): 0.9451, D(G(z)): 0.1374
Epoch: [5/20], Batch Num: [177/600]
Discriminator Loss: 0.4029, Generator Loss: 4.9844
D(x): 0.9431, D(G(z)): 0.1663
Epoch: [5/20], Batch Num: [178/600]
Discriminator Loss: 0.2144, Generator Loss: 5.4801
D(x): 0.9399, D(G(z)): 0.0631
Epoch: [5/20], Batch Num: [179/600]
Discriminator Loss: 0.4367, Generator Loss: 5.6735
D(x): 0.8543, D(G(z)): 0.0461
Epoch: [5/20], Batch Num: [180/600]
Discriminator Loss: 0.2249, Generator Loss: 5.2985
D(x): 0.9051, D(G(z)): 0.0408
Epoch: [5/20], Batch Num: [181/600]
Discriminator Loss: 0.2964, Generator Loss: 4.6406
D(x): 0.8827, D(G(z)): 0.0365
Epoch: [5/20], Batch Num: [182/600]
Discriminator Loss: 0.2765, Generator Loss: 3.7159
D(x): 0.9027, D(G(z)): 0.0663
Epoch: [5/20], Batch Num: [183/600]
Discriminator Loss: 0.2684, Generator Loss: 4.1696
D(x): 0.9806, D(G(z)): 0.1790
Epoch: [5/20], Batch Num: [184/600]
Discriminator Loss: 0.2767, Generator Loss: 4.8946
D(x): 0.9437, D(G(z)): 0.1115
Epoch: [5/20], Batch Num: [185/600]
Discriminator Loss: 0.2559, Generator Loss: 6.1081
D(x): 0.9488, D(G(z)): 0.0895
Epoch: [5/20], Batch Num: [186/600]
Discriminator Loss: 0.1534, Generator Loss: 6.3236
D(x): 0.9235, D(G(z)): 0.0266
Epoch: [5/20], Batch Num: [187/600]
Discriminator Loss: 0.2903, Generator Loss: 5.1729
D(x): 0.8750, D(G(z)): 0.0205
Epoch: [5/20], Batch Num: [188/600]
Discriminator Loss: 0.2378, Generator Loss: 5.0758
D(x): 0.9112, D(G(z)): 0.0449
Epoch: [5/20], Batch Num: [189/600]
Discriminator Loss: 0.1919, Generator Loss: 3.9320
D(x): 0.9424, D(G(z)): 0.0740
Epoch: [5/20], Batch Num: [190/600]
Discriminator Loss: 0.3496, Generator Loss: 5.1738
D(x): 0.9695, D(G(z)): 0.1748
Epoch: [5/20], Batch Num: [191/600]
Discriminator Loss: 0.1179, Generator Loss: 6.2181
D(x): 0.9607, D(G(z)): 0.0408
Epoch: [5/20], Batch Num: [192/600]
Discriminator Loss: 0.4104, Generator Loss: 5.8268
D(x): 0.8598, D(G(z)): 0.0469
Epoch: [5/20], Batch Num: [193/600]
Discriminator Loss: 0.2396, Generator Loss: 5.9033
D(x): 0.9199, D(G(z)): 0.0443
Epoch: [5/20], Batch Num: [194/600]
Discriminator Loss: 0.3085, Generator Loss: 5.0827
D(x): 0.9144, D(G(z)): 0.0686
Epoch: [5/20], Batch Num: [195/600]
Discriminator Loss: 0.2896, Generator Loss: 4.4295
D(x): 0.9249, D(G(z)): 0.0825
Epoch: [5/20], Batch Num: [196/600]
Discriminator Loss: 0.5220, Generator Loss: 4.8991
D(x): 0.9331, D(G(z)): 0.1545
Epoch: [5/20], Batch Num: [197/600]
Discriminator Loss: 0.4387, Generator Loss: 5.2979
D(x): 0.8996, D(G(z)): 0.0918
Epoch: [5/20], Batch Num: [198/600]
Discriminator Loss: 0.4704, Generator Loss: 5.5425
D(x): 0.9111, D(G(z)): 0.0830
Epoch: [5/20], Batch Num: [199/600]
Discriminator Loss: 0.4469, Generator Loss: 5.9171
D(x): 0.9114, D(G(z)): 0.0828
Epoch: 5, Batch Num: [200/600]

Epoch: [5/20], Batch Num: [200/600]
Discriminator Loss: 0.4472, Generator Loss: 5.0455
D(x): 0.8628, D(G(z)): 0.0903
Epoch: [5/20], Batch Num: [201/600]
Discriminator Loss: 0.3336, Generator Loss: 4.5257
D(x): 0.9015, D(G(z)): 0.0862
Epoch: [5/20], Batch Num: [202/600]
Discriminator Loss: 0.6331, Generator Loss: 4.0561
D(x): 0.8383, D(G(z)): 0.1106
Epoch: [5/20], Batch Num: [203/600]
Discriminator Loss: 0.3364, Generator Loss: 4.7706
D(x): 0.9520, D(G(z)): 0.1435
Epoch: [5/20], Batch Num: [204/600]
Discriminator Loss: 0.4398, Generator Loss: 4.3089
D(x): 0.8490, D(G(z)): 0.0746
Epoch: [5/20], Batch Num: [205/600]
Discriminator Loss: 0.2647, Generator Loss: 4.1617
D(x): 0.8805, D(G(z)): 0.0535
Epoch: [5/20], Batch Num: [206/600]
Discriminator Loss: 0.3241, Generator Loss: 3.4259
D(x): 0.9087, D(G(z)): 0.0871
Epoch: [5/20], Batch Num: [207/600]
Discriminator Loss: 0.3251, Generator Loss: 4.1509
D(x): 0.9241, D(G(z)): 0.1265
Epoch: [5/20], Batch Num: [208/600]
Discriminator Loss: 0.1904, Generator Loss: 4.8240
D(x): 0.9445, D(G(z)): 0.0764
Epoch: [5/20], Batch Num: [209/600]
Discriminator Loss: 0.1962, Generator Loss: 4.8202
D(x): 0.9292, D(G(z)): 0.0560
Epoch: [5/20], Batch Num: [210/600]
Discriminator Loss: 0.2745, Generator Loss: 5.3400
D(x): 0.9173, D(G(z)): 0.0663
Epoch: [5/20], Batch Num: [211/600]
Discriminator Loss: 0.1929, Generator Loss: 5.3901
D(x): 0.9231, D(G(z)): 0.0543
Epoch: [5/20], Batch Num: [212/600]
Discriminator Loss: 0.1330, Generator Loss: 5.1968
D(x): 0.9610, D(G(z)): 0.0457
Epoch: [5/20], Batch Num: [213/600]
Discriminator Loss: 0.2864, Generator Loss: 4.8341
D(x): 0.9407, D(G(z)): 0.0868
Epoch: [5/20], Batch Num: [214/600]
Discriminator Loss: 0.3025, Generator Loss: 4.9695
D(x): 0.9166, D(G(z)): 0.0704
Epoch: [5/20], Batch Num: [215/600]
Discriminator Loss: 0.1645, Generator Loss: 4.7205
D(x): 0.9423, D(G(z)): 0.0555
Epoch: [5/20], Batch Num: [216/600]
Discriminator Loss: 0.1875, Generator Loss: 5.2643
D(x): 0.9727, D(G(z)): 0.0909
Epoch: [5/20], Batch Num: [217/600]
Discriminator Loss: 0.2640, Generator Loss: 5.8240
D(x): 0.9461, D(G(z)): 0.0539
Epoch: [5/20], Batch Num: [218/600]
Discriminator Loss: 0.2794, Generator Loss: 5.6278
D(x): 0.9067, D(G(z)): 0.0304
Epoch: [5/20], Batch Num: [219/600]
Discriminator Loss: 0.2421, Generator Loss: 4.8606
D(x): 0.9037, D(G(z)): 0.0326
Epoch: [5/20], Batch Num: [220/600]
Discriminator Loss: 0.3846, Generator Loss: 4.8598
D(x): 0.9309, D(G(z)): 0.1248
Epoch: [5/20], Batch Num: [221/600]
Discriminator Loss: 0.2649, Generator Loss: 4.4356
D(x): 0.9230, D(G(z)): 0.0690
Epoch: [5/20], Batch Num: [222/600]
Discriminator Loss: 0.2395, Generator Loss: 4.9687
D(x): 0.9350, D(G(z)): 0.0843
Epoch: [5/20], Batch Num: [223/600]
Discriminator Loss: 0.2508, Generator Loss: 5.2322
D(x): 0.9310, D(G(z)): 0.0769
Epoch: [5/20], Batch Num: [224/600]
Discriminator Loss: 0.3396, Generator Loss: 5.2561
D(x): 0.8877, D(G(z)): 0.0761
Epoch: [5/20], Batch Num: [225/600]
Discriminator Loss: 0.3309, Generator Loss: 4.8473
D(x): 0.8949, D(G(z)): 0.0940
Epoch: [5/20], Batch Num: [226/600]
Discriminator Loss: 0.2386, Generator Loss: 4.6364
D(x): 0.9395, D(G(z)): 0.0944
Epoch: [5/20], Batch Num: [227/600]
Discriminator Loss: 0.5052, Generator Loss: 4.3677
D(x): 0.8752, D(G(z)): 0.0954
Epoch: [5/20], Batch Num: [228/600]
Discriminator Loss: 0.4089, Generator Loss: 3.9508
D(x): 0.9106, D(G(z)): 0.1414
Epoch: [5/20], Batch Num: [229/600]
Discriminator Loss: 0.4664, Generator Loss: 3.7923
D(x): 0.8803, D(G(z)): 0.1111
Epoch: [5/20], Batch Num: [230/600]
Discriminator Loss: 0.4148, Generator Loss: 4.4160
D(x): 0.9091, D(G(z)): 0.1126
Epoch: [5/20], Batch Num: [231/600]
Discriminator Loss: 0.6454, Generator Loss: 4.2484
D(x): 0.8661, D(G(z)): 0.1375
Epoch: [5/20], Batch Num: [232/600]
Discriminator Loss: 0.5241, Generator Loss: 3.4312
D(x): 0.8332, D(G(z)): 0.0920
Epoch: [5/20], Batch Num: [233/600]
Discriminator Loss: 0.2926, Generator Loss: 3.2515
D(x): 0.9120, D(G(z)): 0.0934
Epoch: [5/20], Batch Num: [234/600]
Discriminator Loss: 0.3608, Generator Loss: 2.9422
D(x): 0.9098, D(G(z)): 0.1389
Epoch: [5/20], Batch Num: [235/600]
Discriminator Loss: 0.3382, Generator Loss: 2.9157
D(x): 0.9074, D(G(z)): 0.1273
Epoch: [5/20], Batch Num: [236/600]
Discriminator Loss: 0.4136, Generator Loss: 2.7176
D(x): 0.9029, D(G(z)): 0.1297
Epoch: [5/20], Batch Num: [237/600]
Discriminator Loss: 0.2354, Generator Loss: 3.2477
D(x): 0.9449, D(G(z)): 0.1165
Epoch: [5/20], Batch Num: [238/600]
Discriminator Loss: 0.3805, Generator Loss: 3.4797
D(x): 0.8559, D(G(z)): 0.1048
Epoch: [5/20], Batch Num: [239/600]
Discriminator Loss: 0.3948, Generator Loss: 3.3112
D(x): 0.8594, D(G(z)): 0.1089
Epoch: [5/20], Batch Num: [240/600]
Discriminator Loss: 0.3750, Generator Loss: 2.9087
D(x): 0.8691, D(G(z)): 0.1086
Epoch: [5/20], Batch Num: [241/600]
Discriminator Loss: 0.3550, Generator Loss: 2.5593
D(x): 0.9033, D(G(z)): 0.1528
Epoch: [5/20], Batch Num: [242/600]
Discriminator Loss: 0.4346, Generator Loss: 2.8800
D(x): 0.9142, D(G(z)): 0.1829
Epoch: [5/20], Batch Num: [243/600]
Discriminator Loss: 0.3392, Generator Loss: 3.5350
D(x): 0.9150, D(G(z)): 0.1413
Epoch: [5/20], Batch Num: [244/600]
Discriminator Loss: 0.3931, Generator Loss: 3.3438
D(x): 0.8612, D(G(z)): 0.0767
Epoch: [5/20], Batch Num: [245/600]
Discriminator Loss: 0.2277, Generator Loss: 3.1189
D(x): 0.8995, D(G(z)): 0.0767
Epoch: [5/20], Batch Num: [246/600]
Discriminator Loss: 0.2758, Generator Loss: 3.0408
D(x): 0.9181, D(G(z)): 0.0959
Epoch: [5/20], Batch Num: [247/600]
Discriminator Loss: 0.2894, Generator Loss: 2.9522
D(x): 0.9273, D(G(z)): 0.1090
Epoch: [5/20], Batch Num: [248/600]
Discriminator Loss: 0.3125, Generator Loss: 3.1865
D(x): 0.9328, D(G(z)): 0.1355
Epoch: [5/20], Batch Num: [249/600]
Discriminator Loss: 0.2415, Generator Loss: 3.4930
D(x): 0.9370, D(G(z)): 0.0933
Epoch: [5/20], Batch Num: [250/600]
Discriminator Loss: 0.1614, Generator Loss: 3.6942
D(x): 0.9487, D(G(z)): 0.0585
Epoch: [5/20], Batch Num: [251/600]
Discriminator Loss: 0.1825, Generator Loss: 3.6986
D(x): 0.9253, D(G(z)): 0.0516
Epoch: [5/20], Batch Num: [252/600]
Discriminator Loss: 0.1647, Generator Loss: 3.5901
D(x): 0.9252, D(G(z)): 0.0445
Epoch: [5/20], Batch Num: [253/600]
Discriminator Loss: 0.2408, Generator Loss: 3.3026
D(x): 0.9108, D(G(z)): 0.0640
Epoch: [5/20], Batch Num: [254/600]
Discriminator Loss: 0.2313, Generator Loss: 3.2589
D(x): 0.9413, D(G(z)): 0.0932
Epoch: [5/20], Batch Num: [255/600]
Discriminator Loss: 0.2706, Generator Loss: 3.3300
D(x): 0.9230, D(G(z)): 0.0964
Epoch: [5/20], Batch Num: [256/600]
Discriminator Loss: 0.2146, Generator Loss: 3.2471
D(x): 0.9637, D(G(z)): 0.1157
Epoch: [5/20], Batch Num: [257/600]
Discriminator Loss: 0.1290, Generator Loss: 4.3433
D(x): 0.9633, D(G(z)): 0.0624
Epoch: [5/20], Batch Num: [258/600]
Discriminator Loss: 0.2393, Generator Loss: 4.0116
D(x): 0.9002, D(G(z)): 0.0341
Epoch: [5/20], Batch Num: [259/600]
Discriminator Loss: 0.2001, Generator Loss: 3.9732
D(x): 0.9327, D(G(z)): 0.0671
Epoch: [5/20], Batch Num: [260/600]
Discriminator Loss: 0.3313, Generator Loss: 3.4329
D(x): 0.9067, D(G(z)): 0.0743
Epoch: [5/20], Batch Num: [261/600]
Discriminator Loss: 0.2153, Generator Loss: 3.2540
D(x): 0.9466, D(G(z)): 0.0790
Epoch: [5/20], Batch Num: [262/600]
Discriminator Loss: 0.2524, Generator Loss: 3.4726
D(x): 0.9445, D(G(z)): 0.1160
Epoch: [5/20], Batch Num: [263/600]
Discriminator Loss: 0.1469, Generator Loss: 3.9907
D(x): 0.9689, D(G(z)): 0.0829
Epoch: [5/20], Batch Num: [264/600]
Discriminator Loss: 0.2159, Generator Loss: 4.1712
D(x): 0.9353, D(G(z)): 0.0603
Epoch: [5/20], Batch Num: [265/600]
Discriminator Loss: 0.1746, Generator Loss: 4.6354
D(x): 0.9345, D(G(z)): 0.0413
Epoch: [5/20], Batch Num: [266/600]
Discriminator Loss: 0.0909, Generator Loss: 4.7462
D(x): 0.9569, D(G(z)): 0.0345
Epoch: [5/20], Batch Num: [267/600]
Discriminator Loss: 0.1839, Generator Loss: 4.6493
D(x): 0.9326, D(G(z)): 0.0488
Epoch: [5/20], Batch Num: [268/600]
Discriminator Loss: 0.1901, Generator Loss: 4.3234
D(x): 0.9462, D(G(z)): 0.0490
Epoch: [5/20], Batch Num: [269/600]
Discriminator Loss: 0.2231, Generator Loss: 3.4640
D(x): 0.9365, D(G(z)): 0.0525
Epoch: [5/20], Batch Num: [270/600]
Discriminator Loss: 0.1794, Generator Loss: 3.7285
D(x): 0.9647, D(G(z)): 0.0823
Epoch: [5/20], Batch Num: [271/600]
Discriminator Loss: 0.2098, Generator Loss: 4.0084
D(x): 0.9318, D(G(z)): 0.0786
Epoch: [5/20], Batch Num: [272/600]
Discriminator Loss: 0.1670, Generator Loss: 4.3554
D(x): 0.9652, D(G(z)): 0.0673
Epoch: [5/20], Batch Num: [273/600]
Discriminator Loss: 0.2462, Generator Loss: 4.7727
D(x): 0.9298, D(G(z)): 0.0507
Epoch: [5/20], Batch Num: [274/600]
Discriminator Loss: 0.2487, Generator Loss: 4.5796
D(x): 0.9285, D(G(z)): 0.0439
Epoch: [5/20], Batch Num: [275/600]
Discriminator Loss: 0.2873, Generator Loss: 3.9842
D(x): 0.9231, D(G(z)): 0.0407
Epoch: [5/20], Batch Num: [276/600]
Discriminator Loss: 0.1767, Generator Loss: 4.1307
D(x): 0.9543, D(G(z)): 0.0661
Epoch: [5/20], Batch Num: [277/600]
Discriminator Loss: 0.2994, Generator Loss: 4.0062
D(x): 0.9284, D(G(z)): 0.0817
Epoch: [5/20], Batch Num: [278/600]
Discriminator Loss: 0.2289, Generator Loss: 4.2536
D(x): 0.9665, D(G(z)): 0.1048
Epoch: [5/20], Batch Num: [279/600]
Discriminator Loss: 0.1857, Generator Loss: 4.3322
D(x): 0.9204, D(G(z)): 0.0397
Epoch: [5/20], Batch Num: [280/600]
Discriminator Loss: 0.2944, Generator Loss: 3.9852
D(x): 0.9129, D(G(z)): 0.0555
Epoch: [5/20], Batch Num: [281/600]
Discriminator Loss: 0.2325, Generator Loss: 4.4308
D(x): 0.9567, D(G(z)): 0.0816
Epoch: [5/20], Batch Num: [282/600]
Discriminator Loss: 0.2405, Generator Loss: 4.3139
D(x): 0.9374, D(G(z)): 0.0787
Epoch: [5/20], Batch Num: [283/600]
Discriminator Loss: 0.1736, Generator Loss: 4.2267
D(x): 0.9427, D(G(z)): 0.0461
Epoch: [5/20], Batch Num: [284/600]
Discriminator Loss: 0.2907, Generator Loss: 3.7504
D(x): 0.8829, D(G(z)): 0.0452
Epoch: [5/20], Batch Num: [285/600]
Discriminator Loss: 0.2354, Generator Loss: 3.4756
D(x): 0.9406, D(G(z)): 0.0813
Epoch: [5/20], Batch Num: [286/600]
Discriminator Loss: 0.3256, Generator Loss: 3.7663
D(x): 0.9480, D(G(z)): 0.1237
Epoch: [5/20], Batch Num: [287/600]
Discriminator Loss: 0.2676, Generator Loss: 3.8906
D(x): 0.9397, D(G(z)): 0.0947
Epoch: [5/20], Batch Num: [288/600]
Discriminator Loss: 0.1686, Generator Loss: 3.9435
D(x): 0.9373, D(G(z)): 0.0549
Epoch: [5/20], Batch Num: [289/600]
Discriminator Loss: 0.3760, Generator Loss: 4.0270
D(x): 0.8952, D(G(z)): 0.0824
Epoch: [5/20], Batch Num: [290/600]
Discriminator Loss: 0.1480, Generator Loss: 4.3107
D(x): 0.9532, D(G(z)): 0.0397
Epoch: [5/20], Batch Num: [291/600]
Discriminator Loss: 0.2899, Generator Loss: 3.8811
D(x): 0.9072, D(G(z)): 0.0391
Epoch: [5/20], Batch Num: [292/600]
Discriminator Loss: 0.3098, Generator Loss: 3.3939
D(x): 0.9454, D(G(z)): 0.1080
Epoch: [5/20], Batch Num: [293/600]
Discriminator Loss: 0.2866, Generator Loss: 4.3705
D(x): 0.9551, D(G(z)): 0.1251
Epoch: [5/20], Batch Num: [294/600]
Discriminator Loss: 0.2845, Generator Loss: 3.9698
D(x): 0.9293, D(G(z)): 0.0793
Epoch: [5/20], Batch Num: [295/600]
Discriminator Loss: 0.3200, Generator Loss: 4.2451
D(x): 0.9127, D(G(z)): 0.0543
Epoch: [5/20], Batch Num: [296/600]
Discriminator Loss: 0.3175, Generator Loss: 4.1103
D(x): 0.9100, D(G(z)): 0.0503
Epoch: [5/20], Batch Num: [297/600]
Discriminator Loss: 0.1954, Generator Loss: 3.9927
D(x): 0.9631, D(G(z)): 0.0873
Epoch: [5/20], Batch Num: [298/600]
Discriminator Loss: 0.2271, Generator Loss: 3.5807
D(x): 0.9527, D(G(z)): 0.0706
Epoch: [5/20], Batch Num: [299/600]
Discriminator Loss: 0.3130, Generator Loss: 3.6272
D(x): 0.8991, D(G(z)): 0.0608
Epoch: 5, Batch Num: [300/600]

Epoch: [5/20], Batch Num: [300/600]
Discriminator Loss: 0.1913, Generator Loss: 3.4515
D(x): 0.9491, D(G(z)): 0.0904
Epoch: [5/20], Batch Num: [301/600]
Discriminator Loss: 0.2186, Generator Loss: 3.6411
D(x): 0.9539, D(G(z)): 0.0908
Epoch: [5/20], Batch Num: [302/600]
Discriminator Loss: 0.2661, Generator Loss: 4.2519
D(x): 0.9462, D(G(z)): 0.1097
Epoch: [5/20], Batch Num: [303/600]
Discriminator Loss: 0.1918, Generator Loss: 4.2480
D(x): 0.9376, D(G(z)): 0.0710
Epoch: [5/20], Batch Num: [304/600]
Discriminator Loss: 0.2573, Generator Loss: 4.1587
D(x): 0.9166, D(G(z)): 0.0600
Epoch: [5/20], Batch Num: [305/600]
Discriminator Loss: 0.3393, Generator Loss: 4.4750
D(x): 0.8970, D(G(z)): 0.0736
Epoch: [5/20], Batch Num: [306/600]
Discriminator Loss: 0.2696, Generator Loss: 4.1544
D(x): 0.9226, D(G(z)): 0.0615
Epoch: [5/20], Batch Num: [307/600]
Discriminator Loss: 0.3449, Generator Loss: 3.7534
D(x): 0.9009, D(G(z)): 0.0644
Epoch: [5/20], Batch Num: [308/600]
Discriminator Loss: 0.2879, Generator Loss: 3.7779
D(x): 0.9408, D(G(z)): 0.1038
Epoch: [5/20], Batch Num: [309/600]
Discriminator Loss: 0.2662, Generator Loss: 3.8654
D(x): 0.9493, D(G(z)): 0.0875
Epoch: [5/20], Batch Num: [310/600]
Discriminator Loss: 0.2512, Generator Loss: 3.9264
D(x): 0.9228, D(G(z)): 0.0805
Epoch: [5/20], Batch Num: [311/600]
Discriminator Loss: 0.1931, Generator Loss: 4.2121
D(x): 0.9545, D(G(z)): 0.0968
Epoch: [5/20], Batch Num: [312/600]
Discriminator Loss: 0.2434, Generator Loss: 4.2272
D(x): 0.9090, D(G(z)): 0.0613
Epoch: [5/20], Batch Num: [313/600]
Discriminator Loss: 0.2426, Generator Loss: 3.9189
D(x): 0.9099, D(G(z)): 0.0495
Epoch: [5/20], Batch Num: [314/600]
Discriminator Loss: 0.2046, Generator Loss: 3.6378
D(x): 0.9397, D(G(z)): 0.0834
Epoch: [5/20], Batch Num: [315/600]
Discriminator Loss: 0.2401, Generator Loss: 3.5543
D(x): 0.9248, D(G(z)): 0.0655
Epoch: [5/20], Batch Num: [316/600]
Discriminator Loss: 0.3762, Generator Loss: 3.9358
D(x): 0.9429, D(G(z)): 0.1478
Epoch: [5/20], Batch Num: [317/600]
Discriminator Loss: 0.5054, Generator Loss: 3.7389
D(x): 0.8359, D(G(z)): 0.0807
Epoch: [5/20], Batch Num: [318/600]
Discriminator Loss: 0.2240, Generator Loss: 3.7251
D(x): 0.9361, D(G(z)): 0.0661
Epoch: [5/20], Batch Num: [319/600]
Discriminator Loss: 0.2438, Generator Loss: 4.3217
D(x): 0.9375, D(G(z)): 0.0867
Epoch: [5/20], Batch Num: [320/600]
Discriminator Loss: 0.1944, Generator Loss: 4.1490
D(x): 0.9357, D(G(z)): 0.0782
Epoch: [5/20], Batch Num: [321/600]
Discriminator Loss: 0.2703, Generator Loss: 4.2780
D(x): 0.8904, D(G(z)): 0.0580
Epoch: [5/20], Batch Num: [322/600]
Discriminator Loss: 0.2468, Generator Loss: 3.7601
D(x): 0.9281, D(G(z)): 0.0666
Epoch: [5/20], Batch Num: [323/600]
Discriminator Loss: 0.4332, Generator Loss: 3.1458
D(x): 0.8649, D(G(z)): 0.0624
Epoch: [5/20], Batch Num: [324/600]
Discriminator Loss: 0.4239, Generator Loss: 2.7096
D(x): 0.8820, D(G(z)): 0.1130
Epoch: [5/20], Batch Num: [325/600]
Discriminator Loss: 0.4575, Generator Loss: 3.3317
D(x): 0.9705, D(G(z)): 0.2308
Epoch: [5/20], Batch Num: [326/600]
Discriminator Loss: 0.4142, Generator Loss: 4.3021
D(x): 0.9466, D(G(z)): 0.1673
Epoch: [5/20], Batch Num: [327/600]
Discriminator Loss: 0.3349, Generator Loss: 4.2880
D(x): 0.8889, D(G(z)): 0.0521
Epoch: [5/20], Batch Num: [328/600]
Discriminator Loss: 0.2959, Generator Loss: 4.1941
D(x): 0.8840, D(G(z)): 0.0317
Epoch: [5/20], Batch Num: [329/600]
Discriminator Loss: 0.3868, Generator Loss: 3.5783
D(x): 0.8525, D(G(z)): 0.0383
Epoch: [5/20], Batch Num: [330/600]
Discriminator Loss: 0.2778, Generator Loss: 3.0435
D(x): 0.8870, D(G(z)): 0.0564
Epoch: [5/20], Batch Num: [331/600]
Discriminator Loss: 0.4525, Generator Loss: 2.8657
D(x): 0.9013, D(G(z)): 0.1536
Epoch: [5/20], Batch Num: [332/600]
Discriminator Loss: 0.4201, Generator Loss: 3.1380
D(x): 0.9249, D(G(z)): 0.1820
Epoch: [5/20], Batch Num: [333/600]
Discriminator Loss: 0.3331, Generator Loss: 3.3753
D(x): 0.9381, D(G(z)): 0.1228
Epoch: [5/20], Batch Num: [334/600]
Discriminator Loss: 0.2275, Generator Loss: 4.0609
D(x): 0.9365, D(G(z)): 0.0837
Epoch: [5/20], Batch Num: [335/600]
Discriminator Loss: 0.2072, Generator Loss: 4.4172
D(x): 0.9289, D(G(z)): 0.0594
Epoch: [5/20], Batch Num: [336/600]
Discriminator Loss: 0.2757, Generator Loss: 4.5633
D(x): 0.8838, D(G(z)): 0.0343
Epoch: [5/20], Batch Num: [337/600]
Discriminator Loss: 0.2103, Generator Loss: 3.7202
D(x): 0.9205, D(G(z)): 0.0426
Epoch: [5/20], Batch Num: [338/600]
Discriminator Loss: 0.2191, Generator Loss: 3.6336
D(x): 0.9346, D(G(z)): 0.0788
Epoch: [5/20], Batch Num: [339/600]
Discriminator Loss: 0.2342, Generator Loss: 3.3096
D(x): 0.9437, D(G(z)): 0.1143
Epoch: [5/20], Batch Num: [340/600]
Discriminator Loss: 0.2273, Generator Loss: 3.7957
D(x): 0.9573, D(G(z)): 0.1183
Epoch: [5/20], Batch Num: [341/600]
Discriminator Loss: 0.1872, Generator Loss: 4.1165
D(x): 0.9549, D(G(z)): 0.0666
Epoch: [5/20], Batch Num: [342/600]
Discriminator Loss: 0.4054, Generator Loss: 4.1554
D(x): 0.8622, D(G(z)): 0.0431
Epoch: [5/20], Batch Num: [343/600]
Discriminator Loss: 0.2141, Generator Loss: 4.0531
D(x): 0.9274, D(G(z)): 0.0639
Epoch: [5/20], Batch Num: [344/600]
Discriminator Loss: 0.2367, Generator Loss: 4.2331
D(x): 0.9466, D(G(z)): 0.1036
Epoch: [5/20], Batch Num: [345/600]
Discriminator Loss: 0.1712, Generator Loss: 3.9924
D(x): 0.9484, D(G(z)): 0.0716
Epoch: [5/20], Batch Num: [346/600]
Discriminator Loss: 0.1548, Generator Loss: 3.7712
D(x): 0.9532, D(G(z)): 0.0738
Epoch: [5/20], Batch Num: [347/600]
Discriminator Loss: 0.4567, Generator Loss: 4.0923
D(x): 0.8560, D(G(z)): 0.0720
Epoch: [5/20], Batch Num: [348/600]
Discriminator Loss: 0.2179, Generator Loss: 3.8262
D(x): 0.9345, D(G(z)): 0.0637
Epoch: [5/20], Batch Num: [349/600]
Discriminator Loss: 0.1267, Generator Loss: 3.7189
D(x): 0.9594, D(G(z)): 0.0550
Epoch: [5/20], Batch Num: [350/600]
Discriminator Loss: 0.2237, Generator Loss: 3.7737
D(x): 0.9456, D(G(z)): 0.0893
Epoch: [5/20], Batch Num: [351/600]
Discriminator Loss: 0.2580, Generator Loss: 3.8078
D(x): 0.9399, D(G(z)): 0.0756
Epoch: [5/20], Batch Num: [352/600]
Discriminator Loss: 0.1393, Generator Loss: 4.1848
D(x): 0.9758, D(G(z)): 0.0837
Epoch: [5/20], Batch Num: [353/600]
Discriminator Loss: 0.3011, Generator Loss: 4.2365
D(x): 0.8959, D(G(z)): 0.0427
Epoch: [5/20], Batch Num: [354/600]
Discriminator Loss: 0.3372, Generator Loss: 4.2204
D(x): 0.8946, D(G(z)): 0.0565
Epoch: [5/20], Batch Num: [355/600]
Discriminator Loss: 0.2061, Generator Loss: 3.8420
D(x): 0.9366, D(G(z)): 0.0619
Epoch: [5/20], Batch Num: [356/600]
Discriminator Loss: 0.2086, Generator Loss: 3.9608
D(x): 0.9536, D(G(z)): 0.1023
Epoch: [5/20], Batch Num: [357/600]
Discriminator Loss: 0.1931, Generator Loss: 4.2576
D(x): 0.9301, D(G(z)): 0.0677
Epoch: [5/20], Batch Num: [358/600]
Discriminator Loss: 0.2600, Generator Loss: 4.0582
D(x): 0.9076, D(G(z)): 0.0519
Epoch: [5/20], Batch Num: [359/600]
Discriminator Loss: 0.4290, Generator Loss: 3.6596
D(x): 0.8740, D(G(z)): 0.0726
Epoch: [5/20], Batch Num: [360/600]
Discriminator Loss: 0.2535, Generator Loss: 3.5356
D(x): 0.9249, D(G(z)): 0.0972
Epoch: [5/20], Batch Num: [361/600]
Discriminator Loss: 0.3840, Generator Loss: 3.8344
D(x): 0.9166, D(G(z)): 0.1154
Epoch: [5/20], Batch Num: [362/600]
Discriminator Loss: 0.2386, Generator Loss: 4.4273
D(x): 0.9500, D(G(z)): 0.1012
Epoch: [5/20], Batch Num: [363/600]
Discriminator Loss: 0.2570, Generator Loss: 4.3929
D(x): 0.8999, D(G(z)): 0.0502
Epoch: [5/20], Batch Num: [364/600]
Discriminator Loss: 0.2735, Generator Loss: 4.1002
D(x): 0.8962, D(G(z)): 0.0435
Epoch: [5/20], Batch Num: [365/600]
Discriminator Loss: 0.2170, Generator Loss: 4.1079
D(x): 0.9520, D(G(z)): 0.0533
Epoch: [5/20], Batch Num: [366/600]
Discriminator Loss: 0.4456, Generator Loss: 3.9943
D(x): 0.8870, D(G(z)): 0.1152
Epoch: [5/20], Batch Num: [367/600]
Discriminator Loss: 0.2434, Generator Loss: 4.0054
D(x): 0.9116, D(G(z)): 0.0673
Epoch: [5/20], Batch Num: [368/600]
Discriminator Loss: 0.2481, Generator Loss: 3.5521
D(x): 0.9202, D(G(z)): 0.0729
Epoch: [5/20], Batch Num: [369/600]
Discriminator Loss: 0.2845, Generator Loss: 4.4980
D(x): 0.9444, D(G(z)): 0.1153
Epoch: [5/20], Batch Num: [370/600]
Discriminator Loss: 0.2871, Generator Loss: 4.6671
D(x): 0.9186, D(G(z)): 0.0631
Epoch: [5/20], Batch Num: [371/600]
Discriminator Loss: 0.3777, Generator Loss: 4.7528
D(x): 0.8684, D(G(z)): 0.0331
Epoch: [5/20], Batch Num: [372/600]
Discriminator Loss: 0.2841, Generator Loss: 4.1444
D(x): 0.9004, D(G(z)): 0.0478
Epoch: [5/20], Batch Num: [373/600]
Discriminator Loss: 0.2955, Generator Loss: 3.3942
D(x): 0.8974, D(G(z)): 0.0645
Epoch: [5/20], Batch Num: [374/600]
Discriminator Loss: 0.3716, Generator Loss: 4.1971
D(x): 0.9524, D(G(z)): 0.1891
Epoch: [5/20], Batch Num: [375/600]
Discriminator Loss: 0.2282, Generator Loss: 4.5638
D(x): 0.9163, D(G(z)): 0.0398
Epoch: [5/20], Batch Num: [376/600]
Discriminator Loss: 0.2050, Generator Loss: 4.3042
D(x): 0.9147, D(G(z)): 0.0289
Epoch: [5/20], Batch Num: [377/600]
Discriminator Loss: 0.3954, Generator Loss: 3.5369
D(x): 0.8661, D(G(z)): 0.0466
Epoch: [5/20], Batch Num: [378/600]
Discriminator Loss: 0.2594, Generator Loss: 3.4758
D(x): 0.9663, D(G(z)): 0.1297
Epoch: [5/20], Batch Num: [379/600]
Discriminator Loss: 0.2520, Generator Loss: 4.0748
D(x): 0.9380, D(G(z)): 0.0944
Epoch: [5/20], Batch Num: [380/600]
Discriminator Loss: 0.2775, Generator Loss: 4.7516
D(x): 0.9285, D(G(z)): 0.0845
Epoch: [5/20], Batch Num: [381/600]
Discriminator Loss: 0.3852, Generator Loss: 4.6103
D(x): 0.8880, D(G(z)): 0.0672
Epoch: [5/20], Batch Num: [382/600]
Discriminator Loss: 0.3680, Generator Loss: 4.0928
D(x): 0.8886, D(G(z)): 0.0491
Epoch: [5/20], Batch Num: [383/600]
Discriminator Loss: 0.2423, Generator Loss: 3.3243
D(x): 0.9173, D(G(z)): 0.0451
Epoch: [5/20], Batch Num: [384/600]
Discriminator Loss: 0.3328, Generator Loss: 3.3193
D(x): 0.9452, D(G(z)): 0.1143
Epoch: [5/20], Batch Num: [385/600]
Discriminator Loss: 0.2267, Generator Loss: 3.6202
D(x): 0.9481, D(G(z)): 0.1034
Epoch: [5/20], Batch Num: [386/600]
Discriminator Loss: 0.3183, Generator Loss: 4.4291
D(x): 0.9208, D(G(z)): 0.0808
Epoch: [5/20], Batch Num: [387/600]
Discriminator Loss: 0.2608, Generator Loss: 3.9210
D(x): 0.8933, D(G(z)): 0.0322
Epoch: [5/20], Batch Num: [388/600]
Discriminator Loss: 0.1669, Generator Loss: 3.3325
D(x): 0.9471, D(G(z)): 0.0553
Epoch: [5/20], Batch Num: [389/600]
Discriminator Loss: 0.3524, Generator Loss: 3.2055
D(x): 0.8990, D(G(z)): 0.1064
Epoch: [5/20], Batch Num: [390/600]
Discriminator Loss: 0.3245, Generator Loss: 4.1214
D(x): 0.9554, D(G(z)): 0.1593
Epoch: [5/20], Batch Num: [391/600]
Discriminator Loss: 0.2088, Generator Loss: 4.6964
D(x): 0.9303, D(G(z)): 0.0596
Epoch: [5/20], Batch Num: [392/600]
Discriminator Loss: 0.3265, Generator Loss: 4.3707
D(x): 0.8721, D(G(z)): 0.0357
Epoch: [5/20], Batch Num: [393/600]
Discriminator Loss: 0.3145, Generator Loss: 3.7428
D(x): 0.8929, D(G(z)): 0.0296
Epoch: [5/20], Batch Num: [394/600]
Discriminator Loss: 0.3757, Generator Loss: 3.0776
D(x): 0.8981, D(G(z)): 0.0876
Epoch: [5/20], Batch Num: [395/600]
Discriminator Loss: 0.4361, Generator Loss: 3.0487
D(x): 0.9156, D(G(z)): 0.1575
Epoch: [5/20], Batch Num: [396/600]
Discriminator Loss: 0.2071, Generator Loss: 3.7428
D(x): 0.9635, D(G(z)): 0.1115
Epoch: [5/20], Batch Num: [397/600]
Discriminator Loss: 0.1201, Generator Loss: 4.2732
D(x): 0.9702, D(G(z)): 0.0705
Epoch: [5/20], Batch Num: [398/600]
Discriminator Loss: 0.1667, Generator Loss: 4.5667
D(x): 0.9286, D(G(z)): 0.0367
Epoch: [5/20], Batch Num: [399/600]
Discriminator Loss: 0.3319, Generator Loss: 4.6347
D(x): 0.8983, D(G(z)): 0.0202
Epoch: 5, Batch Num: [400/600]

Epoch: [5/20], Batch Num: [400/600]
Discriminator Loss: 0.2517, Generator Loss: 4.2165
D(x): 0.9161, D(G(z)): 0.0348
Epoch: [5/20], Batch Num: [401/600]
Discriminator Loss: 0.1940, Generator Loss: 3.6943
D(x): 0.9342, D(G(z)): 0.0391
Epoch: [5/20], Batch Num: [402/600]
Discriminator Loss: 0.2257, Generator Loss: 3.3947
D(x): 0.9456, D(G(z)): 0.0900
Epoch: [5/20], Batch Num: [403/600]
Discriminator Loss: 0.2359, Generator Loss: 3.4097
D(x): 0.9930, D(G(z)): 0.1291
Epoch: [5/20], Batch Num: [404/600]
Discriminator Loss: 0.1777, Generator Loss: 4.3407
D(x): 0.9793, D(G(z)): 0.0939
Epoch: [5/20], Batch Num: [405/600]
Discriminator Loss: 0.1774, Generator Loss: 5.0991
D(x): 0.9397, D(G(z)): 0.0402
Epoch: [5/20], Batch Num: [406/600]
Discriminator Loss: 0.2044, Generator Loss: 5.2275
D(x): 0.9168, D(G(z)): 0.0160
Epoch: [5/20], Batch Num: [407/600]
Discriminator Loss: 0.1708, Generator Loss: 4.8341
D(x): 0.9185, D(G(z)): 0.0211
Epoch: [5/20], Batch Num: [408/600]
Discriminator Loss: 0.5296, Generator Loss: 4.1889
D(x): 0.8537, D(G(z)): 0.0541
Epoch: [5/20], Batch Num: [409/600]
Discriminator Loss: 0.1681, Generator Loss: 3.1770
D(x): 0.9614, D(G(z)): 0.0738
Epoch: [5/20], Batch Num: [410/600]
Discriminator Loss: 0.3470, Generator Loss: 4.4616
D(x): 0.9718, D(G(z)): 0.1648
Epoch: [5/20], Batch Num: [411/600]
Discriminator Loss: 0.2069, Generator Loss: 4.9947
D(x): 0.9528, D(G(z)): 0.0696
Epoch: [5/20], Batch Num: [412/600]
Discriminator Loss: 0.1184, Generator Loss: 5.2735
D(x): 0.9462, D(G(z)): 0.0293
Epoch: [5/20], Batch Num: [413/600]
Discriminator Loss: 0.3493, Generator Loss: 4.7549
D(x): 0.8752, D(G(z)): 0.0150
Epoch: [5/20], Batch Num: [414/600]
Discriminator Loss: 0.3528, Generator Loss: 4.5361
D(x): 0.9158, D(G(z)): 0.0550
Epoch: [5/20], Batch Num: [415/600]
Discriminator Loss: 0.1588, Generator Loss: 3.8056
D(x): 0.9268, D(G(z)): 0.0424
Epoch: [5/20], Batch Num: [416/600]
Discriminator Loss: 0.2233, Generator Loss: 3.4296
D(x): 0.9618, D(G(z)): 0.1069
Epoch: [5/20], Batch Num: [417/600]
Discriminator Loss: 0.2572, Generator Loss: 3.5478
D(x): 0.9524, D(G(z)): 0.1130
Epoch: [5/20], Batch Num: [418/600]
Discriminator Loss: 0.2374, Generator Loss: 3.8701
D(x): 0.9573, D(G(z)): 0.1106
Epoch: [5/20], Batch Num: [419/600]
Discriminator Loss: 0.2945, Generator Loss: 4.4730
D(x): 0.9414, D(G(z)): 0.0982
Epoch: [5/20], Batch Num: [420/600]
Discriminator Loss: 0.2305, Generator Loss: 4.8692
D(x): 0.9355, D(G(z)): 0.0354
Epoch: [5/20], Batch Num: [421/600]
Discriminator Loss: 0.2906, Generator Loss: 4.9421
D(x): 0.9215, D(G(z)): 0.0623
Epoch: [5/20], Batch Num: [422/600]
Discriminator Loss: 0.2197, Generator Loss: 4.3532
D(x): 0.9167, D(G(z)): 0.0332
Epoch: [5/20], Batch Num: [423/600]
Discriminator Loss: 0.1652, Generator Loss: 3.7160
D(x): 0.9449, D(G(z)): 0.0500
Epoch: [5/20], Batch Num: [424/600]
Discriminator Loss: 0.1650, Generator Loss: 3.7176
D(x): 0.9630, D(G(z)): 0.0795
Epoch: [5/20], Batch Num: [425/600]
Discriminator Loss: 0.2883, Generator Loss: 3.6704
D(x): 0.9709, D(G(z)): 0.1403
Epoch: [5/20], Batch Num: [426/600]
Discriminator Loss: 0.2508, Generator Loss: 3.8525
D(x): 0.9306, D(G(z)): 0.0888
Epoch: [5/20], Batch Num: [427/600]
Discriminator Loss: 0.2430, Generator Loss: 3.9266
D(x): 0.9260, D(G(z)): 0.0621
Epoch: [5/20], Batch Num: [428/600]
Discriminator Loss: 0.3583, Generator Loss: 3.8396
D(x): 0.9068, D(G(z)): 0.0826
Epoch: [5/20], Batch Num: [429/600]
Discriminator Loss: 0.2724, Generator Loss: 3.6725
D(x): 0.9131, D(G(z)): 0.0627
Epoch: [5/20], Batch Num: [430/600]
Discriminator Loss: 0.2648, Generator Loss: 3.5180
D(x): 0.9460, D(G(z)): 0.1197
Epoch: [5/20], Batch Num: [431/600]
Discriminator Loss: 0.3612, Generator Loss: 3.5749
D(x): 0.8911, D(G(z)): 0.0985
Epoch: [5/20], Batch Num: [432/600]
Discriminator Loss: 0.3650, Generator Loss: 3.5647
D(x): 0.8862, D(G(z)): 0.1144
Epoch: [5/20], Batch Num: [433/600]
Discriminator Loss: 0.3080, Generator Loss: 3.8109
D(x): 0.9285, D(G(z)): 0.1176
Epoch: [5/20], Batch Num: [434/600]
Discriminator Loss: 0.3428, Generator Loss: 3.4880
D(x): 0.9107, D(G(z)): 0.0967
Epoch: [5/20], Batch Num: [435/600]
Discriminator Loss: 0.3707, Generator Loss: 3.5337
D(x): 0.9113, D(G(z)): 0.1180
Epoch: [5/20], Batch Num: [436/600]
Discriminator Loss: 0.3193, Generator Loss: 3.3045
D(x): 0.8936, D(G(z)): 0.0856
Epoch: [5/20], Batch Num: [437/600]
Discriminator Loss: 0.3551, Generator Loss: 3.1373
D(x): 0.8928, D(G(z)): 0.1115
Epoch: [5/20], Batch Num: [438/600]
Discriminator Loss: 0.3601, Generator Loss: 3.3837
D(x): 0.9321, D(G(z)): 0.1532
Epoch: [5/20], Batch Num: [439/600]
Discriminator Loss: 0.2803, Generator Loss: 3.7091
D(x): 0.9182, D(G(z)): 0.1083
Epoch: [5/20], Batch Num: [440/600]
Discriminator Loss: 0.3304, Generator Loss: 3.5325
D(x): 0.8654, D(G(z)): 0.0658
Epoch: [5/20], Batch Num: [441/600]
Discriminator Loss: 0.4890, Generator Loss: 2.9346
D(x): 0.8409, D(G(z)): 0.1015
Epoch: [5/20], Batch Num: [442/600]
Discriminator Loss: 0.4254, Generator Loss: 3.0214
D(x): 0.9091, D(G(z)): 0.1833
Epoch: [5/20], Batch Num: [443/600]
Discriminator Loss: 0.3796, Generator Loss: 3.1235
D(x): 0.8907, D(G(z)): 0.1344
Epoch: [5/20], Batch Num: [444/600]
Discriminator Loss: 0.3083, Generator Loss: 3.7125
D(x): 0.9086, D(G(z)): 0.1091
Epoch: [5/20], Batch Num: [445/600]
Discriminator Loss: 0.2350, Generator Loss: 3.6968
D(x): 0.9082, D(G(z)): 0.0695
Epoch: [5/20], Batch Num: [446/600]
Discriminator Loss: 0.2557, Generator Loss: 3.5691
D(x): 0.9051, D(G(z)): 0.0671
Epoch: [5/20], Batch Num: [447/600]
Discriminator Loss: 0.2653, Generator Loss: 3.4272
D(x): 0.8989, D(G(z)): 0.0847
Epoch: [5/20], Batch Num: [448/600]
Discriminator Loss: 0.3676, Generator Loss: 2.9421
D(x): 0.8868, D(G(z)): 0.0962
Epoch: [5/20], Batch Num: [449/600]
Discriminator Loss: 0.2910, Generator Loss: 3.2172
D(x): 0.9229, D(G(z)): 0.1148
Epoch: [5/20], Batch Num: [450/600]
Discriminator Loss: 0.2049, Generator Loss: 3.6215
D(x): 0.9645, D(G(z)): 0.1111
Epoch: [5/20], Batch Num: [451/600]
Discriminator Loss: 0.2567, Generator Loss: 3.9271
D(x): 0.9068, D(G(z)): 0.0557
Epoch: [5/20], Batch Num: [452/600]
Discriminator Loss: 0.2215, Generator Loss: 4.0910
D(x): 0.9324, D(G(z)): 0.0906
Epoch: [5/20], Batch Num: [453/600]
Discriminator Loss: 0.2339, Generator Loss: 4.0723
D(x): 0.9071, D(G(z)): 0.0508
Epoch: [5/20], Batch Num: [454/600]
Discriminator Loss: 0.2266, Generator Loss: 3.6505
D(x): 0.9213, D(G(z)): 0.0650
Epoch: [5/20], Batch Num: [455/600]
Discriminator Loss: 0.2655, Generator Loss: 3.3284
D(x): 0.9435, D(G(z)): 0.0891
Epoch: [5/20], Batch Num: [456/600]
Discriminator Loss: 0.3416, Generator Loss: 3.6662
D(x): 0.9482, D(G(z)): 0.1471
Epoch: [5/20], Batch Num: [457/600]
Discriminator Loss: 0.2048, Generator Loss: 4.4980
D(x): 0.9563, D(G(z)): 0.1004
Epoch: [5/20], Batch Num: [458/600]
Discriminator Loss: 0.2188, Generator Loss: 5.0887
D(x): 0.9191, D(G(z)): 0.0419
Epoch: [5/20], Batch Num: [459/600]
Discriminator Loss: 0.2712, Generator Loss: 4.5921
D(x): 0.8811, D(G(z)): 0.0335
Epoch: [5/20], Batch Num: [460/600]
Discriminator Loss: 0.2324, Generator Loss: 3.9376
D(x): 0.9033, D(G(z)): 0.0540
Epoch: [5/20], Batch Num: [461/600]
Discriminator Loss: 0.1703, Generator Loss: 3.2861
D(x): 0.9545, D(G(z)): 0.0841
Epoch: [5/20], Batch Num: [462/600]
Discriminator Loss: 0.2575, Generator Loss: 4.2638
D(x): 0.9499, D(G(z)): 0.1352
Epoch: [5/20], Batch Num: [463/600]
Discriminator Loss: 0.1454, Generator Loss: 4.5039
D(x): 0.9393, D(G(z)): 0.0465
Epoch: [5/20], Batch Num: [464/600]
Discriminator Loss: 0.1137, Generator Loss: 4.7245
D(x): 0.9660, D(G(z)): 0.0374
Epoch: [5/20], Batch Num: [465/600]
Discriminator Loss: 0.1616, Generator Loss: 4.5298
D(x): 0.9428, D(G(z)): 0.0457
Epoch: [5/20], Batch Num: [466/600]
Discriminator Loss: 0.3039, Generator Loss: 4.3898
D(x): 0.9164, D(G(z)): 0.0406
Epoch: [5/20], Batch Num: [467/600]
Discriminator Loss: 0.1269, Generator Loss: 3.9034
D(x): 0.9584, D(G(z)): 0.0509
Epoch: [5/20], Batch Num: [468/600]
Discriminator Loss: 0.2068, Generator Loss: 4.3358
D(x): 0.9515, D(G(z)): 0.0903
Epoch: [5/20], Batch Num: [469/600]
Discriminator Loss: 0.3213, Generator Loss: 4.7761
D(x): 0.9420, D(G(z)): 0.0915
Epoch: [5/20], Batch Num: [470/600]
Discriminator Loss: 0.3144, Generator Loss: 4.2803
D(x): 0.8738, D(G(z)): 0.0561
Epoch: [5/20], Batch Num: [471/600]
Discriminator Loss: 0.2445, Generator Loss: 4.1977
D(x): 0.9330, D(G(z)): 0.0706
Epoch: [5/20], Batch Num: [472/600]
Discriminator Loss: 0.2579, Generator Loss: 4.3564
D(x): 0.9443, D(G(z)): 0.0909
Epoch: [5/20], Batch Num: [473/600]
Discriminator Loss: 0.3097, Generator Loss: 4.6521
D(x): 0.9314, D(G(z)): 0.1088
Epoch: [5/20], Batch Num: [474/600]
Discriminator Loss: 0.2670, Generator Loss: 4.5656
D(x): 0.9335, D(G(z)): 0.0814
Epoch: [5/20], Batch Num: [475/600]
Discriminator Loss: 0.2406, Generator Loss: 4.5115
D(x): 0.9096, D(G(z)): 0.0529
Epoch: [5/20], Batch Num: [476/600]
Discriminator Loss: 0.2728, Generator Loss: 3.8535
D(x): 0.9246, D(G(z)): 0.0492
Epoch: [5/20], Batch Num: [477/600]
Discriminator Loss: 0.2212, Generator Loss: 3.9635
D(x): 0.9638, D(G(z)): 0.0929
Epoch: [5/20], Batch Num: [478/600]
Discriminator Loss: 0.2509, Generator Loss: 4.8972
D(x): 0.9480, D(G(z)): 0.0960
Epoch: [5/20], Batch Num: [479/600]
Discriminator Loss: 0.2651, Generator Loss: 4.7752
D(x): 0.9001, D(G(z)): 0.0334
Epoch: [5/20], Batch Num: [480/600]
Discriminator Loss: 0.2821, Generator Loss: 4.2777
D(x): 0.9008, D(G(z)): 0.0318
Epoch: [5/20], Batch Num: [481/600]
Discriminator Loss: 0.1764, Generator Loss: 3.7452
D(x): 0.9446, D(G(z)): 0.0559
Epoch: [5/20], Batch Num: [482/600]
Discriminator Loss: 0.2822, Generator Loss: 3.7609
D(x): 0.9266, D(G(z)): 0.0884
Epoch: [5/20], Batch Num: [483/600]
Discriminator Loss: 0.3205, Generator Loss: 4.0478
D(x): 0.9498, D(G(z)): 0.1287
Epoch: [5/20], Batch Num: [484/600]
Discriminator Loss: 0.2481, Generator Loss: 4.6859
D(x): 0.9344, D(G(z)): 0.0852
Epoch: [5/20], Batch Num: [485/600]
Discriminator Loss: 0.2669, Generator Loss: 4.5531
D(x): 0.9073, D(G(z)): 0.0407
Epoch: [5/20], Batch Num: [486/600]
Discriminator Loss: 0.4069, Generator Loss: 4.1291
D(x): 0.8812, D(G(z)): 0.0514
Epoch: [5/20], Batch Num: [487/600]
Discriminator Loss: 0.2385, Generator Loss: 3.9124
D(x): 0.9259, D(G(z)): 0.0581
Epoch: [5/20], Batch Num: [488/600]
Discriminator Loss: 0.2789, Generator Loss: 4.0428
D(x): 0.9582, D(G(z)): 0.1095
Epoch: [5/20], Batch Num: [489/600]
Discriminator Loss: 0.2563, Generator Loss: 4.8589
D(x): 0.9667, D(G(z)): 0.1080
Epoch: [5/20], Batch Num: [490/600]
Discriminator Loss: 0.3261, Generator Loss: 5.5886
D(x): 0.9322, D(G(z)): 0.0840
Epoch: [5/20], Batch Num: [491/600]
Discriminator Loss: 0.2816, Generator Loss: 5.7702
D(x): 0.9152, D(G(z)): 0.0329
Epoch: [5/20], Batch Num: [492/600]
Discriminator Loss: 0.3356, Generator Loss: 5.1083
D(x): 0.8954, D(G(z)): 0.0155
Epoch: [5/20], Batch Num: [493/600]
Discriminator Loss: 0.1480, Generator Loss: 4.5330
D(x): 0.9344, D(G(z)): 0.0254
Epoch: [5/20], Batch Num: [494/600]
Discriminator Loss: 0.2988, Generator Loss: 4.2539
D(x): 0.9603, D(G(z)): 0.1072
Epoch: [5/20], Batch Num: [495/600]
Discriminator Loss: 0.1235, Generator Loss: 4.4880
D(x): 0.9802, D(G(z)): 0.0688
Epoch: [5/20], Batch Num: [496/600]
Discriminator Loss: 0.1166, Generator Loss: 4.6531
D(x): 0.9736, D(G(z)): 0.0618
Epoch: [5/20], Batch Num: [497/600]
Discriminator Loss: 0.1888, Generator Loss: 5.2890
D(x): 0.9491, D(G(z)): 0.0584
Epoch: [5/20], Batch Num: [498/600]
Discriminator Loss: 0.1753, Generator Loss: 5.5927
D(x): 0.9439, D(G(z)): 0.0301
Epoch: [5/20], Batch Num: [499/600]
Discriminator Loss: 0.3690, Generator Loss: 5.0932
D(x): 0.8817, D(G(z)): 0.0400
Epoch: 5, Batch Num: [500/600]

Epoch: [5/20], Batch Num: [500/600]
Discriminator Loss: 0.2546, Generator Loss: 5.0093
D(x): 0.9391, D(G(z)): 0.0439
Epoch: [5/20], Batch Num: [501/600]
Discriminator Loss: 0.1924, Generator Loss: 3.7575
D(x): 0.9278, D(G(z)): 0.0410
Epoch: [5/20], Batch Num: [502/600]
Discriminator Loss: 0.1869, Generator Loss: 4.5502
D(x): 0.9971, D(G(z)): 0.1207
Epoch: [5/20], Batch Num: [503/600]
Discriminator Loss: 0.1413, Generator Loss: 5.2511
D(x): 0.9911, D(G(z)): 0.0772
Epoch: [5/20], Batch Num: [504/600]
Discriminator Loss: 0.1681, Generator Loss: 5.9934
D(x): 0.9752, D(G(z)): 0.0735
Epoch: [5/20], Batch Num: [505/600]
Discriminator Loss: 0.1305, Generator Loss: 6.7653
D(x): 0.9518, D(G(z)): 0.0176
Epoch: [5/20], Batch Num: [506/600]
Discriminator Loss: 0.1679, Generator Loss: 6.4464
D(x): 0.9266, D(G(z)): 0.0123
Epoch: [5/20], Batch Num: [507/600]
Discriminator Loss: 0.2133, Generator Loss: 6.0276
D(x): 0.9210, D(G(z)): 0.0194
Epoch: [5/20], Batch Num: [508/600]
Discriminator Loss: 0.3138, Generator Loss: 5.1590
D(x): 0.9250, D(G(z)): 0.0525
Epoch: [5/20], Batch Num: [509/600]
Discriminator Loss: 0.2078, Generator Loss: 4.0547
D(x): 0.9435, D(G(z)): 0.0624
Epoch: [5/20], Batch Num: [510/600]
Discriminator Loss: 0.4631, Generator Loss: 3.8308
D(x): 0.9185, D(G(z)): 0.1763
Epoch: [5/20], Batch Num: [511/600]
Discriminator Loss: 0.2137, Generator Loss: 4.2184
D(x): 0.9590, D(G(z)): 0.0943
Epoch: [5/20], Batch Num: [512/600]
Discriminator Loss: 0.3263, Generator Loss: 4.5611
D(x): 0.9188, D(G(z)): 0.0871
Epoch: [5/20], Batch Num: [513/600]
Discriminator Loss: 0.3225, Generator Loss: 4.0674
D(x): 0.8807, D(G(z)): 0.0611
Epoch: [5/20], Batch Num: [514/600]
Discriminator Loss: 0.2752, Generator Loss: 4.2805
D(x): 0.9413, D(G(z)): 0.1045
Epoch: [5/20], Batch Num: [515/600]
Discriminator Loss: 0.3671, Generator Loss: 4.5210
D(x): 0.9186, D(G(z)): 0.1215
Epoch: [5/20], Batch Num: [516/600]
Discriminator Loss: 0.3530, Generator Loss: 4.6558
D(x): 0.9062, D(G(z)): 0.0754
Epoch: [5/20], Batch Num: [517/600]
Discriminator Loss: 0.3257, Generator Loss: 4.1598
D(x): 0.8912, D(G(z)): 0.0503
Epoch: [5/20], Batch Num: [518/600]
Discriminator Loss: 0.6417, Generator Loss: 4.1383
D(x): 0.8605, D(G(z)): 0.1015
Epoch: [5/20], Batch Num: [519/600]
Discriminator Loss: 0.5545, Generator Loss: 4.5504
D(x): 0.9120, D(G(z)): 0.1522
Epoch: [5/20], Batch Num: [520/600]
Discriminator Loss: 0.5519, Generator Loss: 4.4132
D(x): 0.8562, D(G(z)): 0.1095
Epoch: [5/20], Batch Num: [521/600]
Discriminator Loss: 0.5217, Generator Loss: 4.1176
D(x): 0.8732, D(G(z)): 0.0885
Epoch: [5/20], Batch Num: [522/600]
Discriminator Loss: 0.3495, Generator Loss: 3.7466
D(x): 0.8895, D(G(z)): 0.0636
Epoch: [5/20], Batch Num: [523/600]
Discriminator Loss: 0.5140, Generator Loss: 3.2185
D(x): 0.8496, D(G(z)): 0.0826
Epoch: [5/20], Batch Num: [524/600]
Discriminator Loss: 0.5378, Generator Loss: 2.4662
D(x): 0.8764, D(G(z)): 0.1551
Epoch: [5/20], Batch Num: [525/600]
Discriminator Loss: 0.5459, Generator Loss: 2.9499
D(x): 0.8982, D(G(z)): 0.1976
Epoch: [5/20], Batch Num: [526/600]
Discriminator Loss: 0.5107, Generator Loss: 3.4398
D(x): 0.9135, D(G(z)): 0.1768
Epoch: [5/20], Batch Num: [527/600]
Discriminator Loss: 0.4488, Generator Loss: 3.9837
D(x): 0.8598, D(G(z)): 0.0978
Epoch: [5/20], Batch Num: [528/600]
Discriminator Loss: 0.4607, Generator Loss: 3.9235
D(x): 0.8461, D(G(z)): 0.0596
Epoch: [5/20], Batch Num: [529/600]
Discriminator Loss: 0.3226, Generator Loss: 3.4424
D(x): 0.8885, D(G(z)): 0.0570
Epoch: [5/20], Batch Num: [530/600]
Discriminator Loss: 0.3688, Generator Loss: 3.2224
D(x): 0.8623, D(G(z)): 0.0744
Epoch: [5/20], Batch Num: [531/600]
Discriminator Loss: 0.3161, Generator Loss: 2.6665
D(x): 0.9464, D(G(z)): 0.1408
Epoch: [5/20], Batch Num: [532/600]
Discriminator Loss: 0.2447, Generator Loss: 2.8402
D(x): 0.9559, D(G(z)): 0.1403
Epoch: [5/20], Batch Num: [533/600]
Discriminator Loss: 0.3434, Generator Loss: 3.3567
D(x): 0.9403, D(G(z)): 0.1457
Epoch: [5/20], Batch Num: [534/600]
Discriminator Loss: 0.3220, Generator Loss: 3.7606
D(x): 0.8985, D(G(z)): 0.0978
Epoch: [5/20], Batch Num: [535/600]
Discriminator Loss: 0.3341, Generator Loss: 3.5372
D(x): 0.8520, D(G(z)): 0.0522
Epoch: [5/20], Batch Num: [536/600]
Discriminator Loss: 0.3473, Generator Loss: 3.5265
D(x): 0.8553, D(G(z)): 0.0705
Epoch: [5/20], Batch Num: [537/600]
Discriminator Loss: 0.3077, Generator Loss: 2.7582
D(x): 0.9034, D(G(z)): 0.0929
Epoch: [5/20], Batch Num: [538/600]
Discriminator Loss: 0.3088, Generator Loss: 2.8805
D(x): 0.9246, D(G(z)): 0.1384
Epoch: [5/20], Batch Num: [539/600]
Discriminator Loss: 0.2707, Generator Loss: 2.8370
D(x): 0.9495, D(G(z)): 0.1510
Epoch: [5/20], Batch Num: [540/600]
Discriminator Loss: 0.3069, Generator Loss: 3.3671
D(x): 0.9279, D(G(z)): 0.1222
Epoch: [5/20], Batch Num: [541/600]
Discriminator Loss: 0.2957, Generator Loss: 3.3630
D(x): 0.8959, D(G(z)): 0.0856
Epoch: [5/20], Batch Num: [542/600]
Discriminator Loss: 0.2411, Generator Loss: 3.6212
D(x): 0.9253, D(G(z)): 0.0836
Epoch: [5/20], Batch Num: [543/600]
Discriminator Loss: 0.4519, Generator Loss: 3.5961
D(x): 0.8887, D(G(z)): 0.1280
Epoch: [5/20], Batch Num: [544/600]
Discriminator Loss: 0.4068, Generator Loss: 3.2356
D(x): 0.8613, D(G(z)): 0.0849
Epoch: [5/20], Batch Num: [545/600]
Discriminator Loss: 0.1797, Generator Loss: 3.2324
D(x): 0.9370, D(G(z)): 0.0735
Epoch: [5/20], Batch Num: [546/600]
Discriminator Loss: 0.2182, Generator Loss: 3.0705
D(x): 0.9255, D(G(z)): 0.0886
Epoch: [5/20], Batch Num: [547/600]
Discriminator Loss: 0.2005, Generator Loss: 3.0223
D(x): 0.9573, D(G(z)): 0.0959
Epoch: [5/20], Batch Num: [548/600]
Discriminator Loss: 0.3124, Generator Loss: 3.0398
D(x): 0.9235, D(G(z)): 0.1315
Epoch: [5/20], Batch Num: [549/600]
Discriminator Loss: 0.3376, Generator Loss: 3.7700
D(x): 0.9195, D(G(z)): 0.1460
Epoch: [5/20], Batch Num: [550/600]
Discriminator Loss: 0.1873, Generator Loss: 4.1676
D(x): 0.9573, D(G(z)): 0.0854
Epoch: [5/20], Batch Num: [551/600]
Discriminator Loss: 0.4164, Generator Loss: 4.2275
D(x): 0.8338, D(G(z)): 0.0486
Epoch: [5/20], Batch Num: [552/600]
Discriminator Loss: 0.4105, Generator Loss: 3.8516
D(x): 0.8414, D(G(z)): 0.0382
Epoch: [5/20], Batch Num: [553/600]
Discriminator Loss: 0.2799, Generator Loss: 3.0683
D(x): 0.8875, D(G(z)): 0.0518
Epoch: [5/20], Batch Num: [554/600]
Discriminator Loss: 0.3255, Generator Loss: 2.6289
D(x): 0.9491, D(G(z)): 0.1515
Epoch: [5/20], Batch Num: [555/600]
Discriminator Loss: 0.4262, Generator Loss: 3.1307
D(x): 0.9375, D(G(z)): 0.1948
Epoch: [5/20], Batch Num: [556/600]
Discriminator Loss: 0.3015, Generator Loss: 3.9365
D(x): 0.9195, D(G(z)): 0.1071
Epoch: [5/20], Batch Num: [557/600]
Discriminator Loss: 0.2425, Generator Loss: 3.7953
D(x): 0.8852, D(G(z)): 0.0508
Epoch: [5/20], Batch Num: [558/600]
Discriminator Loss: 0.3398, Generator Loss: 3.7704
D(x): 0.8690, D(G(z)): 0.0662
Epoch: [5/20], Batch Num: [559/600]
Discriminator Loss: 0.2712, Generator Loss: 3.2862
D(x): 0.8849, D(G(z)): 0.0717
Epoch: [5/20], Batch Num: [560/600]
Discriminator Loss: 0.2347, Generator Loss: 2.8166
D(x): 0.9174, D(G(z)): 0.0661
Epoch: [5/20], Batch Num: [561/600]
Discriminator Loss: 0.2988, Generator Loss: 3.2472
D(x): 0.9530, D(G(z)): 0.1559
Epoch: [5/20], Batch Num: [562/600]
Discriminator Loss: 0.2477, Generator Loss: 3.4620
D(x): 0.9426, D(G(z)): 0.0975
Epoch: [5/20], Batch Num: [563/600]
Discriminator Loss: 0.3083, Generator Loss: 3.9568
D(x): 0.8993, D(G(z)): 0.1002
Epoch: [5/20], Batch Num: [564/600]
Discriminator Loss: 0.2517, Generator Loss: 4.1912
D(x): 0.8998, D(G(z)): 0.0498
Epoch: [5/20], Batch Num: [565/600]
Discriminator Loss: 0.2776, Generator Loss: 3.8731
D(x): 0.8962, D(G(z)): 0.0688
Epoch: [5/20], Batch Num: [566/600]
Discriminator Loss: 0.2183, Generator Loss: 3.4573
D(x): 0.9176, D(G(z)): 0.0598
Epoch: [5/20], Batch Num: [567/600]
Discriminator Loss: 0.3374, Generator Loss: 3.5743
D(x): 0.9415, D(G(z)): 0.1571
Epoch: [5/20], Batch Num: [568/600]
Discriminator Loss: 0.2600, Generator Loss: 4.0953
D(x): 0.9039, D(G(z)): 0.0886
Epoch: [5/20], Batch Num: [569/600]
Discriminator Loss: 0.4100, Generator Loss: 3.6270
D(x): 0.8507, D(G(z)): 0.0668
Epoch: [5/20], Batch Num: [570/600]
Discriminator Loss: 0.2053, Generator Loss: 3.4122
D(x): 0.9444, D(G(z)): 0.0554
Epoch: [5/20], Batch Num: [571/600]
Discriminator Loss: 0.1916, Generator Loss: 3.4588
D(x): 0.9464, D(G(z)): 0.0733
Epoch: [5/20], Batch Num: [572/600]
Discriminator Loss: 0.3364, Generator Loss: 3.5555
D(x): 0.9266, D(G(z)): 0.1226
Epoch: [5/20], Batch Num: [573/600]
Discriminator Loss: 0.3339, Generator Loss: 4.0235
D(x): 0.9090, D(G(z)): 0.0937
Epoch: [5/20], Batch Num: [574/600]
Discriminator Loss: 0.2629, Generator Loss: 3.7440
D(x): 0.9073, D(G(z)): 0.0672
Epoch: [5/20], Batch Num: [575/600]
Discriminator Loss: 0.2747, Generator Loss: 3.9205
D(x): 0.8977, D(G(z)): 0.0840
Epoch: [5/20], Batch Num: [576/600]
Discriminator Loss: 0.4156, Generator Loss: 3.1045
D(x): 0.8863, D(G(z)): 0.1099
Epoch: [5/20], Batch Num: [577/600]
Discriminator Loss: 0.3577, Generator Loss: 3.6454
D(x): 0.9136, D(G(z)): 0.1308
Epoch: [5/20], Batch Num: [578/600]
Discriminator Loss: 0.2455, Generator Loss: 3.6534
D(x): 0.9340, D(G(z)): 0.1067
Epoch: [5/20], Batch Num: [579/600]
Discriminator Loss: 0.2217, Generator Loss: 3.9558
D(x): 0.9259, D(G(z)): 0.0784
Epoch: [5/20], Batch Num: [580/600]
Discriminator Loss: 0.3157, Generator Loss: 3.7057
D(x): 0.8908, D(G(z)): 0.0692
Epoch: [5/20], Batch Num: [581/600]
Discriminator Loss: 0.3338, Generator Loss: 3.7020
D(x): 0.9042, D(G(z)): 0.0919
Epoch: [5/20], Batch Num: [582/600]
Discriminator Loss: 0.3366, Generator Loss: 3.4195
D(x): 0.9143, D(G(z)): 0.1180
Epoch: [5/20], Batch Num: [583/600]
Discriminator Loss: 0.2070, Generator Loss: 3.7611
D(x): 0.9401, D(G(z)): 0.0885
Epoch: [5/20], Batch Num: [584/600]
Discriminator Loss: 0.2973, Generator Loss: 3.3777
D(x): 0.9132, D(G(z)): 0.0726
Epoch: [5/20], Batch Num: [585/600]
Discriminator Loss: 0.2384, Generator Loss: 3.5570
D(x): 0.9313, D(G(z)): 0.0785
Epoch: [5/20], Batch Num: [586/600]
Discriminator Loss: 0.4098, Generator Loss: 3.4972
D(x): 0.8904, D(G(z)): 0.1084
Epoch: [5/20], Batch Num: [587/600]
Discriminator Loss: 0.3961, Generator Loss: 3.5378
D(x): 0.9101, D(G(z)): 0.1333
Epoch: [5/20], Batch Num: [588/600]
Discriminator Loss: 0.2847, Generator Loss: 4.1318
D(x): 0.9256, D(G(z)): 0.1015
Epoch: [5/20], Batch Num: [589/600]
Discriminator Loss: 0.2944, Generator Loss: 3.7244
D(x): 0.8955, D(G(z)): 0.0622
Epoch: [5/20], Batch Num: [590/600]
Discriminator Loss: 0.1852, Generator Loss: 3.5971
D(x): 0.9478, D(G(z)): 0.0669
Epoch: [5/20], Batch Num: [591/600]
Discriminator Loss: 0.2548, Generator Loss: 3.2989
D(x): 0.8986, D(G(z)): 0.0736
Epoch: [5/20], Batch Num: [592/600]
Discriminator Loss: 0.1907, Generator Loss: 3.8844
D(x): 0.9742, D(G(z)): 0.1219
Epoch: [5/20], Batch Num: [593/600]
Discriminator Loss: 0.1728, Generator Loss: 4.2704
D(x): 0.9552, D(G(z)): 0.0681
Epoch: [5/20], Batch Num: [594/600]
Discriminator Loss: 0.2118, Generator Loss: 4.2851
D(x): 0.9365, D(G(z)): 0.0478
Epoch: [5/20], Batch Num: [595/600]
Discriminator Loss: 0.1446, Generator Loss: 4.3092
D(x): 0.9461, D(G(z)): 0.0343
Epoch: [5/20], Batch Num: [596/600]
Discriminator Loss: 0.3022, Generator Loss: 4.6517
D(x): 0.9454, D(G(z)): 0.0981
Epoch: [5/20], Batch Num: [597/600]
Discriminator Loss: 0.1504, Generator Loss: 4.6858
D(x): 0.9257, D(G(z)): 0.0331
Epoch: [5/20], Batch Num: [598/600]
Discriminator Loss: 0.1375, Generator Loss: 4.2670
D(x): 0.9524, D(G(z)): 0.0277
Epoch: [5/20], Batch Num: [599/600]
Discriminator Loss: 0.1745, Generator Loss: 3.7661
D(x): 0.9363, D(G(z)): 0.0446
Epoch: 6, Batch Num: [0/600]

Epoch: [6/20], Batch Num: [0/600]
Discriminator Loss: 0.2797, Generator Loss: 3.6841
D(x): 0.9459, D(G(z)): 0.0988
Epoch: [6/20], Batch Num: [1/600]
Discriminator Loss: 0.2495, Generator Loss: 4.1320
D(x): 0.9669, D(G(z)): 0.1014
Epoch: [6/20], Batch Num: [2/600]
Discriminator Loss: 0.2155, Generator Loss: 5.2308
D(x): 0.9532, D(G(z)): 0.0730
Epoch: [6/20], Batch Num: [3/600]
Discriminator Loss: 0.2818, Generator Loss: 5.8174
D(x): 0.9176, D(G(z)): 0.0455
Epoch: [6/20], Batch Num: [4/600]
Discriminator Loss: 0.4154, Generator Loss: 5.0660
D(x): 0.8535, D(G(z)): 0.0405
Epoch: [6/20], Batch Num: [5/600]
Discriminator Loss: 0.0904, Generator Loss: 4.5539
D(x): 0.9660, D(G(z)): 0.0381
Epoch: [6/20], Batch Num: [6/600]
Discriminator Loss: 0.1906, Generator Loss: 4.4636
D(x): 0.9623, D(G(z)): 0.0807
Epoch: [6/20], Batch Num: [7/600]
Discriminator Loss: 0.2037, Generator Loss: 4.4621
D(x): 0.9359, D(G(z)): 0.0516
Epoch: [6/20], Batch Num: [8/600]
Discriminator Loss: 0.2019, Generator Loss: 4.6034
D(x): 0.9646, D(G(z)): 0.0845
Epoch: [6/20], Batch Num: [9/600]
Discriminator Loss: 0.1271, Generator Loss: 4.8254
D(x): 0.9684, D(G(z)): 0.0519
Epoch: [6/20], Batch Num: [10/600]
Discriminator Loss: 0.1654, Generator Loss: 5.1797
D(x): 0.9544, D(G(z)): 0.0513
Epoch: [6/20], Batch Num: [11/600]
Discriminator Loss: 0.1974, Generator Loss: 5.4552
D(x): 0.9362, D(G(z)): 0.0414
Epoch: [6/20], Batch Num: [12/600]
Discriminator Loss: 0.1720, Generator Loss: 4.3438
D(x): 0.9336, D(G(z)): 0.0319
Epoch: [6/20], Batch Num: [13/600]
Discriminator Loss: 0.2591, Generator Loss: 4.5532
D(x): 0.9490, D(G(z)): 0.0896
Epoch: [6/20], Batch Num: [14/600]
Discriminator Loss: 0.2757, Generator Loss: 4.9932
D(x): 0.9409, D(G(z)): 0.0971
Epoch: [6/20], Batch Num: [15/600]
Discriminator Loss: 0.2759, Generator Loss: 4.5145
D(x): 0.9274, D(G(z)): 0.0685
Epoch: [6/20], Batch Num: [16/600]
Discriminator Loss: 0.2527, Generator Loss: 4.2470
D(x): 0.9103, D(G(z)): 0.0450
Epoch: [6/20], Batch Num: [17/600]
Discriminator Loss: 0.2008, Generator Loss: 3.5557
D(x): 0.9451, D(G(z)): 0.0610
Epoch: [6/20], Batch Num: [18/600]
Discriminator Loss: 0.4311, Generator Loss: 3.7312
D(x): 0.9273, D(G(z)): 0.1539
Epoch: [6/20], Batch Num: [19/600]
Discriminator Loss: 0.2253, Generator Loss: 4.5875
D(x): 0.9319, D(G(z)): 0.0770
Epoch: [6/20], Batch Num: [20/600]
Discriminator Loss: 0.2908, Generator Loss: 4.2340
D(x): 0.8976, D(G(z)): 0.0531
Epoch: [6/20], Batch Num: [21/600]
Discriminator Loss: 0.3437, Generator Loss: 4.0690
D(x): 0.8982, D(G(z)): 0.0705
Epoch: [6/20], Batch Num: [22/600]
Discriminator Loss: 0.4352, Generator Loss: 2.9697
D(x): 0.8654, D(G(z)): 0.1000
Epoch: [6/20], Batch Num: [23/600]
Discriminator Loss: 0.5243, Generator Loss: 4.0246
D(x): 0.9337, D(G(z)): 0.2050
Epoch: [6/20], Batch Num: [24/600]
Discriminator Loss: 0.3214, Generator Loss: 4.3483
D(x): 0.9005, D(G(z)): 0.0846
Epoch: [6/20], Batch Num: [25/600]
Discriminator Loss: 0.3051, Generator Loss: 3.7985
D(x): 0.8980, D(G(z)): 0.0486
Epoch: [6/20], Batch Num: [26/600]
Discriminator Loss: 0.3422, Generator Loss: 3.0958
D(x): 0.8772, D(G(z)): 0.0608
Epoch: [6/20], Batch Num: [27/600]
Discriminator Loss: 0.5128, Generator Loss: 2.8951
D(x): 0.8954, D(G(z)): 0.1578
Epoch: [6/20], Batch Num: [28/600]
Discriminator Loss: 0.3213, Generator Loss: 3.5124
D(x): 0.9444, D(G(z)): 0.1626
Epoch: [6/20], Batch Num: [29/600]
Discriminator Loss: 0.3017, Generator Loss: 4.2363
D(x): 0.9101, D(G(z)): 0.0943
Epoch: [6/20], Batch Num: [30/600]
Discriminator Loss: 0.4309, Generator Loss: 3.7427
D(x): 0.8475, D(G(z)): 0.0389
Epoch: [6/20], Batch Num: [31/600]
Discriminator Loss: 0.3898, Generator Loss: 2.8168
D(x): 0.8687, D(G(z)): 0.0639
Epoch: [6/20], Batch Num: [32/600]
Discriminator Loss: 0.2823, Generator Loss: 2.7535
D(x): 0.9233, D(G(z)): 0.1186
Epoch: [6/20], Batch Num: [33/600]
Discriminator Loss: 0.3623, Generator Loss: 3.4245
D(x): 0.9425, D(G(z)): 0.1826
Epoch: [6/20], Batch Num: [34/600]
Discriminator Loss: 0.2283, Generator Loss: 3.9612
D(x): 0.9364, D(G(z)): 0.0881
Epoch: [6/20], Batch Num: [35/600]
Discriminator Loss: 0.1744, Generator Loss: 4.0911
D(x): 0.9315, D(G(z)): 0.0354
Epoch: [6/20], Batch Num: [36/600]
Discriminator Loss: 0.2662, Generator Loss: 3.9727
D(x): 0.9091, D(G(z)): 0.0360
Epoch: [6/20], Batch Num: [37/600]
Discriminator Loss: 0.2441, Generator Loss: 3.6087
D(x): 0.8962, D(G(z)): 0.0423
Epoch: [6/20], Batch Num: [38/600]
Discriminator Loss: 0.2419, Generator Loss: 3.0926
D(x): 0.9225, D(G(z)): 0.0604
Epoch: [6/20], Batch Num: [39/600]
Discriminator Loss: 0.2622, Generator Loss: 2.8966
D(x): 0.9425, D(G(z)): 0.1277
Epoch: [6/20], Batch Num: [40/600]
Discriminator Loss: 0.2852, Generator Loss: 3.5070
D(x): 0.9481, D(G(z)): 0.1305
Epoch: [6/20], Batch Num: [41/600]
Discriminator Loss: 0.1459, Generator Loss: 4.2171
D(x): 0.9564, D(G(z)): 0.0682
Epoch: [6/20], Batch Num: [42/600]
Discriminator Loss: 0.2632, Generator Loss: 4.2484
D(x): 0.9011, D(G(z)): 0.0515
Epoch: [6/20], Batch Num: [43/600]
Discriminator Loss: 0.1779, Generator Loss: 4.0881
D(x): 0.9395, D(G(z)): 0.0347
Epoch: [6/20], Batch Num: [44/600]
Discriminator Loss: 0.1983, Generator Loss: 3.5501
D(x): 0.9211, D(G(z)): 0.0435
Epoch: [6/20], Batch Num: [45/600]
Discriminator Loss: 0.2279, Generator Loss: 3.5669
D(x): 0.9319, D(G(z)): 0.0881
Epoch: [6/20], Batch Num: [46/600]
Discriminator Loss: 0.1481, Generator Loss: 3.6107
D(x): 0.9648, D(G(z)): 0.0851
Epoch: [6/20], Batch Num: [47/600]
Discriminator Loss: 0.2513, Generator Loss: 4.2724
D(x): 0.9209, D(G(z)): 0.0721
Epoch: [6/20], Batch Num: [48/600]
Discriminator Loss: 0.1459, Generator Loss: 3.9344
D(x): 0.9479, D(G(z)): 0.0567
Epoch: [6/20], Batch Num: [49/600]
Discriminator Loss: 0.1434, Generator Loss: 3.9996
D(x): 0.9411, D(G(z)): 0.0492
Epoch: [6/20], Batch Num: [50/600]
Discriminator Loss: 0.2820, Generator Loss: 3.6856
D(x): 0.9025, D(G(z)): 0.0673
Epoch: [6/20], Batch Num: [51/600]
Discriminator Loss: 0.1332, Generator Loss: 3.7144
D(x): 0.9727, D(G(z)): 0.0775
Epoch: [6/20], Batch Num: [52/600]
Discriminator Loss: 0.1485, Generator Loss: 3.9557
D(x): 0.9555, D(G(z)): 0.0515
Epoch: [6/20], Batch Num: [53/600]
Discriminator Loss: 0.1929, Generator Loss: 3.8192
D(x): 0.9519, D(G(z)): 0.0601
Epoch: [6/20], Batch Num: [54/600]
Discriminator Loss: 0.2020, Generator Loss: 3.8831
D(x): 0.9322, D(G(z)): 0.0584
Epoch: [6/20], Batch Num: [55/600]
Discriminator Loss: 0.1570, Generator Loss: 4.3469
D(x): 0.9657, D(G(z)): 0.0677
Epoch: [6/20], Batch Num: [56/600]
Discriminator Loss: 0.1883, Generator Loss: 4.4694
D(x): 0.9338, D(G(z)): 0.0449
Epoch: [6/20], Batch Num: [57/600]
Discriminator Loss: 0.1121, Generator Loss: 4.1787
D(x): 0.9538, D(G(z)): 0.0328
Epoch: [6/20], Batch Num: [58/600]
Discriminator Loss: 0.2112, Generator Loss: 3.7055
D(x): 0.9239, D(G(z)): 0.0553
Epoch: [6/20], Batch Num: [59/600]
Discriminator Loss: 0.2780, Generator Loss: 3.6187
D(x): 0.9120, D(G(z)): 0.0538
Epoch: [6/20], Batch Num: [60/600]
Discriminator Loss: 0.2025, Generator Loss: 3.4358
D(x): 0.9608, D(G(z)): 0.1017
Epoch: [6/20], Batch Num: [61/600]
Discriminator Loss: 0.1573, Generator Loss: 4.3092
D(x): 0.9761, D(G(z)): 0.0937
Epoch: [6/20], Batch Num: [62/600]
Discriminator Loss: 0.3744, Generator Loss: 4.3920
D(x): 0.8825, D(G(z)): 0.0538
Epoch: [6/20], Batch Num: [63/600]
Discriminator Loss: 0.1161, Generator Loss: 4.1443
D(x): 0.9665, D(G(z)): 0.0525
Epoch: [6/20], Batch Num: [64/600]
Discriminator Loss: 0.1896, Generator Loss: 3.9174
D(x): 0.9396, D(G(z)): 0.0469
Epoch: [6/20], Batch Num: [65/600]
Discriminator Loss: 0.2059, Generator Loss: 3.8017
D(x): 0.9456, D(G(z)): 0.0629
Epoch: [6/20], Batch Num: [66/600]
Discriminator Loss: 0.2308, Generator Loss: 4.1039
D(x): 0.9507, D(G(z)): 0.0877
Epoch: [6/20], Batch Num: [67/600]
Discriminator Loss: 0.1658, Generator Loss: 4.4039
D(x): 0.9588, D(G(z)): 0.0545
Epoch: [6/20], Batch Num: [68/600]
Discriminator Loss: 0.1620, Generator Loss: 4.5353
D(x): 0.9442, D(G(z)): 0.0407
Epoch: [6/20], Batch Num: [69/600]
Discriminator Loss: 0.1277, Generator Loss: 4.5136
D(x): 0.9610, D(G(z)): 0.0381
Epoch: [6/20], Batch Num: [70/600]
Discriminator Loss: 0.2718, Generator Loss: 3.8654
D(x): 0.9210, D(G(z)): 0.0576
Epoch: [6/20], Batch Num: [71/600]
Discriminator Loss: 0.2307, Generator Loss: 3.2670
D(x): 0.9095, D(G(z)): 0.0411
Epoch: [6/20], Batch Num: [72/600]
Discriminator Loss: 0.2937, Generator Loss: 3.9999
D(x): 0.9703, D(G(z)): 0.1538
Epoch: [6/20], Batch Num: [73/600]
Discriminator Loss: 0.3480, Generator Loss: 4.0823
D(x): 0.8839, D(G(z)): 0.0615
Epoch: [6/20], Batch Num: [74/600]
Discriminator Loss: 0.2035, Generator Loss: 4.8840
D(x): 0.9697, D(G(z)): 0.0812
Epoch: [6/20], Batch Num: [75/600]
Discriminator Loss: 0.1520, Generator Loss: 4.9238
D(x): 0.9373, D(G(z)): 0.0279
Epoch: [6/20], Batch Num: [76/600]
Discriminator Loss: 0.3807, Generator Loss: 4.5725
D(x): 0.8912, D(G(z)): 0.0260
Epoch: [6/20], Batch Num: [77/600]
Discriminator Loss: 0.2426, Generator Loss: 3.8793
D(x): 0.9424, D(G(z)): 0.0586
Epoch: [6/20], Batch Num: [78/600]
Discriminator Loss: 0.2269, Generator Loss: 3.8819
D(x): 0.9723, D(G(z)): 0.1036
Epoch: [6/20], Batch Num: [79/600]
Discriminator Loss: 0.1303, Generator Loss: 4.1469
D(x): 0.9739, D(G(z)): 0.0766
Epoch: [6/20], Batch Num: [80/600]
Discriminator Loss: 0.2088, Generator Loss: 5.2458
D(x): 0.9487, D(G(z)): 0.0498
Epoch: [6/20], Batch Num: [81/600]
Discriminator Loss: 0.1720, Generator Loss: 4.8946
D(x): 0.9421, D(G(z)): 0.0365
Epoch: [6/20], Batch Num: [82/600]
Discriminator Loss: 0.3174, Generator Loss: 4.2433
D(x): 0.8829, D(G(z)): 0.0218
Epoch: [6/20], Batch Num: [83/600]
Discriminator Loss: 0.2211, Generator Loss: 3.6643
D(x): 0.9441, D(G(z)): 0.0712
Epoch: [6/20], Batch Num: [84/600]
Discriminator Loss: 0.1560, Generator Loss: 3.2302
D(x): 0.9666, D(G(z)): 0.0710
Epoch: [6/20], Batch Num: [85/600]
Discriminator Loss: 0.2779, Generator Loss: 4.1090
D(x): 0.9730, D(G(z)): 0.1344
Epoch: [6/20], Batch Num: [86/600]
Discriminator Loss: 0.1530, Generator Loss: 4.9455
D(x): 0.9625, D(G(z)): 0.0486
Epoch: [6/20], Batch Num: [87/600]
Discriminator Loss: 0.2443, Generator Loss: 4.7781
D(x): 0.9031, D(G(z)): 0.0332
Epoch: [6/20], Batch Num: [88/600]
Discriminator Loss: 0.2943, Generator Loss: 4.3960
D(x): 0.8906, D(G(z)): 0.0360
Epoch: [6/20], Batch Num: [89/600]
Discriminator Loss: 0.1414, Generator Loss: 3.7383
D(x): 0.9458, D(G(z)): 0.0447
Epoch: [6/20], Batch Num: [90/600]
Discriminator Loss: 0.2290, Generator Loss: 3.5185
D(x): 0.9603, D(G(z)): 0.1133
Epoch: [6/20], Batch Num: [91/600]
Discriminator Loss: 0.1836, Generator Loss: 4.0601
D(x): 0.9724, D(G(z)): 0.1012
Epoch: [6/20], Batch Num: [92/600]
Discriminator Loss: 0.1039, Generator Loss: 4.8070
D(x): 0.9668, D(G(z)): 0.0438
Epoch: [6/20], Batch Num: [93/600]
Discriminator Loss: 0.2061, Generator Loss: 5.0156
D(x): 0.9396, D(G(z)): 0.0298
Epoch: [6/20], Batch Num: [94/600]
Discriminator Loss: 0.1203, Generator Loss: 5.0197
D(x): 0.9533, D(G(z)): 0.0226
Epoch: [6/20], Batch Num: [95/600]
Discriminator Loss: 0.1434, Generator Loss: 5.0195
D(x): 0.9501, D(G(z)): 0.0299
Epoch: [6/20], Batch Num: [96/600]
Discriminator Loss: 0.1431, Generator Loss: 4.2683
D(x): 0.9507, D(G(z)): 0.0404
Epoch: [6/20], Batch Num: [97/600]
Discriminator Loss: 0.1886, Generator Loss: 4.2588
D(x): 0.9615, D(G(z)): 0.0837
Epoch: [6/20], Batch Num: [98/600]
Discriminator Loss: 0.1605, Generator Loss: 4.5227
D(x): 0.9748, D(G(z)): 0.0888
Epoch: [6/20], Batch Num: [99/600]
Discriminator Loss: 0.1109, Generator Loss: 4.5255
D(x): 0.9671, D(G(z)): 0.0391
Epoch: 6, Batch Num: [100/600]

Epoch: [6/20], Batch Num: [100/600]
Discriminator Loss: 0.2102, Generator Loss: 4.9271
D(x): 0.9808, D(G(z)): 0.0799
Epoch: [6/20], Batch Num: [101/600]
Discriminator Loss: 0.0751, Generator Loss: 5.5799
D(x): 0.9589, D(G(z)): 0.0157
Epoch: [6/20], Batch Num: [102/600]
Discriminator Loss: 0.1389, Generator Loss: 5.2065
D(x): 0.9367, D(G(z)): 0.0144
Epoch: [6/20], Batch Num: [103/600]
Discriminator Loss: 0.3271, Generator Loss: 4.5007
D(x): 0.9189, D(G(z)): 0.0447
Epoch: [6/20], Batch Num: [104/600]
Discriminator Loss: 0.2085, Generator Loss: 3.8559
D(x): 0.9430, D(G(z)): 0.0668
Epoch: [6/20], Batch Num: [105/600]
Discriminator Loss: 0.2466, Generator Loss: 4.8291
D(x): 0.9750, D(G(z)): 0.1192
Epoch: [6/20], Batch Num: [106/600]
Discriminator Loss: 0.1438, Generator Loss: 5.3262
D(x): 0.9677, D(G(z)): 0.0605
Epoch: [6/20], Batch Num: [107/600]
Discriminator Loss: 0.2102, Generator Loss: 5.4690
D(x): 0.9394, D(G(z)): 0.0303
Epoch: [6/20], Batch Num: [108/600]
Discriminator Loss: 0.3450, Generator Loss: 5.3827
D(x): 0.9219, D(G(z)): 0.0346
Epoch: [6/20], Batch Num: [109/600]
Discriminator Loss: 0.3495, Generator Loss: 4.5752
D(x): 0.8892, D(G(z)): 0.0335
Epoch: [6/20], Batch Num: [110/600]
Discriminator Loss: 0.3294, Generator Loss: 3.8998
D(x): 0.9676, D(G(z)): 0.0920
Epoch: [6/20], Batch Num: [111/600]
Discriminator Loss: 0.1985, Generator Loss: 3.8149
D(x): 0.9621, D(G(z)): 0.0861
Epoch: [6/20], Batch Num: [112/600]
Discriminator Loss: 0.1253, Generator Loss: 4.0540
D(x): 0.9913, D(G(z)): 0.0865
Epoch: [6/20], Batch Num: [113/600]
Discriminator Loss: 0.3192, Generator Loss: 5.1084
D(x): 0.9700, D(G(z)): 0.1201
Epoch: [6/20], Batch Num: [114/600]
Discriminator Loss: 0.1791, Generator Loss: 5.9223
D(x): 0.9319, D(G(z)): 0.0376
Epoch: [6/20], Batch Num: [115/600]
Discriminator Loss: 0.3343, Generator Loss: 5.8448
D(x): 0.8603, D(G(z)): 0.0128
Epoch: [6/20], Batch Num: [116/600]
Discriminator Loss: 0.3501, Generator Loss: 4.4738
D(x): 0.8781, D(G(z)): 0.0254
Epoch: [6/20], Batch Num: [117/600]
Discriminator Loss: 0.1822, Generator Loss: 3.5989
D(x): 0.9667, D(G(z)): 0.0621
Epoch: [6/20], Batch Num: [118/600]
Discriminator Loss: 0.1775, Generator Loss: 3.3959
D(x): 0.9922, D(G(z)): 0.0947
Epoch: [6/20], Batch Num: [119/600]
Discriminator Loss: 0.3193, Generator Loss: 4.5986
D(x): 0.9873, D(G(z)): 0.1566
Epoch: [6/20], Batch Num: [120/600]
Discriminator Loss: 0.1947, Generator Loss: 5.4803
D(x): 0.9484, D(G(z)): 0.0672
Epoch: [6/20], Batch Num: [121/600]
Discriminator Loss: 0.4701, Generator Loss: 5.7029
D(x): 0.9170, D(G(z)): 0.0725
Epoch: [6/20], Batch Num: [122/600]
Discriminator Loss: 0.2769, Generator Loss: 5.7422
D(x): 0.9212, D(G(z)): 0.0434
Epoch: [6/20], Batch Num: [123/600]
Discriminator Loss: 0.2184, Generator Loss: 5.0971
D(x): 0.9210, D(G(z)): 0.0299
Epoch: [6/20], Batch Num: [124/600]
Discriminator Loss: 0.2139, Generator Loss: 5.0734
D(x): 0.9468, D(G(z)): 0.0601
Epoch: [6/20], Batch Num: [125/600]
Discriminator Loss: 0.3170, Generator Loss: 4.0659
D(x): 0.9521, D(G(z)): 0.0909
Epoch: [6/20], Batch Num: [126/600]
Discriminator Loss: 0.2266, Generator Loss: 4.2982
D(x): 0.9570, D(G(z)): 0.0947
Epoch: [6/20], Batch Num: [127/600]
Discriminator Loss: 0.3289, Generator Loss: 4.5784
D(x): 0.9264, D(G(z)): 0.1140
Epoch: [6/20], Batch Num: [128/600]
Discriminator Loss: 0.2645, Generator Loss: 5.2410
D(x): 0.9599, D(G(z)): 0.1237
Epoch: [6/20], Batch Num: [129/600]
Discriminator Loss: 0.2997, Generator Loss: 5.4243
D(x): 0.9006, D(G(z)): 0.0576
Epoch: [6/20], Batch Num: [130/600]
Discriminator Loss: 0.2283, Generator Loss: 4.9961
D(x): 0.9096, D(G(z)): 0.0429
Epoch: [6/20], Batch Num: [131/600]
Discriminator Loss: 0.1960, Generator Loss: 4.7770
D(x): 0.9316, D(G(z)): 0.0397
Epoch: [6/20], Batch Num: [132/600]
Discriminator Loss: 0.2803, Generator Loss: 4.7940
D(x): 0.9210, D(G(z)): 0.0662
Epoch: [6/20], Batch Num: [133/600]
Discriminator Loss: 0.2520, Generator Loss: 4.1308
D(x): 0.9245, D(G(z)): 0.0548
Epoch: [6/20], Batch Num: [134/600]
Discriminator Loss: 0.1979, Generator Loss: 4.5828
D(x): 0.9824, D(G(z)): 0.1177
Epoch: [6/20], Batch Num: [135/600]
Discriminator Loss: 0.2045, Generator Loss: 5.9850
D(x): 0.9719, D(G(z)): 0.1049
Epoch: [6/20], Batch Num: [136/600]
Discriminator Loss: 0.2374, Generator Loss: 7.2914
D(x): 0.9456, D(G(z)): 0.0595
Epoch: [6/20], Batch Num: [137/600]
Discriminator Loss: 0.1827, Generator Loss: 7.4580
D(x): 0.9160, D(G(z)): 0.0221
Epoch: [6/20], Batch Num: [138/600]
Discriminator Loss: 0.3049, Generator Loss: 5.8011
D(x): 0.8812, D(G(z)): 0.0158
Epoch: [6/20], Batch Num: [139/600]
Discriminator Loss: 0.4127, Generator Loss: 4.2254
D(x): 0.8836, D(G(z)): 0.0381
Epoch: [6/20], Batch Num: [140/600]
Discriminator Loss: 0.2407, Generator Loss: 3.2143
D(x): 0.9694, D(G(z)): 0.1212
Epoch: [6/20], Batch Num: [141/600]
Discriminator Loss: 0.5376, Generator Loss: 4.6535
D(x): 0.9599, D(G(z)): 0.2372
Epoch: [6/20], Batch Num: [142/600]
Discriminator Loss: 0.2920, Generator Loss: 6.2743
D(x): 0.9443, D(G(z)): 0.1077
Epoch: [6/20], Batch Num: [143/600]
Discriminator Loss: 0.2097, Generator Loss: 7.4153
D(x): 0.9388, D(G(z)): 0.0385
Epoch: [6/20], Batch Num: [144/600]
Discriminator Loss: 0.4249, Generator Loss: 6.9240
D(x): 0.8472, D(G(z)): 0.0103
Epoch: [6/20], Batch Num: [145/600]
Discriminator Loss: 0.2788, Generator Loss: 5.9643
D(x): 0.8896, D(G(z)): 0.0203
Epoch: [6/20], Batch Num: [146/600]
Discriminator Loss: 0.1468, Generator Loss: 5.0183
D(x): 0.9405, D(G(z)): 0.0405
Epoch: [6/20], Batch Num: [147/600]
Discriminator Loss: 0.3449, Generator Loss: 3.6563
D(x): 0.9166, D(G(z)): 0.0807
Epoch: [6/20], Batch Num: [148/600]
Discriminator Loss: 0.3422, Generator Loss: 3.3915
D(x): 0.9774, D(G(z)): 0.1717
Epoch: [6/20], Batch Num: [149/600]
Discriminator Loss: 0.3045, Generator Loss: 4.7192
D(x): 0.9600, D(G(z)): 0.1430
Epoch: [6/20], Batch Num: [150/600]
Discriminator Loss: 0.2101, Generator Loss: 5.4423
D(x): 0.9400, D(G(z)): 0.0643
Epoch: [6/20], Batch Num: [151/600]
Discriminator Loss: 0.1830, Generator Loss: 6.7976
D(x): 0.9459, D(G(z)): 0.0414
Epoch: [6/20], Batch Num: [152/600]
Discriminator Loss: 0.2184, Generator Loss: 6.1484
D(x): 0.8981, D(G(z)): 0.0199
Epoch: [6/20], Batch Num: [153/600]
Discriminator Loss: 0.2863, Generator Loss: 4.9309
D(x): 0.8808, D(G(z)): 0.0245
Epoch: [6/20], Batch Num: [154/600]
Discriminator Loss: 0.2536, Generator Loss: 3.7540
D(x): 0.9276, D(G(z)): 0.0737
Epoch: [6/20], Batch Num: [155/600]
Discriminator Loss: 0.2542, Generator Loss: 3.6806
D(x): 0.9495, D(G(z)): 0.1244
Epoch: [6/20], Batch Num: [156/600]
Discriminator Loss: 0.3967, Generator Loss: 3.7998
D(x): 0.9353, D(G(z)): 0.1489
Epoch: [6/20], Batch Num: [157/600]
Discriminator Loss: 0.3875, Generator Loss: 4.5031
D(x): 0.9127, D(G(z)): 0.1299
Epoch: [6/20], Batch Num: [158/600]
Discriminator Loss: 0.4277, Generator Loss: 5.0942
D(x): 0.9273, D(G(z)): 0.0898
Epoch: [6/20], Batch Num: [159/600]
Discriminator Loss: 0.3407, Generator Loss: 4.6182
D(x): 0.8775, D(G(z)): 0.0383
Epoch: [6/20], Batch Num: [160/600]
Discriminator Loss: 0.3455, Generator Loss: 3.6774
D(x): 0.8644, D(G(z)): 0.0530
Epoch: [6/20], Batch Num: [161/600]
Discriminator Loss: 0.3046, Generator Loss: 3.3346
D(x): 0.9512, D(G(z)): 0.1237
Epoch: [6/20], Batch Num: [162/600]
Discriminator Loss: 0.3635, Generator Loss: 3.1653
D(x): 0.9197, D(G(z)): 0.1569
Epoch: [6/20], Batch Num: [163/600]
Discriminator Loss: 0.3699, Generator Loss: 3.6206
D(x): 0.8967, D(G(z)): 0.1054
Epoch: [6/20], Batch Num: [164/600]
Discriminator Loss: 0.2504, Generator Loss: 4.2922
D(x): 0.9523, D(G(z)): 0.1092
Epoch: [6/20], Batch Num: [165/600]
Discriminator Loss: 0.2475, Generator Loss: 4.4565
D(x): 0.9129, D(G(z)): 0.0573
Epoch: [6/20], Batch Num: [166/600]
Discriminator Loss: 0.2597, Generator Loss: 4.2272
D(x): 0.9059, D(G(z)): 0.0418
Epoch: [6/20], Batch Num: [167/600]
Discriminator Loss: 0.3183, Generator Loss: 3.9745
D(x): 0.9100, D(G(z)): 0.0480
Epoch: [6/20], Batch Num: [168/600]
Discriminator Loss: 0.2209, Generator Loss: 3.4525
D(x): 0.9328, D(G(z)): 0.0635
Epoch: [6/20], Batch Num: [169/600]
Discriminator Loss: 0.1575, Generator Loss: 2.9837
D(x): 0.9670, D(G(z)): 0.0763
Epoch: [6/20], Batch Num: [170/600]
Discriminator Loss: 0.2401, Generator Loss: 3.6984
D(x): 0.9735, D(G(z)): 0.1242
Epoch: [6/20], Batch Num: [171/600]
Discriminator Loss: 0.1736, Generator Loss: 4.1828
D(x): 0.9439, D(G(z)): 0.0716
Epoch: [6/20], Batch Num: [172/600]
Discriminator Loss: 0.1218, Generator Loss: 4.4812
D(x): 0.9511, D(G(z)): 0.0402
Epoch: [6/20], Batch Num: [173/600]
Discriminator Loss: 0.1947, Generator Loss: 4.3554
D(x): 0.9358, D(G(z)): 0.0547
Epoch: [6/20], Batch Num: [174/600]
Discriminator Loss: 0.1896, Generator Loss: 4.2747
D(x): 0.9354, D(G(z)): 0.0479
Epoch: [6/20], Batch Num: [175/600]
Discriminator Loss: 0.1980, Generator Loss: 4.4555
D(x): 0.9496, D(G(z)): 0.0627
Epoch: [6/20], Batch Num: [176/600]
Discriminator Loss: 0.2553, Generator Loss: 4.0163
D(x): 0.9224, D(G(z)): 0.0560
Epoch: [6/20], Batch Num: [177/600]
Discriminator Loss: 0.1107, Generator Loss: 4.1601
D(x): 0.9729, D(G(z)): 0.0603
Epoch: [6/20], Batch Num: [178/600]
Discriminator Loss: 0.1625, Generator Loss: 3.8523
D(x): 0.9645, D(G(z)): 0.0595
Epoch: [6/20], Batch Num: [179/600]
Discriminator Loss: 0.1909, Generator Loss: 4.4445
D(x): 0.9774, D(G(z)): 0.1026
Epoch: [6/20], Batch Num: [180/600]
Discriminator Loss: 0.2159, Generator Loss: 4.9186
D(x): 0.9458, D(G(z)): 0.0600
Epoch: [6/20], Batch Num: [181/600]
Discriminator Loss: 0.1573, Generator Loss: 4.7967
D(x): 0.9549, D(G(z)): 0.0510
Epoch: [6/20], Batch Num: [182/600]
Discriminator Loss: 0.1153, Generator Loss: 4.9716
D(x): 0.9448, D(G(z)): 0.0207
Epoch: [6/20], Batch Num: [183/600]
Discriminator Loss: 0.2338, Generator Loss: 4.7086
D(x): 0.9307, D(G(z)): 0.0494
Epoch: [6/20], Batch Num: [184/600]
Discriminator Loss: 0.2852, Generator Loss: 4.3577
D(x): 0.9276, D(G(z)): 0.0467
Epoch: [6/20], Batch Num: [185/600]
Discriminator Loss: 0.1466, Generator Loss: 3.9068
D(x): 0.9664, D(G(z)): 0.0722
Epoch: [6/20], Batch Num: [186/600]
Discriminator Loss: 0.1219, Generator Loss: 4.3671
D(x): 0.9791, D(G(z)): 0.0748
Epoch: [6/20], Batch Num: [187/600]
Discriminator Loss: 0.2370, Generator Loss: 4.4899
D(x): 0.9429, D(G(z)): 0.0851
Epoch: [6/20], Batch Num: [188/600]
Discriminator Loss: 0.1806, Generator Loss: 4.4569
D(x): 0.9327, D(G(z)): 0.0434
Epoch: [6/20], Batch Num: [189/600]
Discriminator Loss: 0.1678, Generator Loss: 4.6624
D(x): 0.9630, D(G(z)): 0.0717
Epoch: [6/20], Batch Num: [190/600]
Discriminator Loss: 0.0878, Generator Loss: 4.9878
D(x): 0.9673, D(G(z)): 0.0395
Epoch: [6/20], Batch Num: [191/600]
Discriminator Loss: 0.2758, Generator Loss: 4.4974
D(x): 0.9105, D(G(z)): 0.0537
Epoch: [6/20], Batch Num: [192/600]
Discriminator Loss: 0.2268, Generator Loss: 4.5075
D(x): 0.9397, D(G(z)): 0.0632
Epoch: [6/20], Batch Num: [193/600]
Discriminator Loss: 0.2491, Generator Loss: 4.2612
D(x): 0.9460, D(G(z)): 0.0987
Epoch: [6/20], Batch Num: [194/600]
Discriminator Loss: 0.1918, Generator Loss: 4.4167
D(x): 0.9410, D(G(z)): 0.0519
Epoch: [6/20], Batch Num: [195/600]
Discriminator Loss: 0.1489, Generator Loss: 4.4424
D(x): 0.9486, D(G(z)): 0.0460
Epoch: [6/20], Batch Num: [196/600]
Discriminator Loss: 0.2612, Generator Loss: 4.3059
D(x): 0.9472, D(G(z)): 0.1041
Epoch: [6/20], Batch Num: [197/600]
Discriminator Loss: 0.2961, Generator Loss: 3.6952
D(x): 0.8976, D(G(z)): 0.0478
Epoch: [6/20], Batch Num: [198/600]
Discriminator Loss: 0.3623, Generator Loss: 4.2621
D(x): 0.9216, D(G(z)): 0.1238
Epoch: [6/20], Batch Num: [199/600]
Discriminator Loss: 0.3151, Generator Loss: 4.2087
D(x): 0.8988, D(G(z)): 0.0731
Epoch: 6, Batch Num: [200/600]

Epoch: [6/20], Batch Num: [200/600]
Discriminator Loss: 0.2522, Generator Loss: 4.1496
D(x): 0.9226, D(G(z)): 0.0597
Epoch: [6/20], Batch Num: [201/600]
Discriminator Loss: 0.2456, Generator Loss: 3.9281
D(x): 0.9289, D(G(z)): 0.0806
Epoch: [6/20], Batch Num: [202/600]
Discriminator Loss: 0.3827, Generator Loss: 4.1224
D(x): 0.9006, D(G(z)): 0.0919
Epoch: [6/20], Batch Num: [203/600]
Discriminator Loss: 0.2875, Generator Loss: 3.6152
D(x): 0.9212, D(G(z)): 0.0791
Epoch: [6/20], Batch Num: [204/600]
Discriminator Loss: 0.3990, Generator Loss: 3.0748
D(x): 0.8931, D(G(z)): 0.0973
Epoch: [6/20], Batch Num: [205/600]
Discriminator Loss: 0.4422, Generator Loss: 3.2154
D(x): 0.9057, D(G(z)): 0.1486
Epoch: [6/20], Batch Num: [206/600]
Discriminator Loss: 0.2950, Generator Loss: 4.1985
D(x): 0.9465, D(G(z)): 0.1197
Epoch: [6/20], Batch Num: [207/600]
Discriminator Loss: 0.3543, Generator Loss: 5.2314
D(x): 0.9143, D(G(z)): 0.1093
Epoch: [6/20], Batch Num: [208/600]
Discriminator Loss: 0.3775, Generator Loss: 4.9692
D(x): 0.8714, D(G(z)): 0.0480
Epoch: [6/20], Batch Num: [209/600]
Discriminator Loss: 0.3995, Generator Loss: 4.2579
D(x): 0.8796, D(G(z)): 0.0288
Epoch: [6/20], Batch Num: [210/600]
Discriminator Loss: 0.3207, Generator Loss: 3.2005
D(x): 0.8805, D(G(z)): 0.0603
Epoch: [6/20], Batch Num: [211/600]
Discriminator Loss: 0.4219, Generator Loss: 3.1945
D(x): 0.9337, D(G(z)): 0.1934
Epoch: [6/20], Batch Num: [212/600]
Discriminator Loss: 0.3765, Generator Loss: 3.9637
D(x): 0.9491, D(G(z)): 0.1693
Epoch: [6/20], Batch Num: [213/600]
Discriminator Loss: 0.3783, Generator Loss: 4.0734
D(x): 0.8630, D(G(z)): 0.0722
Epoch: [6/20], Batch Num: [214/600]
Discriminator Loss: 0.4271, Generator Loss: 4.3684
D(x): 0.8404, D(G(z)): 0.0636
Epoch: [6/20], Batch Num: [215/600]
Discriminator Loss: 0.4504, Generator Loss: 4.0334
D(x): 0.8759, D(G(z)): 0.1119
Epoch: [6/20], Batch Num: [216/600]
Discriminator Loss: 0.3113, Generator Loss: 3.3938
D(x): 0.8898, D(G(z)): 0.0901
Epoch: [6/20], Batch Num: [217/600]
Discriminator Loss: 0.3005, Generator Loss: 3.1432
D(x): 0.9340, D(G(z)): 0.1267
Epoch: [6/20], Batch Num: [218/600]
Discriminator Loss: 0.4467, Generator Loss: 3.5842
D(x): 0.9019, D(G(z)): 0.1398
Epoch: [6/20], Batch Num: [219/600]
Discriminator Loss: 0.3927, Generator Loss: 4.2068
D(x): 0.9162, D(G(z)): 0.1466
Epoch: [6/20], Batch Num: [220/600]
Discriminator Loss: 0.2744, Generator Loss: 4.0353
D(x): 0.8876, D(G(z)): 0.0457
Epoch: [6/20], Batch Num: [221/600]
Discriminator Loss: 0.3389, Generator Loss: 4.1212
D(x): 0.8947, D(G(z)): 0.0832
Epoch: [6/20], Batch Num: [222/600]
Discriminator Loss: 0.4462, Generator Loss: 3.3988
D(x): 0.8408, D(G(z)): 0.0781
Epoch: [6/20], Batch Num: [223/600]
Discriminator Loss: 0.4918, Generator Loss: 2.6959
D(x): 0.8655, D(G(z)): 0.1409
Epoch: [6/20], Batch Num: [224/600]
Discriminator Loss: 0.5047, Generator Loss: 2.7127
D(x): 0.9055, D(G(z)): 0.1976
Epoch: [6/20], Batch Num: [225/600]
Discriminator Loss: 0.3736, Generator Loss: 3.4311
D(x): 0.9242, D(G(z)): 0.1665
Epoch: [6/20], Batch Num: [226/600]
Discriminator Loss: 0.3632, Generator Loss: 3.8947
D(x): 0.9039, D(G(z)): 0.0961
Epoch: [6/20], Batch Num: [227/600]
Discriminator Loss: 0.3854, Generator Loss: 4.2335
D(x): 0.8650, D(G(z)): 0.0571
Epoch: [6/20], Batch Num: [228/600]
Discriminator Loss: 0.3316, Generator Loss: 4.1679
D(x): 0.8720, D(G(z)): 0.0644
Epoch: [6/20], Batch Num: [229/600]
Discriminator Loss: 0.2960, Generator Loss: 3.6058
D(x): 0.9009, D(G(z)): 0.0777
Epoch: [6/20], Batch Num: [230/600]
Discriminator Loss: 0.3537, Generator Loss: 3.1395
D(x): 0.8788, D(G(z)): 0.0878
Epoch: [6/20], Batch Num: [231/600]
Discriminator Loss: 0.4791, Generator Loss: 3.1869
D(x): 0.8993, D(G(z)): 0.1602
Epoch: [6/20], Batch Num: [232/600]
Discriminator Loss: 0.5215, Generator Loss: 3.4593
D(x): 0.9252, D(G(z)): 0.2096
Epoch: [6/20], Batch Num: [233/600]
Discriminator Loss: 0.3861, Generator Loss: 4.1325
D(x): 0.9030, D(G(z)): 0.1459
Epoch: [6/20], Batch Num: [234/600]
Discriminator Loss: 0.3371, Generator Loss: 4.5853
D(x): 0.8785, D(G(z)): 0.0595
Epoch: [6/20], Batch Num: [235/600]
Discriminator Loss: 0.3450, Generator Loss: 4.7635
D(x): 0.8721, D(G(z)): 0.0787
Epoch: [6/20], Batch Num: [236/600]
Discriminator Loss: 0.3469, Generator Loss: 3.8041
D(x): 0.8556, D(G(z)): 0.0491
Epoch: [6/20], Batch Num: [237/600]
Discriminator Loss: 0.3931, Generator Loss: 3.3807
D(x): 0.8742, D(G(z)): 0.0901
Epoch: [6/20], Batch Num: [238/600]
Discriminator Loss: 0.6056, Generator Loss: 3.4494
D(x): 0.9625, D(G(z)): 0.2448
Epoch: [6/20], Batch Num: [239/600]
Discriminator Loss: 0.4742, Generator Loss: 4.4301
D(x): 0.9311, D(G(z)): 0.1411
Epoch: [6/20], Batch Num: [240/600]
Discriminator Loss: 0.3695, Generator Loss: 5.4031
D(x): 0.9102, D(G(z)): 0.1252
Epoch: [6/20], Batch Num: [241/600]
Discriminator Loss: 0.5789, Generator Loss: 5.3546
D(x): 0.8082, D(G(z)): 0.1046
Epoch: [6/20], Batch Num: [242/600]
Discriminator Loss: 0.6010, Generator Loss: 4.6888
D(x): 0.7429, D(G(z)): 0.0358
Epoch: [6/20], Batch Num: [243/600]
Discriminator Loss: 0.3899, Generator Loss: 3.8530
D(x): 0.8378, D(G(z)): 0.0703
Epoch: [6/20], Batch Num: [244/600]
Discriminator Loss: 0.4292, Generator Loss: 2.8947
D(x): 0.8939, D(G(z)): 0.1547
Epoch: [6/20], Batch Num: [245/600]
Discriminator Loss: 0.5641, Generator Loss: 2.9749
D(x): 0.9192, D(G(z)): 0.2784
Epoch: [6/20], Batch Num: [246/600]
Discriminator Loss: 0.4027, Generator Loss: 3.5064
D(x): 0.9058, D(G(z)): 0.1814
Epoch: [6/20], Batch Num: [247/600]
Discriminator Loss: 0.3237, Generator Loss: 4.2166
D(x): 0.8703, D(G(z)): 0.0937
Epoch: [6/20], Batch Num: [248/600]
Discriminator Loss: 0.4527, Generator Loss: 3.9139
D(x): 0.8010, D(G(z)): 0.0665
Epoch: [6/20], Batch Num: [249/600]
Discriminator Loss: 0.3517, Generator Loss: 3.3410
D(x): 0.8455, D(G(z)): 0.0603
Epoch: [6/20], Batch Num: [250/600]
Discriminator Loss: 0.3970, Generator Loss: 3.0186
D(x): 0.8280, D(G(z)): 0.1008
Epoch: [6/20], Batch Num: [251/600]
Discriminator Loss: 0.4289, Generator Loss: 2.6208
D(x): 0.8842, D(G(z)): 0.1660
Epoch: [6/20], Batch Num: [252/600]
Discriminator Loss: 0.4718, Generator Loss: 3.1772
D(x): 0.8993, D(G(z)): 0.2123
Epoch: [6/20], Batch Num: [253/600]
Discriminator Loss: 0.5216, Generator Loss: 3.1968
D(x): 0.8398, D(G(z)): 0.1691
Epoch: [6/20], Batch Num: [254/600]
Discriminator Loss: 0.3570, Generator Loss: 3.4094
D(x): 0.8877, D(G(z)): 0.1410
Epoch: [6/20], Batch Num: [255/600]
Discriminator Loss: 0.3633, Generator Loss: 3.6083
D(x): 0.8574, D(G(z)): 0.0997
Epoch: [6/20], Batch Num: [256/600]
Discriminator Loss: 0.4244, Generator Loss: 3.5507
D(x): 0.8282, D(G(z)): 0.1031
Epoch: [6/20], Batch Num: [257/600]
Discriminator Loss: 0.3829, Generator Loss: 3.0242
D(x): 0.8713, D(G(z)): 0.0971
Epoch: [6/20], Batch Num: [258/600]
Discriminator Loss: 0.3639, Generator Loss: 2.8365
D(x): 0.9234, D(G(z)): 0.1703
Epoch: [6/20], Batch Num: [259/600]
Discriminator Loss: 0.3269, Generator Loss: 3.4741
D(x): 0.9027, D(G(z)): 0.1472
Epoch: [6/20], Batch Num: [260/600]
Discriminator Loss: 0.3269, Generator Loss: 3.4180
D(x): 0.8847, D(G(z)): 0.0850
Epoch: [6/20], Batch Num: [261/600]
Discriminator Loss: 0.2086, Generator Loss: 3.4929
D(x): 0.9394, D(G(z)): 0.0971
Epoch: [6/20], Batch Num: [262/600]
Discriminator Loss: 0.3029, Generator Loss: 3.6556
D(x): 0.8990, D(G(z)): 0.0841
Epoch: [6/20], Batch Num: [263/600]
Discriminator Loss: 0.3349, Generator Loss: 3.4961
D(x): 0.8756, D(G(z)): 0.0768
Epoch: [6/20], Batch Num: [264/600]
Discriminator Loss: 0.2814, Generator Loss: 3.4036
D(x): 0.9187, D(G(z)): 0.0970
Epoch: [6/20], Batch Num: [265/600]
Discriminator Loss: 0.3273, Generator Loss: 3.3547
D(x): 0.8987, D(G(z)): 0.1227
Epoch: [6/20], Batch Num: [266/600]
Discriminator Loss: 0.3652, Generator Loss: 2.9834
D(x): 0.8799, D(G(z)): 0.0937
Epoch: [6/20], Batch Num: [267/600]
Discriminator Loss: 0.2737, Generator Loss: 3.2790
D(x): 0.9338, D(G(z)): 0.1334
Epoch: [6/20], Batch Num: [268/600]
Discriminator Loss: 0.4311, Generator Loss: 3.6124
D(x): 0.9043, D(G(z)): 0.1299
Epoch: [6/20], Batch Num: [269/600]
Discriminator Loss: 0.3789, Generator Loss: 3.9602
D(x): 0.8722, D(G(z)): 0.0850
Epoch: [6/20], Batch Num: [270/600]
Discriminator Loss: 0.5174, Generator Loss: 3.3772
D(x): 0.8327, D(G(z)): 0.0860
Epoch: [6/20], Batch Num: [271/600]
Discriminator Loss: 0.2315, Generator Loss: 3.3004
D(x): 0.9416, D(G(z)): 0.0957
Epoch: [6/20], Batch Num: [272/600]
Discriminator Loss: 0.2857, Generator Loss: 3.3631
D(x): 0.9594, D(G(z)): 0.1289
Epoch: [6/20], Batch Num: [273/600]
Discriminator Loss: 0.2602, Generator Loss: 4.0733
D(x): 0.9366, D(G(z)): 0.0758
Epoch: [6/20], Batch Num: [274/600]
Discriminator Loss: 0.1902, Generator Loss: 4.3045
D(x): 0.9322, D(G(z)): 0.0581
Epoch: [6/20], Batch Num: [275/600]
Discriminator Loss: 0.2374, Generator Loss: 4.6223
D(x): 0.9251, D(G(z)): 0.0311
Epoch: [6/20], Batch Num: [276/600]
Discriminator Loss: 0.2607, Generator Loss: 3.7930
D(x): 0.9065, D(G(z)): 0.0377
Epoch: [6/20], Batch Num: [277/600]
Discriminator Loss: 0.2781, Generator Loss: 3.4439
D(x): 0.9156, D(G(z)): 0.0795
Epoch: [6/20], Batch Num: [278/600]
Discriminator Loss: 0.1873, Generator Loss: 3.5317
D(x): 0.9524, D(G(z)): 0.0680
Epoch: [6/20], Batch Num: [279/600]
Discriminator Loss: 0.2201, Generator Loss: 3.8061
D(x): 0.9595, D(G(z)): 0.1163
Epoch: [6/20], Batch Num: [280/600]
Discriminator Loss: 0.2291, Generator Loss: 4.5730
D(x): 0.9420, D(G(z)): 0.0985
Epoch: [6/20], Batch Num: [281/600]
Discriminator Loss: 0.1028, Generator Loss: 4.8677
D(x): 0.9510, D(G(z)): 0.0289
Epoch: [6/20], Batch Num: [282/600]
Discriminator Loss: 0.1527, Generator Loss: 5.1242
D(x): 0.9344, D(G(z)): 0.0170
Epoch: [6/20], Batch Num: [283/600]
Discriminator Loss: 0.2028, Generator Loss: 4.4622
D(x): 0.9225, D(G(z)): 0.0192
Epoch: [6/20], Batch Num: [284/600]
Discriminator Loss: 0.1796, Generator Loss: 3.5989
D(x): 0.9339, D(G(z)): 0.0537
Epoch: [6/20], Batch Num: [285/600]
Discriminator Loss: 0.1853, Generator Loss: 3.5351
D(x): 0.9625, D(G(z)): 0.0924
Epoch: [6/20], Batch Num: [286/600]
Discriminator Loss: 0.2394, Generator Loss: 4.4125
D(x): 0.9719, D(G(z)): 0.0929
Epoch: [6/20], Batch Num: [287/600]
Discriminator Loss: 0.1297, Generator Loss: 5.2757
D(x): 0.9563, D(G(z)): 0.0503
Epoch: [6/20], Batch Num: [288/600]
Discriminator Loss: 0.3326, Generator Loss: 4.7810
D(x): 0.8914, D(G(z)): 0.0280
Epoch: [6/20], Batch Num: [289/600]
Discriminator Loss: 0.1708, Generator Loss: 4.6119
D(x): 0.9560, D(G(z)): 0.0345
Epoch: [6/20], Batch Num: [290/600]
Discriminator Loss: 0.2066, Generator Loss: 4.0964
D(x): 0.9414, D(G(z)): 0.0452
Epoch: [6/20], Batch Num: [291/600]
Discriminator Loss: 0.1623, Generator Loss: 4.1183
D(x): 0.9608, D(G(z)): 0.0729
Epoch: [6/20], Batch Num: [292/600]
Discriminator Loss: 0.1918, Generator Loss: 4.5947
D(x): 0.9543, D(G(z)): 0.0763
Epoch: [6/20], Batch Num: [293/600]
Discriminator Loss: 0.1786, Generator Loss: 4.8194
D(x): 0.9599, D(G(z)): 0.0670
Epoch: [6/20], Batch Num: [294/600]
Discriminator Loss: 0.0960, Generator Loss: 4.9858
D(x): 0.9665, D(G(z)): 0.0418
Epoch: [6/20], Batch Num: [295/600]
Discriminator Loss: 0.2548, Generator Loss: 4.8636
D(x): 0.9166, D(G(z)): 0.0446
Epoch: [6/20], Batch Num: [296/600]
Discriminator Loss: 0.1646, Generator Loss: 4.6252
D(x): 0.9595, D(G(z)): 0.0501
Epoch: [6/20], Batch Num: [297/600]
Discriminator Loss: 0.1773, Generator Loss: 4.4711
D(x): 0.9448, D(G(z)): 0.0442
Epoch: [6/20], Batch Num: [298/600]
Discriminator Loss: 0.1858, Generator Loss: 4.3005
D(x): 0.9456, D(G(z)): 0.0567
Epoch: [6/20], Batch Num: [299/600]
Discriminator Loss: 0.2164, Generator Loss: 4.0437
D(x): 0.9461, D(G(z)): 0.0715
Epoch: 6, Batch Num: [300/600]

Epoch: [6/20], Batch Num: [300/600]
Discriminator Loss: 0.2881, Generator Loss: 4.5919
D(x): 0.9248, D(G(z)): 0.0773
Epoch: [6/20], Batch Num: [301/600]
Discriminator Loss: 0.4075, Generator Loss: 4.9760
D(x): 0.9016, D(G(z)): 0.0986
Epoch: [6/20], Batch Num: [302/600]
Discriminator Loss: 0.3742, Generator Loss: 4.6317
D(x): 0.9168, D(G(z)): 0.0797
Epoch: [6/20], Batch Num: [303/600]
Discriminator Loss: 0.1683, Generator Loss: 4.5116
D(x): 0.9557, D(G(z)): 0.0506
Epoch: [6/20], Batch Num: [304/600]
Discriminator Loss: 0.1489, Generator Loss: 4.1382
D(x): 0.9508, D(G(z)): 0.0535
Epoch: [6/20], Batch Num: [305/600]
Discriminator Loss: 0.4923, Generator Loss: 4.4143
D(x): 0.9337, D(G(z)): 0.1477
Epoch: [6/20], Batch Num: [306/600]
Discriminator Loss: 0.4411, Generator Loss: 4.5950
D(x): 0.8955, D(G(z)): 0.0828
Epoch: [6/20], Batch Num: [307/600]
Discriminator Loss: 0.3816, Generator Loss: 3.9210
D(x): 0.8797, D(G(z)): 0.0540
Epoch: [6/20], Batch Num: [308/600]
Discriminator Loss: 0.3309, Generator Loss: 4.0993
D(x): 0.9478, D(G(z)): 0.1193
Epoch: [6/20], Batch Num: [309/600]
Discriminator Loss: 0.2601, Generator Loss: 4.4921
D(x): 0.9562, D(G(z)): 0.1115
Epoch: [6/20], Batch Num: [310/600]
Discriminator Loss: 0.2567, Generator Loss: 4.8331
D(x): 0.9198, D(G(z)): 0.0786
Epoch: [6/20], Batch Num: [311/600]
Discriminator Loss: 0.2311, Generator Loss: 5.2070
D(x): 0.9294, D(G(z)): 0.0533
Epoch: [6/20], Batch Num: [312/600]
Discriminator Loss: 0.4095, Generator Loss: 4.3422
D(x): 0.8422, D(G(z)): 0.0468
Epoch: [6/20], Batch Num: [313/600]
Discriminator Loss: 0.3387, Generator Loss: 4.2025
D(x): 0.9265, D(G(z)): 0.1122
Epoch: [6/20], Batch Num: [314/600]
Discriminator Loss: 0.2926, Generator Loss: 4.1055
D(x): 0.9556, D(G(z)): 0.1452
Epoch: [6/20], Batch Num: [315/600]
Discriminator Loss: 0.3255, Generator Loss: 4.5782
D(x): 0.9145, D(G(z)): 0.1117
Epoch: [6/20], Batch Num: [316/600]
Discriminator Loss: 0.4853, Generator Loss: 4.1770
D(x): 0.8895, D(G(z)): 0.1073
Epoch: [6/20], Batch Num: [317/600]
Discriminator Loss: 0.3566, Generator Loss: 4.6752
D(x): 0.8873, D(G(z)): 0.0838
Epoch: [6/20], Batch Num: [318/600]
Discriminator Loss: 0.2638, Generator Loss: 4.6599
D(x): 0.9255, D(G(z)): 0.0956
Epoch: [6/20], Batch Num: [319/600]
Discriminator Loss: 0.3502, Generator Loss: 3.7280
D(x): 0.8915, D(G(z)): 0.0626
Epoch: [6/20], Batch Num: [320/600]
Discriminator Loss: 0.3457, Generator Loss: 4.3144
D(x): 0.9055, D(G(z)): 0.1233
Epoch: [6/20], Batch Num: [321/600]
Discriminator Loss: 0.4030, Generator Loss: 4.4997
D(x): 0.9186, D(G(z)): 0.1467
Epoch: [6/20], Batch Num: [322/600]
Discriminator Loss: 0.2448, Generator Loss: 3.8746
D(x): 0.9317, D(G(z)): 0.1004
Epoch: [6/20], Batch Num: [323/600]
Discriminator Loss: 0.3460, Generator Loss: 4.8614
D(x): 0.8912, D(G(z)): 0.1156
Epoch: [6/20], Batch Num: [324/600]
Discriminator Loss: 0.2669, Generator Loss: 4.2966
D(x): 0.8849, D(G(z)): 0.0463
Epoch: [6/20], Batch Num: [325/600]
Discriminator Loss: 0.2590, Generator Loss: 4.5400
D(x): 0.9042, D(G(z)): 0.0771
Epoch: [6/20], Batch Num: [326/600]
Discriminator Loss: 0.2582, Generator Loss: 4.2045
D(x): 0.9334, D(G(z)): 0.1027
Epoch: [6/20], Batch Num: [327/600]
Discriminator Loss: 0.3202, Generator Loss: 4.4544
D(x): 0.9536, D(G(z)): 0.1391
Epoch: [6/20], Batch Num: [328/600]
Discriminator Loss: 0.2948, Generator Loss: 4.9455
D(x): 0.8954, D(G(z)): 0.0668
Epoch: [6/20], Batch Num: [329/600]
Discriminator Loss: 0.2800, Generator Loss: 4.5657
D(x): 0.8905, D(G(z)): 0.0568
Epoch: [6/20], Batch Num: [330/600]
Discriminator Loss: 0.3749, Generator Loss: 4.3646
D(x): 0.8882, D(G(z)): 0.0685
Epoch: [6/20], Batch Num: [331/600]
Discriminator Loss: 0.3614, Generator Loss: 3.3777
D(x): 0.9082, D(G(z)): 0.1163
Epoch: [6/20], Batch Num: [332/600]
Discriminator Loss: 0.3451, Generator Loss: 3.9279
D(x): 0.9245, D(G(z)): 0.1507
Epoch: [6/20], Batch Num: [333/600]
Discriminator Loss: 0.2268, Generator Loss: 4.4624
D(x): 0.9323, D(G(z)): 0.0783
Epoch: [6/20], Batch Num: [334/600]
Discriminator Loss: 0.2188, Generator Loss: 4.4718
D(x): 0.9114, D(G(z)): 0.0649
Epoch: [6/20], Batch Num: [335/600]
Discriminator Loss: 0.2934, Generator Loss: 4.1382
D(x): 0.9005, D(G(z)): 0.0636
Epoch: [6/20], Batch Num: [336/600]
Discriminator Loss: 0.2688, Generator Loss: 3.7454
D(x): 0.9032, D(G(z)): 0.0798
Epoch: [6/20], Batch Num: [337/600]
Discriminator Loss: 0.2084, Generator Loss: 4.1306
D(x): 0.9574, D(G(z)): 0.1012
Epoch: [6/20], Batch Num: [338/600]
Discriminator Loss: 0.1711, Generator Loss: 4.8460
D(x): 0.9576, D(G(z)): 0.0832
Epoch: [6/20], Batch Num: [339/600]
Discriminator Loss: 0.3315, Generator Loss: 4.4310
D(x): 0.8842, D(G(z)): 0.0788
Epoch: [6/20], Batch Num: [340/600]
Discriminator Loss: 0.3255, Generator Loss: 3.5814
D(x): 0.8827, D(G(z)): 0.0600
Epoch: [6/20], Batch Num: [341/600]
Discriminator Loss: 0.2440, Generator Loss: 3.6630
D(x): 0.9434, D(G(z)): 0.1048
Epoch: [6/20], Batch Num: [342/600]
Discriminator Loss: 0.1793, Generator Loss: 4.5273
D(x): 0.9701, D(G(z)): 0.1164
Epoch: [6/20], Batch Num: [343/600]
Discriminator Loss: 0.1792, Generator Loss: 5.8740
D(x): 0.9459, D(G(z)): 0.0675
Epoch: [6/20], Batch Num: [344/600]
Discriminator Loss: 0.3641, Generator Loss: 4.8433
D(x): 0.8766, D(G(z)): 0.0365
Epoch: [6/20], Batch Num: [345/600]
Discriminator Loss: 0.3081, Generator Loss: 3.6633
D(x): 0.8958, D(G(z)): 0.0660
Epoch: [6/20], Batch Num: [346/600]
Discriminator Loss: 0.3406, Generator Loss: 3.5325
D(x): 0.9371, D(G(z)): 0.1528
Epoch: [6/20], Batch Num: [347/600]
Discriminator Loss: 0.3650, Generator Loss: 4.6483
D(x): 0.9368, D(G(z)): 0.1443
Epoch: [6/20], Batch Num: [348/600]
Discriminator Loss: 0.2042, Generator Loss: 5.0505
D(x): 0.9238, D(G(z)): 0.0471
Epoch: [6/20], Batch Num: [349/600]
Discriminator Loss: 0.2378, Generator Loss: 4.1623
D(x): 0.8981, D(G(z)): 0.0410
Epoch: [6/20], Batch Num: [350/600]
Discriminator Loss: 0.2289, Generator Loss: 4.0655
D(x): 0.9409, D(G(z)): 0.0674
Epoch: [6/20], Batch Num: [351/600]
Discriminator Loss: 0.2275, Generator Loss: 3.4068
D(x): 0.9272, D(G(z)): 0.0768
Epoch: [6/20], Batch Num: [352/600]
Discriminator Loss: 0.4502, Generator Loss: 4.1963
D(x): 0.9338, D(G(z)): 0.1573
Epoch: [6/20], Batch Num: [353/600]
Discriminator Loss: 0.3220, Generator Loss: 4.5719
D(x): 0.9169, D(G(z)): 0.0689
Epoch: [6/20], Batch Num: [354/600]
Discriminator Loss: 0.2051, Generator Loss: 4.5463
D(x): 0.9203, D(G(z)): 0.0298
Epoch: [6/20], Batch Num: [355/600]
Discriminator Loss: 0.2218, Generator Loss: 3.7746
D(x): 0.9088, D(G(z)): 0.0354
Epoch: [6/20], Batch Num: [356/600]
Discriminator Loss: 0.2207, Generator Loss: 3.0638
D(x): 0.9297, D(G(z)): 0.0796
Epoch: [6/20], Batch Num: [357/600]
Discriminator Loss: 0.2130, Generator Loss: 3.3467
D(x): 0.9691, D(G(z)): 0.1187
Epoch: [6/20], Batch Num: [358/600]
Discriminator Loss: 0.2679, Generator Loss: 4.8449
D(x): 0.9602, D(G(z)): 0.1310
Epoch: [6/20], Batch Num: [359/600]
Discriminator Loss: 0.2079, Generator Loss: 5.0020
D(x): 0.9183, D(G(z)): 0.0503
Epoch: [6/20], Batch Num: [360/600]
Discriminator Loss: 0.2907, Generator Loss: 4.4907
D(x): 0.8842, D(G(z)): 0.0342
Epoch: [6/20], Batch Num: [361/600]
Discriminator Loss: 0.1741, Generator Loss: 3.9556
D(x): 0.9412, D(G(z)): 0.0577
Epoch: [6/20], Batch Num: [362/600]
Discriminator Loss: 0.3526, Generator Loss: 3.5879
D(x): 0.9169, D(G(z)): 0.0716
Epoch: [6/20], Batch Num: [363/600]
Discriminator Loss: 0.2573, Generator Loss: 3.3338
D(x): 0.9317, D(G(z)): 0.0889
Epoch: [6/20], Batch Num: [364/600]
Discriminator Loss: 0.3612, Generator Loss: 3.5875
D(x): 0.9229, D(G(z)): 0.1366
Epoch: [6/20], Batch Num: [365/600]
Discriminator Loss: 0.2099, Generator Loss: 4.5699
D(x): 0.9660, D(G(z)): 0.0840
Epoch: [6/20], Batch Num: [366/600]
Discriminator Loss: 0.1377, Generator Loss: 5.2374
D(x): 0.9482, D(G(z)): 0.0418
Epoch: [6/20], Batch Num: [367/600]
Discriminator Loss: 0.2015, Generator Loss: 5.1239
D(x): 0.9155, D(G(z)): 0.0443
Epoch: [6/20], Batch Num: [368/600]
Discriminator Loss: 0.3734, Generator Loss: 4.1877
D(x): 0.8901, D(G(z)): 0.0336
Epoch: [6/20], Batch Num: [369/600]
Discriminator Loss: 0.2808, Generator Loss: 3.6560
D(x): 0.9167, D(G(z)): 0.0557
Epoch: [6/20], Batch Num: [370/600]
Discriminator Loss: 0.3291, Generator Loss: 3.2927
D(x): 0.9426, D(G(z)): 0.1092
Epoch: [6/20], Batch Num: [371/600]
Discriminator Loss: 0.2281, Generator Loss: 3.6336
D(x): 0.9569, D(G(z)): 0.1185
Epoch: [6/20], Batch Num: [372/600]
Discriminator Loss: 0.3421, Generator Loss: 4.1770
D(x): 0.9214, D(G(z)): 0.0945
Epoch: [6/20], Batch Num: [373/600]
Discriminator Loss: 0.2320, Generator Loss: 4.2700
D(x): 0.9299, D(G(z)): 0.0616
Epoch: [6/20], Batch Num: [374/600]
Discriminator Loss: 0.2372, Generator Loss: 4.4154
D(x): 0.9148, D(G(z)): 0.0463
Epoch: [6/20], Batch Num: [375/600]
Discriminator Loss: 0.3220, Generator Loss: 3.6841
D(x): 0.8717, D(G(z)): 0.0561
Epoch: [6/20], Batch Num: [376/600]
Discriminator Loss: 0.2251, Generator Loss: 3.6092
D(x): 0.9498, D(G(z)): 0.0823
Epoch: [6/20], Batch Num: [377/600]
Discriminator Loss: 0.2410, Generator Loss: 3.8351
D(x): 0.9537, D(G(z)): 0.1075
Epoch: [6/20], Batch Num: [378/600]
Discriminator Loss: 0.2337, Generator Loss: 4.0966
D(x): 0.9411, D(G(z)): 0.0738
Epoch: [6/20], Batch Num: [379/600]
Discriminator Loss: 0.2343, Generator Loss: 4.1652
D(x): 0.9027, D(G(z)): 0.0482
Epoch: [6/20], Batch Num: [380/600]
Discriminator Loss: 0.3128, Generator Loss: 3.9147
D(x): 0.9158, D(G(z)): 0.0793
Epoch: [6/20], Batch Num: [381/600]
Discriminator Loss: 0.2430, Generator Loss: 3.6687
D(x): 0.9373, D(G(z)): 0.0604
Epoch: [6/20], Batch Num: [382/600]
Discriminator Loss: 0.2442, Generator Loss: 3.7026
D(x): 0.9275, D(G(z)): 0.0857
Epoch: [6/20], Batch Num: [383/600]
Discriminator Loss: 0.1681, Generator Loss: 4.3130
D(x): 0.9513, D(G(z)): 0.0601
Epoch: [6/20], Batch Num: [384/600]
Discriminator Loss: 0.1488, Generator Loss: 3.9498
D(x): 0.9644, D(G(z)): 0.0689
Epoch: [6/20], Batch Num: [385/600]
Discriminator Loss: 0.1225, Generator Loss: 4.1571
D(x): 0.9538, D(G(z)): 0.0481
Epoch: [6/20], Batch Num: [386/600]
Discriminator Loss: 0.1099, Generator Loss: 4.0168
D(x): 0.9580, D(G(z)): 0.0422
Epoch: [6/20], Batch Num: [387/600]
Discriminator Loss: 0.1589, Generator Loss: 4.0980
D(x): 0.9653, D(G(z)): 0.0602
Epoch: [6/20], Batch Num: [388/600]
Discriminator Loss: 0.2178, Generator Loss: 4.1623
D(x): 0.9313, D(G(z)): 0.0410
Epoch: [6/20], Batch Num: [389/600]
Discriminator Loss: 0.2551, Generator Loss: 3.7512
D(x): 0.9255, D(G(z)): 0.0611
Epoch: [6/20], Batch Num: [390/600]
Discriminator Loss: 0.1915, Generator Loss: 3.5106
D(x): 0.9433, D(G(z)): 0.0679
Epoch: [6/20], Batch Num: [391/600]
Discriminator Loss: 0.1386, Generator Loss: 3.6357
D(x): 0.9784, D(G(z)): 0.0799
Epoch: [6/20], Batch Num: [392/600]
Discriminator Loss: 0.1211, Generator Loss: 4.0926
D(x): 0.9802, D(G(z)): 0.0721
Epoch: [6/20], Batch Num: [393/600]
Discriminator Loss: 0.2333, Generator Loss: 4.4890
D(x): 0.9400, D(G(z)): 0.0440
Epoch: [6/20], Batch Num: [394/600]
Discriminator Loss: 0.2409, Generator Loss: 4.3929
D(x): 0.9183, D(G(z)): 0.0419
Epoch: [6/20], Batch Num: [395/600]
Discriminator Loss: 0.1389, Generator Loss: 4.4886
D(x): 0.9746, D(G(z)): 0.0602
Epoch: [6/20], Batch Num: [396/600]
Discriminator Loss: 0.2068, Generator Loss: 4.5245
D(x): 0.9413, D(G(z)): 0.0413
Epoch: [6/20], Batch Num: [397/600]
Discriminator Loss: 0.2019, Generator Loss: 4.5340
D(x): 0.9318, D(G(z)): 0.0369
Epoch: [6/20], Batch Num: [398/600]
Discriminator Loss: 0.1873, Generator Loss: 3.6374
D(x): 0.9625, D(G(z)): 0.0649
Epoch: [6/20], Batch Num: [399/600]
Discriminator Loss: 0.1749, Generator Loss: 3.6931
D(x): 0.9473, D(G(z)): 0.0468
Epoch: 6, Batch Num: [400/600]

Epoch: [6/20], Batch Num: [400/600]
Discriminator Loss: 0.2018, Generator Loss: 3.4410
D(x): 0.9543, D(G(z)): 0.0697
Epoch: [6/20], Batch Num: [401/600]
Discriminator Loss: 0.2158, Generator Loss: 3.8953
D(x): 0.9536, D(G(z)): 0.0990
Epoch: [6/20], Batch Num: [402/600]
Discriminator Loss: 0.1751, Generator Loss: 4.5176
D(x): 0.9570, D(G(z)): 0.0583
Epoch: [6/20], Batch Num: [403/600]
Discriminator Loss: 0.3968, Generator Loss: 4.6974
D(x): 0.9199, D(G(z)): 0.0719
Epoch: [6/20], Batch Num: [404/600]
Discriminator Loss: 0.2221, Generator Loss: 4.6813
D(x): 0.9209, D(G(z)): 0.0479
Epoch: [6/20], Batch Num: [405/600]
Discriminator Loss: 0.1977, Generator Loss: 4.4560
D(x): 0.9379, D(G(z)): 0.0649
Epoch: [6/20], Batch Num: [406/600]
Discriminator Loss: 0.1591, Generator Loss: 4.0833
D(x): 0.9537, D(G(z)): 0.0573
Epoch: [6/20], Batch Num: [407/600]
Discriminator Loss: 0.3154, Generator Loss: 3.6429
D(x): 0.9180, D(G(z)): 0.0885
Epoch: [6/20], Batch Num: [408/600]
Discriminator Loss: 0.4183, Generator Loss: 3.8143
D(x): 0.9474, D(G(z)): 0.1064
Epoch: [6/20], Batch Num: [409/600]
Discriminator Loss: 0.1377, Generator Loss: 4.2119
D(x): 0.9644, D(G(z)): 0.0726
Epoch: [6/20], Batch Num: [410/600]
Discriminator Loss: 0.3279, Generator Loss: 4.0539
D(x): 0.9088, D(G(z)): 0.0582
Epoch: [6/20], Batch Num: [411/600]
Discriminator Loss: 0.2511, Generator Loss: 4.3944
D(x): 0.9267, D(G(z)): 0.0685
Epoch: [6/20], Batch Num: [412/600]
Discriminator Loss: 0.1883, Generator Loss: 4.4250
D(x): 0.9560, D(G(z)): 0.0608
Epoch: [6/20], Batch Num: [413/600]
Discriminator Loss: 0.2985, Generator Loss: 4.6243
D(x): 0.9142, D(G(z)): 0.0671
Epoch: [6/20], Batch Num: [414/600]
Discriminator Loss: 0.2846, Generator Loss: 4.1415
D(x): 0.9184, D(G(z)): 0.0510
Epoch: [6/20], Batch Num: [415/600]
Discriminator Loss: 0.2140, Generator Loss: 3.9270
D(x): 0.9450, D(G(z)): 0.0566
Epoch: [6/20], Batch Num: [416/600]
Discriminator Loss: 0.3195, Generator Loss: 4.0052
D(x): 0.9277, D(G(z)): 0.0999
Epoch: [6/20], Batch Num: [417/600]
Discriminator Loss: 0.3965, Generator Loss: 3.9051
D(x): 0.8986, D(G(z)): 0.0916
Epoch: [6/20], Batch Num: [418/600]
Discriminator Loss: 0.2563, Generator Loss: 4.1033
D(x): 0.9649, D(G(z)): 0.1052
Epoch: [6/20], Batch Num: [419/600]
Discriminator Loss: 0.2830, Generator Loss: 4.6785
D(x): 0.8941, D(G(z)): 0.0634
Epoch: [6/20], Batch Num: [420/600]
Discriminator Loss: 0.1794, Generator Loss: 4.4474
D(x): 0.9477, D(G(z)): 0.0442
Epoch: [6/20], Batch Num: [421/600]
Discriminator Loss: 0.1723, Generator Loss: 4.6391
D(x): 0.9474, D(G(z)): 0.0640
Epoch: [6/20], Batch Num: [422/600]
Discriminator Loss: 0.2511, Generator Loss: 4.3446
D(x): 0.9230, D(G(z)): 0.0779
Epoch: [6/20], Batch Num: [423/600]
Discriminator Loss: 0.2371, Generator Loss: 4.9218
D(x): 0.9260, D(G(z)): 0.0743
Epoch: [6/20], Batch Num: [424/600]
Discriminator Loss: 0.2780, Generator Loss: 4.1096
D(x): 0.8835, D(G(z)): 0.0509
Epoch: [6/20], Batch Num: [425/600]
Discriminator Loss: 0.1697, Generator Loss: 3.9463
D(x): 0.9642, D(G(z)): 0.0880
Epoch: [6/20], Batch Num: [426/600]
Discriminator Loss: 0.2897, Generator Loss: 4.4783
D(x): 0.9302, D(G(z)): 0.1031
Epoch: [6/20], Batch Num: [427/600]
Discriminator Loss: 0.3221, Generator Loss: 4.6844
D(x): 0.8917, D(G(z)): 0.0693
Epoch: [6/20], Batch Num: [428/600]
Discriminator Loss: 0.2260, Generator Loss: 4.5214
D(x): 0.9306, D(G(z)): 0.0820
Epoch: [6/20], Batch Num: [429/600]
Discriminator Loss: 0.2365, Generator Loss: 4.7075
D(x): 0.9373, D(G(z)): 0.0667
Epoch: [6/20], Batch Num: [430/600]
Discriminator Loss: 0.3290, Generator Loss: 4.5124
D(x): 0.9170, D(G(z)): 0.0828
Epoch: [6/20], Batch Num: [431/600]
Discriminator Loss: 0.2713, Generator Loss: 3.9461
D(x): 0.9086, D(G(z)): 0.0839
Epoch: [6/20], Batch Num: [432/600]
Discriminator Loss: 0.4216, Generator Loss: 3.7021
D(x): 0.8961, D(G(z)): 0.1178
Epoch: [6/20], Batch Num: [433/600]
Discriminator Loss: 0.4297, Generator Loss: 4.0576
D(x): 0.8985, D(G(z)): 0.1189
Epoch: [6/20], Batch Num: [434/600]
Discriminator Loss: 0.3480, Generator Loss: 4.6477
D(x): 0.9232, D(G(z)): 0.1479
Epoch: [6/20], Batch Num: [435/600]
Discriminator Loss: 0.4134, Generator Loss: 4.1765
D(x): 0.8391, D(G(z)): 0.0528
Epoch: [6/20], Batch Num: [436/600]
Discriminator Loss: 0.3153, Generator Loss: 4.0945
D(x): 0.9104, D(G(z)): 0.0903
Epoch: [6/20], Batch Num: [437/600]
Discriminator Loss: 0.4560, Generator Loss: 3.3464
D(x): 0.8627, D(G(z)): 0.1211
Epoch: [6/20], Batch Num: [438/600]
Discriminator Loss: 0.3596, Generator Loss: 3.2142
D(x): 0.8940, D(G(z)): 0.1040
Epoch: [6/20], Batch Num: [439/600]
Discriminator Loss: 0.3833, Generator Loss: 3.3928
D(x): 0.8958, D(G(z)): 0.1310
Epoch: [6/20], Batch Num: [440/600]
Discriminator Loss: 0.3472, Generator Loss: 4.2529
D(x): 0.9356, D(G(z)): 0.1442
Epoch: [6/20], Batch Num: [441/600]
Discriminator Loss: 0.2028, Generator Loss: 4.9859
D(x): 0.9123, D(G(z)): 0.0533
Epoch: [6/20], Batch Num: [442/600]
Discriminator Loss: 0.1850, Generator Loss: 4.4792
D(x): 0.9317, D(G(z)): 0.0444
Epoch: [6/20], Batch Num: [443/600]
Discriminator Loss: 0.2596, Generator Loss: 5.2192
D(x): 0.9017, D(G(z)): 0.0528
Epoch: [6/20], Batch Num: [444/600]
Discriminator Loss: 0.2954, Generator Loss: 4.0964
D(x): 0.8998, D(G(z)): 0.0701
Epoch: [6/20], Batch Num: [445/600]
Discriminator Loss: 0.2464, Generator Loss: 4.0821
D(x): 0.9344, D(G(z)): 0.0990
Epoch: [6/20], Batch Num: [446/600]
Discriminator Loss: 0.3660, Generator Loss: 5.5899
D(x): 0.9432, D(G(z)): 0.1443
Epoch: [6/20], Batch Num: [447/600]
Discriminator Loss: 0.2166, Generator Loss: 6.1879
D(x): 0.9158, D(G(z)): 0.0497
Epoch: [6/20], Batch Num: [448/600]
Discriminator Loss: 0.1620, Generator Loss: 6.0830
D(x): 0.9219, D(G(z)): 0.0316
Epoch: [6/20], Batch Num: [449/600]
Discriminator Loss: 0.2453, Generator Loss: 5.0447
D(x): 0.8900, D(G(z)): 0.0164
Epoch: [6/20], Batch Num: [450/600]
Discriminator Loss: 0.2331, Generator Loss: 3.8120
D(x): 0.9079, D(G(z)): 0.0441
Epoch: [6/20], Batch Num: [451/600]
Discriminator Loss: 0.3904, Generator Loss: 4.7932
D(x): 0.9751, D(G(z)): 0.1908
Epoch: [6/20], Batch Num: [452/600]
Discriminator Loss: 0.2353, Generator Loss: 6.0652
D(x): 0.9491, D(G(z)): 0.0841
Epoch: [6/20], Batch Num: [453/600]
Discriminator Loss: 0.1790, Generator Loss: 6.9989
D(x): 0.9310, D(G(z)): 0.0195
Epoch: [6/20], Batch Num: [454/600]
Discriminator Loss: 0.3305, Generator Loss: 6.0619
D(x): 0.8508, D(G(z)): 0.0142
Epoch: [6/20], Batch Num: [455/600]
Discriminator Loss: 0.3659, Generator Loss: 4.2624
D(x): 0.8713, D(G(z)): 0.0331
Epoch: [6/20], Batch Num: [456/600]
Discriminator Loss: 0.3314, Generator Loss: 3.8780
D(x): 0.9677, D(G(z)): 0.1233
Epoch: [6/20], Batch Num: [457/600]
Discriminator Loss: 0.2389, Generator Loss: 4.6189
D(x): 0.9654, D(G(z)): 0.1159
Epoch: [6/20], Batch Num: [458/600]
Discriminator Loss: 0.3653, Generator Loss: 5.4270
D(x): 0.9349, D(G(z)): 0.0997
Epoch: [6/20], Batch Num: [459/600]
Discriminator Loss: 0.1881, Generator Loss: 6.2525
D(x): 0.9226, D(G(z)): 0.0434
Epoch: [6/20], Batch Num: [460/600]
Discriminator Loss: 0.3120, Generator Loss: 5.6257
D(x): 0.8756, D(G(z)): 0.0300
Epoch: [6/20], Batch Num: [461/600]
Discriminator Loss: 0.2537, Generator Loss: 4.8991
D(x): 0.9135, D(G(z)): 0.0294
Epoch: [6/20], Batch Num: [462/600]
Discriminator Loss: 0.2332, Generator Loss: 4.1545
D(x): 0.9470, D(G(z)): 0.1003
Epoch: [6/20], Batch Num: [463/600]
Discriminator Loss: 0.3785, Generator Loss: 5.5297
D(x): 0.9827, D(G(z)): 0.1658
Epoch: [6/20], Batch Num: [464/600]
Discriminator Loss: 0.3147, Generator Loss: 5.9941
D(x): 0.9134, D(G(z)): 0.0606
Epoch: [6/20], Batch Num: [465/600]
Discriminator Loss: 0.4621, Generator Loss: 5.7671
D(x): 0.8404, D(G(z)): 0.0516
Epoch: [6/20], Batch Num: [466/600]
Discriminator Loss: 0.3137, Generator Loss: 4.3945
D(x): 0.8718, D(G(z)): 0.0343
Epoch: [6/20], Batch Num: [467/600]
Discriminator Loss: 0.5130, Generator Loss: 3.9467
D(x): 0.9299, D(G(z)): 0.1377
Epoch: [6/20], Batch Num: [468/600]
Discriminator Loss: 0.4664, Generator Loss: 4.5087
D(x): 0.9442, D(G(z)): 0.1724
Epoch: [6/20], Batch Num: [469/600]
Discriminator Loss: 0.3994, Generator Loss: 6.0099
D(x): 0.8808, D(G(z)): 0.0920
Epoch: [6/20], Batch Num: [470/600]
Discriminator Loss: 0.5625, Generator Loss: 5.2408
D(x): 0.8194, D(G(z)): 0.0720
Epoch: [6/20], Batch Num: [471/600]
Discriminator Loss: 0.4394, Generator Loss: 3.6594
D(x): 0.8308, D(G(z)): 0.0387
Epoch: [6/20], Batch Num: [472/600]
Discriminator Loss: 0.3187, Generator Loss: 3.5244
D(x): 0.9421, D(G(z)): 0.1521
Epoch: [6/20], Batch Num: [473/600]
Discriminator Loss: 0.3871, Generator Loss: 4.3247
D(x): 0.9158, D(G(z)): 0.1550
Epoch: [6/20], Batch Num: [474/600]
Discriminator Loss: 0.3845, Generator Loss: 3.9339
D(x): 0.8617, D(G(z)): 0.0790
Epoch: [6/20], Batch Num: [475/600]
Discriminator Loss: 0.3469, Generator Loss: 4.2574
D(x): 0.8917, D(G(z)): 0.0842
Epoch: [6/20], Batch Num: [476/600]
Discriminator Loss: 0.3798, Generator Loss: 3.7529
D(x): 0.8736, D(G(z)): 0.1019
Epoch: [6/20], Batch Num: [477/600]
Discriminator Loss: 0.3236, Generator Loss: 3.9738
D(x): 0.9140, D(G(z)): 0.1120
Epoch: [6/20], Batch Num: [478/600]
Discriminator Loss: 0.5424, Generator Loss: 3.6135
D(x): 0.8789, D(G(z)): 0.1460
Epoch: [6/20], Batch Num: [479/600]
Discriminator Loss: 0.3809, Generator Loss: 4.0935
D(x): 0.8935, D(G(z)): 0.1112
Epoch: [6/20], Batch Num: [480/600]
Discriminator Loss: 0.4549, Generator Loss: 4.0395
D(x): 0.8454, D(G(z)): 0.0858
Epoch: [6/20], Batch Num: [481/600]
Discriminator Loss: 0.3049, Generator Loss: 3.6657
D(x): 0.8915, D(G(z)): 0.0915
Epoch: [6/20], Batch Num: [482/600]
Discriminator Loss: 0.3259, Generator Loss: 3.1344
D(x): 0.8947, D(G(z)): 0.0934
Epoch: [6/20], Batch Num: [483/600]
Discriminator Loss: 0.4806, Generator Loss: 3.2347
D(x): 0.8934, D(G(z)): 0.1491
Epoch: [6/20], Batch Num: [484/600]
Discriminator Loss: 0.2935, Generator Loss: 3.6989
D(x): 0.9447, D(G(z)): 0.1280
Epoch: [6/20], Batch Num: [485/600]
Discriminator Loss: 0.4834, Generator Loss: 3.6118
D(x): 0.8194, D(G(z)): 0.0900
Epoch: [6/20], Batch Num: [486/600]
Discriminator Loss: 0.3858, Generator Loss: 3.2345
D(x): 0.8560, D(G(z)): 0.1014
Epoch: [6/20], Batch Num: [487/600]
Discriminator Loss: 0.4579, Generator Loss: 3.1352
D(x): 0.8579, D(G(z)): 0.1216
Epoch: [6/20], Batch Num: [488/600]
Discriminator Loss: 0.3843, Generator Loss: 3.0878
D(x): 0.8823, D(G(z)): 0.1420
Epoch: [6/20], Batch Num: [489/600]
Discriminator Loss: 0.4556, Generator Loss: 3.5850
D(x): 0.9058, D(G(z)): 0.1981
Epoch: [6/20], Batch Num: [490/600]
Discriminator Loss: 0.3459, Generator Loss: 4.0117
D(x): 0.8916, D(G(z)): 0.1021
Epoch: [6/20], Batch Num: [491/600]
Discriminator Loss: 0.3438, Generator Loss: 3.8084
D(x): 0.8655, D(G(z)): 0.0503
Epoch: [6/20], Batch Num: [492/600]
Discriminator Loss: 0.1958, Generator Loss: 3.4847
D(x): 0.9109, D(G(z)): 0.0651
Epoch: [6/20], Batch Num: [493/600]
Discriminator Loss: 0.3460, Generator Loss: 2.9660
D(x): 0.8771, D(G(z)): 0.0901
Epoch: [6/20], Batch Num: [494/600]
Discriminator Loss: 0.2258, Generator Loss: 3.1044
D(x): 0.9473, D(G(z)): 0.1228
Epoch: [6/20], Batch Num: [495/600]
Discriminator Loss: 0.2917, Generator Loss: 3.6205
D(x): 0.9214, D(G(z)): 0.1166
Epoch: [6/20], Batch Num: [496/600]
Discriminator Loss: 0.3036, Generator Loss: 4.0656
D(x): 0.8987, D(G(z)): 0.0915
Epoch: [6/20], Batch Num: [497/600]
Discriminator Loss: 0.3180, Generator Loss: 4.2482
D(x): 0.8968, D(G(z)): 0.0979
Epoch: [6/20], Batch Num: [498/600]
Discriminator Loss: 0.1951, Generator Loss: 4.2781
D(x): 0.9208, D(G(z)): 0.0666
Epoch: [6/20], Batch Num: [499/600]
Discriminator Loss: 0.1402, Generator Loss: 4.9455
D(x): 0.9752, D(G(z)): 0.0892
Epoch: 6, Batch Num: [500/600]

Epoch: [6/20], Batch Num: [500/600]
Discriminator Loss: 0.2708, Generator Loss: 4.8953
D(x): 0.8758, D(G(z)): 0.0628
Epoch: [6/20], Batch Num: [501/600]
Discriminator Loss: 0.2142, Generator Loss: 4.6695
D(x): 0.9147, D(G(z)): 0.0601
Epoch: [6/20], Batch Num: [502/600]
Discriminator Loss: 0.2153, Generator Loss: 4.2154
D(x): 0.9305, D(G(z)): 0.0602
Epoch: [6/20], Batch Num: [503/600]
Discriminator Loss: 0.2223, Generator Loss: 4.4596
D(x): 0.9448, D(G(z)): 0.1009
Epoch: [6/20], Batch Num: [504/600]
Discriminator Loss: 0.1777, Generator Loss: 4.6772
D(x): 0.9586, D(G(z)): 0.0677
Epoch: [6/20], Batch Num: [505/600]
Discriminator Loss: 0.2492, Generator Loss: 6.1317
D(x): 0.9506, D(G(z)): 0.0970
Epoch: [6/20], Batch Num: [506/600]
Discriminator Loss: 0.4914, Generator Loss: 6.2315
D(x): 0.8895, D(G(z)): 0.0755
Epoch: [6/20], Batch Num: [507/600]
Discriminator Loss: 0.2890, Generator Loss: 5.9629
D(x): 0.8731, D(G(z)): 0.0395
Epoch: [6/20], Batch Num: [508/600]
Discriminator Loss: 0.2166, Generator Loss: 4.8428
D(x): 0.9266, D(G(z)): 0.0628
Epoch: [6/20], Batch Num: [509/600]
Discriminator Loss: 0.4273, Generator Loss: 4.6628
D(x): 0.9284, D(G(z)): 0.1452
Epoch: [6/20], Batch Num: [510/600]
Discriminator Loss: 0.3307, Generator Loss: 5.7046
D(x): 0.9687, D(G(z)): 0.1324
Epoch: [6/20], Batch Num: [511/600]
Discriminator Loss: 0.5303, Generator Loss: 5.7323
D(x): 0.8844, D(G(z)): 0.0718
Epoch: [6/20], Batch Num: [512/600]
Discriminator Loss: 0.5423, Generator Loss: 5.0027
D(x): 0.8561, D(G(z)): 0.0576
Epoch: [6/20], Batch Num: [513/600]
Discriminator Loss: 0.3942, Generator Loss: 2.9077
D(x): 0.8713, D(G(z)): 0.0801
Epoch: [6/20], Batch Num: [514/600]
Discriminator Loss: 0.5719, Generator Loss: 3.5069
D(x): 0.9427, D(G(z)): 0.2469
Epoch: [6/20], Batch Num: [515/600]
Discriminator Loss: 0.7140, Generator Loss: 4.9472
D(x): 0.8609, D(G(z)): 0.2266
Epoch: [6/20], Batch Num: [516/600]
Discriminator Loss: 0.3812, Generator Loss: 5.2988
D(x): 0.8383, D(G(z)): 0.0682
Epoch: [6/20], Batch Num: [517/600]
Discriminator Loss: 0.4708, Generator Loss: 4.3949
D(x): 0.8116, D(G(z)): 0.0420
Epoch: [6/20], Batch Num: [518/600]
Discriminator Loss: 0.4584, Generator Loss: 3.2455
D(x): 0.8521, D(G(z)): 0.1143
Epoch: [6/20], Batch Num: [519/600]
Discriminator Loss: 0.5263, Generator Loss: 2.7009
D(x): 0.9035, D(G(z)): 0.2086
Epoch: [6/20], Batch Num: [520/600]
Discriminator Loss: 0.6434, Generator Loss: 2.9829
D(x): 0.8416, D(G(z)): 0.1836
Epoch: [6/20], Batch Num: [521/600]
Discriminator Loss: 0.5016, Generator Loss: 3.8083
D(x): 0.8922, D(G(z)): 0.1896
Epoch: [6/20], Batch Num: [522/600]
Discriminator Loss: 0.4144, Generator Loss: 4.5480
D(x): 0.8747, D(G(z)): 0.1364
Epoch: [6/20], Batch Num: [523/600]
Discriminator Loss: 0.4878, Generator Loss: 4.2365
D(x): 0.8220, D(G(z)): 0.0792
Epoch: [6/20], Batch Num: [524/600]
Discriminator Loss: 0.3872, Generator Loss: 3.4853
D(x): 0.8529, D(G(z)): 0.0944
Epoch: [6/20], Batch Num: [525/600]
Discriminator Loss: 0.4190, Generator Loss: 3.6452
D(x): 0.8967, D(G(z)): 0.1632
Epoch: [6/20], Batch Num: [526/600]
Discriminator Loss: 0.3702, Generator Loss: 3.3361
D(x): 0.8871, D(G(z)): 0.1553
Epoch: [6/20], Batch Num: [527/600]
Discriminator Loss: 0.4575, Generator Loss: 3.7457
D(x): 0.8537, D(G(z)): 0.1356
Epoch: [6/20], Batch Num: [528/600]
Discriminator Loss: 0.4177, Generator Loss: 3.5059
D(x): 0.8671, D(G(z)): 0.1228
Epoch: [6/20], Batch Num: [529/600]
Discriminator Loss: 0.2482, Generator Loss: 4.1717
D(x): 0.9191, D(G(z)): 0.1169
Epoch: [6/20], Batch Num: [530/600]
Discriminator Loss: 0.3072, Generator Loss: 4.0295
D(x): 0.8985, D(G(z)): 0.0981
Epoch: [6/20], Batch Num: [531/600]
Discriminator Loss: 0.3396, Generator Loss: 3.8580
D(x): 0.8755, D(G(z)): 0.0745
Epoch: [6/20], Batch Num: [532/600]
Discriminator Loss: 0.3299, Generator Loss: 3.9045
D(x): 0.8939, D(G(z)): 0.0894
Epoch: [6/20], Batch Num: [533/600]
Discriminator Loss: 0.2677, Generator Loss: 3.6590
D(x): 0.9252, D(G(z)): 0.1118
Epoch: [6/20], Batch Num: [534/600]
Discriminator Loss: 0.3349, Generator Loss: 3.8436
D(x): 0.9159, D(G(z)): 0.1387
Epoch: [6/20], Batch Num: [535/600]
Discriminator Loss: 0.2621, Generator Loss: 4.5636
D(x): 0.9161, D(G(z)): 0.0712
Epoch: [6/20], Batch Num: [536/600]
Discriminator Loss: 0.2614, Generator Loss: 5.2751
D(x): 0.9359, D(G(z)): 0.1088
Epoch: [6/20], Batch Num: [537/600]
Discriminator Loss: 0.3968, Generator Loss: 4.3715
D(x): 0.8492, D(G(z)): 0.0544
Epoch: [6/20], Batch Num: [538/600]
Discriminator Loss: 0.3865, Generator Loss: 3.5730
D(x): 0.8781, D(G(z)): 0.0764
Epoch: [6/20], Batch Num: [539/600]
Discriminator Loss: 0.3873, Generator Loss: 2.9836
D(x): 0.9046, D(G(z)): 0.1154
Epoch: [6/20], Batch Num: [540/600]
Discriminator Loss: 0.3852, Generator Loss: 4.1331
D(x): 0.9562, D(G(z)): 0.1910
Epoch: [6/20], Batch Num: [541/600]
Discriminator Loss: 0.2662, Generator Loss: 5.5479
D(x): 0.9097, D(G(z)): 0.0864
Epoch: [6/20], Batch Num: [542/600]
Discriminator Loss: 0.4004, Generator Loss: 4.9274
D(x): 0.8408, D(G(z)): 0.0520
Epoch: [6/20], Batch Num: [543/600]
Discriminator Loss: 0.4393, Generator Loss: 3.0157
D(x): 0.8206, D(G(z)): 0.0522
Epoch: [6/20], Batch Num: [544/600]
Discriminator Loss: 0.3672, Generator Loss: 2.6372
D(x): 0.9503, D(G(z)): 0.1888
Epoch: [6/20], Batch Num: [545/600]
Discriminator Loss: 0.4625, Generator Loss: 4.7300
D(x): 0.9393, D(G(z)): 0.2310
Epoch: [6/20], Batch Num: [546/600]
Discriminator Loss: 0.3910, Generator Loss: 4.8467
D(x): 0.8506, D(G(z)): 0.0678
Epoch: [6/20], Batch Num: [547/600]
Discriminator Loss: 0.5557, Generator Loss: 4.4246
D(x): 0.8173, D(G(z)): 0.0692
Epoch: [6/20], Batch Num: [548/600]
Discriminator Loss: 0.4171, Generator Loss: 3.2847
D(x): 0.8760, D(G(z)): 0.0778
Epoch: [6/20], Batch Num: [549/600]
Discriminator Loss: 0.4604, Generator Loss: 3.3988
D(x): 0.8981, D(G(z)): 0.1691
Epoch: [6/20], Batch Num: [550/600]
Discriminator Loss: 0.5825, Generator Loss: 3.3637
D(x): 0.8728, D(G(z)): 0.1615
Epoch: [6/20], Batch Num: [551/600]
Discriminator Loss: 0.6153, Generator Loss: 3.8832
D(x): 0.8574, D(G(z)): 0.1943
Epoch: [6/20], Batch Num: [552/600]
Discriminator Loss: 0.5931, Generator Loss: 3.5102
D(x): 0.7949, D(G(z)): 0.0908
Epoch: [6/20], Batch Num: [553/600]
Discriminator Loss: 0.4221, Generator Loss: 3.2276
D(x): 0.8768, D(G(z)): 0.1271
Epoch: [6/20], Batch Num: [554/600]
Discriminator Loss: 0.5592, Generator Loss: 3.2454
D(x): 0.8580, D(G(z)): 0.1562
Epoch: [6/20], Batch Num: [555/600]
Discriminator Loss: 0.4573, Generator Loss: 3.7253
D(x): 0.9074, D(G(z)): 0.1517
Epoch: [6/20], Batch Num: [556/600]
Discriminator Loss: 0.2279, Generator Loss: 3.4870
D(x): 0.9060, D(G(z)): 0.0594
Epoch: [6/20], Batch Num: [557/600]
Discriminator Loss: 0.4080, Generator Loss: 3.2863
D(x): 0.8566, D(G(z)): 0.0868
Epoch: [6/20], Batch Num: [558/600]
Discriminator Loss: 0.2932, Generator Loss: 3.0081
D(x): 0.9070, D(G(z)): 0.0727
Epoch: [6/20], Batch Num: [559/600]
Discriminator Loss: 0.4525, Generator Loss: 3.4558
D(x): 0.9109, D(G(z)): 0.1596
Epoch: [6/20], Batch Num: [560/600]
Discriminator Loss: 0.3235, Generator Loss: 3.7339
D(x): 0.9179, D(G(z)): 0.1092
Epoch: [6/20], Batch Num: [561/600]
Discriminator Loss: 0.3323, Generator Loss: 3.8435
D(x): 0.8662, D(G(z)): 0.0551
Epoch: [6/20], Batch Num: [562/600]
Discriminator Loss: 0.2089, Generator Loss: 3.8197
D(x): 0.9396, D(G(z)): 0.0775
Epoch: [6/20], Batch Num: [563/600]
Discriminator Loss: 0.1694, Generator Loss: 3.9688
D(x): 0.9413, D(G(z)): 0.0644
Epoch: [6/20], Batch Num: [564/600]
Discriminator Loss: 0.2793, Generator Loss: 4.0995
D(x): 0.9201, D(G(z)): 0.0780
Epoch: [6/20], Batch Num: [565/600]
Discriminator Loss: 0.2776, Generator Loss: 4.0558
D(x): 0.9093, D(G(z)): 0.0818
Epoch: [6/20], Batch Num: [566/600]
Discriminator Loss: 0.1844, Generator Loss: 3.7299
D(x): 0.9279, D(G(z)): 0.0632
Epoch: [6/20], Batch Num: [567/600]
Discriminator Loss: 0.1708, Generator Loss: 3.7706
D(x): 0.9430, D(G(z)): 0.0772
Epoch: [6/20], Batch Num: [568/600]
Discriminator Loss: 0.2592, Generator Loss: 3.9619
D(x): 0.9416, D(G(z)): 0.0777
Epoch: [6/20], Batch Num: [569/600]
Discriminator Loss: 0.1245, Generator Loss: 3.9499
D(x): 0.9720, D(G(z)): 0.0708
Epoch: [6/20], Batch Num: [570/600]
Discriminator Loss: 0.1588, Generator Loss: 4.1737
D(x): 0.9381, D(G(z)): 0.0416
Epoch: [6/20], Batch Num: [571/600]
Discriminator Loss: 0.1546, Generator Loss: 4.6316
D(x): 0.9615, D(G(z)): 0.0448
Epoch: [6/20], Batch Num: [572/600]
Discriminator Loss: 0.1431, Generator Loss: 4.3549
D(x): 0.9504, D(G(z)): 0.0357
Epoch: [6/20], Batch Num: [573/600]
Discriminator Loss: 0.1306, Generator Loss: 4.3491
D(x): 0.9308, D(G(z)): 0.0276
Epoch: [6/20], Batch Num: [574/600]
Discriminator Loss: 0.2005, Generator Loss: 3.7879
D(x): 0.9280, D(G(z)): 0.0491
Epoch: [6/20], Batch Num: [575/600]
Discriminator Loss: 0.1725, Generator Loss: 3.1827
D(x): 0.9572, D(G(z)): 0.0669
Epoch: [6/20], Batch Num: [576/600]
Discriminator Loss: 0.2939, Generator Loss: 3.8008
D(x): 0.9680, D(G(z)): 0.1588
Epoch: [6/20], Batch Num: [577/600]
Discriminator Loss: 0.3764, Generator Loss: 5.2113
D(x): 0.9482, D(G(z)): 0.1466
Epoch: [6/20], Batch Num: [578/600]
Discriminator Loss: 0.2790, Generator Loss: 5.3532
D(x): 0.8761, D(G(z)): 0.0185
Epoch: [6/20], Batch Num: [579/600]
Discriminator Loss: 0.2396, Generator Loss: 4.8661
D(x): 0.8924, D(G(z)): 0.0118
Epoch: [6/20], Batch Num: [580/600]
Discriminator Loss: 0.2670, Generator Loss: 4.3546
D(x): 0.9063, D(G(z)): 0.0433
Epoch: [6/20], Batch Num: [581/600]
Discriminator Loss: 0.2436, Generator Loss: 3.1942
D(x): 0.9037, D(G(z)): 0.0481
Epoch: [6/20], Batch Num: [582/600]
Discriminator Loss: 0.2979, Generator Loss: 3.1293
D(x): 0.9537, D(G(z)): 0.1422
Epoch: [6/20], Batch Num: [583/600]
Discriminator Loss: 0.3429, Generator Loss: 4.5306
D(x): 0.9627, D(G(z)): 0.1886
Epoch: [6/20], Batch Num: [584/600]
Discriminator Loss: 0.3413, Generator Loss: 4.8217
D(x): 0.8752, D(G(z)): 0.0370
Epoch: [6/20], Batch Num: [585/600]
Discriminator Loss: 0.2644, Generator Loss: 4.9682
D(x): 0.8911, D(G(z)): 0.0268
Epoch: [6/20], Batch Num: [586/600]
Discriminator Loss: 0.2707, Generator Loss: 4.3326
D(x): 0.8936, D(G(z)): 0.0297
Epoch: [6/20], Batch Num: [587/600]
Discriminator Loss: 0.2891, Generator Loss: 4.1931
D(x): 0.9168, D(G(z)): 0.0708
Epoch: [6/20], Batch Num: [588/600]
Discriminator Loss: 0.2963, Generator Loss: 3.7675
D(x): 0.9507, D(G(z)): 0.1142
Epoch: [6/20], Batch Num: [589/600]
Discriminator Loss: 0.3823, Generator Loss: 3.8858
D(x): 0.9274, D(G(z)): 0.1096
Epoch: [6/20], Batch Num: [590/600]
Discriminator Loss: 0.3307, Generator Loss: 4.3058
D(x): 0.9277, D(G(z)): 0.1097
Epoch: [6/20], Batch Num: [591/600]
Discriminator Loss: 0.3009, Generator Loss: 4.3692
D(x): 0.9135, D(G(z)): 0.0840
Epoch: [6/20], Batch Num: [592/600]
Discriminator Loss: 0.3825, Generator Loss: 4.3377
D(x): 0.8966, D(G(z)): 0.0548
Epoch: [6/20], Batch Num: [593/600]
Discriminator Loss: 0.3163, Generator Loss: 3.9374
D(x): 0.9077, D(G(z)): 0.0724
Epoch: [6/20], Batch Num: [594/600]
Discriminator Loss: 0.2508, Generator Loss: 3.7784
D(x): 0.9186, D(G(z)): 0.0786
Epoch: [6/20], Batch Num: [595/600]
Discriminator Loss: 0.2543, Generator Loss: 4.0549
D(x): 0.9551, D(G(z)): 0.1083
Epoch: [6/20], Batch Num: [596/600]
Discriminator Loss: 0.1850, Generator Loss: 4.3017
D(x): 0.9324, D(G(z)): 0.0660
Epoch: [6/20], Batch Num: [597/600]
Discriminator Loss: 0.3024, Generator Loss: 4.1694
D(x): 0.9062, D(G(z)): 0.0415
Epoch: [6/20], Batch Num: [598/600]
Discriminator Loss: 0.3874, Generator Loss: 3.7637
D(x): 0.8838, D(G(z)): 0.0699
Epoch: [6/20], Batch Num: [599/600]
Discriminator Loss: 0.3121, Generator Loss: 3.6808
D(x): 0.9086, D(G(z)): 0.0740
Epoch: 7, Batch Num: [0/600]

Epoch: [7/20], Batch Num: [0/600]
Discriminator Loss: 0.2915, Generator Loss: 3.5438
D(x): 0.9127, D(G(z)): 0.1098
Epoch: [7/20], Batch Num: [1/600]
Discriminator Loss: 0.3691, Generator Loss: 3.9567
D(x): 0.9206, D(G(z)): 0.1340
Epoch: [7/20], Batch Num: [2/600]
Discriminator Loss: 0.2331, Generator Loss: 4.4415
D(x): 0.9125, D(G(z)): 0.0586
Epoch: [7/20], Batch Num: [3/600]
Discriminator Loss: 0.2546, Generator Loss: 4.2082
D(x): 0.9252, D(G(z)): 0.0639
Epoch: [7/20], Batch Num: [4/600]
Discriminator Loss: 0.3475, Generator Loss: 3.7615
D(x): 0.8948, D(G(z)): 0.0848
Epoch: [7/20], Batch Num: [5/600]
Discriminator Loss: 0.3326, Generator Loss: 3.7920
D(x): 0.9182, D(G(z)): 0.1059
Epoch: [7/20], Batch Num: [6/600]
Discriminator Loss: 0.3353, Generator Loss: 4.2407
D(x): 0.9336, D(G(z)): 0.1128
Epoch: [7/20], Batch Num: [7/600]
Discriminator Loss: 0.2374, Generator Loss: 4.2718
D(x): 0.9291, D(G(z)): 0.0693
Epoch: [7/20], Batch Num: [8/600]
Discriminator Loss: 0.2353, Generator Loss: 4.3081
D(x): 0.9445, D(G(z)): 0.0706
Epoch: [7/20], Batch Num: [9/600]
Discriminator Loss: 0.4593, Generator Loss: 3.8367
D(x): 0.8485, D(G(z)): 0.0581
Epoch: [7/20], Batch Num: [10/600]
Discriminator Loss: 0.4634, Generator Loss: 2.9007
D(x): 0.8694, D(G(z)): 0.0800
Epoch: [7/20], Batch Num: [11/600]
Discriminator Loss: 0.3653, Generator Loss: 2.7389
D(x): 0.9127, D(G(z)): 0.1199
Epoch: [7/20], Batch Num: [12/600]
Discriminator Loss: 0.4588, Generator Loss: 3.1673
D(x): 0.9129, D(G(z)): 0.1992
Epoch: [7/20], Batch Num: [13/600]
Discriminator Loss: 0.3124, Generator Loss: 4.0503
D(x): 0.9351, D(G(z)): 0.1363
Epoch: [7/20], Batch Num: [14/600]
Discriminator Loss: 0.2487, Generator Loss: 4.4337
D(x): 0.9154, D(G(z)): 0.0572
Epoch: [7/20], Batch Num: [15/600]
Discriminator Loss: 0.4586, Generator Loss: 3.9545
D(x): 0.8330, D(G(z)): 0.0373
Epoch: [7/20], Batch Num: [16/600]
Discriminator Loss: 0.4676, Generator Loss: 3.2223
D(x): 0.8076, D(G(z)): 0.0381
Epoch: [7/20], Batch Num: [17/600]
Discriminator Loss: 0.3529, Generator Loss: 2.7501
D(x): 0.9210, D(G(z)): 0.1408
Epoch: [7/20], Batch Num: [18/600]
Discriminator Loss: 0.4297, Generator Loss: 2.7633
D(x): 0.9376, D(G(z)): 0.2166
Epoch: [7/20], Batch Num: [19/600]
Discriminator Loss: 0.4274, Generator Loss: 3.2972
D(x): 0.8956, D(G(z)): 0.1736
Epoch: [7/20], Batch Num: [20/600]
Discriminator Loss: 0.1766, Generator Loss: 4.0235
D(x): 0.9635, D(G(z)): 0.1120
Epoch: [7/20], Batch Num: [21/600]
Discriminator Loss: 0.2623, Generator Loss: 4.5372
D(x): 0.9073, D(G(z)): 0.0652
Epoch: [7/20], Batch Num: [22/600]
Discriminator Loss: 0.3507, Generator Loss: 4.3593
D(x): 0.8393, D(G(z)): 0.0407
Epoch: [7/20], Batch Num: [23/600]
Discriminator Loss: 0.3794, Generator Loss: 3.6431
D(x): 0.8788, D(G(z)): 0.0415
Epoch: [7/20], Batch Num: [24/600]
Discriminator Loss: 0.3280, Generator Loss: 3.0967
D(x): 0.8895, D(G(z)): 0.0820
Epoch: [7/20], Batch Num: [25/600]
Discriminator Loss: 0.2417, Generator Loss: 2.8322
D(x): 0.9483, D(G(z)): 0.1282
Epoch: [7/20], Batch Num: [26/600]
Discriminator Loss: 0.2733, Generator Loss: 3.2141
D(x): 0.9645, D(G(z)): 0.1679
Epoch: [7/20], Batch Num: [27/600]
Discriminator Loss: 0.1893, Generator Loss: 3.9150
D(x): 0.9519, D(G(z)): 0.1077
Epoch: [7/20], Batch Num: [28/600]
Discriminator Loss: 0.2278, Generator Loss: 4.0738
D(x): 0.9225, D(G(z)): 0.0701
Epoch: [7/20], Batch Num: [29/600]
Discriminator Loss: 0.3323, Generator Loss: 4.4564
D(x): 0.8702, D(G(z)): 0.0456
Epoch: [7/20], Batch Num: [30/600]
Discriminator Loss: 0.3115, Generator Loss: 4.1066
D(x): 0.8825, D(G(z)): 0.0584
Epoch: [7/20], Batch Num: [31/600]
Discriminator Loss: 0.1574, Generator Loss: 4.0557
D(x): 0.9514, D(G(z)): 0.0616
Epoch: [7/20], Batch Num: [32/600]
Discriminator Loss: 0.2454, Generator Loss: 3.6892
D(x): 0.9188, D(G(z)): 0.0805
Epoch: [7/20], Batch Num: [33/600]
Discriminator Loss: 0.2555, Generator Loss: 3.1555
D(x): 0.9355, D(G(z)): 0.0946
Epoch: [7/20], Batch Num: [34/600]
Discriminator Loss: 0.1538, Generator Loss: 3.3108
D(x): 0.9829, D(G(z)): 0.1089
Epoch: [7/20], Batch Num: [35/600]
Discriminator Loss: 0.2995, Generator Loss: 3.6176
D(x): 0.9391, D(G(z)): 0.1244
Epoch: [7/20], Batch Num: [36/600]
Discriminator Loss: 0.2738, Generator Loss: 4.1639
D(x): 0.9328, D(G(z)): 0.1133
Epoch: [7/20], Batch Num: [37/600]
Discriminator Loss: 0.2433, Generator Loss: 4.5253
D(x): 0.9288, D(G(z)): 0.0605
Epoch: [7/20], Batch Num: [38/600]
Discriminator Loss: 0.2197, Generator Loss: 4.5665
D(x): 0.9085, D(G(z)): 0.0382
Epoch: [7/20], Batch Num: [39/600]
Discriminator Loss: 0.2515, Generator Loss: 4.3798
D(x): 0.9125, D(G(z)): 0.0630
Epoch: [7/20], Batch Num: [40/600]
Discriminator Loss: 0.2500, Generator Loss: 3.5939
D(x): 0.9167, D(G(z)): 0.0749
Epoch: [7/20], Batch Num: [41/600]
Discriminator Loss: 0.2523, Generator Loss: 3.7177
D(x): 0.9336, D(G(z)): 0.1085
Epoch: [7/20], Batch Num: [42/600]
Discriminator Loss: 0.2001, Generator Loss: 3.6477
D(x): 0.9444, D(G(z)): 0.0921
Epoch: [7/20], Batch Num: [43/600]
Discriminator Loss: 0.1579, Generator Loss: 4.2859
D(x): 0.9906, D(G(z)): 0.1130
Epoch: [7/20], Batch Num: [44/600]
Discriminator Loss: 0.1384, Generator Loss: 4.8634
D(x): 0.9561, D(G(z)): 0.0574
Epoch: [7/20], Batch Num: [45/600]
Discriminator Loss: 0.2444, Generator Loss: 5.0869
D(x): 0.8923, D(G(z)): 0.0367
Epoch: [7/20], Batch Num: [46/600]
Discriminator Loss: 0.3851, Generator Loss: 4.5721
D(x): 0.8702, D(G(z)): 0.0509
Epoch: [7/20], Batch Num: [47/600]
Discriminator Loss: 0.1662, Generator Loss: 4.3093
D(x): 0.9358, D(G(z)): 0.0383
Epoch: [7/20], Batch Num: [48/600]
Discriminator Loss: 0.2352, Generator Loss: 3.8819
D(x): 0.9531, D(G(z)): 0.1173
Epoch: [7/20], Batch Num: [49/600]
Discriminator Loss: 0.3487, Generator Loss: 4.0641
D(x): 0.9523, D(G(z)): 0.1611
Epoch: [7/20], Batch Num: [50/600]
Discriminator Loss: 0.2001, Generator Loss: 4.4669
D(x): 0.9473, D(G(z)): 0.0751
Epoch: [7/20], Batch Num: [51/600]
Discriminator Loss: 0.1600, Generator Loss: 5.0063
D(x): 0.9499, D(G(z)): 0.0641
Epoch: [7/20], Batch Num: [52/600]
Discriminator Loss: 0.2505, Generator Loss: 5.2797
D(x): 0.8996, D(G(z)): 0.0344
Epoch: [7/20], Batch Num: [53/600]
Discriminator Loss: 0.2536, Generator Loss: 4.9645
D(x): 0.9085, D(G(z)): 0.0509
Epoch: [7/20], Batch Num: [54/600]
Discriminator Loss: 0.2765, Generator Loss: 4.0309
D(x): 0.8982, D(G(z)): 0.0458
Epoch: [7/20], Batch Num: [55/600]
Discriminator Loss: 0.3080, Generator Loss: 3.4533
D(x): 0.9190, D(G(z)): 0.0796
Epoch: [7/20], Batch Num: [56/600]
Discriminator Loss: 0.3234, Generator Loss: 2.9897
D(x): 0.9414, D(G(z)): 0.1412
Epoch: [7/20], Batch Num: [57/600]
Discriminator Loss: 0.5307, Generator Loss: 4.1876
D(x): 0.9726, D(G(z)): 0.2619
Epoch: [7/20], Batch Num: [58/600]
Discriminator Loss: 0.2492, Generator Loss: 5.2062
D(x): 0.9018, D(G(z)): 0.0811
Epoch: [7/20], Batch Num: [59/600]
Discriminator Loss: 0.2944, Generator Loss: 5.1712
D(x): 0.8892, D(G(z)): 0.0520
Epoch: [7/20], Batch Num: [60/600]
Discriminator Loss: 0.3086, Generator Loss: 4.8279
D(x): 0.8711, D(G(z)): 0.0456
Epoch: [7/20], Batch Num: [61/600]
Discriminator Loss: 0.4240, Generator Loss: 3.8285
D(x): 0.8514, D(G(z)): 0.0579
Epoch: [7/20], Batch Num: [62/600]
Discriminator Loss: 0.2217, Generator Loss: 3.1211
D(x): 0.9305, D(G(z)): 0.0969
Epoch: [7/20], Batch Num: [63/600]
Discriminator Loss: 0.5049, Generator Loss: 3.4230
D(x): 0.9384, D(G(z)): 0.2357
Epoch: [7/20], Batch Num: [64/600]
Discriminator Loss: 0.4081, Generator Loss: 4.1234
D(x): 0.9013, D(G(z)): 0.1337
Epoch: [7/20], Batch Num: [65/600]
Discriminator Loss: 0.3279, Generator Loss: 4.9016
D(x): 0.9038, D(G(z)): 0.0998
Epoch: [7/20], Batch Num: [66/600]
Discriminator Loss: 0.2802, Generator Loss: 4.8601
D(x): 0.8922, D(G(z)): 0.0609
Epoch: [7/20], Batch Num: [67/600]
Discriminator Loss: 0.3695, Generator Loss: 4.3677
D(x): 0.8645, D(G(z)): 0.0783
Epoch: [7/20], Batch Num: [68/600]
Discriminator Loss: 0.2941, Generator Loss: 4.4912
D(x): 0.9235, D(G(z)): 0.1153
Epoch: [7/20], Batch Num: [69/600]
Discriminator Loss: 0.3119, Generator Loss: 4.3860
D(x): 0.9371, D(G(z)): 0.1179
Epoch: [7/20], Batch Num: [70/600]
Discriminator Loss: 0.2984, Generator Loss: 5.7246
D(x): 0.9215, D(G(z)): 0.1104
Epoch: [7/20], Batch Num: [71/600]
Discriminator Loss: 0.2244, Generator Loss: 5.5691
D(x): 0.9294, D(G(z)): 0.0790
Epoch: [7/20], Batch Num: [72/600]
Discriminator Loss: 0.2025, Generator Loss: 5.9319
D(x): 0.9396, D(G(z)): 0.0709
Epoch: [7/20], Batch Num: [73/600]
Discriminator Loss: 0.3830, Generator Loss: 5.5639
D(x): 0.8612, D(G(z)): 0.0559
Epoch: [7/20], Batch Num: [74/600]
Discriminator Loss: 0.2713, Generator Loss: 5.1399
D(x): 0.9225, D(G(z)): 0.0861
Epoch: [7/20], Batch Num: [75/600]
Discriminator Loss: 0.2800, Generator Loss: 4.4686
D(x): 0.9045, D(G(z)): 0.0753
Epoch: [7/20], Batch Num: [76/600]
Discriminator Loss: 0.5516, Generator Loss: 6.0560
D(x): 0.9280, D(G(z)): 0.2232
Epoch: [7/20], Batch Num: [77/600]
Discriminator Loss: 0.4086, Generator Loss: 6.1784
D(x): 0.8384, D(G(z)): 0.0692
Epoch: [7/20], Batch Num: [78/600]
Discriminator Loss: 0.5061, Generator Loss: 4.4908
D(x): 0.8115, D(G(z)): 0.0422
Epoch: [7/20], Batch Num: [79/600]
Discriminator Loss: 0.3035, Generator Loss: 3.3257
D(x): 0.9092, D(G(z)): 0.1065
Epoch: [7/20], Batch Num: [80/600]
Discriminator Loss: 0.5961, Generator Loss: 4.3507
D(x): 0.9355, D(G(z)): 0.2530
Epoch: [7/20], Batch Num: [81/600]
Discriminator Loss: 0.3068, Generator Loss: 5.6625
D(x): 0.9243, D(G(z)): 0.1015
Epoch: [7/20], Batch Num: [82/600]
Discriminator Loss: 0.5469, Generator Loss: 5.0158
D(x): 0.8009, D(G(z)): 0.0517
Epoch: [7/20], Batch Num: [83/600]
Discriminator Loss: 0.7012, Generator Loss: 2.7822
D(x): 0.7731, D(G(z)): 0.0461
Epoch: [7/20], Batch Num: [84/600]
Discriminator Loss: 0.4909, Generator Loss: 2.7294
D(x): 0.9274, D(G(z)): 0.2206
Epoch: [7/20], Batch Num: [85/600]
Discriminator Loss: 0.4674, Generator Loss: 2.9345
D(x): 0.9304, D(G(z)): 0.1975
Epoch: [7/20], Batch Num: [86/600]
Discriminator Loss: 0.5152, Generator Loss: 4.3745
D(x): 0.9276, D(G(z)): 0.1989
Epoch: [7/20], Batch Num: [87/600]
Discriminator Loss: 0.3831, Generator Loss: 5.1150
D(x): 0.8597, D(G(z)): 0.0500
Epoch: [7/20], Batch Num: [88/600]
Discriminator Loss: 0.4336, Generator Loss: 4.9029
D(x): 0.8332, D(G(z)): 0.0558
Epoch: [7/20], Batch Num: [89/600]
Discriminator Loss: 0.5089, Generator Loss: 3.2860
D(x): 0.8026, D(G(z)): 0.0625
Epoch: [7/20], Batch Num: [90/600]
Discriminator Loss: 0.4912, Generator Loss: 2.6700
D(x): 0.8988, D(G(z)): 0.1901
Epoch: [7/20], Batch Num: [91/600]
Discriminator Loss: 0.4233, Generator Loss: 2.8076
D(x): 0.9123, D(G(z)): 0.1949
Epoch: [7/20], Batch Num: [92/600]
Discriminator Loss: 0.3046, Generator Loss: 3.2701
D(x): 0.8992, D(G(z)): 0.1101
Epoch: [7/20], Batch Num: [93/600]
Discriminator Loss: 0.3059, Generator Loss: 3.6499
D(x): 0.9083, D(G(z)): 0.1036
Epoch: [7/20], Batch Num: [94/600]
Discriminator Loss: 0.3515, Generator Loss: 3.6585
D(x): 0.8908, D(G(z)): 0.0652
Epoch: [7/20], Batch Num: [95/600]
Discriminator Loss: 0.3632, Generator Loss: 3.2536
D(x): 0.8730, D(G(z)): 0.0657
Epoch: [7/20], Batch Num: [96/600]
Discriminator Loss: 0.3456, Generator Loss: 3.3066
D(x): 0.8730, D(G(z)): 0.0710
Epoch: [7/20], Batch Num: [97/600]
Discriminator Loss: 0.2951, Generator Loss: 2.8496
D(x): 0.9068, D(G(z)): 0.0824
Epoch: [7/20], Batch Num: [98/600]
Discriminator Loss: 0.2639, Generator Loss: 2.7102
D(x): 0.9372, D(G(z)): 0.1188
Epoch: [7/20], Batch Num: [99/600]
Discriminator Loss: 0.3637, Generator Loss: 3.2954
D(x): 0.9195, D(G(z)): 0.1496
Epoch: 7, Batch Num: [100/600]

Epoch: [7/20], Batch Num: [100/600]
Discriminator Loss: 0.1547, Generator Loss: 3.9251
D(x): 0.9680, D(G(z)): 0.0762
Epoch: [7/20], Batch Num: [101/600]
Discriminator Loss: 0.1721, Generator Loss: 4.3288
D(x): 0.9386, D(G(z)): 0.0507
Epoch: [7/20], Batch Num: [102/600]
Discriminator Loss: 0.2005, Generator Loss: 4.3109
D(x): 0.9122, D(G(z)): 0.0174
Epoch: [7/20], Batch Num: [103/600]
Discriminator Loss: 0.2105, Generator Loss: 4.1030
D(x): 0.9170, D(G(z)): 0.0261
Epoch: [7/20], Batch Num: [104/600]
Discriminator Loss: 0.2666, Generator Loss: 3.3884
D(x): 0.9030, D(G(z)): 0.0470
Epoch: [7/20], Batch Num: [105/600]
Discriminator Loss: 0.2071, Generator Loss: 3.1203
D(x): 0.9542, D(G(z)): 0.0933
Epoch: [7/20], Batch Num: [106/600]
Discriminator Loss: 0.1770, Generator Loss: 3.5723
D(x): 0.9737, D(G(z)): 0.0951
Epoch: [7/20], Batch Num: [107/600]
Discriminator Loss: 0.0957, Generator Loss: 4.2692
D(x): 0.9853, D(G(z)): 0.0682
Epoch: [7/20], Batch Num: [108/600]
Discriminator Loss: 0.2718, Generator Loss: 4.7262
D(x): 0.9343, D(G(z)): 0.0559
Epoch: [7/20], Batch Num: [109/600]
Discriminator Loss: 0.2016, Generator Loss: 4.9567
D(x): 0.9352, D(G(z)): 0.0320
Epoch: [7/20], Batch Num: [110/600]
Discriminator Loss: 0.2031, Generator Loss: 4.6199
D(x): 0.9132, D(G(z)): 0.0315
Epoch: [7/20], Batch Num: [111/600]
Discriminator Loss: 0.2157, Generator Loss: 3.8389
D(x): 0.9064, D(G(z)): 0.0340
Epoch: [7/20], Batch Num: [112/600]
Discriminator Loss: 0.0922, Generator Loss: 3.1191
D(x): 0.9849, D(G(z)): 0.0579
Epoch: [7/20], Batch Num: [113/600]
Discriminator Loss: 0.1825, Generator Loss: 3.6602
D(x): 0.9697, D(G(z)): 0.1008
Epoch: [7/20], Batch Num: [114/600]
Discriminator Loss: 0.2754, Generator Loss: 4.6401
D(x): 0.9442, D(G(z)): 0.0899
Epoch: [7/20], Batch Num: [115/600]
Discriminator Loss: 0.1211, Generator Loss: 5.0168
D(x): 0.9652, D(G(z)): 0.0478
Epoch: [7/20], Batch Num: [116/600]
Discriminator Loss: 0.1787, Generator Loss: 5.2831
D(x): 0.9295, D(G(z)): 0.0277
Epoch: [7/20], Batch Num: [117/600]
Discriminator Loss: 0.2153, Generator Loss: 5.0833
D(x): 0.9215, D(G(z)): 0.0253
Epoch: [7/20], Batch Num: [118/600]
Discriminator Loss: 0.2765, Generator Loss: 4.2528
D(x): 0.9067, D(G(z)): 0.0398
Epoch: [7/20], Batch Num: [119/600]
Discriminator Loss: 0.2797, Generator Loss: 4.2119
D(x): 0.9219, D(G(z)): 0.0461
Epoch: [7/20], Batch Num: [120/600]
Discriminator Loss: 0.2827, Generator Loss: 4.4612
D(x): 0.9626, D(G(z)): 0.1239
Epoch: [7/20], Batch Num: [121/600]
Discriminator Loss: 0.3446, Generator Loss: 4.8569
D(x): 0.9195, D(G(z)): 0.0856
Epoch: [7/20], Batch Num: [122/600]
Discriminator Loss: 0.2759, Generator Loss: 4.5197
D(x): 0.8966, D(G(z)): 0.0402
Epoch: [7/20], Batch Num: [123/600]
Discriminator Loss: 0.2320, Generator Loss: 4.5345
D(x): 0.9182, D(G(z)): 0.0511
Epoch: [7/20], Batch Num: [124/600]
Discriminator Loss: 0.1721, Generator Loss: 4.0435
D(x): 0.9544, D(G(z)): 0.0738
Epoch: [7/20], Batch Num: [125/600]
Discriminator Loss: 0.2090, Generator Loss: 3.9841
D(x): 0.9439, D(G(z)): 0.0803
Epoch: [7/20], Batch Num: [126/600]
Discriminator Loss: 0.2621, Generator Loss: 3.8390
D(x): 0.9198, D(G(z)): 0.0534
Epoch: [7/20], Batch Num: [127/600]
Discriminator Loss: 0.2055, Generator Loss: 3.6245
D(x): 0.9415, D(G(z)): 0.0929
Epoch: [7/20], Batch Num: [128/600]
Discriminator Loss: 0.2489, Generator Loss: 4.0659
D(x): 0.9467, D(G(z)): 0.0814
Epoch: [7/20], Batch Num: [129/600]
Discriminator Loss: 0.2007, Generator Loss: 4.2664
D(x): 0.9384, D(G(z)): 0.0591
Epoch: [7/20], Batch Num: [130/600]
Discriminator Loss: 0.1773, Generator Loss: 4.2170
D(x): 0.9366, D(G(z)): 0.0587
Epoch: [7/20], Batch Num: [131/600]
Discriminator Loss: 0.3033, Generator Loss: 3.9353
D(x): 0.9232, D(G(z)): 0.0905
Epoch: [7/20], Batch Num: [132/600]
Discriminator Loss: 0.2610, Generator Loss: 3.9840
D(x): 0.9223, D(G(z)): 0.0809
Epoch: [7/20], Batch Num: [133/600]
Discriminator Loss: 0.1954, Generator Loss: 4.0177
D(x): 0.9534, D(G(z)): 0.0733
Epoch: [7/20], Batch Num: [134/600]
Discriminator Loss: 0.2635, Generator Loss: 3.9980
D(x): 0.9266, D(G(z)): 0.0929
Epoch: [7/20], Batch Num: [135/600]
Discriminator Loss: 0.2833, Generator Loss: 4.1745
D(x): 0.9075, D(G(z)): 0.0565
Epoch: [7/20], Batch Num: [136/600]
Discriminator Loss: 0.2741, Generator Loss: 4.1581
D(x): 0.9126, D(G(z)): 0.0585
Epoch: [7/20], Batch Num: [137/600]
Discriminator Loss: 0.2224, Generator Loss: 3.7671
D(x): 0.9379, D(G(z)): 0.0702
Epoch: [7/20], Batch Num: [138/600]
Discriminator Loss: 0.1423, Generator Loss: 3.6259
D(x): 0.9540, D(G(z)): 0.0648
Epoch: [7/20], Batch Num: [139/600]
Discriminator Loss: 0.1180, Generator Loss: 4.0679
D(x): 0.9621, D(G(z)): 0.0570
Epoch: [7/20], Batch Num: [140/600]
Discriminator Loss: 0.2409, Generator Loss: 4.3146
D(x): 0.9384, D(G(z)): 0.0742
Epoch: [7/20], Batch Num: [141/600]
Discriminator Loss: 0.2031, Generator Loss: 3.9453
D(x): 0.9048, D(G(z)): 0.0339
Epoch: [7/20], Batch Num: [142/600]
Discriminator Loss: 0.3085, Generator Loss: 3.6968
D(x): 0.9254, D(G(z)): 0.0676
Epoch: [7/20], Batch Num: [143/600]
Discriminator Loss: 0.2834, Generator Loss: 3.5521
D(x): 0.9187, D(G(z)): 0.0612
Epoch: [7/20], Batch Num: [144/600]
Discriminator Loss: 0.3269, Generator Loss: 3.7432
D(x): 0.9344, D(G(z)): 0.1180
Epoch: [7/20], Batch Num: [145/600]
Discriminator Loss: 0.3503, Generator Loss: 4.2567
D(x): 0.9154, D(G(z)): 0.0745
Epoch: [7/20], Batch Num: [146/600]
Discriminator Loss: 0.1842, Generator Loss: 4.3956
D(x): 0.9356, D(G(z)): 0.0464
Epoch: [7/20], Batch Num: [147/600]
Discriminator Loss: 0.2473, Generator Loss: 3.9308
D(x): 0.9097, D(G(z)): 0.0407
Epoch: [7/20], Batch Num: [148/600]
Discriminator Loss: 0.1536, Generator Loss: 3.7024
D(x): 0.9557, D(G(z)): 0.0642
Epoch: [7/20], Batch Num: [149/600]
Discriminator Loss: 0.2137, Generator Loss: 3.6938
D(x): 0.9485, D(G(z)): 0.0718
Epoch: [7/20], Batch Num: [150/600]
Discriminator Loss: 0.2303, Generator Loss: 3.4287
D(x): 0.9354, D(G(z)): 0.0678
Epoch: [7/20], Batch Num: [151/600]
Discriminator Loss: 0.2424, Generator Loss: 4.2214
D(x): 0.9506, D(G(z)): 0.0942
Epoch: [7/20], Batch Num: [152/600]
Discriminator Loss: 0.3034, Generator Loss: 4.3698
D(x): 0.9233, D(G(z)): 0.0932
Epoch: [7/20], Batch Num: [153/600]
Discriminator Loss: 0.3095, Generator Loss: 4.1378
D(x): 0.8893, D(G(z)): 0.0370
Epoch: [7/20], Batch Num: [154/600]
Discriminator Loss: 0.4180, Generator Loss: 3.8431
D(x): 0.9377, D(G(z)): 0.0800
Epoch: [7/20], Batch Num: [155/600]
Discriminator Loss: 0.2253, Generator Loss: 3.9712
D(x): 0.9524, D(G(z)): 0.0672
Epoch: [7/20], Batch Num: [156/600]
Discriminator Loss: 0.1523, Generator Loss: 4.2143
D(x): 0.9555, D(G(z)): 0.0681
Epoch: [7/20], Batch Num: [157/600]
Discriminator Loss: 0.3187, Generator Loss: 4.0698
D(x): 0.8831, D(G(z)): 0.0501
Epoch: [7/20], Batch Num: [158/600]
Discriminator Loss: 0.2371, Generator Loss: 3.3303
D(x): 0.9286, D(G(z)): 0.0860
Epoch: [7/20], Batch Num: [159/600]
Discriminator Loss: 0.2251, Generator Loss: 3.9717
D(x): 0.9758, D(G(z)): 0.1041
Epoch: [7/20], Batch Num: [160/600]
Discriminator Loss: 0.2477, Generator Loss: 3.9877
D(x): 0.9190, D(G(z)): 0.0549
Epoch: [7/20], Batch Num: [161/600]
Discriminator Loss: 0.1447, Generator Loss: 4.3395
D(x): 0.9744, D(G(z)): 0.0753
Epoch: [7/20], Batch Num: [162/600]
Discriminator Loss: 0.2587, Generator Loss: 4.9644
D(x): 0.9525, D(G(z)): 0.0828
Epoch: [7/20], Batch Num: [163/600]
Discriminator Loss: 0.2645, Generator Loss: 4.8506
D(x): 0.9081, D(G(z)): 0.0383
Epoch: [7/20], Batch Num: [164/600]
Discriminator Loss: 0.2870, Generator Loss: 4.3344
D(x): 0.9029, D(G(z)): 0.0521
Epoch: [7/20], Batch Num: [165/600]
Discriminator Loss: 0.2011, Generator Loss: 3.9957
D(x): 0.9511, D(G(z)): 0.0646
Epoch: [7/20], Batch Num: [166/600]
Discriminator Loss: 0.2846, Generator Loss: 3.9917
D(x): 0.9435, D(G(z)): 0.0803
Epoch: [7/20], Batch Num: [167/600]
Discriminator Loss: 0.2099, Generator Loss: 3.8400
D(x): 0.9327, D(G(z)): 0.0825
Epoch: [7/20], Batch Num: [168/600]
Discriminator Loss: 0.1985, Generator Loss: 4.0642
D(x): 0.9753, D(G(z)): 0.1064
Epoch: [7/20], Batch Num: [169/600]
Discriminator Loss: 0.2324, Generator Loss: 4.3223
D(x): 0.9124, D(G(z)): 0.0507
Epoch: [7/20], Batch Num: [170/600]
Discriminator Loss: 0.2170, Generator Loss: 3.8920
D(x): 0.9182, D(G(z)): 0.0473
Epoch: [7/20], Batch Num: [171/600]
Discriminator Loss: 0.2119, Generator Loss: 4.0104
D(x): 0.9492, D(G(z)): 0.0717
Epoch: [7/20], Batch Num: [172/600]
Discriminator Loss: 0.2410, Generator Loss: 4.3914
D(x): 0.9373, D(G(z)): 0.0715
Epoch: [7/20], Batch Num: [173/600]
Discriminator Loss: 0.1822, Generator Loss: 4.4092
D(x): 0.9410, D(G(z)): 0.0638
Epoch: [7/20], Batch Num: [174/600]
Discriminator Loss: 0.1833, Generator Loss: 4.5506
D(x): 0.9377, D(G(z)): 0.0354
Epoch: [7/20], Batch Num: [175/600]
Discriminator Loss: 0.2574, Generator Loss: 3.9963
D(x): 0.9149, D(G(z)): 0.0337
Epoch: [7/20], Batch Num: [176/600]
Discriminator Loss: 0.1683, Generator Loss: 3.6419
D(x): 0.9483, D(G(z)): 0.0743
Epoch: [7/20], Batch Num: [177/600]
Discriminator Loss: 0.2452, Generator Loss: 4.1723
D(x): 0.9590, D(G(z)): 0.1092
Epoch: [7/20], Batch Num: [178/600]
Discriminator Loss: 0.2506, Generator Loss: 4.9102
D(x): 0.9586, D(G(z)): 0.1058
Epoch: [7/20], Batch Num: [179/600]
Discriminator Loss: 0.1215, Generator Loss: 5.5180
D(x): 0.9726, D(G(z)): 0.0466
Epoch: [7/20], Batch Num: [180/600]
Discriminator Loss: 0.2078, Generator Loss: 5.5317
D(x): 0.9415, D(G(z)): 0.0389
Epoch: [7/20], Batch Num: [181/600]
Discriminator Loss: 0.1370, Generator Loss: 5.1789
D(x): 0.9532, D(G(z)): 0.0264
Epoch: [7/20], Batch Num: [182/600]
Discriminator Loss: 0.4388, Generator Loss: 4.8833
D(x): 0.8799, D(G(z)): 0.0339
Epoch: [7/20], Batch Num: [183/600]
Discriminator Loss: 0.2172, Generator Loss: 3.7727
D(x): 0.9393, D(G(z)): 0.0428
Epoch: [7/20], Batch Num: [184/600]
Discriminator Loss: 0.2930, Generator Loss: 3.4869
D(x): 0.9504, D(G(z)): 0.1076
Epoch: [7/20], Batch Num: [185/600]
Discriminator Loss: 0.2680, Generator Loss: 4.8187
D(x): 0.9902, D(G(z)): 0.1483
Epoch: [7/20], Batch Num: [186/600]
Discriminator Loss: 0.1412, Generator Loss: 5.6152
D(x): 0.9515, D(G(z)): 0.0457
Epoch: [7/20], Batch Num: [187/600]
Discriminator Loss: 0.3343, Generator Loss: 5.4848
D(x): 0.8731, D(G(z)): 0.0248
Epoch: [7/20], Batch Num: [188/600]
Discriminator Loss: 0.1695, Generator Loss: 4.9115
D(x): 0.9389, D(G(z)): 0.0260
Epoch: [7/20], Batch Num: [189/600]
Discriminator Loss: 0.2245, Generator Loss: 4.8414
D(x): 0.9466, D(G(z)): 0.0405
Epoch: [7/20], Batch Num: [190/600]
Discriminator Loss: 0.1523, Generator Loss: 4.5022
D(x): 0.9474, D(G(z)): 0.0361
Epoch: [7/20], Batch Num: [191/600]
Discriminator Loss: 0.2168, Generator Loss: 4.1120
D(x): 0.9488, D(G(z)): 0.0739
Epoch: [7/20], Batch Num: [192/600]
Discriminator Loss: 0.2758, Generator Loss: 3.7338
D(x): 0.9444, D(G(z)): 0.0715
Epoch: [7/20], Batch Num: [193/600]
Discriminator Loss: 0.3570, Generator Loss: 4.6984
D(x): 0.9428, D(G(z)): 0.1520
Epoch: [7/20], Batch Num: [194/600]
Discriminator Loss: 0.2212, Generator Loss: 5.5076
D(x): 0.9393, D(G(z)): 0.0593
Epoch: [7/20], Batch Num: [195/600]
Discriminator Loss: 0.2100, Generator Loss: 5.8367
D(x): 0.9344, D(G(z)): 0.0640
Epoch: [7/20], Batch Num: [196/600]
Discriminator Loss: 0.2140, Generator Loss: 5.6931
D(x): 0.9171, D(G(z)): 0.0273
Epoch: [7/20], Batch Num: [197/600]
Discriminator Loss: 0.4057, Generator Loss: 5.2137
D(x): 0.8621, D(G(z)): 0.0202
Epoch: [7/20], Batch Num: [198/600]
Discriminator Loss: 0.3279, Generator Loss: 4.2478
D(x): 0.8892, D(G(z)): 0.0578
Epoch: [7/20], Batch Num: [199/600]
Discriminator Loss: 0.4317, Generator Loss: 3.6436
D(x): 0.9031, D(G(z)): 0.1063
Epoch: 7, Batch Num: [200/600]

Epoch: [7/20], Batch Num: [200/600]
Discriminator Loss: 0.3593, Generator Loss: 3.5671
D(x): 0.9195, D(G(z)): 0.0960
Epoch: [7/20], Batch Num: [201/600]
Discriminator Loss: 0.4019, Generator Loss: 3.5719
D(x): 0.9259, D(G(z)): 0.1347
Epoch: [7/20], Batch Num: [202/600]
Discriminator Loss: 0.2875, Generator Loss: 3.8636
D(x): 0.9105, D(G(z)): 0.0982
Epoch: [7/20], Batch Num: [203/600]
Discriminator Loss: 0.2334, Generator Loss: 4.0502
D(x): 0.9076, D(G(z)): 0.0675
Epoch: [7/20], Batch Num: [204/600]
Discriminator Loss: 0.3636, Generator Loss: 3.5945
D(x): 0.8709, D(G(z)): 0.0794
Epoch: [7/20], Batch Num: [205/600]
Discriminator Loss: 0.2455, Generator Loss: 3.8901
D(x): 0.9256, D(G(z)): 0.0889
Epoch: [7/20], Batch Num: [206/600]
Discriminator Loss: 0.3075, Generator Loss: 3.3440
D(x): 0.8984, D(G(z)): 0.0834
Epoch: [7/20], Batch Num: [207/600]
Discriminator Loss: 0.3304, Generator Loss: 3.7564
D(x): 0.9078, D(G(z)): 0.1165
Epoch: [7/20], Batch Num: [208/600]
Discriminator Loss: 0.3625, Generator Loss: 3.1936
D(x): 0.8904, D(G(z)): 0.1341
Epoch: [7/20], Batch Num: [209/600]
Discriminator Loss: 0.3817, Generator Loss: 3.4585
D(x): 0.8952, D(G(z)): 0.1222
Epoch: [7/20], Batch Num: [210/600]
Discriminator Loss: 0.2457, Generator Loss: 3.4072
D(x): 0.9304, D(G(z)): 0.0982
Epoch: [7/20], Batch Num: [211/600]
Discriminator Loss: 0.2786, Generator Loss: 3.5356
D(x): 0.9096, D(G(z)): 0.0989
Epoch: [7/20], Batch Num: [212/600]
Discriminator Loss: 0.3573, Generator Loss: 3.6006
D(x): 0.8942, D(G(z)): 0.1035
Epoch: [7/20], Batch Num: [213/600]
Discriminator Loss: 0.3198, Generator Loss: 3.7884
D(x): 0.9034, D(G(z)): 0.0919
Epoch: [7/20], Batch Num: [214/600]
Discriminator Loss: 0.2834, Generator Loss: 3.7825
D(x): 0.8839, D(G(z)): 0.0678
Epoch: [7/20], Batch Num: [215/600]
Discriminator Loss: 0.2757, Generator Loss: 3.6125
D(x): 0.9100, D(G(z)): 0.0961
Epoch: [7/20], Batch Num: [216/600]
Discriminator Loss: 0.4245, Generator Loss: 3.5109
D(x): 0.8639, D(G(z)): 0.1011
Epoch: [7/20], Batch Num: [217/600]
Discriminator Loss: 0.2758, Generator Loss: 3.2649
D(x): 0.9142, D(G(z)): 0.0969
Epoch: [7/20], Batch Num: [218/600]
Discriminator Loss: 0.2615, Generator Loss: 3.3306
D(x): 0.9180, D(G(z)): 0.0919
Epoch: [7/20], Batch Num: [219/600]
Discriminator Loss: 0.2977, Generator Loss: 3.6992
D(x): 0.9329, D(G(z)): 0.1195
Epoch: [7/20], Batch Num: [220/600]
Discriminator Loss: 0.2940, Generator Loss: 3.8473
D(x): 0.9036, D(G(z)): 0.0848
Epoch: [7/20], Batch Num: [221/600]
Discriminator Loss: 0.2142, Generator Loss: 4.1954
D(x): 0.9280, D(G(z)): 0.0557
Epoch: [7/20], Batch Num: [222/600]
Discriminator Loss: 0.3159, Generator Loss: 3.9513
D(x): 0.8906, D(G(z)): 0.0638
Epoch: [7/20], Batch Num: [223/600]
Discriminator Loss: 0.2700, Generator Loss: 3.5213
D(x): 0.8926, D(G(z)): 0.0574
Epoch: [7/20], Batch Num: [224/600]
Discriminator Loss: 0.2078, Generator Loss: 3.2752
D(x): 0.9556, D(G(z)): 0.0999
Epoch: [7/20], Batch Num: [225/600]
Discriminator Loss: 0.2865, Generator Loss: 3.8063
D(x): 0.9538, D(G(z)): 0.1503
Epoch: [7/20], Batch Num: [226/600]
Discriminator Loss: 0.1980, Generator Loss: 4.8160
D(x): 0.9519, D(G(z)): 0.1001
Epoch: [7/20], Batch Num: [227/600]
Discriminator Loss: 0.4570, Generator Loss: 5.1509
D(x): 0.8261, D(G(z)): 0.0565
Epoch: [7/20], Batch Num: [228/600]
Discriminator Loss: 0.4557, Generator Loss: 3.7992
D(x): 0.8414, D(G(z)): 0.0339
Epoch: [7/20], Batch Num: [229/600]
Discriminator Loss: 0.1960, Generator Loss: 3.1127
D(x): 0.9379, D(G(z)): 0.0730
Epoch: [7/20], Batch Num: [230/600]
Discriminator Loss: 0.2160, Generator Loss: 3.4121
D(x): 0.9690, D(G(z)): 0.1257
Epoch: [7/20], Batch Num: [231/600]
Discriminator Loss: 0.2013, Generator Loss: 3.7658
D(x): 0.9408, D(G(z)): 0.0885
Epoch: [7/20], Batch Num: [232/600]
Discriminator Loss: 0.1888, Generator Loss: 4.4261
D(x): 0.9483, D(G(z)): 0.0867
Epoch: [7/20], Batch Num: [233/600]
Discriminator Loss: 0.2210, Generator Loss: 4.7504
D(x): 0.9155, D(G(z)): 0.0498
Epoch: [7/20], Batch Num: [234/600]
Discriminator Loss: 0.1851, Generator Loss: 5.0547
D(x): 0.9341, D(G(z)): 0.0513
Epoch: [7/20], Batch Num: [235/600]
Discriminator Loss: 0.1483, Generator Loss: 4.4897
D(x): 0.9414, D(G(z)): 0.0370
Epoch: [7/20], Batch Num: [236/600]
Discriminator Loss: 0.3014, Generator Loss: 3.9652
D(x): 0.8804, D(G(z)): 0.0524
Epoch: [7/20], Batch Num: [237/600]
Discriminator Loss: 0.2156, Generator Loss: 3.6317
D(x): 0.9378, D(G(z)): 0.0820
Epoch: [7/20], Batch Num: [238/600]
Discriminator Loss: 0.3154, Generator Loss: 3.3626
D(x): 0.9212, D(G(z)): 0.1274
Epoch: [7/20], Batch Num: [239/600]
Discriminator Loss: 0.4072, Generator Loss: 4.1129
D(x): 0.9549, D(G(z)): 0.1794
Epoch: [7/20], Batch Num: [240/600]
Discriminator Loss: 0.2800, Generator Loss: 4.3824
D(x): 0.8920, D(G(z)): 0.0535
Epoch: [7/20], Batch Num: [241/600]
Discriminator Loss: 0.2481, Generator Loss: 4.6225
D(x): 0.9069, D(G(z)): 0.0395
Epoch: [7/20], Batch Num: [242/600]
Discriminator Loss: 0.2135, Generator Loss: 4.4464
D(x): 0.9183, D(G(z)): 0.0394
Epoch: [7/20], Batch Num: [243/600]
Discriminator Loss: 0.2846, Generator Loss: 3.4027
D(x): 0.9038, D(G(z)): 0.0709
Epoch: [7/20], Batch Num: [244/600]
Discriminator Loss: 0.3421, Generator Loss: 2.8061
D(x): 0.8994, D(G(z)): 0.0990
Epoch: [7/20], Batch Num: [245/600]
Discriminator Loss: 0.3367, Generator Loss: 3.1342
D(x): 0.9516, D(G(z)): 0.1768
Epoch: [7/20], Batch Num: [246/600]
Discriminator Loss: 0.3310, Generator Loss: 4.1127
D(x): 0.9326, D(G(z)): 0.1325
Epoch: [7/20], Batch Num: [247/600]
Discriminator Loss: 0.3594, Generator Loss: 4.4340
D(x): 0.8863, D(G(z)): 0.0589
Epoch: [7/20], Batch Num: [248/600]
Discriminator Loss: 0.1892, Generator Loss: 4.1010
D(x): 0.9194, D(G(z)): 0.0357
Epoch: [7/20], Batch Num: [249/600]
Discriminator Loss: 0.3674, Generator Loss: 3.5848
D(x): 0.8839, D(G(z)): 0.0573
Epoch: [7/20], Batch Num: [250/600]
Discriminator Loss: 0.2539, Generator Loss: 3.2537
D(x): 0.9071, D(G(z)): 0.0639
Epoch: [7/20], Batch Num: [251/600]
Discriminator Loss: 0.1754, Generator Loss: 3.1658
D(x): 0.9730, D(G(z)): 0.1139
Epoch: [7/20], Batch Num: [252/600]
Discriminator Loss: 0.3013, Generator Loss: 3.5571
D(x): 0.9215, D(G(z)): 0.1071
Epoch: [7/20], Batch Num: [253/600]
Discriminator Loss: 0.3494, Generator Loss: 3.4803
D(x): 0.8979, D(G(z)): 0.0820
Epoch: [7/20], Batch Num: [254/600]
Discriminator Loss: 0.1923, Generator Loss: 3.5076
D(x): 0.9543, D(G(z)): 0.0986
Epoch: [7/20], Batch Num: [255/600]
Discriminator Loss: 0.2461, Generator Loss: 3.8403
D(x): 0.9221, D(G(z)): 0.0708
Epoch: [7/20], Batch Num: [256/600]
Discriminator Loss: 0.1121, Generator Loss: 4.1479
D(x): 0.9601, D(G(z)): 0.0390
Epoch: [7/20], Batch Num: [257/600]
Discriminator Loss: 0.2990, Generator Loss: 3.9146
D(x): 0.9311, D(G(z)): 0.0660
Epoch: [7/20], Batch Num: [258/600]
Discriminator Loss: 0.1892, Generator Loss: 3.7270
D(x): 0.9365, D(G(z)): 0.0623
Epoch: [7/20], Batch Num: [259/600]
Discriminator Loss: 0.1850, Generator Loss: 3.1608
D(x): 0.9481, D(G(z)): 0.0516
Epoch: [7/20], Batch Num: [260/600]
Discriminator Loss: 0.1893, Generator Loss: 3.3004
D(x): 0.9678, D(G(z)): 0.0969
Epoch: [7/20], Batch Num: [261/600]
Discriminator Loss: 0.2489, Generator Loss: 4.1527
D(x): 0.9485, D(G(z)): 0.0788
Epoch: [7/20], Batch Num: [262/600]
Discriminator Loss: 0.2844, Generator Loss: 4.6657
D(x): 0.9371, D(G(z)): 0.0852
Epoch: [7/20], Batch Num: [263/600]
Discriminator Loss: 0.2130, Generator Loss: 5.6181
D(x): 0.9470, D(G(z)): 0.0542
Epoch: [7/20], Batch Num: [264/600]
Discriminator Loss: 0.2825, Generator Loss: 5.0149
D(x): 0.8790, D(G(z)): 0.0143
Epoch: [7/20], Batch Num: [265/600]
Discriminator Loss: 0.2130, Generator Loss: 3.9324
D(x): 0.9085, D(G(z)): 0.0216
Epoch: [7/20], Batch Num: [266/600]
Discriminator Loss: 0.1600, Generator Loss: 2.7747
D(x): 0.9499, D(G(z)): 0.0623
Epoch: [7/20], Batch Num: [267/600]
Discriminator Loss: 0.2445, Generator Loss: 3.2083
D(x): 0.9736, D(G(z)): 0.1195
Epoch: [7/20], Batch Num: [268/600]
Discriminator Loss: 0.3390, Generator Loss: 4.3534
D(x): 0.9594, D(G(z)): 0.1463
Epoch: [7/20], Batch Num: [269/600]
Discriminator Loss: 0.2461, Generator Loss: 4.5270
D(x): 0.9061, D(G(z)): 0.0502
Epoch: [7/20], Batch Num: [270/600]
Discriminator Loss: 0.3980, Generator Loss: 4.9312
D(x): 0.8995, D(G(z)): 0.0600
Epoch: [7/20], Batch Num: [271/600]
Discriminator Loss: 0.2424, Generator Loss: 4.4410
D(x): 0.8948, D(G(z)): 0.0332
Epoch: [7/20], Batch Num: [272/600]
Discriminator Loss: 0.2627, Generator Loss: 3.8033
D(x): 0.8976, D(G(z)): 0.0244
Epoch: [7/20], Batch Num: [273/600]
Discriminator Loss: 0.2206, Generator Loss: 3.0039
D(x): 0.9332, D(G(z)): 0.0721
Epoch: [7/20], Batch Num: [274/600]
Discriminator Loss: 0.3009, Generator Loss: 3.4250
D(x): 0.9580, D(G(z)): 0.1404
Epoch: [7/20], Batch Num: [275/600]
Discriminator Loss: 0.2557, Generator Loss: 4.1148
D(x): 0.9452, D(G(z)): 0.1060
Epoch: [7/20], Batch Num: [276/600]
Discriminator Loss: 0.1083, Generator Loss: 4.5425
D(x): 0.9694, D(G(z)): 0.0575
Epoch: [7/20], Batch Num: [277/600]
Discriminator Loss: 0.2431, Generator Loss: 5.1695
D(x): 0.9192, D(G(z)): 0.0543
Epoch: [7/20], Batch Num: [278/600]
Discriminator Loss: 0.1549, Generator Loss: 4.7256
D(x): 0.9348, D(G(z)): 0.0331
Epoch: [7/20], Batch Num: [279/600]
Discriminator Loss: 0.0904, Generator Loss: 4.9953
D(x): 0.9698, D(G(z)): 0.0280
Epoch: [7/20], Batch Num: [280/600]
Discriminator Loss: 0.1526, Generator Loss: 5.0059
D(x): 0.9816, D(G(z)): 0.0616
Epoch: [7/20], Batch Num: [281/600]
Discriminator Loss: 0.1634, Generator Loss: 5.0128
D(x): 0.9464, D(G(z)): 0.0438
Epoch: [7/20], Batch Num: [282/600]
Discriminator Loss: 0.2991, Generator Loss: 4.6104
D(x): 0.9384, D(G(z)): 0.0722
Epoch: [7/20], Batch Num: [283/600]
Discriminator Loss: 0.1591, Generator Loss: 3.9678
D(x): 0.9311, D(G(z)): 0.0326
Epoch: [7/20], Batch Num: [284/600]
Discriminator Loss: 0.1502, Generator Loss: 3.9002
D(x): 0.9715, D(G(z)): 0.0554
Epoch: [7/20], Batch Num: [285/600]
Discriminator Loss: 0.2031, Generator Loss: 4.1779
D(x): 0.9575, D(G(z)): 0.1030
Epoch: [7/20], Batch Num: [286/600]
Discriminator Loss: 0.2173, Generator Loss: 4.8090
D(x): 0.9662, D(G(z)): 0.0843
Epoch: [7/20], Batch Num: [287/600]
Discriminator Loss: 0.3039, Generator Loss: 5.0506
D(x): 0.9347, D(G(z)): 0.0440
Epoch: [7/20], Batch Num: [288/600]
Discriminator Loss: 0.2257, Generator Loss: 5.1283
D(x): 0.9152, D(G(z)): 0.0240
Epoch: [7/20], Batch Num: [289/600]
Discriminator Loss: 0.2529, Generator Loss: 4.9427
D(x): 0.9430, D(G(z)): 0.0586
Epoch: [7/20], Batch Num: [290/600]
Discriminator Loss: 0.1773, Generator Loss: 4.8380
D(x): 0.9118, D(G(z)): 0.0197
Epoch: [7/20], Batch Num: [291/600]
Discriminator Loss: 0.1592, Generator Loss: 3.9922
D(x): 0.9594, D(G(z)): 0.0549
Epoch: [7/20], Batch Num: [292/600]
Discriminator Loss: 0.1345, Generator Loss: 4.2161
D(x): 0.9579, D(G(z)): 0.0531
Epoch: [7/20], Batch Num: [293/600]
Discriminator Loss: 0.1817, Generator Loss: 4.3075
D(x): 0.9500, D(G(z)): 0.0671
Epoch: [7/20], Batch Num: [294/600]
Discriminator Loss: 0.1696, Generator Loss: 4.6060
D(x): 0.9632, D(G(z)): 0.0776
Epoch: [7/20], Batch Num: [295/600]
Discriminator Loss: 0.2149, Generator Loss: 5.3933
D(x): 0.9371, D(G(z)): 0.0423
Epoch: [7/20], Batch Num: [296/600]
Discriminator Loss: 0.1538, Generator Loss: 5.5617
D(x): 0.9483, D(G(z)): 0.0334
Epoch: [7/20], Batch Num: [297/600]
Discriminator Loss: 0.2203, Generator Loss: 5.6679
D(x): 0.9440, D(G(z)): 0.0507
Epoch: [7/20], Batch Num: [298/600]
Discriminator Loss: 0.2476, Generator Loss: 4.9469
D(x): 0.9132, D(G(z)): 0.0218
Epoch: [7/20], Batch Num: [299/600]
Discriminator Loss: 0.1686, Generator Loss: 4.5746
D(x): 0.9464, D(G(z)): 0.0518
Epoch: 7, Batch Num: [300/600]

Epoch: [7/20], Batch Num: [300/600]
Discriminator Loss: 0.2077, Generator Loss: 3.8056
D(x): 0.9289, D(G(z)): 0.0572
Epoch: [7/20], Batch Num: [301/600]
Discriminator Loss: 0.3204, Generator Loss: 4.6292
D(x): 0.9606, D(G(z)): 0.1375
Epoch: [7/20], Batch Num: [302/600]
Discriminator Loss: 0.3830, Generator Loss: 5.1436
D(x): 0.9267, D(G(z)): 0.1028
Epoch: [7/20], Batch Num: [303/600]
Discriminator Loss: 0.1401, Generator Loss: 5.2466
D(x): 0.9490, D(G(z)): 0.0409
Epoch: [7/20], Batch Num: [304/600]
Discriminator Loss: 0.1794, Generator Loss: 5.2272
D(x): 0.9222, D(G(z)): 0.0303
Epoch: [7/20], Batch Num: [305/600]
Discriminator Loss: 0.1743, Generator Loss: 4.5316
D(x): 0.9164, D(G(z)): 0.0199
Epoch: [7/20], Batch Num: [306/600]
Discriminator Loss: 0.1970, Generator Loss: 4.0533
D(x): 0.9392, D(G(z)): 0.0600
Epoch: [7/20], Batch Num: [307/600]
Discriminator Loss: 0.1577, Generator Loss: 3.8771
D(x): 0.9831, D(G(z)): 0.0993
Epoch: [7/20], Batch Num: [308/600]
Discriminator Loss: 0.1131, Generator Loss: 4.3012
D(x): 0.9631, D(G(z)): 0.0574
Epoch: [7/20], Batch Num: [309/600]
Discriminator Loss: 0.1769, Generator Loss: 4.6210
D(x): 0.9310, D(G(z)): 0.0567
Epoch: [7/20], Batch Num: [310/600]
Discriminator Loss: 0.2141, Generator Loss: 3.8619
D(x): 0.9139, D(G(z)): 0.0432
Epoch: [7/20], Batch Num: [311/600]
Discriminator Loss: 0.2509, Generator Loss: 4.4762
D(x): 0.9292, D(G(z)): 0.0786
Epoch: [7/20], Batch Num: [312/600]
Discriminator Loss: 0.1843, Generator Loss: 4.5906
D(x): 0.9699, D(G(z)): 0.0878
Epoch: [7/20], Batch Num: [313/600]
Discriminator Loss: 0.2082, Generator Loss: 4.8813
D(x): 0.9492, D(G(z)): 0.0716
Epoch: [7/20], Batch Num: [314/600]
Discriminator Loss: 0.3106, Generator Loss: 4.0378
D(x): 0.8916, D(G(z)): 0.0545
Epoch: [7/20], Batch Num: [315/600]
Discriminator Loss: 0.1761, Generator Loss: 4.1411
D(x): 0.9559, D(G(z)): 0.0630
Epoch: [7/20], Batch Num: [316/600]
Discriminator Loss: 0.1485, Generator Loss: 3.9120
D(x): 0.9502, D(G(z)): 0.0574
Epoch: [7/20], Batch Num: [317/600]
Discriminator Loss: 0.3078, Generator Loss: 4.2234
D(x): 0.9306, D(G(z)): 0.1040
Epoch: [7/20], Batch Num: [318/600]
Discriminator Loss: 0.2174, Generator Loss: 4.4574
D(x): 0.9498, D(G(z)): 0.0713
Epoch: [7/20], Batch Num: [319/600]
Discriminator Loss: 0.1931, Generator Loss: 4.3944
D(x): 0.9321, D(G(z)): 0.0522
Epoch: [7/20], Batch Num: [320/600]
Discriminator Loss: 0.2763, Generator Loss: 3.9875
D(x): 0.8986, D(G(z)): 0.0524
Epoch: [7/20], Batch Num: [321/600]
Discriminator Loss: 0.1945, Generator Loss: 3.6900
D(x): 0.9465, D(G(z)): 0.0731
Epoch: [7/20], Batch Num: [322/600]
Discriminator Loss: 0.2921, Generator Loss: 4.4065
D(x): 0.9537, D(G(z)): 0.1236
Epoch: [7/20], Batch Num: [323/600]
Discriminator Loss: 0.2368, Generator Loss: 5.4200
D(x): 0.9447, D(G(z)): 0.0878
Epoch: [7/20], Batch Num: [324/600]
Discriminator Loss: 0.3752, Generator Loss: 4.6907
D(x): 0.8487, D(G(z)): 0.0365
Epoch: [7/20], Batch Num: [325/600]
Discriminator Loss: 0.1446, Generator Loss: 4.2536
D(x): 0.9518, D(G(z)): 0.0528
Epoch: [7/20], Batch Num: [326/600]
Discriminator Loss: 0.2215, Generator Loss: 3.7658
D(x): 0.9238, D(G(z)): 0.0588
Epoch: [7/20], Batch Num: [327/600]
Discriminator Loss: 0.2771, Generator Loss: 3.6527
D(x): 0.9397, D(G(z)): 0.1066
Epoch: [7/20], Batch Num: [328/600]
Discriminator Loss: 0.2849, Generator Loss: 4.6979
D(x): 0.9717, D(G(z)): 0.1466
Epoch: [7/20], Batch Num: [329/600]
Discriminator Loss: 0.2202, Generator Loss: 5.6143
D(x): 0.9163, D(G(z)): 0.0531
Epoch: [7/20], Batch Num: [330/600]
Discriminator Loss: 0.2254, Generator Loss: 5.2103
D(x): 0.9281, D(G(z)): 0.0308
Epoch: [7/20], Batch Num: [331/600]
Discriminator Loss: 0.2793, Generator Loss: 4.9132
D(x): 0.8948, D(G(z)): 0.0551
Epoch: [7/20], Batch Num: [332/600]
Discriminator Loss: 0.2125, Generator Loss: 4.2474
D(x): 0.9298, D(G(z)): 0.0737
Epoch: [7/20], Batch Num: [333/600]
Discriminator Loss: 0.1796, Generator Loss: 4.1783
D(x): 0.9591, D(G(z)): 0.0701
Epoch: [7/20], Batch Num: [334/600]
Discriminator Loss: 0.1727, Generator Loss: 4.3258
D(x): 0.9507, D(G(z)): 0.0743
Epoch: [7/20], Batch Num: [335/600]
Discriminator Loss: 0.2739, Generator Loss: 4.5302
D(x): 0.9082, D(G(z)): 0.0566
Epoch: [7/20], Batch Num: [336/600]
Discriminator Loss: 0.2622, Generator Loss: 4.6872
D(x): 0.9568, D(G(z)): 0.0891
Epoch: [7/20], Batch Num: [337/600]
Discriminator Loss: 0.2198, Generator Loss: 4.3338
D(x): 0.9131, D(G(z)): 0.0559
Epoch: [7/20], Batch Num: [338/600]
Discriminator Loss: 0.2665, Generator Loss: 4.2119
D(x): 0.9187, D(G(z)): 0.0750
Epoch: [7/20], Batch Num: [339/600]
Discriminator Loss: 0.2521, Generator Loss: 4.4900
D(x): 0.9455, D(G(z)): 0.0881
Epoch: [7/20], Batch Num: [340/600]
Discriminator Loss: 0.3071, Generator Loss: 4.2439
D(x): 0.9068, D(G(z)): 0.0852
Epoch: [7/20], Batch Num: [341/600]
Discriminator Loss: 0.2148, Generator Loss: 4.8107
D(x): 0.9637, D(G(z)): 0.0975
Epoch: [7/20], Batch Num: [342/600]
Discriminator Loss: 0.3322, Generator Loss: 4.6267
D(x): 0.8988, D(G(z)): 0.0579
Epoch: [7/20], Batch Num: [343/600]
Discriminator Loss: 0.2508, Generator Loss: 4.5768
D(x): 0.8957, D(G(z)): 0.0418
Epoch: [7/20], Batch Num: [344/600]
Discriminator Loss: 0.1411, Generator Loss: 4.2137
D(x): 0.9521, D(G(z)): 0.0514
Epoch: [7/20], Batch Num: [345/600]
Discriminator Loss: 0.2451, Generator Loss: 4.0313
D(x): 0.9400, D(G(z)): 0.0861
Epoch: [7/20], Batch Num: [346/600]
Discriminator Loss: 0.2498, Generator Loss: 3.8649
D(x): 0.9418, D(G(z)): 0.0969
Epoch: [7/20], Batch Num: [347/600]
Discriminator Loss: 0.4223, Generator Loss: 4.0213
D(x): 0.9189, D(G(z)): 0.1300
Epoch: [7/20], Batch Num: [348/600]
Discriminator Loss: 0.2685, Generator Loss: 4.8499
D(x): 0.9269, D(G(z)): 0.0630
Epoch: [7/20], Batch Num: [349/600]
Discriminator Loss: 0.2320, Generator Loss: 4.8599
D(x): 0.9229, D(G(z)): 0.0788
Epoch: [7/20], Batch Num: [350/600]
Discriminator Loss: 0.2589, Generator Loss: 4.3552
D(x): 0.9090, D(G(z)): 0.0414
Epoch: [7/20], Batch Num: [351/600]
Discriminator Loss: 0.2044, Generator Loss: 3.7889
D(x): 0.9338, D(G(z)): 0.0523
Epoch: [7/20], Batch Num: [352/600]
Discriminator Loss: 0.2785, Generator Loss: 3.7177
D(x): 0.9072, D(G(z)): 0.0912
Epoch: [7/20], Batch Num: [353/600]
Discriminator Loss: 0.2913, Generator Loss: 3.8212
D(x): 0.9431, D(G(z)): 0.1112
Epoch: [7/20], Batch Num: [354/600]
Discriminator Loss: 0.2105, Generator Loss: 4.0910
D(x): 0.9523, D(G(z)): 0.0886
Epoch: [7/20], Batch Num: [355/600]
Discriminator Loss: 0.3284, Generator Loss: 5.0509
D(x): 0.9301, D(G(z)): 0.0995
Epoch: [7/20], Batch Num: [356/600]
Discriminator Loss: 0.3809, Generator Loss: 5.0990
D(x): 0.8722, D(G(z)): 0.0507
Epoch: [7/20], Batch Num: [357/600]
Discriminator Loss: 0.2826, Generator Loss: 4.3122
D(x): 0.9015, D(G(z)): 0.0612
Epoch: [7/20], Batch Num: [358/600]
Discriminator Loss: 0.2590, Generator Loss: 3.7792
D(x): 0.9217, D(G(z)): 0.0652
Epoch: [7/20], Batch Num: [359/600]
Discriminator Loss: 0.2543, Generator Loss: 3.8273
D(x): 0.9386, D(G(z)): 0.0993
Epoch: [7/20], Batch Num: [360/600]
Discriminator Loss: 0.2424, Generator Loss: 3.9090
D(x): 0.9410, D(G(z)): 0.0986
Epoch: [7/20], Batch Num: [361/600]
Discriminator Loss: 0.2805, Generator Loss: 4.6435
D(x): 0.9383, D(G(z)): 0.0727
Epoch: [7/20], Batch Num: [362/600]
Discriminator Loss: 0.2584, Generator Loss: 5.2777
D(x): 0.9362, D(G(z)): 0.0729
Epoch: [7/20], Batch Num: [363/600]
Discriminator Loss: 0.3340, Generator Loss: 5.0083
D(x): 0.8914, D(G(z)): 0.0501
Epoch: [7/20], Batch Num: [364/600]
Discriminator Loss: 0.4527, Generator Loss: 4.2926
D(x): 0.8852, D(G(z)): 0.0760
Epoch: [7/20], Batch Num: [365/600]
Discriminator Loss: 0.3426, Generator Loss: 3.1194
D(x): 0.8905, D(G(z)): 0.0876
Epoch: [7/20], Batch Num: [366/600]
Discriminator Loss: 0.3287, Generator Loss: 3.5082
D(x): 0.9623, D(G(z)): 0.1420
Epoch: [7/20], Batch Num: [367/600]
Discriminator Loss: 0.3896, Generator Loss: 4.5122
D(x): 0.9232, D(G(z)): 0.1503
Epoch: [7/20], Batch Num: [368/600]
Discriminator Loss: 0.3383, Generator Loss: 4.9677
D(x): 0.9324, D(G(z)): 0.0807
Epoch: [7/20], Batch Num: [369/600]
Discriminator Loss: 0.4250, Generator Loss: 4.9576
D(x): 0.8511, D(G(z)): 0.0390
Epoch: [7/20], Batch Num: [370/600]
Discriminator Loss: 0.4698, Generator Loss: 4.2541
D(x): 0.8381, D(G(z)): 0.0600
Epoch: [7/20], Batch Num: [371/600]
Discriminator Loss: 0.1821, Generator Loss: 3.6994
D(x): 0.9480, D(G(z)): 0.0696
Epoch: [7/20], Batch Num: [372/600]
Discriminator Loss: 0.2448, Generator Loss: 3.1952
D(x): 0.9437, D(G(z)): 0.1087
Epoch: [7/20], Batch Num: [373/600]
Discriminator Loss: 0.3245, Generator Loss: 4.2061
D(x): 0.9820, D(G(z)): 0.1654
Epoch: [7/20], Batch Num: [374/600]
Discriminator Loss: 0.2915, Generator Loss: 5.0888
D(x): 0.9122, D(G(z)): 0.0746
Epoch: [7/20], Batch Num: [375/600]
Discriminator Loss: 0.2022, Generator Loss: 5.4200
D(x): 0.9121, D(G(z)): 0.0292
Epoch: [7/20], Batch Num: [376/600]
Discriminator Loss: 0.2337, Generator Loss: 4.9899
D(x): 0.9121, D(G(z)): 0.0356
Epoch: [7/20], Batch Num: [377/600]
Discriminator Loss: 0.2445, Generator Loss: 5.0047
D(x): 0.9290, D(G(z)): 0.0889
Epoch: [7/20], Batch Num: [378/600]
Discriminator Loss: 0.2301, Generator Loss: 4.6773
D(x): 0.9266, D(G(z)): 0.0588
Epoch: [7/20], Batch Num: [379/600]
Discriminator Loss: 0.1638, Generator Loss: 4.3033
D(x): 0.9558, D(G(z)): 0.0678
Epoch: [7/20], Batch Num: [380/600]
Discriminator Loss: 0.2525, Generator Loss: 4.3632
D(x): 0.9082, D(G(z)): 0.0626
Epoch: [7/20], Batch Num: [381/600]
Discriminator Loss: 0.2653, Generator Loss: 4.1280
D(x): 0.9216, D(G(z)): 0.0912
Epoch: [7/20], Batch Num: [382/600]
Discriminator Loss: 0.1982, Generator Loss: 4.8287
D(x): 0.9430, D(G(z)): 0.0888
Epoch: [7/20], Batch Num: [383/600]
Discriminator Loss: 0.3678, Generator Loss: 5.3729
D(x): 0.8986, D(G(z)): 0.0816
Epoch: [7/20], Batch Num: [384/600]
Discriminator Loss: 0.2878, Generator Loss: 5.1399
D(x): 0.9073, D(G(z)): 0.0474
Epoch: [7/20], Batch Num: [385/600]
Discriminator Loss: 0.2056, Generator Loss: 4.5684
D(x): 0.9141, D(G(z)): 0.0363
Epoch: [7/20], Batch Num: [386/600]
Discriminator Loss: 0.2531, Generator Loss: 3.8958
D(x): 0.9089, D(G(z)): 0.0392
Epoch: [7/20], Batch Num: [387/600]
Discriminator Loss: 0.2306, Generator Loss: 3.4673
D(x): 0.9554, D(G(z)): 0.1064
Epoch: [7/20], Batch Num: [388/600]
Discriminator Loss: 0.3479, Generator Loss: 4.3482
D(x): 0.9406, D(G(z)): 0.1470
Epoch: [7/20], Batch Num: [389/600]
Discriminator Loss: 0.2413, Generator Loss: 4.5692
D(x): 0.9449, D(G(z)): 0.0915
Epoch: [7/20], Batch Num: [390/600]
Discriminator Loss: 0.2963, Generator Loss: 5.1588
D(x): 0.9204, D(G(z)): 0.0806
Epoch: [7/20], Batch Num: [391/600]
Discriminator Loss: 0.1906, Generator Loss: 4.7003
D(x): 0.9121, D(G(z)): 0.0344
Epoch: [7/20], Batch Num: [392/600]
Discriminator Loss: 0.0992, Generator Loss: 4.7765
D(x): 0.9594, D(G(z)): 0.0360
Epoch: [7/20], Batch Num: [393/600]
Discriminator Loss: 0.1790, Generator Loss: 4.4190
D(x): 0.9360, D(G(z)): 0.0500
Epoch: [7/20], Batch Num: [394/600]
Discriminator Loss: 0.2861, Generator Loss: 4.2621
D(x): 0.9453, D(G(z)): 0.1193
Epoch: [7/20], Batch Num: [395/600]
Discriminator Loss: 0.3163, Generator Loss: 4.9872
D(x): 0.9411, D(G(z)): 0.1095
Epoch: [7/20], Batch Num: [396/600]
Discriminator Loss: 0.2131, Generator Loss: 5.5305
D(x): 0.9227, D(G(z)): 0.0456
Epoch: [7/20], Batch Num: [397/600]
Discriminator Loss: 0.2592, Generator Loss: 4.4463
D(x): 0.9060, D(G(z)): 0.0393
Epoch: [7/20], Batch Num: [398/600]
Discriminator Loss: 0.3922, Generator Loss: 3.4837
D(x): 0.8533, D(G(z)): 0.0652
Epoch: [7/20], Batch Num: [399/600]
Discriminator Loss: 0.3148, Generator Loss: 3.4909
D(x): 0.9523, D(G(z)): 0.1276
Epoch: 7, Batch Num: [400/600]

Epoch: [7/20], Batch Num: [400/600]
Discriminator Loss: 0.3094, Generator Loss: 4.2217
D(x): 0.9245, D(G(z)): 0.1314
Epoch: [7/20], Batch Num: [401/600]
Discriminator Loss: 0.3761, Generator Loss: 4.3338
D(x): 0.8574, D(G(z)): 0.0756
Epoch: [7/20], Batch Num: [402/600]
Discriminator Loss: 0.5214, Generator Loss: 4.3980
D(x): 0.8864, D(G(z)): 0.1335
Epoch: [7/20], Batch Num: [403/600]
Discriminator Loss: 0.3482, Generator Loss: 4.6515
D(x): 0.9190, D(G(z)): 0.1104
Epoch: [7/20], Batch Num: [404/600]
Discriminator Loss: 0.4774, Generator Loss: 4.2219
D(x): 0.8561, D(G(z)): 0.0604
Epoch: [7/20], Batch Num: [405/600]
Discriminator Loss: 0.2749, Generator Loss: 3.6085
D(x): 0.8979, D(G(z)): 0.0619
Epoch: [7/20], Batch Num: [406/600]
Discriminator Loss: 0.3105, Generator Loss: 3.1188
D(x): 0.9045, D(G(z)): 0.0912
Epoch: [7/20], Batch Num: [407/600]
Discriminator Loss: 0.3307, Generator Loss: 3.8967
D(x): 0.9725, D(G(z)): 0.1597
Epoch: [7/20], Batch Num: [408/600]
Discriminator Loss: 0.2081, Generator Loss: 4.5873
D(x): 0.9446, D(G(z)): 0.0928
Epoch: [7/20], Batch Num: [409/600]
Discriminator Loss: 0.2145, Generator Loss: 5.3884
D(x): 0.9181, D(G(z)): 0.0464
Epoch: [7/20], Batch Num: [410/600]
Discriminator Loss: 0.4392, Generator Loss: 4.6050
D(x): 0.8603, D(G(z)): 0.0276
Epoch: [7/20], Batch Num: [411/600]
Discriminator Loss: 0.2896, Generator Loss: 3.6988
D(x): 0.8690, D(G(z)): 0.0357
Epoch: [7/20], Batch Num: [412/600]
Discriminator Loss: 0.3253, Generator Loss: 2.5278
D(x): 0.9447, D(G(z)): 0.1106
Epoch: [7/20], Batch Num: [413/600]
Discriminator Loss: 0.5937, Generator Loss: 4.1323
D(x): 0.9638, D(G(z)): 0.2724
Epoch: [7/20], Batch Num: [414/600]
Discriminator Loss: 0.2337, Generator Loss: 5.4065
D(x): 0.9198, D(G(z)): 0.0817
Epoch: [7/20], Batch Num: [415/600]
Discriminator Loss: 0.4669, Generator Loss: 5.2607
D(x): 0.8413, D(G(z)): 0.0447
Epoch: [7/20], Batch Num: [416/600]
Discriminator Loss: 0.4104, Generator Loss: 4.4893
D(x): 0.8222, D(G(z)): 0.0207
Epoch: [7/20], Batch Num: [417/600]
Discriminator Loss: 0.2478, Generator Loss: 3.5907
D(x): 0.9223, D(G(z)): 0.0681
Epoch: [7/20], Batch Num: [418/600]
Discriminator Loss: 0.2137, Generator Loss: 3.2849
D(x): 0.9629, D(G(z)): 0.1247
Epoch: [7/20], Batch Num: [419/600]
Discriminator Loss: 0.3370, Generator Loss: 3.2188
D(x): 0.9548, D(G(z)): 0.1488
Epoch: [7/20], Batch Num: [420/600]
Discriminator Loss: 0.3694, Generator Loss: 4.4303
D(x): 0.9410, D(G(z)): 0.1607
Epoch: [7/20], Batch Num: [421/600]
Discriminator Loss: 0.2743, Generator Loss: 4.9066
D(x): 0.9145, D(G(z)): 0.0777
Epoch: [7/20], Batch Num: [422/600]
Discriminator Loss: 0.4740, Generator Loss: 4.5698
D(x): 0.8147, D(G(z)): 0.0277
Epoch: [7/20], Batch Num: [423/600]
Discriminator Loss: 0.3106, Generator Loss: 3.5034
D(x): 0.8875, D(G(z)): 0.0596
Epoch: [7/20], Batch Num: [424/600]
Discriminator Loss: 0.2711, Generator Loss: 3.0421
D(x): 0.9185, D(G(z)): 0.0903
Epoch: [7/20], Batch Num: [425/600]
Discriminator Loss: 0.3244, Generator Loss: 3.4692
D(x): 0.9783, D(G(z)): 0.1972
Epoch: [7/20], Batch Num: [426/600]
Discriminator Loss: 0.2497, Generator Loss: 4.6282
D(x): 0.9567, D(G(z)): 0.1355
Epoch: [7/20], Batch Num: [427/600]
Discriminator Loss: 0.5426, Generator Loss: 4.4962
D(x): 0.8119, D(G(z)): 0.0594
Epoch: [7/20], Batch Num: [428/600]
Discriminator Loss: 0.3512, Generator Loss: 4.4266
D(x): 0.8760, D(G(z)): 0.0540
Epoch: [7/20], Batch Num: [429/600]
Discriminator Loss: 0.4570, Generator Loss: 3.3346
D(x): 0.8295, D(G(z)): 0.0484
Epoch: [7/20], Batch Num: [430/600]
Discriminator Loss: 0.2523, Generator Loss: 2.4408
D(x): 0.9235, D(G(z)): 0.0942
Epoch: [7/20], Batch Num: [431/600]
Discriminator Loss: 0.4074, Generator Loss: 3.0765
D(x): 0.9594, D(G(z)): 0.2271
Epoch: [7/20], Batch Num: [432/600]
Discriminator Loss: 0.4384, Generator Loss: 4.1273
D(x): 0.9161, D(G(z)): 0.1773
Epoch: [7/20], Batch Num: [433/600]
Discriminator Loss: 0.2777, Generator Loss: 4.4793
D(x): 0.8841, D(G(z)): 0.0455
Epoch: [7/20], Batch Num: [434/600]
Discriminator Loss: 0.2272, Generator Loss: 4.5767
D(x): 0.8911, D(G(z)): 0.0317
Epoch: [7/20], Batch Num: [435/600]
Discriminator Loss: 0.2519, Generator Loss: 4.0207
D(x): 0.8926, D(G(z)): 0.0390
Epoch: [7/20], Batch Num: [436/600]
Discriminator Loss: 0.2087, Generator Loss: 3.4546
D(x): 0.9206, D(G(z)): 0.0528
Epoch: [7/20], Batch Num: [437/600]
Discriminator Loss: 0.3135, Generator Loss: 3.2288
D(x): 0.9289, D(G(z)): 0.1170
Epoch: [7/20], Batch Num: [438/600]
Discriminator Loss: 0.3857, Generator Loss: 3.6122
D(x): 0.9204, D(G(z)): 0.1416
Epoch: [7/20], Batch Num: [439/600]
Discriminator Loss: 0.3020, Generator Loss: 3.7444
D(x): 0.9147, D(G(z)): 0.1011
Epoch: [7/20], Batch Num: [440/600]
Discriminator Loss: 0.3164, Generator Loss: 3.9749
D(x): 0.9068, D(G(z)): 0.0776
Epoch: [7/20], Batch Num: [441/600]
Discriminator Loss: 0.3227, Generator Loss: 4.2507
D(x): 0.8858, D(G(z)): 0.0809
Epoch: [7/20], Batch Num: [442/600]
Discriminator Loss: 0.2175, Generator Loss: 3.6761
D(x): 0.9185, D(G(z)): 0.0729
Epoch: [7/20], Batch Num: [443/600]
Discriminator Loss: 0.3440, Generator Loss: 3.8974
D(x): 0.8965, D(G(z)): 0.0985
Epoch: [7/20], Batch Num: [444/600]
Discriminator Loss: 0.2946, Generator Loss: 4.0830
D(x): 0.9138, D(G(z)): 0.1015
Epoch: [7/20], Batch Num: [445/600]
Discriminator Loss: 0.3127, Generator Loss: 4.0278
D(x): 0.9182, D(G(z)): 0.1106
Epoch: [7/20], Batch Num: [446/600]
Discriminator Loss: 0.3203, Generator Loss: 4.0334
D(x): 0.8820, D(G(z)): 0.0664
Epoch: [7/20], Batch Num: [447/600]
Discriminator Loss: 0.2428, Generator Loss: 4.5566
D(x): 0.9268, D(G(z)): 0.0578
Epoch: [7/20], Batch Num: [448/600]
Discriminator Loss: 0.2173, Generator Loss: 4.3761
D(x): 0.9332, D(G(z)): 0.0760
Epoch: [7/20], Batch Num: [449/600]
Discriminator Loss: 0.2644, Generator Loss: 4.2854
D(x): 0.9007, D(G(z)): 0.0664
Epoch: [7/20], Batch Num: [450/600]
Discriminator Loss: 0.3002, Generator Loss: 4.6130
D(x): 0.9221, D(G(z)): 0.1075
Epoch: [7/20], Batch Num: [451/600]
Discriminator Loss: 0.2675, Generator Loss: 4.8198
D(x): 0.9608, D(G(z)): 0.1090
Epoch: [7/20], Batch Num: [452/600]
Discriminator Loss: 0.3726, Generator Loss: 5.2992
D(x): 0.8721, D(G(z)): 0.0817
Epoch: [7/20], Batch Num: [453/600]
Discriminator Loss: 0.2526, Generator Loss: 5.0835
D(x): 0.9102, D(G(z)): 0.0748
Epoch: [7/20], Batch Num: [454/600]
Discriminator Loss: 0.3057, Generator Loss: 4.0839
D(x): 0.8841, D(G(z)): 0.0642
Epoch: [7/20], Batch Num: [455/600]
Discriminator Loss: 0.4930, Generator Loss: 3.8543
D(x): 0.8958, D(G(z)): 0.1366
Epoch: [7/20], Batch Num: [456/600]
Discriminator Loss: 0.2991, Generator Loss: 4.4169
D(x): 0.9498, D(G(z)): 0.1449
Epoch: [7/20], Batch Num: [457/600]
Discriminator Loss: 0.3201, Generator Loss: 5.3880
D(x): 0.9243, D(G(z)): 0.1113
Epoch: [7/20], Batch Num: [458/600]
Discriminator Loss: 0.3444, Generator Loss: 4.9822
D(x): 0.8926, D(G(z)): 0.0553
Epoch: [7/20], Batch Num: [459/600]
Discriminator Loss: 0.3240, Generator Loss: 4.6812
D(x): 0.8906, D(G(z)): 0.0800
Epoch: [7/20], Batch Num: [460/600]
Discriminator Loss: 0.4420, Generator Loss: 3.7421
D(x): 0.8743, D(G(z)): 0.0833
Epoch: [7/20], Batch Num: [461/600]
Discriminator Loss: 0.4344, Generator Loss: 3.5890
D(x): 0.9111, D(G(z)): 0.1689
Epoch: [7/20], Batch Num: [462/600]
Discriminator Loss: 0.4477, Generator Loss: 3.9913
D(x): 0.8966, D(G(z)): 0.1720
Epoch: [7/20], Batch Num: [463/600]
Discriminator Loss: 0.3228, Generator Loss: 4.4569
D(x): 0.9045, D(G(z)): 0.1130
Epoch: [7/20], Batch Num: [464/600]
Discriminator Loss: 0.3892, Generator Loss: 3.9804
D(x): 0.8533, D(G(z)): 0.0676
Epoch: [7/20], Batch Num: [465/600]
Discriminator Loss: 0.5027, Generator Loss: 3.2008
D(x): 0.8521, D(G(z)): 0.0884
Epoch: [7/20], Batch Num: [466/600]
Discriminator Loss: 0.3638, Generator Loss: 3.1263
D(x): 0.9441, D(G(z)): 0.1388
Epoch: [7/20], Batch Num: [467/600]
Discriminator Loss: 0.3822, Generator Loss: 4.1247
D(x): 0.9140, D(G(z)): 0.1603
Epoch: [7/20], Batch Num: [468/600]
Discriminator Loss: 0.4641, Generator Loss: 4.0984
D(x): 0.8632, D(G(z)): 0.1194
Epoch: [7/20], Batch Num: [469/600]
Discriminator Loss: 0.3676, Generator Loss: 4.4048
D(x): 0.8740, D(G(z)): 0.1006
Epoch: [7/20], Batch Num: [470/600]
Discriminator Loss: 0.3939, Generator Loss: 4.0442
D(x): 0.8580, D(G(z)): 0.0758
Epoch: [7/20], Batch Num: [471/600]
Discriminator Loss: 0.3226, Generator Loss: 3.1742
D(x): 0.8954, D(G(z)): 0.0863
Epoch: [7/20], Batch Num: [472/600]
Discriminator Loss: 0.4307, Generator Loss: 2.8403
D(x): 0.8732, D(G(z)): 0.1043
Epoch: [7/20], Batch Num: [473/600]
Discriminator Loss: 0.4892, Generator Loss: 3.3026
D(x): 0.9182, D(G(z)): 0.2062
Epoch: [7/20], Batch Num: [474/600]
Discriminator Loss: 0.4121, Generator Loss: 3.5961
D(x): 0.9072, D(G(z)): 0.1457
Epoch: [7/20], Batch Num: [475/600]
Discriminator Loss: 0.2676, Generator Loss: 3.9050
D(x): 0.9061, D(G(z)): 0.0819
Epoch: [7/20], Batch Num: [476/600]
Discriminator Loss: 0.3326, Generator Loss: 4.1374
D(x): 0.8831, D(G(z)): 0.0717
Epoch: [7/20], Batch Num: [477/600]
Discriminator Loss: 0.3871, Generator Loss: 3.9356
D(x): 0.8622, D(G(z)): 0.0850
Epoch: [7/20], Batch Num: [478/600]
Discriminator Loss: 0.4229, Generator Loss: 3.1313
D(x): 0.8256, D(G(z)): 0.0644
Epoch: [7/20], Batch Num: [479/600]
Discriminator Loss: 0.3715, Generator Loss: 3.4729
D(x): 0.9263, D(G(z)): 0.1592
Epoch: [7/20], Batch Num: [480/600]
Discriminator Loss: 0.3887, Generator Loss: 3.3879
D(x): 0.9102, D(G(z)): 0.1683
Epoch: [7/20], Batch Num: [481/600]
Discriminator Loss: 0.3230, Generator Loss: 4.0384
D(x): 0.9316, D(G(z)): 0.1627
Epoch: [7/20], Batch Num: [482/600]
Discriminator Loss: 0.3241, Generator Loss: 4.5174
D(x): 0.8856, D(G(z)): 0.0866
Epoch: [7/20], Batch Num: [483/600]
Discriminator Loss: 0.2637, Generator Loss: 4.8956
D(x): 0.8844, D(G(z)): 0.0547
Epoch: [7/20], Batch Num: [484/600]
Discriminator Loss: 0.3343, Generator Loss: 4.3353
D(x): 0.8689, D(G(z)): 0.0478
Epoch: [7/20], Batch Num: [485/600]
Discriminator Loss: 0.3457, Generator Loss: 3.7023
D(x): 0.8737, D(G(z)): 0.0719
Epoch: [7/20], Batch Num: [486/600]
Discriminator Loss: 0.2279, Generator Loss: 3.7651
D(x): 0.9549, D(G(z)): 0.1243
Epoch: [7/20], Batch Num: [487/600]
Discriminator Loss: 0.3499, Generator Loss: 3.1594
D(x): 0.9049, D(G(z)): 0.1240
Epoch: [7/20], Batch Num: [488/600]
Discriminator Loss: 0.2829, Generator Loss: 3.7646
D(x): 0.9667, D(G(z)): 0.1351
Epoch: [7/20], Batch Num: [489/600]
Discriminator Loss: 0.3137, Generator Loss: 4.5320
D(x): 0.9204, D(G(z)): 0.1016
Epoch: [7/20], Batch Num: [490/600]
Discriminator Loss: 0.3625, Generator Loss: 4.6636
D(x): 0.8660, D(G(z)): 0.0700
Epoch: [7/20], Batch Num: [491/600]
Discriminator Loss: 0.2521, Generator Loss: 4.1337
D(x): 0.9120, D(G(z)): 0.0671
Epoch: [7/20], Batch Num: [492/600]
Discriminator Loss: 0.3040, Generator Loss: 4.2182
D(x): 0.8687, D(G(z)): 0.0715
Epoch: [7/20], Batch Num: [493/600]
Discriminator Loss: 0.2005, Generator Loss: 3.9004
D(x): 0.9459, D(G(z)): 0.0812
Epoch: [7/20], Batch Num: [494/600]
Discriminator Loss: 0.3152, Generator Loss: 3.9609
D(x): 0.9027, D(G(z)): 0.0883
Epoch: [7/20], Batch Num: [495/600]
Discriminator Loss: 0.3611, Generator Loss: 4.2531
D(x): 0.9310, D(G(z)): 0.1388
Epoch: [7/20], Batch Num: [496/600]
Discriminator Loss: 0.1817, Generator Loss: 4.4688
D(x): 0.9478, D(G(z)): 0.0767
Epoch: [7/20], Batch Num: [497/600]
Discriminator Loss: 0.2078, Generator Loss: 4.4704
D(x): 0.9040, D(G(z)): 0.0562
Epoch: [7/20], Batch Num: [498/600]
Discriminator Loss: 0.2255, Generator Loss: 4.5427
D(x): 0.9093, D(G(z)): 0.0709
Epoch: [7/20], Batch Num: [499/600]
Discriminator Loss: 0.3606, Generator Loss: 4.1934
D(x): 0.8680, D(G(z)): 0.0654
Epoch: 7, Batch Num: [500/600]

Epoch: [7/20], Batch Num: [500/600]
Discriminator Loss: 0.1986, Generator Loss: 3.9867
D(x): 0.9503, D(G(z)): 0.0816
Epoch: [7/20], Batch Num: [501/600]
Discriminator Loss: 0.2667, Generator Loss: 3.6074
D(x): 0.9416, D(G(z)): 0.1229
Epoch: [7/20], Batch Num: [502/600]
Discriminator Loss: 0.2954, Generator Loss: 4.8514
D(x): 0.9342, D(G(z)): 0.1214
Epoch: [7/20], Batch Num: [503/600]
Discriminator Loss: 0.2082, Generator Loss: 5.4174
D(x): 0.9251, D(G(z)): 0.0414
Epoch: [7/20], Batch Num: [504/600]
Discriminator Loss: 0.2683, Generator Loss: 4.9641
D(x): 0.8921, D(G(z)): 0.0398
Epoch: [7/20], Batch Num: [505/600]
Discriminator Loss: 0.1679, Generator Loss: 4.4355
D(x): 0.9324, D(G(z)): 0.0578
Epoch: [7/20], Batch Num: [506/600]
Discriminator Loss: 0.3509, Generator Loss: 4.3216
D(x): 0.9045, D(G(z)): 0.0922
Epoch: [7/20], Batch Num: [507/600]
Discriminator Loss: 0.3527, Generator Loss: 4.1304
D(x): 0.9181, D(G(z)): 0.0878
Epoch: [7/20], Batch Num: [508/600]
Discriminator Loss: 0.2176, Generator Loss: 4.8420
D(x): 0.9520, D(G(z)): 0.0952
Epoch: [7/20], Batch Num: [509/600]
Discriminator Loss: 0.2707, Generator Loss: 4.8443
D(x): 0.9037, D(G(z)): 0.0735
Epoch: [7/20], Batch Num: [510/600]
Discriminator Loss: 0.3783, Generator Loss: 4.4489
D(x): 0.8962, D(G(z)): 0.0550
Epoch: [7/20], Batch Num: [511/600]
Discriminator Loss: 0.3166, Generator Loss: 3.7532
D(x): 0.9112, D(G(z)): 0.0662
Epoch: [7/20], Batch Num: [512/600]
Discriminator Loss: 0.2496, Generator Loss: 3.9108
D(x): 0.9442, D(G(z)): 0.1091
Epoch: [7/20], Batch Num: [513/600]
Discriminator Loss: 0.3046, Generator Loss: 4.9628
D(x): 0.9475, D(G(z)): 0.1271
Epoch: [7/20], Batch Num: [514/600]
Discriminator Loss: 0.2982, Generator Loss: 4.9181
D(x): 0.8996, D(G(z)): 0.0494
Epoch: [7/20], Batch Num: [515/600]
Discriminator Loss: 0.2640, Generator Loss: 4.5864
D(x): 0.9128, D(G(z)): 0.0689
Epoch: [7/20], Batch Num: [516/600]
Discriminator Loss: 0.3127, Generator Loss: 4.0150
D(x): 0.8918, D(G(z)): 0.0699
Epoch: [7/20], Batch Num: [517/600]
Discriminator Loss: 0.2995, Generator Loss: 3.9672
D(x): 0.9460, D(G(z)): 0.1219
Epoch: [7/20], Batch Num: [518/600]
Discriminator Loss: 0.3354, Generator Loss: 4.0647
D(x): 0.8948, D(G(z)): 0.0897
Epoch: [7/20], Batch Num: [519/600]
Discriminator Loss: 0.3034, Generator Loss: 3.7723
D(x): 0.9057, D(G(z)): 0.0887
Epoch: [7/20], Batch Num: [520/600]
Discriminator Loss: 0.3664, Generator Loss: 3.8371
D(x): 0.8899, D(G(z)): 0.0862
Epoch: [7/20], Batch Num: [521/600]
Discriminator Loss: 0.3342, Generator Loss: 4.1195
D(x): 0.9454, D(G(z)): 0.1319
Epoch: [7/20], Batch Num: [522/600]
Discriminator Loss: 0.2631, Generator Loss: 4.2655
D(x): 0.9147, D(G(z)): 0.0545
Epoch: [7/20], Batch Num: [523/600]
Discriminator Loss: 0.2607, Generator Loss: 3.9190
D(x): 0.9121, D(G(z)): 0.0772
Epoch: [7/20], Batch Num: [524/600]
Discriminator Loss: 0.3713, Generator Loss: 3.9355
D(x): 0.8826, D(G(z)): 0.0693
Epoch: [7/20], Batch Num: [525/600]
Discriminator Loss: 0.3721, Generator Loss: 3.3851
D(x): 0.9007, D(G(z)): 0.1022
Epoch: [7/20], Batch Num: [526/600]
Discriminator Loss: 0.4159, Generator Loss: 3.5747
D(x): 0.9223, D(G(z)): 0.1599
Epoch: [7/20], Batch Num: [527/600]
Discriminator Loss: 0.2923, Generator Loss: 4.3355
D(x): 0.9394, D(G(z)): 0.1104
Epoch: [7/20], Batch Num: [528/600]
Discriminator Loss: 0.3170, Generator Loss: 4.2291
D(x): 0.9099, D(G(z)): 0.0672
Epoch: [7/20], Batch Num: [529/600]
Discriminator Loss: 0.3187, Generator Loss: 4.4777
D(x): 0.8945, D(G(z)): 0.0637
Epoch: [7/20], Batch Num: [530/600]
Discriminator Loss: 0.2609, Generator Loss: 3.5727
D(x): 0.9112, D(G(z)): 0.0504
Epoch: [7/20], Batch Num: [531/600]
Discriminator Loss: 0.3431, Generator Loss: 3.3595
D(x): 0.9274, D(G(z)): 0.1397
Epoch: [7/20], Batch Num: [532/600]
Discriminator Loss: 0.3534, Generator Loss: 4.2230
D(x): 0.9335, D(G(z)): 0.1292
Epoch: [7/20], Batch Num: [533/600]
Discriminator Loss: 0.2329, Generator Loss: 4.7254
D(x): 0.9213, D(G(z)): 0.0670
Epoch: [7/20], Batch Num: [534/600]
Discriminator Loss: 0.1444, Generator Loss: 4.7093
D(x): 0.9394, D(G(z)): 0.0522
Epoch: [7/20], Batch Num: [535/600]
Discriminator Loss: 0.4309, Generator Loss: 3.6997
D(x): 0.8400, D(G(z)): 0.0437
Epoch: [7/20], Batch Num: [536/600]
Discriminator Loss: 0.1744, Generator Loss: 3.5195
D(x): 0.9425, D(G(z)): 0.0693
Epoch: [7/20], Batch Num: [537/600]
Discriminator Loss: 0.1825, Generator Loss: 3.3973
D(x): 0.9561, D(G(z)): 0.1003
Epoch: [7/20], Batch Num: [538/600]
Discriminator Loss: 0.4039, Generator Loss: 4.1842
D(x): 0.9484, D(G(z)): 0.1659
Epoch: [7/20], Batch Num: [539/600]
Discriminator Loss: 0.3695, Generator Loss: 4.3979
D(x): 0.8814, D(G(z)): 0.0790
Epoch: [7/20], Batch Num: [540/600]
Discriminator Loss: 0.1938, Generator Loss: 4.3262
D(x): 0.9171, D(G(z)): 0.0409
Epoch: [7/20], Batch Num: [541/600]
Discriminator Loss: 0.3749, Generator Loss: 3.4694
D(x): 0.8470, D(G(z)): 0.0344
Epoch: [7/20], Batch Num: [542/600]
Discriminator Loss: 0.2910, Generator Loss: 3.1232
D(x): 0.9320, D(G(z)): 0.1107
Epoch: [7/20], Batch Num: [543/600]
Discriminator Loss: 0.2266, Generator Loss: 3.2163
D(x): 0.9710, D(G(z)): 0.1336
Epoch: [7/20], Batch Num: [544/600]
Discriminator Loss: 0.3390, Generator Loss: 3.8502
D(x): 0.9202, D(G(z)): 0.1391
Epoch: [7/20], Batch Num: [545/600]
Discriminator Loss: 0.3073, Generator Loss: 4.1100
D(x): 0.8797, D(G(z)): 0.0695
Epoch: [7/20], Batch Num: [546/600]
Discriminator Loss: 0.3785, Generator Loss: 4.1796
D(x): 0.8643, D(G(z)): 0.0569
Epoch: [7/20], Batch Num: [547/600]
Discriminator Loss: 0.2464, Generator Loss: 3.4659
D(x): 0.9113, D(G(z)): 0.0753
Epoch: [7/20], Batch Num: [548/600]
Discriminator Loss: 0.2763, Generator Loss: 3.1543
D(x): 0.9285, D(G(z)): 0.0965
Epoch: [7/20], Batch Num: [549/600]
Discriminator Loss: 0.4107, Generator Loss: 3.6082
D(x): 0.9281, D(G(z)): 0.1646
Epoch: [7/20], Batch Num: [550/600]
Discriminator Loss: 0.2600, Generator Loss: 4.6730
D(x): 0.9496, D(G(z)): 0.1031
Epoch: [7/20], Batch Num: [551/600]
Discriminator Loss: 0.2539, Generator Loss: 4.7896
D(x): 0.8957, D(G(z)): 0.0522
Epoch: [7/20], Batch Num: [552/600]
Discriminator Loss: 0.2983, Generator Loss: 4.2335
D(x): 0.8868, D(G(z)): 0.0364
Epoch: [7/20], Batch Num: [553/600]
Discriminator Loss: 0.3113, Generator Loss: 3.6550
D(x): 0.8764, D(G(z)): 0.0503
Epoch: [7/20], Batch Num: [554/600]
Discriminator Loss: 0.2906, Generator Loss: 2.8478
D(x): 0.8969, D(G(z)): 0.0502
Epoch: [7/20], Batch Num: [555/600]
Discriminator Loss: 0.2943, Generator Loss: 3.3255
D(x): 0.9924, D(G(z)): 0.1691
Epoch: [7/20], Batch Num: [556/600]
Discriminator Loss: 0.3293, Generator Loss: 4.7109
D(x): 0.9592, D(G(z)): 0.1654
Epoch: [7/20], Batch Num: [557/600]
Discriminator Loss: 0.2720, Generator Loss: 4.8811
D(x): 0.8914, D(G(z)): 0.0464
Epoch: [7/20], Batch Num: [558/600]
Discriminator Loss: 0.5516, Generator Loss: 4.5798
D(x): 0.8285, D(G(z)): 0.0251
Epoch: [7/20], Batch Num: [559/600]
Discriminator Loss: 0.2632, Generator Loss: 3.8788
D(x): 0.9093, D(G(z)): 0.0504
Epoch: [7/20], Batch Num: [560/600]
Discriminator Loss: 0.3777, Generator Loss: 3.0767
D(x): 0.9116, D(G(z)): 0.0920
Epoch: [7/20], Batch Num: [561/600]
Discriminator Loss: 0.2410, Generator Loss: 3.0303
D(x): 0.9709, D(G(z)): 0.1267
Epoch: [7/20], Batch Num: [562/600]
Discriminator Loss: 0.3227, Generator Loss: 3.7996
D(x): 0.9554, D(G(z)): 0.1446
Epoch: [7/20], Batch Num: [563/600]
Discriminator Loss: 0.1947, Generator Loss: 4.4499
D(x): 0.9433, D(G(z)): 0.0758
Epoch: [7/20], Batch Num: [564/600]
Discriminator Loss: 0.3234, Generator Loss: 4.3432
D(x): 0.8791, D(G(z)): 0.0405
Epoch: [7/20], Batch Num: [565/600]
Discriminator Loss: 0.2775, Generator Loss: 4.1492
D(x): 0.9104, D(G(z)): 0.0230
Epoch: [7/20], Batch Num: [566/600]
Discriminator Loss: 0.2290, Generator Loss: 3.5767
D(x): 0.9203, D(G(z)): 0.0610
Epoch: [7/20], Batch Num: [567/600]
Discriminator Loss: 0.2823, Generator Loss: 3.1601
D(x): 0.9341, D(G(z)): 0.0896
Epoch: [7/20], Batch Num: [568/600]
Discriminator Loss: 0.2262, Generator Loss: 3.3667
D(x): 0.9493, D(G(z)): 0.1062
Epoch: [7/20], Batch Num: [569/600]
Discriminator Loss: 0.3186, Generator Loss: 3.8940
D(x): 0.9095, D(G(z)): 0.1096
Epoch: [7/20], Batch Num: [570/600]
Discriminator Loss: 0.3556, Generator Loss: 4.1022
D(x): 0.9191, D(G(z)): 0.0898
Epoch: [7/20], Batch Num: [571/600]
Discriminator Loss: 0.3075, Generator Loss: 4.4625
D(x): 0.9111, D(G(z)): 0.0733
Epoch: [7/20], Batch Num: [572/600]
Discriminator Loss: 0.3757, Generator Loss: 3.7345
D(x): 0.8631, D(G(z)): 0.0611
Epoch: [7/20], Batch Num: [573/600]
Discriminator Loss: 0.2543, Generator Loss: 3.0938
D(x): 0.9102, D(G(z)): 0.0600
Epoch: [7/20], Batch Num: [574/600]
Discriminator Loss: 0.2946, Generator Loss: 2.8487
D(x): 0.9323, D(G(z)): 0.1068
Epoch: [7/20], Batch Num: [575/600]
Discriminator Loss: 0.2259, Generator Loss: 3.5125
D(x): 0.9747, D(G(z)): 0.1517
Epoch: [7/20], Batch Num: [576/600]
Discriminator Loss: 0.2975, Generator Loss: 3.8601
D(x): 0.9211, D(G(z)): 0.0890
Epoch: [7/20], Batch Num: [577/600]
Discriminator Loss: 0.4910, Generator Loss: 4.0608
D(x): 0.8708, D(G(z)): 0.0894
Epoch: [7/20], Batch Num: [578/600]
Discriminator Loss: 0.2757, Generator Loss: 4.2343
D(x): 0.9131, D(G(z)): 0.0611
Epoch: [7/20], Batch Num: [579/600]
Discriminator Loss: 0.1560, Generator Loss: 4.2372
D(x): 0.9439, D(G(z)): 0.0618
Epoch: [7/20], Batch Num: [580/600]
Discriminator Loss: 0.3766, Generator Loss: 4.1529
D(x): 0.9010, D(G(z)): 0.0789
Epoch: [7/20], Batch Num: [581/600]
Discriminator Loss: 0.2727, Generator Loss: 4.0346
D(x): 0.9222, D(G(z)): 0.0626
Epoch: [7/20], Batch Num: [582/600]
Discriminator Loss: 0.1860, Generator Loss: 3.8190
D(x): 0.9411, D(G(z)): 0.0643
Epoch: [7/20], Batch Num: [583/600]
Discriminator Loss: 0.1977, Generator Loss: 3.8583
D(x): 0.9366, D(G(z)): 0.0649
Epoch: [7/20], Batch Num: [584/600]
Discriminator Loss: 0.2622, Generator Loss: 3.8998
D(x): 0.9312, D(G(z)): 0.0715
Epoch: [7/20], Batch Num: [585/600]
Discriminator Loss: 0.1719, Generator Loss: 3.8072
D(x): 0.9686, D(G(z)): 0.0767
Epoch: [7/20], Batch Num: [586/600]
Discriminator Loss: 0.3177, Generator Loss: 3.8531
D(x): 0.9021, D(G(z)): 0.0525
Epoch: [7/20], Batch Num: [587/600]
Discriminator Loss: 0.3785, Generator Loss: 3.6559
D(x): 0.8793, D(G(z)): 0.0794
Epoch: [7/20], Batch Num: [588/600]
Discriminator Loss: 0.3232, Generator Loss: 3.9293
D(x): 0.9374, D(G(z)): 0.1235
Epoch: [7/20], Batch Num: [589/600]
Discriminator Loss: 0.2113, Generator Loss: 4.2802
D(x): 0.9687, D(G(z)): 0.0977
Epoch: [7/20], Batch Num: [590/600]
Discriminator Loss: 0.3152, Generator Loss: 4.1818
D(x): 0.8928, D(G(z)): 0.0457
Epoch: [7/20], Batch Num: [591/600]
Discriminator Loss: 0.2520, Generator Loss: 3.7692
D(x): 0.9028, D(G(z)): 0.0428
Epoch: [7/20], Batch Num: [592/600]
Discriminator Loss: 0.2989, Generator Loss: 3.3596
D(x): 0.9142, D(G(z)): 0.0843
Epoch: [7/20], Batch Num: [593/600]
Discriminator Loss: 0.1876, Generator Loss: 3.2874
D(x): 0.9649, D(G(z)): 0.0995
Epoch: [7/20], Batch Num: [594/600]
Discriminator Loss: 0.2115, Generator Loss: 4.3060
D(x): 0.9644, D(G(z)): 0.0987
Epoch: [7/20], Batch Num: [595/600]
Discriminator Loss: 0.2421, Generator Loss: 4.2359
D(x): 0.9235, D(G(z)): 0.0848
Epoch: [7/20], Batch Num: [596/600]
Discriminator Loss: 0.4528, Generator Loss: 4.4945
D(x): 0.8740, D(G(z)): 0.0733
Epoch: [7/20], Batch Num: [597/600]
Discriminator Loss: 0.2843, Generator Loss: 3.8141
D(x): 0.9006, D(G(z)): 0.0425
Epoch: [7/20], Batch Num: [598/600]
Discriminator Loss: 0.4463, Generator Loss: 3.0126
D(x): 0.8733, D(G(z)): 0.0712
Epoch: [7/20], Batch Num: [599/600]
Discriminator Loss: 0.2908, Generator Loss: 3.0027
D(x): 0.9660, D(G(z)): 0.1522
Epoch: 8, Batch Num: [0/600]

Epoch: [8/20], Batch Num: [0/600]
Discriminator Loss: 0.2972, Generator Loss: 3.8671
D(x): 0.9553, D(G(z)): 0.1476
Epoch: [8/20], Batch Num: [1/600]
Discriminator Loss: 0.3030, Generator Loss: 4.4037
D(x): 0.9168, D(G(z)): 0.0766
Epoch: [8/20], Batch Num: [2/600]
Discriminator Loss: 0.3984, Generator Loss: 4.1119
D(x): 0.8328, D(G(z)): 0.0430
Epoch: [8/20], Batch Num: [3/600]
Discriminator Loss: 0.2880, Generator Loss: 3.1991
D(x): 0.8838, D(G(z)): 0.0513
Epoch: [8/20], Batch Num: [4/600]
Discriminator Loss: 0.2419, Generator Loss: 2.5141
D(x): 0.9297, D(G(z)): 0.0935
Epoch: [8/20], Batch Num: [5/600]
Discriminator Loss: 0.4253, Generator Loss: 2.8198
D(x): 0.9446, D(G(z)): 0.2139
Epoch: [8/20], Batch Num: [6/600]
Discriminator Loss: 0.3938, Generator Loss: 3.8721
D(x): 0.9237, D(G(z)): 0.1640
Epoch: [8/20], Batch Num: [7/600]
Discriminator Loss: 0.3496, Generator Loss: 4.6216
D(x): 0.8870, D(G(z)): 0.0822
Epoch: [8/20], Batch Num: [8/600]
Discriminator Loss: 0.4140, Generator Loss: 4.4749
D(x): 0.8482, D(G(z)): 0.0413
Epoch: [8/20], Batch Num: [9/600]
Discriminator Loss: 0.4921, Generator Loss: 3.5448
D(x): 0.8316, D(G(z)): 0.0387
Epoch: [8/20], Batch Num: [10/600]
Discriminator Loss: 0.2822, Generator Loss: 2.8835
D(x): 0.9232, D(G(z)): 0.0851
Epoch: [8/20], Batch Num: [11/600]
Discriminator Loss: 0.3421, Generator Loss: 3.1035
D(x): 0.9601, D(G(z)): 0.1574
Epoch: [8/20], Batch Num: [12/600]
Discriminator Loss: 0.2978, Generator Loss: 3.0082
D(x): 0.9242, D(G(z)): 0.1131
Epoch: [8/20], Batch Num: [13/600]
Discriminator Loss: 0.2376, Generator Loss: 3.7274
D(x): 0.9629, D(G(z)): 0.1178
Epoch: [8/20], Batch Num: [14/600]
Discriminator Loss: 0.2851, Generator Loss: 4.2297
D(x): 0.9329, D(G(z)): 0.0926
Epoch: [8/20], Batch Num: [15/600]
Discriminator Loss: 0.4073, Generator Loss: 4.6372
D(x): 0.8623, D(G(z)): 0.0667
Epoch: [8/20], Batch Num: [16/600]
Discriminator Loss: 0.3454, Generator Loss: 4.0462
D(x): 0.8774, D(G(z)): 0.0268
Epoch: [8/20], Batch Num: [17/600]
Discriminator Loss: 0.3149, Generator Loss: 4.1662
D(x): 0.8794, D(G(z)): 0.0591
Epoch: [8/20], Batch Num: [18/600]
Discriminator Loss: 0.2807, Generator Loss: 3.0999
D(x): 0.9635, D(G(z)): 0.1085
Epoch: [8/20], Batch Num: [19/600]
Discriminator Loss: 0.2025, Generator Loss: 3.5785
D(x): 0.9593, D(G(z)): 0.1051
Epoch: [8/20], Batch Num: [20/600]
Discriminator Loss: 0.3301, Generator Loss: 4.4041
D(x): 0.9117, D(G(z)): 0.1155
Epoch: [8/20], Batch Num: [21/600]
Discriminator Loss: 0.3697, Generator Loss: 3.7663
D(x): 0.8787, D(G(z)): 0.0510
Epoch: [8/20], Batch Num: [22/600]
Discriminator Loss: 0.3217, Generator Loss: 3.6192
D(x): 0.8820, D(G(z)): 0.0666
Epoch: [8/20], Batch Num: [23/600]
Discriminator Loss: 0.2027, Generator Loss: 3.0806
D(x): 0.9237, D(G(z)): 0.0652
Epoch: [8/20], Batch Num: [24/600]
Discriminator Loss: 0.4076, Generator Loss: 3.1018
D(x): 0.8987, D(G(z)): 0.1195
Epoch: [8/20], Batch Num: [25/600]
Discriminator Loss: 0.3723, Generator Loss: 3.1137
D(x): 0.9158, D(G(z)): 0.1399
Epoch: [8/20], Batch Num: [26/600]
Discriminator Loss: 0.3013, Generator Loss: 3.1111
D(x): 0.9179, D(G(z)): 0.1192
Epoch: [8/20], Batch Num: [27/600]
Discriminator Loss: 0.4440, Generator Loss: 3.2741
D(x): 0.8798, D(G(z)): 0.1133
Epoch: [8/20], Batch Num: [28/600]
Discriminator Loss: 0.3597, Generator Loss: 3.1988
D(x): 0.8885, D(G(z)): 0.0863
Epoch: [8/20], Batch Num: [29/600]
Discriminator Loss: 0.2473, Generator Loss: 3.3935
D(x): 0.9386, D(G(z)): 0.1113
Epoch: [8/20], Batch Num: [30/600]
Discriminator Loss: 0.2895, Generator Loss: 3.2554
D(x): 0.9014, D(G(z)): 0.0631
Epoch: [8/20], Batch Num: [31/600]
Discriminator Loss: 0.3333, Generator Loss: 3.1231
D(x): 0.8771, D(G(z)): 0.0753
Epoch: [8/20], Batch Num: [32/600]
Discriminator Loss: 0.4118, Generator Loss: 2.9606
D(x): 0.9084, D(G(z)): 0.1283
Epoch: [8/20], Batch Num: [33/600]
Discriminator Loss: 0.3356, Generator Loss: 3.3132
D(x): 0.9164, D(G(z)): 0.1271
Epoch: [8/20], Batch Num: [34/600]
Discriminator Loss: 0.2900, Generator Loss: 3.9851
D(x): 0.9229, D(G(z)): 0.1040
Epoch: [8/20], Batch Num: [35/600]
Discriminator Loss: 0.3080, Generator Loss: 3.8337
D(x): 0.8748, D(G(z)): 0.0524
Epoch: [8/20], Batch Num: [36/600]
Discriminator Loss: 0.2343, Generator Loss: 3.6530
D(x): 0.9286, D(G(z)): 0.0795
Epoch: [8/20], Batch Num: [37/600]
Discriminator Loss: 0.2165, Generator Loss: 3.6896
D(x): 0.9326, D(G(z)): 0.0726
Epoch: [8/20], Batch Num: [38/600]
Discriminator Loss: 0.3075, Generator Loss: 3.6012
D(x): 0.8945, D(G(z)): 0.0911
Epoch: [8/20], Batch Num: [39/600]
Discriminator Loss: 0.3396, Generator Loss: 3.6923
D(x): 0.8940, D(G(z)): 0.1003
Epoch: [8/20], Batch Num: [40/600]
Discriminator Loss: 0.4957, Generator Loss: 3.3705
D(x): 0.8545, D(G(z)): 0.0978
Epoch: [8/20], Batch Num: [41/600]
Discriminator Loss: 0.2681, Generator Loss: 3.2738
D(x): 0.9364, D(G(z)): 0.1182
Epoch: [8/20], Batch Num: [42/600]
Discriminator Loss: 0.2122, Generator Loss: 3.5918
D(x): 0.9347, D(G(z)): 0.0916
Epoch: [8/20], Batch Num: [43/600]
Discriminator Loss: 0.3363, Generator Loss: 3.7750
D(x): 0.8906, D(G(z)): 0.0858
Epoch: [8/20], Batch Num: [44/600]
Discriminator Loss: 0.2941, Generator Loss: 3.7038
D(x): 0.9062, D(G(z)): 0.0867
Epoch: [8/20], Batch Num: [45/600]
Discriminator Loss: 0.4352, Generator Loss: 3.4975
D(x): 0.8846, D(G(z)): 0.1123
Epoch: [8/20], Batch Num: [46/600]
Discriminator Loss: 0.3606, Generator Loss: 3.9533
D(x): 0.9077, D(G(z)): 0.1054
Epoch: [8/20], Batch Num: [47/600]
Discriminator Loss: 0.3700, Generator Loss: 3.9009
D(x): 0.9048, D(G(z)): 0.1023
Epoch: [8/20], Batch Num: [48/600]
Discriminator Loss: 0.2519, Generator Loss: 4.0460
D(x): 0.9253, D(G(z)): 0.0694
Epoch: [8/20], Batch Num: [49/600]
Discriminator Loss: 0.3113, Generator Loss: 4.1005
D(x): 0.8959, D(G(z)): 0.0739
Epoch: [8/20], Batch Num: [50/600]
Discriminator Loss: 0.3545, Generator Loss: 3.6899
D(x): 0.8856, D(G(z)): 0.0890
Epoch: [8/20], Batch Num: [51/600]
Discriminator Loss: 0.3509, Generator Loss: 3.1707
D(x): 0.9144, D(G(z)): 0.0934
Epoch: [8/20], Batch Num: [52/600]
Discriminator Loss: 0.3891, Generator Loss: 3.4638
D(x): 0.8834, D(G(z)): 0.1204
Epoch: [8/20], Batch Num: [53/600]
Discriminator Loss: 0.3625, Generator Loss: 4.3502
D(x): 0.9447, D(G(z)): 0.1494
Epoch: [8/20], Batch Num: [54/600]
Discriminator Loss: 0.2900, Generator Loss: 4.3275
D(x): 0.8725, D(G(z)): 0.0453
Epoch: [8/20], Batch Num: [55/600]
Discriminator Loss: 0.3352, Generator Loss: 4.1160
D(x): 0.8897, D(G(z)): 0.0891
Epoch: [8/20], Batch Num: [56/600]
Discriminator Loss: 0.3033, Generator Loss: 3.2814
D(x): 0.8999, D(G(z)): 0.0703
Epoch: [8/20], Batch Num: [57/600]
Discriminator Loss: 0.3740, Generator Loss: 3.2889
D(x): 0.8905, D(G(z)): 0.1137
Epoch: [8/20], Batch Num: [58/600]
Discriminator Loss: 0.2870, Generator Loss: 3.6483
D(x): 0.9402, D(G(z)): 0.1101
Epoch: [8/20], Batch Num: [59/600]
Discriminator Loss: 0.4603, Generator Loss: 3.8878
D(x): 0.8673, D(G(z)): 0.1185
Epoch: [8/20], Batch Num: [60/600]
Discriminator Loss: 0.5497, Generator Loss: 3.4921
D(x): 0.8425, D(G(z)): 0.0964
Epoch: [8/20], Batch Num: [61/600]
Discriminator Loss: 0.3332, Generator Loss: 3.2701
D(x): 0.8770, D(G(z)): 0.0756
Epoch: [8/20], Batch Num: [62/600]
Discriminator Loss: 0.2936, Generator Loss: 3.5365
D(x): 0.9101, D(G(z)): 0.1153
Epoch: [8/20], Batch Num: [63/600]
Discriminator Loss: 0.3230, Generator Loss: 3.6090
D(x): 0.9407, D(G(z)): 0.1321
Epoch: [8/20], Batch Num: [64/600]
Discriminator Loss: 0.2972, Generator Loss: 4.0728
D(x): 0.9294, D(G(z)): 0.1075
Epoch: [8/20], Batch Num: [65/600]
Discriminator Loss: 0.3052, Generator Loss: 4.7897
D(x): 0.8897, D(G(z)): 0.0618
Epoch: [8/20], Batch Num: [66/600]
Discriminator Loss: 0.3357, Generator Loss: 4.2001
D(x): 0.8698, D(G(z)): 0.0589
Epoch: [8/20], Batch Num: [67/600]
Discriminator Loss: 0.3801, Generator Loss: 3.5011
D(x): 0.8568, D(G(z)): 0.0428
Epoch: [8/20], Batch Num: [68/600]
Discriminator Loss: 0.4058, Generator Loss: 2.9934
D(x): 0.9405, D(G(z)): 0.1639
Epoch: [8/20], Batch Num: [69/600]
Discriminator Loss: 0.3621, Generator Loss: 3.7986
D(x): 0.9362, D(G(z)): 0.1361
Epoch: [8/20], Batch Num: [70/600]
Discriminator Loss: 0.4140, Generator Loss: 4.2117
D(x): 0.9219, D(G(z)): 0.1308
Epoch: [8/20], Batch Num: [71/600]
Discriminator Loss: 0.4410, Generator Loss: 5.0273
D(x): 0.8669, D(G(z)): 0.0657
Epoch: [8/20], Batch Num: [72/600]
Discriminator Loss: 0.3531, Generator Loss: 4.8249
D(x): 0.8671, D(G(z)): 0.0572
Epoch: [8/20], Batch Num: [73/600]
Discriminator Loss: 0.3648, Generator Loss: 4.0711
D(x): 0.8547, D(G(z)): 0.0673
Epoch: [8/20], Batch Num: [74/600]
Discriminator Loss: 0.2754, Generator Loss: 3.0788
D(x): 0.9172, D(G(z)): 0.0883
Epoch: [8/20], Batch Num: [75/600]
Discriminator Loss: 0.2819, Generator Loss: 3.3893
D(x): 0.9482, D(G(z)): 0.1312
Epoch: [8/20], Batch Num: [76/600]
Discriminator Loss: 0.3327, Generator Loss: 4.3341
D(x): 0.9492, D(G(z)): 0.1675
Epoch: [8/20], Batch Num: [77/600]
Discriminator Loss: 0.3657, Generator Loss: 4.4229
D(x): 0.8511, D(G(z)): 0.0825
Epoch: [8/20], Batch Num: [78/600]
Discriminator Loss: 0.2943, Generator Loss: 4.5839
D(x): 0.8854, D(G(z)): 0.0653
Epoch: [8/20], Batch Num: [79/600]
Discriminator Loss: 0.4855, Generator Loss: 3.9631
D(x): 0.8508, D(G(z)): 0.0964
Epoch: [8/20], Batch Num: [80/600]
Discriminator Loss: 0.4243, Generator Loss: 4.0959
D(x): 0.9065, D(G(z)): 0.1192
Epoch: [8/20], Batch Num: [81/600]
Discriminator Loss: 0.5149, Generator Loss: 3.4443
D(x): 0.9033, D(G(z)): 0.1479
Epoch: [8/20], Batch Num: [82/600]
Discriminator Loss: 0.2481, Generator Loss: 4.0930
D(x): 0.9211, D(G(z)): 0.0805
Epoch: [8/20], Batch Num: [83/600]
Discriminator Loss: 0.2652, Generator Loss: 4.2778
D(x): 0.9205, D(G(z)): 0.0962
Epoch: [8/20], Batch Num: [84/600]
Discriminator Loss: 0.3444, Generator Loss: 4.4191
D(x): 0.8651, D(G(z)): 0.0892
Epoch: [8/20], Batch Num: [85/600]
Discriminator Loss: 0.3420, Generator Loss: 3.9518
D(x): 0.8746, D(G(z)): 0.0995
Epoch: [8/20], Batch Num: [86/600]
Discriminator Loss: 0.2671, Generator Loss: 4.1814
D(x): 0.9212, D(G(z)): 0.1000
Epoch: [8/20], Batch Num: [87/600]
Discriminator Loss: 0.3269, Generator Loss: 3.6936
D(x): 0.9218, D(G(z)): 0.1162
Epoch: [8/20], Batch Num: [88/600]
Discriminator Loss: 0.4257, Generator Loss: 4.6989
D(x): 0.8779, D(G(z)): 0.0959
Epoch: [8/20], Batch Num: [89/600]
Discriminator Loss: 0.3774, Generator Loss: 4.1801
D(x): 0.8598, D(G(z)): 0.0627
Epoch: [8/20], Batch Num: [90/600]
Discriminator Loss: 0.3880, Generator Loss: 3.6064
D(x): 0.8676, D(G(z)): 0.1025
Epoch: [8/20], Batch Num: [91/600]
Discriminator Loss: 0.2967, Generator Loss: 3.5224
D(x): 0.9238, D(G(z)): 0.1040
Epoch: [8/20], Batch Num: [92/600]
Discriminator Loss: 0.2898, Generator Loss: 3.4925
D(x): 0.9428, D(G(z)): 0.1193
Epoch: [8/20], Batch Num: [93/600]
Discriminator Loss: 0.3013, Generator Loss: 4.0275
D(x): 0.9134, D(G(z)): 0.1212
Epoch: [8/20], Batch Num: [94/600]
Discriminator Loss: 0.2843, Generator Loss: 4.7131
D(x): 0.9238, D(G(z)): 0.0765
Epoch: [8/20], Batch Num: [95/600]
Discriminator Loss: 0.2931, Generator Loss: 4.5978
D(x): 0.8919, D(G(z)): 0.0821
Epoch: [8/20], Batch Num: [96/600]
Discriminator Loss: 0.3287, Generator Loss: 4.2293
D(x): 0.8738, D(G(z)): 0.0785
Epoch: [8/20], Batch Num: [97/600]
Discriminator Loss: 0.3416, Generator Loss: 4.1006
D(x): 0.8804, D(G(z)): 0.1031
Epoch: [8/20], Batch Num: [98/600]
Discriminator Loss: 0.3240, Generator Loss: 3.9315
D(x): 0.9130, D(G(z)): 0.0947
Epoch: [8/20], Batch Num: [99/600]
Discriminator Loss: 0.3630, Generator Loss: 3.5637
D(x): 0.8837, D(G(z)): 0.1055
Epoch: 8, Batch Num: [100/600]

Epoch: [8/20], Batch Num: [100/600]
Discriminator Loss: 0.2708, Generator Loss: 3.4144
D(x): 0.9240, D(G(z)): 0.0937
Epoch: [8/20], Batch Num: [101/600]
Discriminator Loss: 0.3345, Generator Loss: 4.1711
D(x): 0.9113, D(G(z)): 0.1270
Epoch: [8/20], Batch Num: [102/600]
Discriminator Loss: 0.2460, Generator Loss: 4.8013
D(x): 0.9272, D(G(z)): 0.0539
Epoch: [8/20], Batch Num: [103/600]
Discriminator Loss: 0.3271, Generator Loss: 5.3758
D(x): 0.8922, D(G(z)): 0.0883
Epoch: [8/20], Batch Num: [104/600]
Discriminator Loss: 0.3235, Generator Loss: 4.4775
D(x): 0.8769, D(G(z)): 0.0533
Epoch: [8/20], Batch Num: [105/600]
Discriminator Loss: 0.3577, Generator Loss: 3.8007
D(x): 0.8997, D(G(z)): 0.0703
Epoch: [8/20], Batch Num: [106/600]
Discriminator Loss: 0.4591, Generator Loss: 3.3894
D(x): 0.8949, D(G(z)): 0.1190
Epoch: [8/20], Batch Num: [107/600]
Discriminator Loss: 0.5135, Generator Loss: 4.2245
D(x): 0.9029, D(G(z)): 0.1761
Epoch: [8/20], Batch Num: [108/600]
Discriminator Loss: 0.3506, Generator Loss: 4.4838
D(x): 0.9054, D(G(z)): 0.1060
Epoch: [8/20], Batch Num: [109/600]
Discriminator Loss: 0.3594, Generator Loss: 4.2348
D(x): 0.8858, D(G(z)): 0.0697
Epoch: [8/20], Batch Num: [110/600]
Discriminator Loss: 0.2582, Generator Loss: 4.2725
D(x): 0.9032, D(G(z)): 0.0532
Epoch: [8/20], Batch Num: [111/600]
Discriminator Loss: 0.2682, Generator Loss: 3.9380
D(x): 0.9205, D(G(z)): 0.0795
Epoch: [8/20], Batch Num: [112/600]
Discriminator Loss: 0.3660, Generator Loss: 3.3906
D(x): 0.8803, D(G(z)): 0.0909
Epoch: [8/20], Batch Num: [113/600]
Discriminator Loss: 0.3792, Generator Loss: 3.3978
D(x): 0.9372, D(G(z)): 0.1738
Epoch: [8/20], Batch Num: [114/600]
Discriminator Loss: 0.4270, Generator Loss: 4.3988
D(x): 0.9151, D(G(z)): 0.1390
Epoch: [8/20], Batch Num: [115/600]
Discriminator Loss: 0.3106, Generator Loss: 4.5260
D(x): 0.9060, D(G(z)): 0.0528
Epoch: [8/20], Batch Num: [116/600]
Discriminator Loss: 0.5483, Generator Loss: 4.3664
D(x): 0.8420, D(G(z)): 0.0450
Epoch: [8/20], Batch Num: [117/600]
Discriminator Loss: 0.5209, Generator Loss: 3.4270
D(x): 0.8545, D(G(z)): 0.1038
Epoch: [8/20], Batch Num: [118/600]
Discriminator Loss: 0.3916, Generator Loss: 3.0160
D(x): 0.9232, D(G(z)): 0.1556
Epoch: [8/20], Batch Num: [119/600]
Discriminator Loss: 0.4851, Generator Loss: 3.3791
D(x): 0.9036, D(G(z)): 0.1565
Epoch: [8/20], Batch Num: [120/600]
Discriminator Loss: 0.2956, Generator Loss: 4.1154
D(x): 0.9312, D(G(z)): 0.1300
Epoch: [8/20], Batch Num: [121/600]
Discriminator Loss: 0.3946, Generator Loss: 4.1857
D(x): 0.8777, D(G(z)): 0.0757
Epoch: [8/20], Batch Num: [122/600]
Discriminator Loss: 0.5525, Generator Loss: 3.5084
D(x): 0.7799, D(G(z)): 0.0570
Epoch: [8/20], Batch Num: [123/600]
Discriminator Loss: 0.4272, Generator Loss: 2.6289
D(x): 0.8337, D(G(z)): 0.0844
Epoch: [8/20], Batch Num: [124/600]
Discriminator Loss: 0.3814, Generator Loss: 2.6631
D(x): 0.9452, D(G(z)): 0.1963
Epoch: [8/20], Batch Num: [125/600]
Discriminator Loss: 0.4178, Generator Loss: 3.0164
D(x): 0.9144, D(G(z)): 0.1914
Epoch: [8/20], Batch Num: [126/600]
Discriminator Loss: 0.4252, Generator Loss: 3.7567
D(x): 0.9103, D(G(z)): 0.1679
Epoch: [8/20], Batch Num: [127/600]
Discriminator Loss: 0.3456, Generator Loss: 4.3626
D(x): 0.8734, D(G(z)): 0.0623
Epoch: [8/20], Batch Num: [128/600]
Discriminator Loss: 0.4531, Generator Loss: 3.7081
D(x): 0.8190, D(G(z)): 0.0680
Epoch: [8/20], Batch Num: [129/600]
Discriminator Loss: 0.5510, Generator Loss: 2.7252
D(x): 0.8004, D(G(z)): 0.0568
Epoch: [8/20], Batch Num: [130/600]
Discriminator Loss: 0.4924, Generator Loss: 2.1664
D(x): 0.8853, D(G(z)): 0.1702
Epoch: [8/20], Batch Num: [131/600]
Discriminator Loss: 0.5907, Generator Loss: 2.2781
D(x): 0.9275, D(G(z)): 0.2540
Epoch: [8/20], Batch Num: [132/600]
Discriminator Loss: 0.3484, Generator Loss: 3.3790
D(x): 0.9594, D(G(z)): 0.2147
Epoch: [8/20], Batch Num: [133/600]
Discriminator Loss: 0.3503, Generator Loss: 4.3182
D(x): 0.8836, D(G(z)): 0.0804
Epoch: [8/20], Batch Num: [134/600]
Discriminator Loss: 0.4810, Generator Loss: 4.7329
D(x): 0.8219, D(G(z)): 0.0358
Epoch: [8/20], Batch Num: [135/600]
Discriminator Loss: 0.5887, Generator Loss: 3.8905
D(x): 0.7770, D(G(z)): 0.0345
Epoch: [8/20], Batch Num: [136/600]
Discriminator Loss: 0.3392, Generator Loss: 2.9333
D(x): 0.8646, D(G(z)): 0.0525
Epoch: [8/20], Batch Num: [137/600]
Discriminator Loss: 0.4667, Generator Loss: 2.4439
D(x): 0.8638, D(G(z)): 0.1412
Epoch: [8/20], Batch Num: [138/600]
Discriminator Loss: 0.4522, Generator Loss: 2.3906
D(x): 0.9330, D(G(z)): 0.2265
Epoch: [8/20], Batch Num: [139/600]
Discriminator Loss: 0.4106, Generator Loss: 2.6703
D(x): 0.9394, D(G(z)): 0.2229
Epoch: [8/20], Batch Num: [140/600]
Discriminator Loss: 0.3307, Generator Loss: 3.5659
D(x): 0.9283, D(G(z)): 0.1577
Epoch: [8/20], Batch Num: [141/600]
Discriminator Loss: 0.3407, Generator Loss: 3.8553
D(x): 0.8644, D(G(z)): 0.0707
Epoch: [8/20], Batch Num: [142/600]
Discriminator Loss: 0.3713, Generator Loss: 4.0330
D(x): 0.8357, D(G(z)): 0.0502
Epoch: [8/20], Batch Num: [143/600]
Discriminator Loss: 0.3676, Generator Loss: 3.5049
D(x): 0.8443, D(G(z)): 0.0612
Epoch: [8/20], Batch Num: [144/600]
Discriminator Loss: 0.2910, Generator Loss: 3.1948
D(x): 0.8658, D(G(z)): 0.0543
Epoch: [8/20], Batch Num: [145/600]
Discriminator Loss: 0.3033, Generator Loss: 2.6982
D(x): 0.9084, D(G(z)): 0.1115
Epoch: [8/20], Batch Num: [146/600]
Discriminator Loss: 0.3905, Generator Loss: 2.6384
D(x): 0.8976, D(G(z)): 0.1529
Epoch: [8/20], Batch Num: [147/600]
Discriminator Loss: 0.2848, Generator Loss: 2.5240
D(x): 0.9552, D(G(z)): 0.1627
Epoch: [8/20], Batch Num: [148/600]
Discriminator Loss: 0.2765, Generator Loss: 3.2064
D(x): 0.9523, D(G(z)): 0.1589
Epoch: [8/20], Batch Num: [149/600]
Discriminator Loss: 0.3210, Generator Loss: 3.7269
D(x): 0.8801, D(G(z)): 0.0845
Epoch: [8/20], Batch Num: [150/600]
Discriminator Loss: 0.4139, Generator Loss: 3.4128
D(x): 0.8399, D(G(z)): 0.0746
Epoch: [8/20], Batch Num: [151/600]
Discriminator Loss: 0.3514, Generator Loss: 3.5024
D(x): 0.8568, D(G(z)): 0.0794
Epoch: [8/20], Batch Num: [152/600]
Discriminator Loss: 0.3181, Generator Loss: 3.0432
D(x): 0.8909, D(G(z)): 0.0944
Epoch: [8/20], Batch Num: [153/600]
Discriminator Loss: 0.3328, Generator Loss: 3.0952
D(x): 0.9079, D(G(z)): 0.1413
Epoch: [8/20], Batch Num: [154/600]
Discriminator Loss: 0.3168, Generator Loss: 3.0022
D(x): 0.9226, D(G(z)): 0.1468
Epoch: [8/20], Batch Num: [155/600]
Discriminator Loss: 0.3246, Generator Loss: 3.1277
D(x): 0.9389, D(G(z)): 0.1436
Epoch: [8/20], Batch Num: [156/600]
Discriminator Loss: 0.2821, Generator Loss: 3.9412
D(x): 0.9376, D(G(z)): 0.0900
Epoch: [8/20], Batch Num: [157/600]
Discriminator Loss: 0.3599, Generator Loss: 4.4888
D(x): 0.8847, D(G(z)): 0.0797
Epoch: [8/20], Batch Num: [158/600]
Discriminator Loss: 0.2301, Generator Loss: 4.0080
D(x): 0.9011, D(G(z)): 0.0598
Epoch: [8/20], Batch Num: [159/600]
Discriminator Loss: 0.3527, Generator Loss: 3.9684
D(x): 0.8887, D(G(z)): 0.0783
Epoch: [8/20], Batch Num: [160/600]
Discriminator Loss: 0.4212, Generator Loss: 3.3855
D(x): 0.8661, D(G(z)): 0.1038
Epoch: [8/20], Batch Num: [161/600]
Discriminator Loss: 0.3318, Generator Loss: 3.6198
D(x): 0.9357, D(G(z)): 0.1604
Epoch: [8/20], Batch Num: [162/600]
Discriminator Loss: 0.3400, Generator Loss: 3.6534
D(x): 0.9187, D(G(z)): 0.1178
Epoch: [8/20], Batch Num: [163/600]
Discriminator Loss: 0.3594, Generator Loss: 3.9005
D(x): 0.9072, D(G(z)): 0.1408
Epoch: [8/20], Batch Num: [164/600]
Discriminator Loss: 0.4570, Generator Loss: 3.9718
D(x): 0.8659, D(G(z)): 0.1013
Epoch: [8/20], Batch Num: [165/600]
Discriminator Loss: 0.3193, Generator Loss: 3.4721
D(x): 0.8934, D(G(z)): 0.0945
Epoch: [8/20], Batch Num: [166/600]
Discriminator Loss: 0.4060, Generator Loss: 2.6861
D(x): 0.8546, D(G(z)): 0.0909
Epoch: [8/20], Batch Num: [167/600]
Discriminator Loss: 0.3887, Generator Loss: 2.8512
D(x): 0.9027, D(G(z)): 0.1890
Epoch: [8/20], Batch Num: [168/600]
Discriminator Loss: 0.3645, Generator Loss: 3.5687
D(x): 0.9367, D(G(z)): 0.1900
Epoch: [8/20], Batch Num: [169/600]
Discriminator Loss: 0.4084, Generator Loss: 3.8327
D(x): 0.8643, D(G(z)): 0.1117
Epoch: [8/20], Batch Num: [170/600]
Discriminator Loss: 0.4077, Generator Loss: 4.1742
D(x): 0.8394, D(G(z)): 0.1082
Epoch: [8/20], Batch Num: [171/600]
Discriminator Loss: 0.3717, Generator Loss: 3.6801
D(x): 0.8643, D(G(z)): 0.0965
Epoch: [8/20], Batch Num: [172/600]
Discriminator Loss: 0.3629, Generator Loss: 3.1919
D(x): 0.8819, D(G(z)): 0.1065
Epoch: [8/20], Batch Num: [173/600]
Discriminator Loss: 0.4405, Generator Loss: 3.0998
D(x): 0.8851, D(G(z)): 0.1629
Epoch: [8/20], Batch Num: [174/600]
Discriminator Loss: 0.3860, Generator Loss: 3.3416
D(x): 0.9170, D(G(z)): 0.1497
Epoch: [8/20], Batch Num: [175/600]
Discriminator Loss: 0.2628, Generator Loss: 3.6080
D(x): 0.8846, D(G(z)): 0.0828
Epoch: [8/20], Batch Num: [176/600]
Discriminator Loss: 0.2890, Generator Loss: 3.5216
D(x): 0.9078, D(G(z)): 0.0981
Epoch: [8/20], Batch Num: [177/600]
Discriminator Loss: 0.3698, Generator Loss: 3.8338
D(x): 0.8866, D(G(z)): 0.1103
Epoch: [8/20], Batch Num: [178/600]
Discriminator Loss: 0.3124, Generator Loss: 3.5790
D(x): 0.8916, D(G(z)): 0.0832
Epoch: [8/20], Batch Num: [179/600]
Discriminator Loss: 0.2684, Generator Loss: 3.9017
D(x): 0.9110, D(G(z)): 0.0963
Epoch: [8/20], Batch Num: [180/600]
Discriminator Loss: 0.3095, Generator Loss: 3.4668
D(x): 0.9043, D(G(z)): 0.0829
Epoch: [8/20], Batch Num: [181/600]
Discriminator Loss: 0.2707, Generator Loss: 3.4718
D(x): 0.9235, D(G(z)): 0.0993
Epoch: [8/20], Batch Num: [182/600]
Discriminator Loss: 0.3116, Generator Loss: 3.7607
D(x): 0.9132, D(G(z)): 0.1103
Epoch: [8/20], Batch Num: [183/600]
Discriminator Loss: 0.2821, Generator Loss: 3.3846
D(x): 0.9279, D(G(z)): 0.0880
Epoch: [8/20], Batch Num: [184/600]
Discriminator Loss: 0.4176, Generator Loss: 3.6460
D(x): 0.8951, D(G(z)): 0.1034
Epoch: [8/20], Batch Num: [185/600]
Discriminator Loss: 0.3569, Generator Loss: 4.1108
D(x): 0.9055, D(G(z)): 0.1155
Epoch: [8/20], Batch Num: [186/600]
Discriminator Loss: 0.2828, Generator Loss: 4.3208
D(x): 0.9092, D(G(z)): 0.0853
Epoch: [8/20], Batch Num: [187/600]
Discriminator Loss: 0.3925, Generator Loss: 3.2820
D(x): 0.8591, D(G(z)): 0.0730
Epoch: [8/20], Batch Num: [188/600]
Discriminator Loss: 0.3528, Generator Loss: 3.5890
D(x): 0.9087, D(G(z)): 0.1200
Epoch: [8/20], Batch Num: [189/600]
Discriminator Loss: 0.3306, Generator Loss: 3.6215
D(x): 0.9113, D(G(z)): 0.1169
Epoch: [8/20], Batch Num: [190/600]
Discriminator Loss: 0.2997, Generator Loss: 4.4238
D(x): 0.9335, D(G(z)): 0.1303
Epoch: [8/20], Batch Num: [191/600]
Discriminator Loss: 0.3477, Generator Loss: 4.6819
D(x): 0.8739, D(G(z)): 0.0684
Epoch: [8/20], Batch Num: [192/600]
Discriminator Loss: 0.3202, Generator Loss: 3.4149
D(x): 0.8346, D(G(z)): 0.0408
Epoch: [8/20], Batch Num: [193/600]
Discriminator Loss: 0.3375, Generator Loss: 3.3862
D(x): 0.9219, D(G(z)): 0.1389
Epoch: [8/20], Batch Num: [194/600]
Discriminator Loss: 0.4081, Generator Loss: 4.1327
D(x): 0.9332, D(G(z)): 0.1843
Epoch: [8/20], Batch Num: [195/600]
Discriminator Loss: 0.4018, Generator Loss: 4.5585
D(x): 0.8836, D(G(z)): 0.1042
Epoch: [8/20], Batch Num: [196/600]
Discriminator Loss: 0.2858, Generator Loss: 4.5193
D(x): 0.9124, D(G(z)): 0.0564
Epoch: [8/20], Batch Num: [197/600]
Discriminator Loss: 0.3602, Generator Loss: 3.6016
D(x): 0.8515, D(G(z)): 0.0467
Epoch: [8/20], Batch Num: [198/600]
Discriminator Loss: 0.3608, Generator Loss: 2.9426
D(x): 0.8775, D(G(z)): 0.0922
Epoch: [8/20], Batch Num: [199/600]
Discriminator Loss: 0.4907, Generator Loss: 3.4923
D(x): 0.9405, D(G(z)): 0.1999
Epoch: 8, Batch Num: [200/600]

Epoch: [8/20], Batch Num: [200/600]
Discriminator Loss: 0.3382, Generator Loss: 4.0488
D(x): 0.9202, D(G(z)): 0.1147
Epoch: [8/20], Batch Num: [201/600]
Discriminator Loss: 0.2842, Generator Loss: 4.4123
D(x): 0.9108, D(G(z)): 0.0830
Epoch: [8/20], Batch Num: [202/600]
Discriminator Loss: 0.3579, Generator Loss: 4.7336
D(x): 0.8653, D(G(z)): 0.0707
Epoch: [8/20], Batch Num: [203/600]
Discriminator Loss: 0.3974, Generator Loss: 4.1545
D(x): 0.8599, D(G(z)): 0.0844
Epoch: [8/20], Batch Num: [204/600]
Discriminator Loss: 0.5486, Generator Loss: 3.0643
D(x): 0.8460, D(G(z)): 0.1016
Epoch: [8/20], Batch Num: [205/600]
Discriminator Loss: 0.3994, Generator Loss: 2.7916
D(x): 0.9007, D(G(z)): 0.1352
Epoch: [8/20], Batch Num: [206/600]
Discriminator Loss: 0.3516, Generator Loss: 2.9320
D(x): 0.9245, D(G(z)): 0.1472
Epoch: [8/20], Batch Num: [207/600]
Discriminator Loss: 0.4436, Generator Loss: 4.1923
D(x): 0.9051, D(G(z)): 0.1706
Epoch: [8/20], Batch Num: [208/600]
Discriminator Loss: 0.4093, Generator Loss: 4.1260
D(x): 0.8545, D(G(z)): 0.0678
Epoch: [8/20], Batch Num: [209/600]
Discriminator Loss: 0.5976, Generator Loss: 3.4486
D(x): 0.8020, D(G(z)): 0.0437
Epoch: [8/20], Batch Num: [210/600]
Discriminator Loss: 0.3641, Generator Loss: 2.5986
D(x): 0.8680, D(G(z)): 0.0914
Epoch: [8/20], Batch Num: [211/600]
Discriminator Loss: 0.3346, Generator Loss: 2.3751
D(x): 0.9228, D(G(z)): 0.1569
Epoch: [8/20], Batch Num: [212/600]
Discriminator Loss: 0.4416, Generator Loss: 2.4966
D(x): 0.9264, D(G(z)): 0.2049
Epoch: [8/20], Batch Num: [213/600]
Discriminator Loss: 0.3678, Generator Loss: 3.4740
D(x): 0.9209, D(G(z)): 0.1548
Epoch: [8/20], Batch Num: [214/600]
Discriminator Loss: 0.3576, Generator Loss: 3.8234
D(x): 0.8763, D(G(z)): 0.0863
Epoch: [8/20], Batch Num: [215/600]
Discriminator Loss: 0.4295, Generator Loss: 4.0149
D(x): 0.8613, D(G(z)): 0.0783
Epoch: [8/20], Batch Num: [216/600]
Discriminator Loss: 0.5140, Generator Loss: 3.4562
D(x): 0.8361, D(G(z)): 0.0818
Epoch: [8/20], Batch Num: [217/600]
Discriminator Loss: 0.4065, Generator Loss: 3.0906
D(x): 0.8606, D(G(z)): 0.0901
Epoch: [8/20], Batch Num: [218/600]
Discriminator Loss: 0.2710, Generator Loss: 2.5216
D(x): 0.9287, D(G(z)): 0.1069
Epoch: [8/20], Batch Num: [219/600]
Discriminator Loss: 0.4785, Generator Loss: 2.3395
D(x): 0.8609, D(G(z)): 0.1342
Epoch: [8/20], Batch Num: [220/600]
Discriminator Loss: 0.4271, Generator Loss: 2.6909
D(x): 0.9276, D(G(z)): 0.2163
Epoch: [8/20], Batch Num: [221/600]
Discriminator Loss: 0.3466, Generator Loss: 3.6853
D(x): 0.9507, D(G(z)): 0.1796
Epoch: [8/20], Batch Num: [222/600]
Discriminator Loss: 0.3671, Generator Loss: 3.9859
D(x): 0.8750, D(G(z)): 0.0628
Epoch: [8/20], Batch Num: [223/600]
Discriminator Loss: 0.3316, Generator Loss: 4.1919
D(x): 0.8543, D(G(z)): 0.0578
Epoch: [8/20], Batch Num: [224/600]
Discriminator Loss: 0.3137, Generator Loss: 3.5729
D(x): 0.8743, D(G(z)): 0.0476
Epoch: [8/20], Batch Num: [225/600]
Discriminator Loss: 0.2960, Generator Loss: 3.0127
D(x): 0.8658, D(G(z)): 0.0560
Epoch: [8/20], Batch Num: [226/600]
Discriminator Loss: 0.2275, Generator Loss: 2.8023
D(x): 0.9384, D(G(z)): 0.0952
Epoch: [8/20], Batch Num: [227/600]
Discriminator Loss: 0.3901, Generator Loss: 2.5705
D(x): 0.8900, D(G(z)): 0.1400
Epoch: [8/20], Batch Num: [228/600]
Discriminator Loss: 0.3537, Generator Loss: 2.9067
D(x): 0.9148, D(G(z)): 0.1559
Epoch: [8/20], Batch Num: [229/600]
Discriminator Loss: 0.3240, Generator Loss: 3.3118
D(x): 0.9434, D(G(z)): 0.1634
Epoch: [8/20], Batch Num: [230/600]
Discriminator Loss: 0.3345, Generator Loss: 3.7007
D(x): 0.8887, D(G(z)): 0.1017
Epoch: [8/20], Batch Num: [231/600]
Discriminator Loss: 0.2454, Generator Loss: 3.6937
D(x): 0.8986, D(G(z)): 0.0625
Epoch: [8/20], Batch Num: [232/600]
Discriminator Loss: 0.2522, Generator Loss: 3.7985
D(x): 0.8855, D(G(z)): 0.0487
Epoch: [8/20], Batch Num: [233/600]
Discriminator Loss: 0.2797, Generator Loss: 3.3297
D(x): 0.8958, D(G(z)): 0.0636
Epoch: [8/20], Batch Num: [234/600]
Discriminator Loss: 0.1892, Generator Loss: 3.1851
D(x): 0.9304, D(G(z)): 0.0616
Epoch: [8/20], Batch Num: [235/600]
Discriminator Loss: 0.3779, Generator Loss: 2.7614
D(x): 0.9031, D(G(z)): 0.1358
Epoch: [8/20], Batch Num: [236/600]
Discriminator Loss: 0.3643, Generator Loss: 3.3022
D(x): 0.9420, D(G(z)): 0.1736
Epoch: [8/20], Batch Num: [237/600]
Discriminator Loss: 0.2646, Generator Loss: 4.1089
D(x): 0.9627, D(G(z)): 0.1183
Epoch: [8/20], Batch Num: [238/600]
Discriminator Loss: 0.2891, Generator Loss: 4.5439
D(x): 0.8953, D(G(z)): 0.0496
Epoch: [8/20], Batch Num: [239/600]
Discriminator Loss: 0.3781, Generator Loss: 4.1033
D(x): 0.8403, D(G(z)): 0.0335
Epoch: [8/20], Batch Num: [240/600]
Discriminator Loss: 0.2704, Generator Loss: 3.5021
D(x): 0.8811, D(G(z)): 0.0501
Epoch: [8/20], Batch Num: [241/600]
Discriminator Loss: 0.1953, Generator Loss: 2.8758
D(x): 0.9339, D(G(z)): 0.0690
Epoch: [8/20], Batch Num: [242/600]
Discriminator Loss: 0.2064, Generator Loss: 2.9222
D(x): 0.9816, D(G(z)): 0.1310
Epoch: [8/20], Batch Num: [243/600]
Discriminator Loss: 0.1777, Generator Loss: 3.4940
D(x): 0.9712, D(G(z)): 0.1081
Epoch: [8/20], Batch Num: [244/600]
Discriminator Loss: 0.2460, Generator Loss: 3.7695
D(x): 0.9294, D(G(z)): 0.1028
Epoch: [8/20], Batch Num: [245/600]
Discriminator Loss: 0.1407, Generator Loss: 4.9120
D(x): 0.9553, D(G(z)): 0.0561
Epoch: [8/20], Batch Num: [246/600]
Discriminator Loss: 0.2502, Generator Loss: 4.9258
D(x): 0.9042, D(G(z)): 0.0472
Epoch: [8/20], Batch Num: [247/600]
Discriminator Loss: 0.2907, Generator Loss: 4.4318
D(x): 0.8739, D(G(z)): 0.0493
Epoch: [8/20], Batch Num: [248/600]
Discriminator Loss: 0.1955, Generator Loss: 3.4466
D(x): 0.9275, D(G(z)): 0.0694
Epoch: [8/20], Batch Num: [249/600]
Discriminator Loss: 0.4246, Generator Loss: 3.4970
D(x): 0.9099, D(G(z)): 0.1608
Epoch: [8/20], Batch Num: [250/600]
Discriminator Loss: 0.3130, Generator Loss: 4.7942
D(x): 0.9426, D(G(z)): 0.1381
Epoch: [8/20], Batch Num: [251/600]
Discriminator Loss: 0.2993, Generator Loss: 4.4530
D(x): 0.8701, D(G(z)): 0.0590
Epoch: [8/20], Batch Num: [252/600]
Discriminator Loss: 0.2368, Generator Loss: 4.4189
D(x): 0.9067, D(G(z)): 0.0530
Epoch: [8/20], Batch Num: [253/600]
Discriminator Loss: 0.2938, Generator Loss: 3.7805
D(x): 0.9172, D(G(z)): 0.0923
Epoch: [8/20], Batch Num: [254/600]
Discriminator Loss: 0.3334, Generator Loss: 3.5314
D(x): 0.9172, D(G(z)): 0.1045
Epoch: [8/20], Batch Num: [255/600]
Discriminator Loss: 0.3019, Generator Loss: 3.9515
D(x): 0.9406, D(G(z)): 0.1275
Epoch: [8/20], Batch Num: [256/600]
Discriminator Loss: 0.2889, Generator Loss: 4.2248
D(x): 0.9222, D(G(z)): 0.0962
Epoch: [8/20], Batch Num: [257/600]
Discriminator Loss: 0.1855, Generator Loss: 5.0595
D(x): 0.9405, D(G(z)): 0.0708
Epoch: [8/20], Batch Num: [258/600]
Discriminator Loss: 0.4113, Generator Loss: 4.2522
D(x): 0.8440, D(G(z)): 0.0391
Epoch: [8/20], Batch Num: [259/600]
Discriminator Loss: 0.2424, Generator Loss: 3.3187
D(x): 0.9166, D(G(z)): 0.0627
Epoch: [8/20], Batch Num: [260/600]
Discriminator Loss: 0.3198, Generator Loss: 3.6734
D(x): 0.9321, D(G(z)): 0.1079
Epoch: [8/20], Batch Num: [261/600]
Discriminator Loss: 0.3730, Generator Loss: 3.7993
D(x): 0.9310, D(G(z)): 0.1631
Epoch: [8/20], Batch Num: [262/600]
Discriminator Loss: 0.4648, Generator Loss: 4.2438
D(x): 0.8980, D(G(z)): 0.1255
Epoch: [8/20], Batch Num: [263/600]
Discriminator Loss: 0.3498, Generator Loss: 4.9872
D(x): 0.8878, D(G(z)): 0.0644
Epoch: [8/20], Batch Num: [264/600]
Discriminator Loss: 0.3019, Generator Loss: 4.3074
D(x): 0.8747, D(G(z)): 0.0469
Epoch: [8/20], Batch Num: [265/600]
Discriminator Loss: 0.4255, Generator Loss: 3.3506
D(x): 0.8502, D(G(z)): 0.0409
Epoch: [8/20], Batch Num: [266/600]
Discriminator Loss: 0.2605, Generator Loss: 2.9101
D(x): 0.9434, D(G(z)): 0.1119
Epoch: [8/20], Batch Num: [267/600]
Discriminator Loss: 0.4365, Generator Loss: 3.1987
D(x): 0.9233, D(G(z)): 0.1853
Epoch: [8/20], Batch Num: [268/600]
Discriminator Loss: 0.3343, Generator Loss: 3.7746
D(x): 0.9225, D(G(z)): 0.1299
Epoch: [8/20], Batch Num: [269/600]
Discriminator Loss: 0.2519, Generator Loss: 4.4100
D(x): 0.9103, D(G(z)): 0.0776
Epoch: [8/20], Batch Num: [270/600]
Discriminator Loss: 0.4157, Generator Loss: 4.3142
D(x): 0.8530, D(G(z)): 0.0628
Epoch: [8/20], Batch Num: [271/600]
Discriminator Loss: 0.4944, Generator Loss: 3.1687
D(x): 0.8157, D(G(z)): 0.0441
Epoch: [8/20], Batch Num: [272/600]
Discriminator Loss: 0.2638, Generator Loss: 2.7905
D(x): 0.9391, D(G(z)): 0.1105
Epoch: [8/20], Batch Num: [273/600]
Discriminator Loss: 0.3062, Generator Loss: 2.8598
D(x): 0.9627, D(G(z)): 0.1811
Epoch: [8/20], Batch Num: [274/600]
Discriminator Loss: 0.4387, Generator Loss: 3.2644
D(x): 0.8901, D(G(z)): 0.1350
Epoch: [8/20], Batch Num: [275/600]
Discriminator Loss: 0.3408, Generator Loss: 3.8489
D(x): 0.9160, D(G(z)): 0.1216
Epoch: [8/20], Batch Num: [276/600]
Discriminator Loss: 0.3527, Generator Loss: 3.9460
D(x): 0.8748, D(G(z)): 0.0638
Epoch: [8/20], Batch Num: [277/600]
Discriminator Loss: 0.3234, Generator Loss: 4.1225
D(x): 0.8895, D(G(z)): 0.0676
Epoch: [8/20], Batch Num: [278/600]
Discriminator Loss: 0.4242, Generator Loss: 3.8000
D(x): 0.8252, D(G(z)): 0.0514
Epoch: [8/20], Batch Num: [279/600]
Discriminator Loss: 0.4137, Generator Loss: 2.7970
D(x): 0.8792, D(G(z)): 0.0963
Epoch: [8/20], Batch Num: [280/600]
Discriminator Loss: 0.3405, Generator Loss: 3.1598
D(x): 0.9626, D(G(z)): 0.1802
Epoch: [8/20], Batch Num: [281/600]
Discriminator Loss: 0.3718, Generator Loss: 3.4648
D(x): 0.9181, D(G(z)): 0.1595
Epoch: [8/20], Batch Num: [282/600]
Discriminator Loss: 0.2287, Generator Loss: 4.0116
D(x): 0.9271, D(G(z)): 0.0698
Epoch: [8/20], Batch Num: [283/600]
Discriminator Loss: 0.3847, Generator Loss: 4.0611
D(x): 0.8662, D(G(z)): 0.0674
Epoch: [8/20], Batch Num: [284/600]
Discriminator Loss: 0.4147, Generator Loss: 3.5988
D(x): 0.8539, D(G(z)): 0.0589
Epoch: [8/20], Batch Num: [285/600]
Discriminator Loss: 0.2404, Generator Loss: 3.1640
D(x): 0.9073, D(G(z)): 0.0760
Epoch: [8/20], Batch Num: [286/600]
Discriminator Loss: 0.3906, Generator Loss: 2.8101
D(x): 0.8959, D(G(z)): 0.1180
Epoch: [8/20], Batch Num: [287/600]
Discriminator Loss: 0.3578, Generator Loss: 3.3132
D(x): 0.9483, D(G(z)): 0.1771
Epoch: [8/20], Batch Num: [288/600]
Discriminator Loss: 0.2889, Generator Loss: 4.5767
D(x): 0.9182, D(G(z)): 0.0996
Epoch: [8/20], Batch Num: [289/600]
Discriminator Loss: 0.2000, Generator Loss: 4.1131
D(x): 0.9251, D(G(z)): 0.0732
Epoch: [8/20], Batch Num: [290/600]
Discriminator Loss: 0.3633, Generator Loss: 4.3961
D(x): 0.8650, D(G(z)): 0.0604
Epoch: [8/20], Batch Num: [291/600]
Discriminator Loss: 0.3900, Generator Loss: 3.7017
D(x): 0.8454, D(G(z)): 0.0451
Epoch: [8/20], Batch Num: [292/600]
Discriminator Loss: 0.2206, Generator Loss: 2.6620
D(x): 0.9286, D(G(z)): 0.0756
Epoch: [8/20], Batch Num: [293/600]
Discriminator Loss: 0.4341, Generator Loss: 3.4904
D(x): 0.9361, D(G(z)): 0.1913
Epoch: [8/20], Batch Num: [294/600]
Discriminator Loss: 0.3324, Generator Loss: 3.6620
D(x): 0.8988, D(G(z)): 0.1162
Epoch: [8/20], Batch Num: [295/600]
Discriminator Loss: 0.2748, Generator Loss: 4.1811
D(x): 0.9225, D(G(z)): 0.0961
Epoch: [8/20], Batch Num: [296/600]
Discriminator Loss: 0.2150, Generator Loss: 4.4925
D(x): 0.9077, D(G(z)): 0.0489
Epoch: [8/20], Batch Num: [297/600]
Discriminator Loss: 0.2980, Generator Loss: 4.4052
D(x): 0.8872, D(G(z)): 0.0589
Epoch: [8/20], Batch Num: [298/600]
Discriminator Loss: 0.3101, Generator Loss: 3.8000
D(x): 0.8677, D(G(z)): 0.0451
Epoch: [8/20], Batch Num: [299/600]
Discriminator Loss: 0.2818, Generator Loss: 3.0343
D(x): 0.9065, D(G(z)): 0.0984
Epoch: 8, Batch Num: [300/600]

Epoch: [8/20], Batch Num: [300/600]
Discriminator Loss: 0.3204, Generator Loss: 3.1982
D(x): 0.9298, D(G(z)): 0.1213
Epoch: [8/20], Batch Num: [301/600]
Discriminator Loss: 0.3484, Generator Loss: 3.5425
D(x): 0.9305, D(G(z)): 0.1413
Epoch: [8/20], Batch Num: [302/600]
Discriminator Loss: 0.2361, Generator Loss: 3.9834
D(x): 0.9327, D(G(z)): 0.0992
Epoch: [8/20], Batch Num: [303/600]
Discriminator Loss: 0.2433, Generator Loss: 4.2917
D(x): 0.9144, D(G(z)): 0.0879
Epoch: [8/20], Batch Num: [304/600]
Discriminator Loss: 0.1885, Generator Loss: 3.9562
D(x): 0.9155, D(G(z)): 0.0472
Epoch: [8/20], Batch Num: [305/600]
Discriminator Loss: 0.2695, Generator Loss: 4.0996
D(x): 0.8929, D(G(z)): 0.0396
Epoch: [8/20], Batch Num: [306/600]
Discriminator Loss: 0.2440, Generator Loss: 3.5804
D(x): 0.9207, D(G(z)): 0.0610
Epoch: [8/20], Batch Num: [307/600]
Discriminator Loss: 0.2216, Generator Loss: 2.9776
D(x): 0.9318, D(G(z)): 0.0799
Epoch: [8/20], Batch Num: [308/600]
Discriminator Loss: 0.2437, Generator Loss: 3.5605
D(x): 0.9564, D(G(z)): 0.1366
Epoch: [8/20], Batch Num: [309/600]
Discriminator Loss: 0.3698, Generator Loss: 3.9644
D(x): 0.9437, D(G(z)): 0.1516
Epoch: [8/20], Batch Num: [310/600]
Discriminator Loss: 0.2029, Generator Loss: 4.9400
D(x): 0.9679, D(G(z)): 0.0938
Epoch: [8/20], Batch Num: [311/600]
Discriminator Loss: 0.4468, Generator Loss: 5.0082
D(x): 0.8204, D(G(z)): 0.0353
Epoch: [8/20], Batch Num: [312/600]
Discriminator Loss: 0.3580, Generator Loss: 4.2270
D(x): 0.8390, D(G(z)): 0.0146
Epoch: [8/20], Batch Num: [313/600]
Discriminator Loss: 0.2498, Generator Loss: 2.7670
D(x): 0.9093, D(G(z)): 0.0465
Epoch: [8/20], Batch Num: [314/600]
Discriminator Loss: 0.3011, Generator Loss: 3.2132
D(x): 0.9798, D(G(z)): 0.1650
Epoch: [8/20], Batch Num: [315/600]
Discriminator Loss: 0.2690, Generator Loss: 3.9906
D(x): 0.9767, D(G(z)): 0.1570
Epoch: [8/20], Batch Num: [316/600]
Discriminator Loss: 0.2866, Generator Loss: 4.8138
D(x): 0.9307, D(G(z)): 0.0664
Epoch: [8/20], Batch Num: [317/600]
Discriminator Loss: 0.1803, Generator Loss: 5.2433
D(x): 0.9247, D(G(z)): 0.0492
Epoch: [8/20], Batch Num: [318/600]
Discriminator Loss: 0.4567, Generator Loss: 4.9964
D(x): 0.8640, D(G(z)): 0.0467
Epoch: [8/20], Batch Num: [319/600]
Discriminator Loss: 0.2143, Generator Loss: 4.2381
D(x): 0.9031, D(G(z)): 0.0243
Epoch: [8/20], Batch Num: [320/600]
Discriminator Loss: 0.3585, Generator Loss: 3.5741
D(x): 0.8899, D(G(z)): 0.0828
Epoch: [8/20], Batch Num: [321/600]
Discriminator Loss: 0.3028, Generator Loss: 3.0713
D(x): 0.9344, D(G(z)): 0.1140
Epoch: [8/20], Batch Num: [322/600]
Discriminator Loss: 0.3528, Generator Loss: 3.5537
D(x): 0.9459, D(G(z)): 0.1530
Epoch: [8/20], Batch Num: [323/600]
Discriminator Loss: 0.2011, Generator Loss: 4.2101
D(x): 0.9590, D(G(z)): 0.0917
Epoch: [8/20], Batch Num: [324/600]
Discriminator Loss: 0.3825, Generator Loss: 4.6254
D(x): 0.8783, D(G(z)): 0.0919
Epoch: [8/20], Batch Num: [325/600]
Discriminator Loss: 0.4049, Generator Loss: 4.1869
D(x): 0.8450, D(G(z)): 0.0551
Epoch: [8/20], Batch Num: [326/600]
Discriminator Loss: 0.2236, Generator Loss: 3.9840
D(x): 0.8835, D(G(z)): 0.0390
Epoch: [8/20], Batch Num: [327/600]
Discriminator Loss: 0.2184, Generator Loss: 3.5678
D(x): 0.9284, D(G(z)): 0.0696
Epoch: [8/20], Batch Num: [328/600]
Discriminator Loss: 0.2566, Generator Loss: 3.8322
D(x): 0.9484, D(G(z)): 0.1293
Epoch: [8/20], Batch Num: [329/600]
Discriminator Loss: 0.2185, Generator Loss: 3.9975
D(x): 0.9545, D(G(z)): 0.1102
Epoch: [8/20], Batch Num: [330/600]
Discriminator Loss: 0.2589, Generator Loss: 3.7629
D(x): 0.9324, D(G(z)): 0.0919
Epoch: [8/20], Batch Num: [331/600]
Discriminator Loss: 0.2498, Generator Loss: 4.6784
D(x): 0.9254, D(G(z)): 0.0728
Epoch: [8/20], Batch Num: [332/600]
Discriminator Loss: 0.2316, Generator Loss: 4.7750
D(x): 0.9302, D(G(z)): 0.0815
Epoch: [8/20], Batch Num: [333/600]
Discriminator Loss: 0.4435, Generator Loss: 4.3966
D(x): 0.8412, D(G(z)): 0.0396
Epoch: [8/20], Batch Num: [334/600]
Discriminator Loss: 0.1950, Generator Loss: 4.1177
D(x): 0.9174, D(G(z)): 0.0374
Epoch: [8/20], Batch Num: [335/600]
Discriminator Loss: 0.2510, Generator Loss: 3.3865
D(x): 0.9216, D(G(z)): 0.0928
Epoch: [8/20], Batch Num: [336/600]
Discriminator Loss: 0.4190, Generator Loss: 3.4028
D(x): 0.9373, D(G(z)): 0.1515
Epoch: [8/20], Batch Num: [337/600]
Discriminator Loss: 0.4421, Generator Loss: 3.5937
D(x): 0.9193, D(G(z)): 0.1284
Epoch: [8/20], Batch Num: [338/600]
Discriminator Loss: 0.3184, Generator Loss: 4.1145
D(x): 0.9277, D(G(z)): 0.0867
Epoch: [8/20], Batch Num: [339/600]
Discriminator Loss: 0.3282, Generator Loss: 4.4191
D(x): 0.9046, D(G(z)): 0.0709
Epoch: [8/20], Batch Num: [340/600]
Discriminator Loss: 0.3072, Generator Loss: 4.4881
D(x): 0.8787, D(G(z)): 0.0668
Epoch: [8/20], Batch Num: [341/600]
Discriminator Loss: 0.3214, Generator Loss: 3.9604
D(x): 0.8946, D(G(z)): 0.0626
Epoch: [8/20], Batch Num: [342/600]
Discriminator Loss: 0.3006, Generator Loss: 3.8982
D(x): 0.9245, D(G(z)): 0.1119
Epoch: [8/20], Batch Num: [343/600]
Discriminator Loss: 0.3760, Generator Loss: 3.4005
D(x): 0.9112, D(G(z)): 0.1129
Epoch: [8/20], Batch Num: [344/600]
Discriminator Loss: 0.3044, Generator Loss: 4.4066
D(x): 0.9463, D(G(z)): 0.1479
Epoch: [8/20], Batch Num: [345/600]
Discriminator Loss: 0.3185, Generator Loss: 4.3540
D(x): 0.8929, D(G(z)): 0.0694
Epoch: [8/20], Batch Num: [346/600]
Discriminator Loss: 0.3004, Generator Loss: 4.3324
D(x): 0.9115, D(G(z)): 0.0551
Epoch: [8/20], Batch Num: [347/600]
Discriminator Loss: 0.1685, Generator Loss: 4.3367
D(x): 0.9311, D(G(z)): 0.0606
Epoch: [8/20], Batch Num: [348/600]
Discriminator Loss: 0.3349, Generator Loss: 3.9204
D(x): 0.8832, D(G(z)): 0.0507
Epoch: [8/20], Batch Num: [349/600]
Discriminator Loss: 0.2119, Generator Loss: 3.2624
D(x): 0.9161, D(G(z)): 0.0640
Epoch: [8/20], Batch Num: [350/600]
Discriminator Loss: 0.3319, Generator Loss: 3.2524
D(x): 0.9409, D(G(z)): 0.1307
Epoch: [8/20], Batch Num: [351/600]
Discriminator Loss: 0.2691, Generator Loss: 3.5410
D(x): 0.9417, D(G(z)): 0.1166
Epoch: [8/20], Batch Num: [352/600]
Discriminator Loss: 0.2126, Generator Loss: 3.6454
D(x): 0.9103, D(G(z)): 0.0675
Epoch: [8/20], Batch Num: [353/600]
Discriminator Loss: 0.2576, Generator Loss: 4.1319
D(x): 0.9252, D(G(z)): 0.0868
Epoch: [8/20], Batch Num: [354/600]
Discriminator Loss: 0.3019, Generator Loss: 4.0051
D(x): 0.9239, D(G(z)): 0.1014
Epoch: [8/20], Batch Num: [355/600]
Discriminator Loss: 0.2637, Generator Loss: 4.2176
D(x): 0.9152, D(G(z)): 0.0860
Epoch: [8/20], Batch Num: [356/600]
Discriminator Loss: 0.4906, Generator Loss: 4.0628
D(x): 0.8516, D(G(z)): 0.0870
Epoch: [8/20], Batch Num: [357/600]
Discriminator Loss: 0.3254, Generator Loss: 3.7323
D(x): 0.9026, D(G(z)): 0.0911
Epoch: [8/20], Batch Num: [358/600]
Discriminator Loss: 0.3545, Generator Loss: 3.3663
D(x): 0.9017, D(G(z)): 0.0895
Epoch: [8/20], Batch Num: [359/600]
Discriminator Loss: 0.4308, Generator Loss: 3.6322
D(x): 0.9217, D(G(z)): 0.1419
Epoch: [8/20], Batch Num: [360/600]
Discriminator Loss: 0.2249, Generator Loss: 3.7312
D(x): 0.9435, D(G(z)): 0.0800
Epoch: [8/20], Batch Num: [361/600]
Discriminator Loss: 0.3757, Generator Loss: 3.8350
D(x): 0.8660, D(G(z)): 0.0537
Epoch: [8/20], Batch Num: [362/600]
Discriminator Loss: 0.3307, Generator Loss: 3.4853
D(x): 0.9379, D(G(z)): 0.1311
Epoch: [8/20], Batch Num: [363/600]
Discriminator Loss: 0.3481, Generator Loss: 3.7011
D(x): 0.9014, D(G(z)): 0.1201
Epoch: [8/20], Batch Num: [364/600]
Discriminator Loss: 0.2151, Generator Loss: 3.8242
D(x): 0.9259, D(G(z)): 0.0648
Epoch: [8/20], Batch Num: [365/600]
Discriminator Loss: 0.2627, Generator Loss: 4.2483
D(x): 0.9184, D(G(z)): 0.0882
Epoch: [8/20], Batch Num: [366/600]
Discriminator Loss: 0.4088, Generator Loss: 3.7066
D(x): 0.8629, D(G(z)): 0.0750
Epoch: [8/20], Batch Num: [367/600]
Discriminator Loss: 0.3318, Generator Loss: 3.2257
D(x): 0.9078, D(G(z)): 0.0936
Epoch: [8/20], Batch Num: [368/600]
Discriminator Loss: 0.2386, Generator Loss: 3.1783
D(x): 0.9158, D(G(z)): 0.0958
Epoch: [8/20], Batch Num: [369/600]
Discriminator Loss: 0.3710, Generator Loss: 3.0828
D(x): 0.8978, D(G(z)): 0.1240
Epoch: [8/20], Batch Num: [370/600]
Discriminator Loss: 0.3182, Generator Loss: 3.4845
D(x): 0.9147, D(G(z)): 0.1088
Epoch: [8/20], Batch Num: [371/600]
Discriminator Loss: 0.3110, Generator Loss: 3.9884
D(x): 0.9378, D(G(z)): 0.1284
Epoch: [8/20], Batch Num: [372/600]
Discriminator Loss: 0.2577, Generator Loss: 4.3323
D(x): 0.8975, D(G(z)): 0.0667
Epoch: [8/20], Batch Num: [373/600]
Discriminator Loss: 0.2453, Generator Loss: 4.1424
D(x): 0.9073, D(G(z)): 0.0661
Epoch: [8/20], Batch Num: [374/600]
Discriminator Loss: 0.3092, Generator Loss: 4.2203
D(x): 0.8747, D(G(z)): 0.0393
Epoch: [8/20], Batch Num: [375/600]
Discriminator Loss: 0.2824, Generator Loss: 3.0579
D(x): 0.8980, D(G(z)): 0.0691
Epoch: [8/20], Batch Num: [376/600]
Discriminator Loss: 0.2490, Generator Loss: 2.5552
D(x): 0.9299, D(G(z)): 0.0962
Epoch: [8/20], Batch Num: [377/600]
Discriminator Loss: 0.4382, Generator Loss: 3.0997
D(x): 0.9166, D(G(z)): 0.1768
Epoch: [8/20], Batch Num: [378/600]
Discriminator Loss: 0.3739, Generator Loss: 3.7287
D(x): 0.9313, D(G(z)): 0.1743
Epoch: [8/20], Batch Num: [379/600]
Discriminator Loss: 0.3790, Generator Loss: 4.7740
D(x): 0.8807, D(G(z)): 0.0987
Epoch: [8/20], Batch Num: [380/600]
Discriminator Loss: 0.4073, Generator Loss: 4.5643
D(x): 0.8658, D(G(z)): 0.0591
Epoch: [8/20], Batch Num: [381/600]
Discriminator Loss: 0.2985, Generator Loss: 3.9681
D(x): 0.8969, D(G(z)): 0.0519
Epoch: [8/20], Batch Num: [382/600]
Discriminator Loss: 0.3951, Generator Loss: 3.4297
D(x): 0.8683, D(G(z)): 0.0716
Epoch: [8/20], Batch Num: [383/600]
Discriminator Loss: 0.4044, Generator Loss: 3.1556
D(x): 0.9191, D(G(z)): 0.1208
Epoch: [8/20], Batch Num: [384/600]
Discriminator Loss: 0.3929, Generator Loss: 3.3238
D(x): 0.9188, D(G(z)): 0.1465
Epoch: [8/20], Batch Num: [385/600]
Discriminator Loss: 0.3800, Generator Loss: 3.4626
D(x): 0.8871, D(G(z)): 0.1127
Epoch: [8/20], Batch Num: [386/600]
Discriminator Loss: 0.3083, Generator Loss: 3.9299
D(x): 0.9041, D(G(z)): 0.0740
Epoch: [8/20], Batch Num: [387/600]
Discriminator Loss: 0.2830, Generator Loss: 3.8984
D(x): 0.8977, D(G(z)): 0.0549
Epoch: [8/20], Batch Num: [388/600]
Discriminator Loss: 0.2212, Generator Loss: 3.3234
D(x): 0.9248, D(G(z)): 0.0738
Epoch: [8/20], Batch Num: [389/600]
Discriminator Loss: 0.4275, Generator Loss: 3.3579
D(x): 0.8844, D(G(z)): 0.1023
Epoch: [8/20], Batch Num: [390/600]
Discriminator Loss: 0.3198, Generator Loss: 2.9965
D(x): 0.9039, D(G(z)): 0.1077
Epoch: [8/20], Batch Num: [391/600]
Discriminator Loss: 0.2542, Generator Loss: 3.1518
D(x): 0.9332, D(G(z)): 0.1157
Epoch: [8/20], Batch Num: [392/600]
Discriminator Loss: 0.2730, Generator Loss: 3.5869
D(x): 0.9447, D(G(z)): 0.1235
Epoch: [8/20], Batch Num: [393/600]
Discriminator Loss: 0.5306, Generator Loss: 3.9907
D(x): 0.8476, D(G(z)): 0.0895
Epoch: [8/20], Batch Num: [394/600]
Discriminator Loss: 0.2501, Generator Loss: 4.0189
D(x): 0.9177, D(G(z)): 0.0662
Epoch: [8/20], Batch Num: [395/600]
Discriminator Loss: 0.2815, Generator Loss: 3.7702
D(x): 0.8998, D(G(z)): 0.0718
Epoch: [8/20], Batch Num: [396/600]
Discriminator Loss: 0.3282, Generator Loss: 3.4351
D(x): 0.9008, D(G(z)): 0.0872
Epoch: [8/20], Batch Num: [397/600]
Discriminator Loss: 0.3007, Generator Loss: 3.6849
D(x): 0.9211, D(G(z)): 0.1091
Epoch: [8/20], Batch Num: [398/600]
Discriminator Loss: 0.2871, Generator Loss: 3.6557
D(x): 0.9129, D(G(z)): 0.1000
Epoch: [8/20], Batch Num: [399/600]
Discriminator Loss: 0.2434, Generator Loss: 3.8345
D(x): 0.9278, D(G(z)): 0.0716
Epoch: 8, Batch Num: [400/600]

Epoch: [8/20], Batch Num: [400/600]
Discriminator Loss: 0.3934, Generator Loss: 3.7179
D(x): 0.8659, D(G(z)): 0.0859
Epoch: [8/20], Batch Num: [401/600]
Discriminator Loss: 0.2789, Generator Loss: 3.3502
D(x): 0.9152, D(G(z)): 0.0774
Epoch: [8/20], Batch Num: [402/600]
Discriminator Loss: 0.3636, Generator Loss: 3.6724
D(x): 0.9094, D(G(z)): 0.1268
Epoch: [8/20], Batch Num: [403/600]
Discriminator Loss: 0.3359, Generator Loss: 3.4826
D(x): 0.9011, D(G(z)): 0.1015
Epoch: [8/20], Batch Num: [404/600]
Discriminator Loss: 0.2131, Generator Loss: 3.8234
D(x): 0.9494, D(G(z)): 0.0909
Epoch: [8/20], Batch Num: [405/600]
Discriminator Loss: 0.2684, Generator Loss: 4.5445
D(x): 0.9174, D(G(z)): 0.0907
Epoch: [8/20], Batch Num: [406/600]
Discriminator Loss: 0.1518, Generator Loss: 4.6907
D(x): 0.9487, D(G(z)): 0.0559
Epoch: [8/20], Batch Num: [407/600]
Discriminator Loss: 0.1756, Generator Loss: 4.2935
D(x): 0.9252, D(G(z)): 0.0349
Epoch: [8/20], Batch Num: [408/600]
Discriminator Loss: 0.1886, Generator Loss: 4.0413
D(x): 0.9307, D(G(z)): 0.0425
Epoch: [8/20], Batch Num: [409/600]
Discriminator Loss: 0.2399, Generator Loss: 3.2960
D(x): 0.9165, D(G(z)): 0.0632
Epoch: [8/20], Batch Num: [410/600]
Discriminator Loss: 0.4016, Generator Loss: 4.3062
D(x): 0.9546, D(G(z)): 0.1756
Epoch: [8/20], Batch Num: [411/600]
Discriminator Loss: 0.4932, Generator Loss: 5.1013
D(x): 0.8895, D(G(z)): 0.1104
Epoch: [8/20], Batch Num: [412/600]
Discriminator Loss: 0.3196, Generator Loss: 5.5938
D(x): 0.9145, D(G(z)): 0.0688
Epoch: [8/20], Batch Num: [413/600]
Discriminator Loss: 0.3309, Generator Loss: 5.0161
D(x): 0.8996, D(G(z)): 0.0343
Epoch: [8/20], Batch Num: [414/600]
Discriminator Loss: 0.2745, Generator Loss: 4.0637
D(x): 0.8928, D(G(z)): 0.0221
Epoch: [8/20], Batch Num: [415/600]
Discriminator Loss: 0.4439, Generator Loss: 3.8044
D(x): 0.9468, D(G(z)): 0.1582
Epoch: [8/20], Batch Num: [416/600]
Discriminator Loss: 0.3596, Generator Loss: 4.6974
D(x): 0.9232, D(G(z)): 0.1155
Epoch: [8/20], Batch Num: [417/600]
Discriminator Loss: 0.3533, Generator Loss: 3.9547
D(x): 0.8758, D(G(z)): 0.0632
Epoch: [8/20], Batch Num: [418/600]
Discriminator Loss: 0.2531, Generator Loss: 3.5841
D(x): 0.9249, D(G(z)): 0.0802
Epoch: [8/20], Batch Num: [419/600]
Discriminator Loss: 0.2138, Generator Loss: 3.8690
D(x): 0.9452, D(G(z)): 0.1044
Epoch: [8/20], Batch Num: [420/600]
Discriminator Loss: 0.1871, Generator Loss: 4.1543
D(x): 0.9362, D(G(z)): 0.0708
Epoch: [8/20], Batch Num: [421/600]
Discriminator Loss: 0.3995, Generator Loss: 4.0363
D(x): 0.8768, D(G(z)): 0.0465
Epoch: [8/20], Batch Num: [422/600]
Discriminator Loss: 0.2815, Generator Loss: 3.4383
D(x): 0.9056, D(G(z)): 0.0579
Epoch: [8/20], Batch Num: [423/600]
Discriminator Loss: 0.2026, Generator Loss: 2.8998
D(x): 0.9484, D(G(z)): 0.0903
Epoch: [8/20], Batch Num: [424/600]
Discriminator Loss: 0.3418, Generator Loss: 3.3985
D(x): 0.9368, D(G(z)): 0.1448
Epoch: [8/20], Batch Num: [425/600]
Discriminator Loss: 0.3918, Generator Loss: 4.3659
D(x): 0.9159, D(G(z)): 0.1282
Epoch: [8/20], Batch Num: [426/600]
Discriminator Loss: 0.2227, Generator Loss: 4.6940
D(x): 0.9081, D(G(z)): 0.0496
Epoch: [8/20], Batch Num: [427/600]
Discriminator Loss: 0.4250, Generator Loss: 3.9952
D(x): 0.8594, D(G(z)): 0.0416
Epoch: [8/20], Batch Num: [428/600]
Discriminator Loss: 0.2142, Generator Loss: 3.7490
D(x): 0.9110, D(G(z)): 0.0590
Epoch: [8/20], Batch Num: [429/600]
Discriminator Loss: 0.2654, Generator Loss: 2.7358
D(x): 0.9256, D(G(z)): 0.0737
Epoch: [8/20], Batch Num: [430/600]
Discriminator Loss: 0.4372, Generator Loss: 3.3479
D(x): 0.9396, D(G(z)): 0.1785
Epoch: [8/20], Batch Num: [431/600]
Discriminator Loss: 0.4909, Generator Loss: 4.3747
D(x): 0.9157, D(G(z)): 0.1835
Epoch: [8/20], Batch Num: [432/600]
Discriminator Loss: 0.3370, Generator Loss: 4.7559
D(x): 0.8740, D(G(z)): 0.0584
Epoch: [8/20], Batch Num: [433/600]
Discriminator Loss: 0.2726, Generator Loss: 4.5098
D(x): 0.8952, D(G(z)): 0.0335
Epoch: [8/20], Batch Num: [434/600]
Discriminator Loss: 0.1624, Generator Loss: 4.1587
D(x): 0.9253, D(G(z)): 0.0314
Epoch: [8/20], Batch Num: [435/600]
Discriminator Loss: 0.3457, Generator Loss: 4.0700
D(x): 0.9135, D(G(z)): 0.1080
Epoch: [8/20], Batch Num: [436/600]
Discriminator Loss: 0.2695, Generator Loss: 3.7242
D(x): 0.9171, D(G(z)): 0.0801
Epoch: [8/20], Batch Num: [437/600]
Discriminator Loss: 0.3242, Generator Loss: 3.8561
D(x): 0.8854, D(G(z)): 0.0854
Epoch: [8/20], Batch Num: [438/600]
Discriminator Loss: 0.2080, Generator Loss: 3.3600
D(x): 0.9310, D(G(z)): 0.0875
Epoch: [8/20], Batch Num: [439/600]
Discriminator Loss: 0.3086, Generator Loss: 3.3917
D(x): 0.9031, D(G(z)): 0.1071
Epoch: [8/20], Batch Num: [440/600]
Discriminator Loss: 0.3581, Generator Loss: 3.8111
D(x): 0.9178, D(G(z)): 0.1360
Epoch: [8/20], Batch Num: [441/600]
Discriminator Loss: 0.3385, Generator Loss: 3.9616
D(x): 0.9164, D(G(z)): 0.1249
Epoch: [8/20], Batch Num: [442/600]
Discriminator Loss: 0.2773, Generator Loss: 4.2141
D(x): 0.8825, D(G(z)): 0.0564
Epoch: [8/20], Batch Num: [443/600]
Discriminator Loss: 0.4614, Generator Loss: 3.3740
D(x): 0.8283, D(G(z)): 0.0540
Epoch: [8/20], Batch Num: [444/600]
Discriminator Loss: 0.3605, Generator Loss: 2.9402
D(x): 0.9032, D(G(z)): 0.1219
Epoch: [8/20], Batch Num: [445/600]
Discriminator Loss: 0.5693, Generator Loss: 3.6317
D(x): 0.9079, D(G(z)): 0.2272
Epoch: [8/20], Batch Num: [446/600]
Discriminator Loss: 0.3981, Generator Loss: 4.4645
D(x): 0.9066, D(G(z)): 0.1373
Epoch: [8/20], Batch Num: [447/600]
Discriminator Loss: 0.4888, Generator Loss: 4.4223
D(x): 0.7973, D(G(z)): 0.0502
Epoch: [8/20], Batch Num: [448/600]
Discriminator Loss: 0.3196, Generator Loss: 3.6056
D(x): 0.8673, D(G(z)): 0.0528
Epoch: [8/20], Batch Num: [449/600]
Discriminator Loss: 0.3555, Generator Loss: 2.8721
D(x): 0.8791, D(G(z)): 0.0868
Epoch: [8/20], Batch Num: [450/600]
Discriminator Loss: 0.4143, Generator Loss: 2.5453
D(x): 0.9220, D(G(z)): 0.1802
Epoch: [8/20], Batch Num: [451/600]
Discriminator Loss: 0.4171, Generator Loss: 3.4536
D(x): 0.9506, D(G(z)): 0.2137
Epoch: [8/20], Batch Num: [452/600]
Discriminator Loss: 0.4742, Generator Loss: 3.8974
D(x): 0.8279, D(G(z)): 0.1023
Epoch: [8/20], Batch Num: [453/600]
Discriminator Loss: 0.2663, Generator Loss: 4.0517
D(x): 0.8984, D(G(z)): 0.0750
Epoch: [8/20], Batch Num: [454/600]
Discriminator Loss: 0.4154, Generator Loss: 3.7255
D(x): 0.8367, D(G(z)): 0.0437
Epoch: [8/20], Batch Num: [455/600]
Discriminator Loss: 0.3422, Generator Loss: 3.2974
D(x): 0.8966, D(G(z)): 0.0943
Epoch: [8/20], Batch Num: [456/600]
Discriminator Loss: 0.4560, Generator Loss: 3.4298
D(x): 0.9073, D(G(z)): 0.1722
Epoch: [8/20], Batch Num: [457/600]
Discriminator Loss: 0.2967, Generator Loss: 3.6076
D(x): 0.9378, D(G(z)): 0.1394
Epoch: [8/20], Batch Num: [458/600]
Discriminator Loss: 0.3146, Generator Loss: 3.6549
D(x): 0.9048, D(G(z)): 0.0917
Epoch: [8/20], Batch Num: [459/600]
Discriminator Loss: 0.3254, Generator Loss: 4.4530
D(x): 0.9050, D(G(z)): 0.0965
Epoch: [8/20], Batch Num: [460/600]
Discriminator Loss: 0.3312, Generator Loss: 4.0373
D(x): 0.8647, D(G(z)): 0.0745
Epoch: [8/20], Batch Num: [461/600]
Discriminator Loss: 0.3267, Generator Loss: 3.9887
D(x): 0.8611, D(G(z)): 0.0573
Epoch: [8/20], Batch Num: [462/600]
Discriminator Loss: 0.2876, Generator Loss: 2.9335
D(x): 0.9082, D(G(z)): 0.1045
Epoch: [8/20], Batch Num: [463/600]
Discriminator Loss: 0.2930, Generator Loss: 2.5440
D(x): 0.9297, D(G(z)): 0.1049
Epoch: [8/20], Batch Num: [464/600]
Discriminator Loss: 0.3243, Generator Loss: 2.8780
D(x): 0.9185, D(G(z)): 0.1449
Epoch: [8/20], Batch Num: [465/600]
Discriminator Loss: 0.3196, Generator Loss: 3.4630
D(x): 0.9040, D(G(z)): 0.1100
Epoch: [8/20], Batch Num: [466/600]
Discriminator Loss: 0.3719, Generator Loss: 4.3599
D(x): 0.8734, D(G(z)): 0.0850
Epoch: [8/20], Batch Num: [467/600]
Discriminator Loss: 0.4642, Generator Loss: 3.9563
D(x): 0.8494, D(G(z)): 0.1089
Epoch: [8/20], Batch Num: [468/600]
Discriminator Loss: 0.4942, Generator Loss: 3.0477
D(x): 0.8433, D(G(z)): 0.1279
Epoch: [8/20], Batch Num: [469/600]
Discriminator Loss: 0.3295, Generator Loss: 2.7984
D(x): 0.9287, D(G(z)): 0.1183
Epoch: [8/20], Batch Num: [470/600]
Discriminator Loss: 0.3546, Generator Loss: 3.0644
D(x): 0.9246, D(G(z)): 0.1495
Epoch: [8/20], Batch Num: [471/600]
Discriminator Loss: 0.3005, Generator Loss: 3.6803
D(x): 0.9213, D(G(z)): 0.1101
Epoch: [8/20], Batch Num: [472/600]
Discriminator Loss: 0.3048, Generator Loss: 4.1174
D(x): 0.9048, D(G(z)): 0.0987
Epoch: [8/20], Batch Num: [473/600]
Discriminator Loss: 0.2956, Generator Loss: 4.4539
D(x): 0.8883, D(G(z)): 0.0711
Epoch: [8/20], Batch Num: [474/600]
Discriminator Loss: 0.4512, Generator Loss: 3.8727
D(x): 0.8176, D(G(z)): 0.0705
Epoch: [8/20], Batch Num: [475/600]
Discriminator Loss: 0.2715, Generator Loss: 3.1930
D(x): 0.8884, D(G(z)): 0.0624
Epoch: [8/20], Batch Num: [476/600]
Discriminator Loss: 0.3344, Generator Loss: 2.1923
D(x): 0.8895, D(G(z)): 0.0974
Epoch: [8/20], Batch Num: [477/600]
Discriminator Loss: 0.3907, Generator Loss: 2.5650
D(x): 0.9554, D(G(z)): 0.2069
Epoch: [8/20], Batch Num: [478/600]
Discriminator Loss: 0.5166, Generator Loss: 4.1374
D(x): 0.9294, D(G(z)): 0.2151
Epoch: [8/20], Batch Num: [479/600]
Discriminator Loss: 0.2960, Generator Loss: 4.6652
D(x): 0.8778, D(G(z)): 0.0550
Epoch: [8/20], Batch Num: [480/600]
Discriminator Loss: 0.5625, Generator Loss: 4.6099
D(x): 0.7775, D(G(z)): 0.0196
Epoch: [8/20], Batch Num: [481/600]
Discriminator Loss: 0.4566, Generator Loss: 3.2437
D(x): 0.8220, D(G(z)): 0.0471
Epoch: [8/20], Batch Num: [482/600]
Discriminator Loss: 0.2573, Generator Loss: 2.3753
D(x): 0.9446, D(G(z)): 0.1089
Epoch: [8/20], Batch Num: [483/600]
Discriminator Loss: 0.4227, Generator Loss: 2.7621
D(x): 0.9686, D(G(z)): 0.2036
Epoch: [8/20], Batch Num: [484/600]
Discriminator Loss: 0.3447, Generator Loss: 3.5673
D(x): 0.9392, D(G(z)): 0.1707
Epoch: [8/20], Batch Num: [485/600]
Discriminator Loss: 0.3938, Generator Loss: 4.2305
D(x): 0.8621, D(G(z)): 0.0870
Epoch: [8/20], Batch Num: [486/600]
Discriminator Loss: 0.3945, Generator Loss: 4.0671
D(x): 0.8558, D(G(z)): 0.0380
Epoch: [8/20], Batch Num: [487/600]
Discriminator Loss: 0.3264, Generator Loss: 3.6992
D(x): 0.8853, D(G(z)): 0.0522
Epoch: [8/20], Batch Num: [488/600]
Discriminator Loss: 0.2183, Generator Loss: 3.1183
D(x): 0.9251, D(G(z)): 0.0680
Epoch: [8/20], Batch Num: [489/600]
Discriminator Loss: 0.2839, Generator Loss: 3.1013
D(x): 0.9291, D(G(z)): 0.1148
Epoch: [8/20], Batch Num: [490/600]
Discriminator Loss: 0.3199, Generator Loss: 3.1678
D(x): 0.9353, D(G(z)): 0.1438
Epoch: [8/20], Batch Num: [491/600]
Discriminator Loss: 0.3077, Generator Loss: 4.2309
D(x): 0.9343, D(G(z)): 0.1165
Epoch: [8/20], Batch Num: [492/600]
Discriminator Loss: 0.2311, Generator Loss: 4.7968
D(x): 0.8875, D(G(z)): 0.0267
Epoch: [8/20], Batch Num: [493/600]
Discriminator Loss: 0.3334, Generator Loss: 4.1890
D(x): 0.8724, D(G(z)): 0.0356
Epoch: [8/20], Batch Num: [494/600]
Discriminator Loss: 0.3018, Generator Loss: 3.5575
D(x): 0.8760, D(G(z)): 0.0534
Epoch: [8/20], Batch Num: [495/600]
Discriminator Loss: 0.1981, Generator Loss: 3.2655
D(x): 0.9630, D(G(z)): 0.0916
Epoch: [8/20], Batch Num: [496/600]
Discriminator Loss: 0.3044, Generator Loss: 3.4518
D(x): 0.9491, D(G(z)): 0.1222
Epoch: [8/20], Batch Num: [497/600]
Discriminator Loss: 0.1974, Generator Loss: 3.7046
D(x): 0.9455, D(G(z)): 0.0685
Epoch: [8/20], Batch Num: [498/600]
Discriminator Loss: 0.2340, Generator Loss: 4.1437
D(x): 0.9432, D(G(z)): 0.0676
Epoch: [8/20], Batch Num: [499/600]
Discriminator Loss: 0.1631, Generator Loss: 4.6345
D(x): 0.9421, D(G(z)): 0.0331
Epoch: 8, Batch Num: [500/600]

Epoch: [8/20], Batch Num: [500/600]
Discriminator Loss: 0.1932, Generator Loss: 4.1089
D(x): 0.9166, D(G(z)): 0.0471
Epoch: [8/20], Batch Num: [501/600]
Discriminator Loss: 0.1718, Generator Loss: 3.8499
D(x): 0.9310, D(G(z)): 0.0527
Epoch: [8/20], Batch Num: [502/600]
Discriminator Loss: 0.2533, Generator Loss: 3.6971
D(x): 0.9248, D(G(z)): 0.0832
Epoch: [8/20], Batch Num: [503/600]
Discriminator Loss: 0.2357, Generator Loss: 3.8354
D(x): 0.9435, D(G(z)): 0.0975
Epoch: [8/20], Batch Num: [504/600]
Discriminator Loss: 0.3915, Generator Loss: 3.4466
D(x): 0.8909, D(G(z)): 0.1052
Epoch: [8/20], Batch Num: [505/600]
Discriminator Loss: 0.2817, Generator Loss: 3.7807
D(x): 0.9080, D(G(z)): 0.0882
Epoch: [8/20], Batch Num: [506/600]
Discriminator Loss: 0.3872, Generator Loss: 3.6457
D(x): 0.8989, D(G(z)): 0.1052
Epoch: [8/20], Batch Num: [507/600]
Discriminator Loss: 0.3701, Generator Loss: 3.6065
D(x): 0.8859, D(G(z)): 0.0995
Epoch: [8/20], Batch Num: [508/600]
Discriminator Loss: 0.3830, Generator Loss: 3.9568
D(x): 0.9011, D(G(z)): 0.1027
Epoch: [8/20], Batch Num: [509/600]
Discriminator Loss: 0.1711, Generator Loss: 4.0618
D(x): 0.9549, D(G(z)): 0.0755
Epoch: [8/20], Batch Num: [510/600]
Discriminator Loss: 0.1327, Generator Loss: 4.1665
D(x): 0.9635, D(G(z)): 0.0612
Epoch: [8/20], Batch Num: [511/600]
Discriminator Loss: 0.1768, Generator Loss: 4.6932
D(x): 0.9589, D(G(z)): 0.0757
Epoch: [8/20], Batch Num: [512/600]
Discriminator Loss: 0.4001, Generator Loss: 4.0653
D(x): 0.8665, D(G(z)): 0.0405
Epoch: [8/20], Batch Num: [513/600]
Discriminator Loss: 0.4484, Generator Loss: 4.1443
D(x): 0.9047, D(G(z)): 0.0963
Epoch: [8/20], Batch Num: [514/600]
Discriminator Loss: 0.2780, Generator Loss: 3.6804
D(x): 0.9123, D(G(z)): 0.0687
Epoch: [8/20], Batch Num: [515/600]
Discriminator Loss: 0.4668, Generator Loss: 3.2535
D(x): 0.8590, D(G(z)): 0.1006
Epoch: [8/20], Batch Num: [516/600]
Discriminator Loss: 0.2692, Generator Loss: 3.6172
D(x): 0.9596, D(G(z)): 0.1367
Epoch: [8/20], Batch Num: [517/600]
Discriminator Loss: 0.2555, Generator Loss: 3.8731
D(x): 0.9264, D(G(z)): 0.0941
Epoch: [8/20], Batch Num: [518/600]
Discriminator Loss: 0.3612, Generator Loss: 4.5156
D(x): 0.8837, D(G(z)): 0.0789
Epoch: [8/20], Batch Num: [519/600]
Discriminator Loss: 0.2649, Generator Loss: 4.3349
D(x): 0.9073, D(G(z)): 0.0555
Epoch: [8/20], Batch Num: [520/600]
Discriminator Loss: 0.1821, Generator Loss: 3.9204
D(x): 0.9295, D(G(z)): 0.0534
Epoch: [8/20], Batch Num: [521/600]
Discriminator Loss: 0.2978, Generator Loss: 3.6991
D(x): 0.9059, D(G(z)): 0.0713
Epoch: [8/20], Batch Num: [522/600]
Discriminator Loss: 0.2292, Generator Loss: 3.5787
D(x): 0.9306, D(G(z)): 0.0911
Epoch: [8/20], Batch Num: [523/600]
Discriminator Loss: 0.3218, Generator Loss: 3.7861
D(x): 0.9245, D(G(z)): 0.1384
Epoch: [8/20], Batch Num: [524/600]
Discriminator Loss: 0.3475, Generator Loss: 4.3713
D(x): 0.8971, D(G(z)): 0.1091
Epoch: [8/20], Batch Num: [525/600]
Discriminator Loss: 0.2819, Generator Loss: 4.6032
D(x): 0.9150, D(G(z)): 0.0661
Epoch: [8/20], Batch Num: [526/600]
Discriminator Loss: 0.4323, Generator Loss: 4.6227
D(x): 0.8689, D(G(z)): 0.0463
Epoch: [8/20], Batch Num: [527/600]
Discriminator Loss: 0.2948, Generator Loss: 3.6015
D(x): 0.8686, D(G(z)): 0.0606
Epoch: [8/20], Batch Num: [528/600]
Discriminator Loss: 0.2625, Generator Loss: 3.3714
D(x): 0.9266, D(G(z)): 0.1021
Epoch: [8/20], Batch Num: [529/600]
Discriminator Loss: 0.2412, Generator Loss: 3.7289
D(x): 0.9540, D(G(z)): 0.1100
Epoch: [8/20], Batch Num: [530/600]
Discriminator Loss: 0.3166, Generator Loss: 3.9936
D(x): 0.9266, D(G(z)): 0.1161
Epoch: [8/20], Batch Num: [531/600]
Discriminator Loss: 0.2075, Generator Loss: 4.6632
D(x): 0.9214, D(G(z)): 0.0630
Epoch: [8/20], Batch Num: [532/600]
Discriminator Loss: 0.3719, Generator Loss: 4.1129
D(x): 0.8563, D(G(z)): 0.0666
Epoch: [8/20], Batch Num: [533/600]
Discriminator Loss: 0.3797, Generator Loss: 3.6735
D(x): 0.8790, D(G(z)): 0.0821
Epoch: [8/20], Batch Num: [534/600]
Discriminator Loss: 0.3879, Generator Loss: 3.7770
D(x): 0.9530, D(G(z)): 0.1592
Epoch: [8/20], Batch Num: [535/600]
Discriminator Loss: 0.4718, Generator Loss: 4.2698
D(x): 0.8895, D(G(z)): 0.1305
Epoch: [8/20], Batch Num: [536/600]
Discriminator Loss: 0.3998, Generator Loss: 3.9906
D(x): 0.8885, D(G(z)): 0.0930
Epoch: [8/20], Batch Num: [537/600]
Discriminator Loss: 0.3835, Generator Loss: 3.6340
D(x): 0.8618, D(G(z)): 0.0855
Epoch: [8/20], Batch Num: [538/600]
Discriminator Loss: 0.2359, Generator Loss: 3.8047
D(x): 0.9199, D(G(z)): 0.0807
Epoch: [8/20], Batch Num: [539/600]
Discriminator Loss: 0.2533, Generator Loss: 3.6458
D(x): 0.9327, D(G(z)): 0.1002
Epoch: [8/20], Batch Num: [540/600]
Discriminator Loss: 0.2269, Generator Loss: 3.9098
D(x): 0.9382, D(G(z)): 0.0893
Epoch: [8/20], Batch Num: [541/600]
Discriminator Loss: 0.2351, Generator Loss: 3.9422
D(x): 0.9052, D(G(z)): 0.0678
Epoch: [8/20], Batch Num: [542/600]
Discriminator Loss: 0.3423, Generator Loss: 3.8420
D(x): 0.8841, D(G(z)): 0.0903
Epoch: [8/20], Batch Num: [543/600]
Discriminator Loss: 0.2584, Generator Loss: 3.8533
D(x): 0.9156, D(G(z)): 0.0833
Epoch: [8/20], Batch Num: [544/600]
Discriminator Loss: 0.3455, Generator Loss: 3.3957
D(x): 0.8781, D(G(z)): 0.0776
Epoch: [8/20], Batch Num: [545/600]
Discriminator Loss: 0.2983, Generator Loss: 3.4519
D(x): 0.9132, D(G(z)): 0.1089
Epoch: [8/20], Batch Num: [546/600]
Discriminator Loss: 0.3384, Generator Loss: 4.3489
D(x): 0.9550, D(G(z)): 0.1513
Epoch: [8/20], Batch Num: [547/600]
Discriminator Loss: 0.3324, Generator Loss: 4.7951
D(x): 0.8997, D(G(z)): 0.0889
Epoch: [8/20], Batch Num: [548/600]
Discriminator Loss: 0.3011, Generator Loss: 4.9558
D(x): 0.8791, D(G(z)): 0.0468
Epoch: [8/20], Batch Num: [549/600]
Discriminator Loss: 0.3188, Generator Loss: 3.9366
D(x): 0.8875, D(G(z)): 0.0618
Epoch: [8/20], Batch Num: [550/600]
Discriminator Loss: 0.2654, Generator Loss: 3.6215
D(x): 0.9087, D(G(z)): 0.0737
Epoch: [8/20], Batch Num: [551/600]
Discriminator Loss: 0.3839, Generator Loss: 3.6944
D(x): 0.9406, D(G(z)): 0.1559
Epoch: [8/20], Batch Num: [552/600]
Discriminator Loss: 0.4868, Generator Loss: 4.0539
D(x): 0.8845, D(G(z)): 0.1643
Epoch: [8/20], Batch Num: [553/600]
Discriminator Loss: 0.4546, Generator Loss: 4.6918
D(x): 0.8663, D(G(z)): 0.1254
Epoch: [8/20], Batch Num: [554/600]
Discriminator Loss: 0.3803, Generator Loss: 3.9513
D(x): 0.8437, D(G(z)): 0.0735
Epoch: [8/20], Batch Num: [555/600]
Discriminator Loss: 0.5177, Generator Loss: 3.2412
D(x): 0.8238, D(G(z)): 0.0936
Epoch: [8/20], Batch Num: [556/600]
Discriminator Loss: 0.3754, Generator Loss: 2.3381
D(x): 0.8871, D(G(z)): 0.1318
Epoch: [8/20], Batch Num: [557/600]
Discriminator Loss: 0.3406, Generator Loss: 2.5405
D(x): 0.9472, D(G(z)): 0.1894
Epoch: [8/20], Batch Num: [558/600]
Discriminator Loss: 0.4441, Generator Loss: 3.6586
D(x): 0.9194, D(G(z)): 0.2127
Epoch: [8/20], Batch Num: [559/600]
Discriminator Loss: 0.3853, Generator Loss: 4.3465
D(x): 0.8870, D(G(z)): 0.1126
Epoch: [8/20], Batch Num: [560/600]
Discriminator Loss: 0.4578, Generator Loss: 4.2369
D(x): 0.8422, D(G(z)): 0.0559
Epoch: [8/20], Batch Num: [561/600]
Discriminator Loss: 0.4973, Generator Loss: 3.3753
D(x): 0.7863, D(G(z)): 0.0446
Epoch: [8/20], Batch Num: [562/600]
Discriminator Loss: 0.4673, Generator Loss: 2.3349
D(x): 0.8353, D(G(z)): 0.0981
Epoch: [8/20], Batch Num: [563/600]
Discriminator Loss: 0.4162, Generator Loss: 2.2138
D(x): 0.9436, D(G(z)): 0.2270
Epoch: [8/20], Batch Num: [564/600]
Discriminator Loss: 0.4773, Generator Loss: 2.5513
D(x): 0.9139, D(G(z)): 0.2288
Epoch: [8/20], Batch Num: [565/600]
Discriminator Loss: 0.3628, Generator Loss: 3.7022
D(x): 0.9267, D(G(z)): 0.1806
Epoch: [8/20], Batch Num: [566/600]
Discriminator Loss: 0.4182, Generator Loss: 4.4852
D(x): 0.8577, D(G(z)): 0.0879
Epoch: [8/20], Batch Num: [567/600]
Discriminator Loss: 0.5750, Generator Loss: 4.0976
D(x): 0.7703, D(G(z)): 0.0526
Epoch: [8/20], Batch Num: [568/600]
Discriminator Loss: 0.5452, Generator Loss: 3.0511
D(x): 0.7856, D(G(z)): 0.0629
Epoch: [8/20], Batch Num: [569/600]
Discriminator Loss: 0.3206, Generator Loss: 2.8100
D(x): 0.9129, D(G(z)): 0.1253
Epoch: [8/20], Batch Num: [570/600]
Discriminator Loss: 0.3482, Generator Loss: 2.4632
D(x): 0.9299, D(G(z)): 0.1587
Epoch: [8/20], Batch Num: [571/600]
Discriminator Loss: 0.3518, Generator Loss: 2.8900
D(x): 0.9075, D(G(z)): 0.1460
Epoch: [8/20], Batch Num: [572/600]
Discriminator Loss: 0.4282, Generator Loss: 3.1903
D(x): 0.8632, D(G(z)): 0.1504
Epoch: [8/20], Batch Num: [573/600]
Discriminator Loss: 0.4269, Generator Loss: 3.5820
D(x): 0.8622, D(G(z)): 0.1259
Epoch: [8/20], Batch Num: [574/600]
Discriminator Loss: 0.2966, Generator Loss: 3.8343
D(x): 0.9119, D(G(z)): 0.1101
Epoch: [8/20], Batch Num: [575/600]
Discriminator Loss: 0.3250, Generator Loss: 4.0682
D(x): 0.8840, D(G(z)): 0.0618
Epoch: [8/20], Batch Num: [576/600]
Discriminator Loss: 0.4339, Generator Loss: 4.0753
D(x): 0.8395, D(G(z)): 0.0743
Epoch: [8/20], Batch Num: [577/600]
Discriminator Loss: 0.4240, Generator Loss: 2.8554
D(x): 0.8416, D(G(z)): 0.0835
Epoch: [8/20], Batch Num: [578/600]
Discriminator Loss: 0.3567, Generator Loss: 2.8928
D(x): 0.9163, D(G(z)): 0.1496
Epoch: [8/20], Batch Num: [579/600]
Discriminator Loss: 0.3295, Generator Loss: 3.3616
D(x): 0.9546, D(G(z)): 0.1860
Epoch: [8/20], Batch Num: [580/600]
Discriminator Loss: 0.3178, Generator Loss: 3.8892
D(x): 0.9082, D(G(z)): 0.1173
Epoch: [8/20], Batch Num: [581/600]
Discriminator Loss: 0.2270, Generator Loss: 4.4001
D(x): 0.9302, D(G(z)): 0.0907
Epoch: [8/20], Batch Num: [582/600]
Discriminator Loss: 0.5279, Generator Loss: 4.5433
D(x): 0.8288, D(G(z)): 0.0651
Epoch: [8/20], Batch Num: [583/600]
Discriminator Loss: 0.3365, Generator Loss: 3.8801
D(x): 0.8548, D(G(z)): 0.0540
Epoch: [8/20], Batch Num: [584/600]
Discriminator Loss: 0.4387, Generator Loss: 3.1058
D(x): 0.8609, D(G(z)): 0.0939
Epoch: [8/20], Batch Num: [585/600]
Discriminator Loss: 0.3964, Generator Loss: 2.6654
D(x): 0.9200, D(G(z)): 0.1770
Epoch: [8/20], Batch Num: [586/600]
Discriminator Loss: 0.3817, Generator Loss: 3.5965
D(x): 0.9227, D(G(z)): 0.1674
Epoch: [8/20], Batch Num: [587/600]
Discriminator Loss: 0.3835, Generator Loss: 3.5346
D(x): 0.8943, D(G(z)): 0.1168
Epoch: [8/20], Batch Num: [588/600]
Discriminator Loss: 0.3877, Generator Loss: 3.9548
D(x): 0.8617, D(G(z)): 0.0941
Epoch: [8/20], Batch Num: [589/600]
Discriminator Loss: 0.3444, Generator Loss: 3.5284
D(x): 0.8963, D(G(z)): 0.0969
Epoch: [8/20], Batch Num: [590/600]
Discriminator Loss: 0.4074, Generator Loss: 3.3736
D(x): 0.8614, D(G(z)): 0.0847
Epoch: [8/20], Batch Num: [591/600]
Discriminator Loss: 0.3401, Generator Loss: 3.3644
D(x): 0.8946, D(G(z)): 0.1028
Epoch: [8/20], Batch Num: [592/600]
Discriminator Loss: 0.4137, Generator Loss: 2.9730
D(x): 0.8853, D(G(z)): 0.1313
Epoch: [8/20], Batch Num: [593/600]
Discriminator Loss: 0.2978, Generator Loss: 3.0128
D(x): 0.9516, D(G(z)): 0.1583
Epoch: [8/20], Batch Num: [594/600]
Discriminator Loss: 0.4466, Generator Loss: 3.5151
D(x): 0.8502, D(G(z)): 0.1247
Epoch: [8/20], Batch Num: [595/600]
Discriminator Loss: 0.3802, Generator Loss: 3.8457
D(x): 0.8903, D(G(z)): 0.1044
Epoch: [8/20], Batch Num: [596/600]
Discriminator Loss: 0.3015, Generator Loss: 4.1149
D(x): 0.8867, D(G(z)): 0.0766
Epoch: [8/20], Batch Num: [597/600]
Discriminator Loss: 0.4189, Generator Loss: 3.8379
D(x): 0.8889, D(G(z)): 0.0851
Epoch: [8/20], Batch Num: [598/600]
Discriminator Loss: 0.3236, Generator Loss: 3.3222
D(x): 0.8992, D(G(z)): 0.0768
Epoch: [8/20], Batch Num: [599/600]
Discriminator Loss: 0.3255, Generator Loss: 3.0499
D(x): 0.8976, D(G(z)): 0.1158
Epoch: 9, Batch Num: [0/600]

Epoch: [9/20], Batch Num: [0/600]
Discriminator Loss: 0.3256, Generator Loss: 3.1667
D(x): 0.9334, D(G(z)): 0.1597
Epoch: [9/20], Batch Num: [1/600]
Discriminator Loss: 0.4365, Generator Loss: 3.5704
D(x): 0.8622, D(G(z)): 0.1020
Epoch: [9/20], Batch Num: [2/600]
Discriminator Loss: 0.3171, Generator Loss: 3.6814
D(x): 0.9262, D(G(z)): 0.1245
Epoch: [9/20], Batch Num: [3/600]
Discriminator Loss: 0.2851, Generator Loss: 3.9183
D(x): 0.8994, D(G(z)): 0.0837
Epoch: [9/20], Batch Num: [4/600]
Discriminator Loss: 0.3380, Generator Loss: 3.8295
D(x): 0.8576, D(G(z)): 0.0647
Epoch: [9/20], Batch Num: [5/600]
Discriminator Loss: 0.4038, Generator Loss: 3.3155
D(x): 0.8958, D(G(z)): 0.1123
Epoch: [9/20], Batch Num: [6/600]
Discriminator Loss: 0.3269, Generator Loss: 3.2793
D(x): 0.9131, D(G(z)): 0.1184
Epoch: [9/20], Batch Num: [7/600]
Discriminator Loss: 0.3019, Generator Loss: 3.5225
D(x): 0.9347, D(G(z)): 0.1311
Epoch: [9/20], Batch Num: [8/600]
Discriminator Loss: 0.3348, Generator Loss: 3.5183
D(x): 0.9091, D(G(z)): 0.1115
Epoch: [9/20], Batch Num: [9/600]
Discriminator Loss: 0.3632, Generator Loss: 3.7469
D(x): 0.8802, D(G(z)): 0.0951
Epoch: [9/20], Batch Num: [10/600]
Discriminator Loss: 0.3482, Generator Loss: 3.8100
D(x): 0.8638, D(G(z)): 0.0633
Epoch: [9/20], Batch Num: [11/600]
Discriminator Loss: 0.3874, Generator Loss: 3.4225
D(x): 0.8704, D(G(z)): 0.1115
Epoch: [9/20], Batch Num: [12/600]
Discriminator Loss: 0.2900, Generator Loss: 3.5381
D(x): 0.9389, D(G(z)): 0.1222
Epoch: [9/20], Batch Num: [13/600]
Discriminator Loss: 0.3291, Generator Loss: 3.9571
D(x): 0.9513, D(G(z)): 0.1568
Epoch: [9/20], Batch Num: [14/600]
Discriminator Loss: 0.2322, Generator Loss: 4.5629
D(x): 0.9123, D(G(z)): 0.0715
Epoch: [9/20], Batch Num: [15/600]
Discriminator Loss: 0.2145, Generator Loss: 4.7362
D(x): 0.9150, D(G(z)): 0.0511
Epoch: [9/20], Batch Num: [16/600]
Discriminator Loss: 0.1872, Generator Loss: 4.8347
D(x): 0.9092, D(G(z)): 0.0326
Epoch: [9/20], Batch Num: [17/600]
Discriminator Loss: 0.3584, Generator Loss: 3.7835
D(x): 0.8627, D(G(z)): 0.0391
Epoch: [9/20], Batch Num: [18/600]
Discriminator Loss: 0.2802, Generator Loss: 3.0185
D(x): 0.9174, D(G(z)): 0.0875
Epoch: [9/20], Batch Num: [19/600]
Discriminator Loss: 0.2387, Generator Loss: 3.5306
D(x): 0.9813, D(G(z)): 0.1556
Epoch: [9/20], Batch Num: [20/600]
Discriminator Loss: 0.3693, Generator Loss: 4.2105
D(x): 0.9133, D(G(z)): 0.1387
Epoch: [9/20], Batch Num: [21/600]
Discriminator Loss: 0.4332, Generator Loss: 4.9221
D(x): 0.8854, D(G(z)): 0.0707
Epoch: [9/20], Batch Num: [22/600]
Discriminator Loss: 0.2749, Generator Loss: 4.6405
D(x): 0.8956, D(G(z)): 0.0465
Epoch: [9/20], Batch Num: [23/600]
Discriminator Loss: 0.3469, Generator Loss: 4.6532
D(x): 0.8941, D(G(z)): 0.0528
Epoch: [9/20], Batch Num: [24/600]
Discriminator Loss: 0.3546, Generator Loss: 3.4820
D(x): 0.8698, D(G(z)): 0.0679
Epoch: [9/20], Batch Num: [25/600]
Discriminator Loss: 0.3319, Generator Loss: 3.1020
D(x): 0.9307, D(G(z)): 0.1327
Epoch: [9/20], Batch Num: [26/600]
Discriminator Loss: 0.3383, Generator Loss: 3.4163
D(x): 0.9212, D(G(z)): 0.1461
Epoch: [9/20], Batch Num: [27/600]
Discriminator Loss: 0.2174, Generator Loss: 4.2461
D(x): 0.9650, D(G(z)): 0.1224
Epoch: [9/20], Batch Num: [28/600]
Discriminator Loss: 0.2865, Generator Loss: 4.9014
D(x): 0.9073, D(G(z)): 0.0765
Epoch: [9/20], Batch Num: [29/600]
Discriminator Loss: 0.2503, Generator Loss: 4.9103
D(x): 0.8844, D(G(z)): 0.0186
Epoch: [9/20], Batch Num: [30/600]
Discriminator Loss: 0.2172, Generator Loss: 4.2401
D(x): 0.9067, D(G(z)): 0.0353
Epoch: [9/20], Batch Num: [31/600]
Discriminator Loss: 0.3232, Generator Loss: 3.4294
D(x): 0.9180, D(G(z)): 0.1102
Epoch: [9/20], Batch Num: [32/600]
Discriminator Loss: 0.3914, Generator Loss: 3.8976
D(x): 0.9381, D(G(z)): 0.1360
Epoch: [9/20], Batch Num: [33/600]
Discriminator Loss: 0.4594, Generator Loss: 3.8999
D(x): 0.8842, D(G(z)): 0.1038
Epoch: [9/20], Batch Num: [34/600]
Discriminator Loss: 0.2755, Generator Loss: 3.6768
D(x): 0.8929, D(G(z)): 0.0664
Epoch: [9/20], Batch Num: [35/600]
Discriminator Loss: 0.3039, Generator Loss: 4.0235
D(x): 0.9313, D(G(z)): 0.1131
Epoch: [9/20], Batch Num: [36/600]
Discriminator Loss: 0.5064, Generator Loss: 3.4662
D(x): 0.8478, D(G(z)): 0.0929
Epoch: [9/20], Batch Num: [37/600]
Discriminator Loss: 0.3782, Generator Loss: 3.8010
D(x): 0.8832, D(G(z)): 0.1329
Epoch: [9/20], Batch Num: [38/600]
Discriminator Loss: 0.3743, Generator Loss: 3.2686
D(x): 0.8970, D(G(z)): 0.1297
Epoch: [9/20], Batch Num: [39/600]
Discriminator Loss: 0.4350, Generator Loss: 2.8817
D(x): 0.8651, D(G(z)): 0.0801
Epoch: [9/20], Batch Num: [40/600]
Discriminator Loss: 0.3738, Generator Loss: 3.2832
D(x): 0.9270, D(G(z)): 0.1665
Epoch: [9/20], Batch Num: [41/600]
Discriminator Loss: 0.3293, Generator Loss: 3.1622
D(x): 0.9014, D(G(z)): 0.1194
Epoch: [9/20], Batch Num: [42/600]
Discriminator Loss: 0.3658, Generator Loss: 3.7605
D(x): 0.8827, D(G(z)): 0.0943
Epoch: [9/20], Batch Num: [43/600]
Discriminator Loss: 0.4238, Generator Loss: 3.2047
D(x): 0.8536, D(G(z)): 0.0790
Epoch: [9/20], Batch Num: [44/600]
Discriminator Loss: 0.2871, Generator Loss: 3.2049
D(x): 0.9148, D(G(z)): 0.1200
Epoch: [9/20], Batch Num: [45/600]
Discriminator Loss: 0.2202, Generator Loss: 3.4615
D(x): 0.9425, D(G(z)): 0.0935
Epoch: [9/20], Batch Num: [46/600]
Discriminator Loss: 0.2817, Generator Loss: 3.4186
D(x): 0.8998, D(G(z)): 0.0888
Epoch: [9/20], Batch Num: [47/600]
Discriminator Loss: 0.2717, Generator Loss: 3.2105
D(x): 0.8997, D(G(z)): 0.0769
Epoch: [9/20], Batch Num: [48/600]
Discriminator Loss: 0.2292, Generator Loss: 3.2651
D(x): 0.9270, D(G(z)): 0.0931
Epoch: [9/20], Batch Num: [49/600]
Discriminator Loss: 0.2075, Generator Loss: 3.5791
D(x): 0.9457, D(G(z)): 0.0884
Epoch: [9/20], Batch Num: [50/600]
Discriminator Loss: 0.3135, Generator Loss: 3.7587
D(x): 0.9237, D(G(z)): 0.0981
Epoch: [9/20], Batch Num: [51/600]
Discriminator Loss: 0.2405, Generator Loss: 4.2952
D(x): 0.9103, D(G(z)): 0.0459
Epoch: [9/20], Batch Num: [52/600]
Discriminator Loss: 0.3154, Generator Loss: 4.1999
D(x): 0.9017, D(G(z)): 0.0766
Epoch: [9/20], Batch Num: [53/600]
Discriminator Loss: 0.4965, Generator Loss: 3.0892
D(x): 0.8688, D(G(z)): 0.1107
Epoch: [9/20], Batch Num: [54/600]
Discriminator Loss: 0.3237, Generator Loss: 2.9016
D(x): 0.9078, D(G(z)): 0.1016
Epoch: [9/20], Batch Num: [55/600]
Discriminator Loss: 0.4160, Generator Loss: 3.4086
D(x): 0.9041, D(G(z)): 0.1868
Epoch: [9/20], Batch Num: [56/600]
Discriminator Loss: 0.3241, Generator Loss: 3.7152
D(x): 0.8953, D(G(z)): 0.1027
Epoch: [9/20], Batch Num: [57/600]
Discriminator Loss: 0.3473, Generator Loss: 3.9956
D(x): 0.9249, D(G(z)): 0.0940
Epoch: [9/20], Batch Num: [58/600]
Discriminator Loss: 0.3900, Generator Loss: 4.4159
D(x): 0.8708, D(G(z)): 0.0715
Epoch: [9/20], Batch Num: [59/600]
Discriminator Loss: 0.4474, Generator Loss: 4.1359
D(x): 0.8660, D(G(z)): 0.0657
Epoch: [9/20], Batch Num: [60/600]
Discriminator Loss: 0.3710, Generator Loss: 2.9123
D(x): 0.8695, D(G(z)): 0.0693
Epoch: [9/20], Batch Num: [61/600]
Discriminator Loss: 0.5135, Generator Loss: 2.5798
D(x): 0.8846, D(G(z)): 0.1575
Epoch: [9/20], Batch Num: [62/600]
Discriminator Loss: 0.3726, Generator Loss: 2.7955
D(x): 0.9525, D(G(z)): 0.1822
Epoch: [9/20], Batch Num: [63/600]
Discriminator Loss: 0.4369, Generator Loss: 3.3868
D(x): 0.8656, D(G(z)): 0.1023
Epoch: [9/20], Batch Num: [64/600]
Discriminator Loss: 0.4043, Generator Loss: 3.5101
D(x): 0.8880, D(G(z)): 0.1021
Epoch: [9/20], Batch Num: [65/600]
Discriminator Loss: 0.3485, Generator Loss: 3.9838
D(x): 0.8824, D(G(z)): 0.0936
Epoch: [9/20], Batch Num: [66/600]
Discriminator Loss: 0.2480, Generator Loss: 3.9022
D(x): 0.9115, D(G(z)): 0.0787
Epoch: [9/20], Batch Num: [67/600]
Discriminator Loss: 0.3644, Generator Loss: 3.2809
D(x): 0.8487, D(G(z)): 0.0807
Epoch: [9/20], Batch Num: [68/600]
Discriminator Loss: 0.2865, Generator Loss: 3.0380
D(x): 0.9332, D(G(z)): 0.1051
Epoch: [9/20], Batch Num: [69/600]
Discriminator Loss: 0.3114, Generator Loss: 3.4067
D(x): 0.9195, D(G(z)): 0.1348
Epoch: [9/20], Batch Num: [70/600]
Discriminator Loss: 0.4691, Generator Loss: 3.7276
D(x): 0.8827, D(G(z)): 0.1406
Epoch: [9/20], Batch Num: [71/600]
Discriminator Loss: 0.4792, Generator Loss: 3.4767
D(x): 0.8590, D(G(z)): 0.1061
Epoch: [9/20], Batch Num: [72/600]
Discriminator Loss: 0.6685, Generator Loss: 3.5642
D(x): 0.8297, D(G(z)): 0.1355
Epoch: [9/20], Batch Num: [73/600]
Discriminator Loss: 0.5008, Generator Loss: 3.0102
D(x): 0.8592, D(G(z)): 0.1143
Epoch: [9/20], Batch Num: [74/600]
Discriminator Loss: 0.3512, Generator Loss: 3.3782
D(x): 0.8928, D(G(z)): 0.1130
Epoch: [9/20], Batch Num: [75/600]
Discriminator Loss: 0.3273, Generator Loss: 3.2238
D(x): 0.9333, D(G(z)): 0.1326
Epoch: [9/20], Batch Num: [76/600]
Discriminator Loss: 0.3893, Generator Loss: 3.8665
D(x): 0.9015, D(G(z)): 0.1289
Epoch: [9/20], Batch Num: [77/600]
Discriminator Loss: 0.3313, Generator Loss: 3.7927
D(x): 0.8907, D(G(z)): 0.0893
Epoch: [9/20], Batch Num: [78/600]
Discriminator Loss: 0.4509, Generator Loss: 4.0036
D(x): 0.8703, D(G(z)): 0.0754
Epoch: [9/20], Batch Num: [79/600]
Discriminator Loss: 0.5299, Generator Loss: 2.8537
D(x): 0.8152, D(G(z)): 0.0714
Epoch: [9/20], Batch Num: [80/600]
Discriminator Loss: 0.5835, Generator Loss: 2.6972
D(x): 0.8800, D(G(z)): 0.1809
Epoch: [9/20], Batch Num: [81/600]
Discriminator Loss: 0.2508, Generator Loss: 2.8699
D(x): 0.9606, D(G(z)): 0.1543
Epoch: [9/20], Batch Num: [82/600]
Discriminator Loss: 0.3221, Generator Loss: 3.7870
D(x): 0.9305, D(G(z)): 0.1281
Epoch: [9/20], Batch Num: [83/600]
Discriminator Loss: 0.3985, Generator Loss: 3.9510
D(x): 0.8428, D(G(z)): 0.0658
Epoch: [9/20], Batch Num: [84/600]
Discriminator Loss: 0.4871, Generator Loss: 3.8230
D(x): 0.8524, D(G(z)): 0.0702
Epoch: [9/20], Batch Num: [85/600]
Discriminator Loss: 0.2440, Generator Loss: 3.6487
D(x): 0.9009, D(G(z)): 0.0572
Epoch: [9/20], Batch Num: [86/600]
Discriminator Loss: 0.3371, Generator Loss: 2.8898
D(x): 0.8963, D(G(z)): 0.0865
Epoch: [9/20], Batch Num: [87/600]
Discriminator Loss: 0.3533, Generator Loss: 2.7513
D(x): 0.9150, D(G(z)): 0.1232
Epoch: [9/20], Batch Num: [88/600]
Discriminator Loss: 0.3995, Generator Loss: 2.8381
D(x): 0.9215, D(G(z)): 0.1705
Epoch: [9/20], Batch Num: [89/600]
Discriminator Loss: 0.3270, Generator Loss: 3.7674
D(x): 0.9285, D(G(z)): 0.1496
Epoch: [9/20], Batch Num: [90/600]
Discriminator Loss: 0.3159, Generator Loss: 4.0650
D(x): 0.8981, D(G(z)): 0.0741
Epoch: [9/20], Batch Num: [91/600]
Discriminator Loss: 0.3212, Generator Loss: 3.9232
D(x): 0.8814, D(G(z)): 0.0573
Epoch: [9/20], Batch Num: [92/600]
Discriminator Loss: 0.3263, Generator Loss: 3.7013
D(x): 0.8675, D(G(z)): 0.0475
Epoch: [9/20], Batch Num: [93/600]
Discriminator Loss: 0.2432, Generator Loss: 3.3172
D(x): 0.9143, D(G(z)): 0.0633
Epoch: [9/20], Batch Num: [94/600]
Discriminator Loss: 0.3692, Generator Loss: 3.2219
D(x): 0.9241, D(G(z)): 0.1380
Epoch: [9/20], Batch Num: [95/600]
Discriminator Loss: 0.3430, Generator Loss: 3.4042
D(x): 0.9371, D(G(z)): 0.1316
Epoch: [9/20], Batch Num: [96/600]
Discriminator Loss: 0.2524, Generator Loss: 3.5920
D(x): 0.9478, D(G(z)): 0.1144
Epoch: [9/20], Batch Num: [97/600]
Discriminator Loss: 0.3744, Generator Loss: 3.7539
D(x): 0.8741, D(G(z)): 0.0888
Epoch: [9/20], Batch Num: [98/600]
Discriminator Loss: 0.5510, Generator Loss: 3.5174
D(x): 0.8409, D(G(z)): 0.1001
Epoch: [9/20], Batch Num: [99/600]
Discriminator Loss: 0.7405, Generator Loss: 2.7531
D(x): 0.7959, D(G(z)): 0.1026
Epoch: 9, Batch Num: [100/600]

Epoch: [9/20], Batch Num: [100/600]
Discriminator Loss: 0.3922, Generator Loss: 2.2030
D(x): 0.9008, D(G(z)): 0.1655
Epoch: [9/20], Batch Num: [101/600]
Discriminator Loss: 0.4467, Generator Loss: 2.7157
D(x): 0.9211, D(G(z)): 0.2170
Epoch: [9/20], Batch Num: [102/600]
Discriminator Loss: 0.6164, Generator Loss: 3.5962
D(x): 0.8913, D(G(z)): 0.2295
Epoch: [9/20], Batch Num: [103/600]
Discriminator Loss: 0.3939, Generator Loss: 4.0356
D(x): 0.8497, D(G(z)): 0.0863
Epoch: [9/20], Batch Num: [104/600]
Discriminator Loss: 0.4318, Generator Loss: 3.8985
D(x): 0.8260, D(G(z)): 0.0575
Epoch: [9/20], Batch Num: [105/600]
Discriminator Loss: 0.6140, Generator Loss: 2.9188
D(x): 0.7959, D(G(z)): 0.0889
Epoch: [9/20], Batch Num: [106/600]
Discriminator Loss: 0.4443, Generator Loss: 2.2304
D(x): 0.8516, D(G(z)): 0.1013
Epoch: [9/20], Batch Num: [107/600]
Discriminator Loss: 0.3519, Generator Loss: 1.9808
D(x): 0.9181, D(G(z)): 0.1612
Epoch: [9/20], Batch Num: [108/600]
Discriminator Loss: 0.3951, Generator Loss: 2.4288
D(x): 0.9407, D(G(z)): 0.2160
Epoch: [9/20], Batch Num: [109/600]
Discriminator Loss: 0.3471, Generator Loss: 3.3394
D(x): 0.9242, D(G(z)): 0.1812
Epoch: [9/20], Batch Num: [110/600]
Discriminator Loss: 0.2580, Generator Loss: 3.9781
D(x): 0.9241, D(G(z)): 0.0867
Epoch: [9/20], Batch Num: [111/600]
Discriminator Loss: 0.3743, Generator Loss: 4.4859
D(x): 0.8518, D(G(z)): 0.0551
Epoch: [9/20], Batch Num: [112/600]
Discriminator Loss: 0.4193, Generator Loss: 3.8134
D(x): 0.8127, D(G(z)): 0.0398
Epoch: [9/20], Batch Num: [113/600]
Discriminator Loss: 0.3461, Generator Loss: 3.2486
D(x): 0.8791, D(G(z)): 0.0709
Epoch: [9/20], Batch Num: [114/600]
Discriminator Loss: 0.4028, Generator Loss: 2.5628
D(x): 0.8849, D(G(z)): 0.1415
Epoch: [9/20], Batch Num: [115/600]
Discriminator Loss: 0.3954, Generator Loss: 2.6932
D(x): 0.9483, D(G(z)): 0.1830
Epoch: [9/20], Batch Num: [116/600]
Discriminator Loss: 0.4500, Generator Loss: 3.2289
D(x): 0.9067, D(G(z)): 0.1717
Epoch: [9/20], Batch Num: [117/600]
Discriminator Loss: 0.3632, Generator Loss: 3.3402
D(x): 0.8896, D(G(z)): 0.0993
Epoch: [9/20], Batch Num: [118/600]
Discriminator Loss: 0.2636, Generator Loss: 4.1743
D(x): 0.9360, D(G(z)): 0.1187
Epoch: [9/20], Batch Num: [119/600]
Discriminator Loss: 0.4770, Generator Loss: 3.7700
D(x): 0.8294, D(G(z)): 0.0645
Epoch: [9/20], Batch Num: [120/600]
Discriminator Loss: 0.4556, Generator Loss: 3.4093
D(x): 0.8179, D(G(z)): 0.0630
Epoch: [9/20], Batch Num: [121/600]
Discriminator Loss: 0.3746, Generator Loss: 2.7203
D(x): 0.8545, D(G(z)): 0.0976
Epoch: [9/20], Batch Num: [122/600]
Discriminator Loss: 0.6755, Generator Loss: 2.5213
D(x): 0.8690, D(G(z)): 0.2413
Epoch: [9/20], Batch Num: [123/600]
Discriminator Loss: 0.3424, Generator Loss: 2.9391
D(x): 0.9405, D(G(z)): 0.1777
Epoch: [9/20], Batch Num: [124/600]
Discriminator Loss: 0.3859, Generator Loss: 3.9299
D(x): 0.9340, D(G(z)): 0.1832
Epoch: [9/20], Batch Num: [125/600]
Discriminator Loss: 0.3583, Generator Loss: 4.1676
D(x): 0.8377, D(G(z)): 0.0602
Epoch: [9/20], Batch Num: [126/600]
Discriminator Loss: 0.4101, Generator Loss: 3.5511
D(x): 0.8162, D(G(z)): 0.0495
Epoch: [9/20], Batch Num: [127/600]
Discriminator Loss: 0.4297, Generator Loss: 2.9757
D(x): 0.8082, D(G(z)): 0.0464
Epoch: [9/20], Batch Num: [128/600]
Discriminator Loss: 0.3394, Generator Loss: 2.2732
D(x): 0.9090, D(G(z)): 0.1437
Epoch: [9/20], Batch Num: [129/600]
Discriminator Loss: 0.5130, Generator Loss: 2.4531
D(x): 0.9594, D(G(z)): 0.2715
Epoch: [9/20], Batch Num: [130/600]
Discriminator Loss: 0.4240, Generator Loss: 3.3887
D(x): 0.9038, D(G(z)): 0.1838
Epoch: [9/20], Batch Num: [131/600]
Discriminator Loss: 0.3945, Generator Loss: 3.9119
D(x): 0.8720, D(G(z)): 0.1193
Epoch: [9/20], Batch Num: [132/600]
Discriminator Loss: 0.5176, Generator Loss: 4.1526
D(x): 0.8253, D(G(z)): 0.0913
Epoch: [9/20], Batch Num: [133/600]
Discriminator Loss: 0.3479, Generator Loss: 4.0320
D(x): 0.8801, D(G(z)): 0.0894
Epoch: [9/20], Batch Num: [134/600]
Discriminator Loss: 0.5833, Generator Loss: 3.4534
D(x): 0.7632, D(G(z)): 0.0488
Epoch: [9/20], Batch Num: [135/600]
Discriminator Loss: 0.3615, Generator Loss: 2.5546
D(x): 0.8796, D(G(z)): 0.1108
Epoch: [9/20], Batch Num: [136/600]
Discriminator Loss: 0.5078, Generator Loss: 2.5487
D(x): 0.8783, D(G(z)): 0.1874
Epoch: [9/20], Batch Num: [137/600]
Discriminator Loss: 0.4071, Generator Loss: 2.6286
D(x): 0.9431, D(G(z)): 0.2242
Epoch: [9/20], Batch Num: [138/600]
Discriminator Loss: 0.4494, Generator Loss: 3.3483
D(x): 0.8625, D(G(z)): 0.1334
Epoch: [9/20], Batch Num: [139/600]
Discriminator Loss: 0.4244, Generator Loss: 3.1112
D(x): 0.8521, D(G(z)): 0.1163
Epoch: [9/20], Batch Num: [140/600]
Discriminator Loss: 0.4725, Generator Loss: 2.7823
D(x): 0.8389, D(G(z)): 0.0943
Epoch: [9/20], Batch Num: [141/600]
Discriminator Loss: 0.3684, Generator Loss: 2.8455
D(x): 0.8616, D(G(z)): 0.1114
Epoch: [9/20], Batch Num: [142/600]
Discriminator Loss: 0.4489, Generator Loss: 2.9583
D(x): 0.8749, D(G(z)): 0.1325
Epoch: [9/20], Batch Num: [143/600]
Discriminator Loss: 0.3489, Generator Loss: 2.9281
D(x): 0.8676, D(G(z)): 0.1206
Epoch: [9/20], Batch Num: [144/600]
Discriminator Loss: 0.3746, Generator Loss: 3.0051
D(x): 0.8910, D(G(z)): 0.1278
Epoch: [9/20], Batch Num: [145/600]
Discriminator Loss: 0.3081, Generator Loss: 3.0536
D(x): 0.8956, D(G(z)): 0.0971
Epoch: [9/20], Batch Num: [146/600]
Discriminator Loss: 0.4393, Generator Loss: 3.3567
D(x): 0.8611, D(G(z)): 0.1127
Epoch: [9/20], Batch Num: [147/600]
Discriminator Loss: 0.4132, Generator Loss: 2.9954
D(x): 0.8519, D(G(z)): 0.1212
Epoch: [9/20], Batch Num: [148/600]
Discriminator Loss: 0.3709, Generator Loss: 2.7798
D(x): 0.8753, D(G(z)): 0.1048
Epoch: [9/20], Batch Num: [149/600]
Discriminator Loss: 0.3367, Generator Loss: 3.2957
D(x): 0.9413, D(G(z)): 0.1873
Epoch: [9/20], Batch Num: [150/600]
Discriminator Loss: 0.3773, Generator Loss: 3.9880
D(x): 0.8807, D(G(z)): 0.1166
Epoch: [9/20], Batch Num: [151/600]
Discriminator Loss: 0.3814, Generator Loss: 3.9571
D(x): 0.8444, D(G(z)): 0.0610
Epoch: [9/20], Batch Num: [152/600]
Discriminator Loss: 0.4136, Generator Loss: 3.3254
D(x): 0.8341, D(G(z)): 0.0598
Epoch: [9/20], Batch Num: [153/600]
Discriminator Loss: 0.2419, Generator Loss: 3.1060
D(x): 0.9361, D(G(z)): 0.0950
Epoch: [9/20], Batch Num: [154/600]
Discriminator Loss: 0.3321, Generator Loss: 3.2586
D(x): 0.9368, D(G(z)): 0.1644
Epoch: [9/20], Batch Num: [155/600]
Discriminator Loss: 0.4151, Generator Loss: 3.6307
D(x): 0.8843, D(G(z)): 0.1680
Epoch: [9/20], Batch Num: [156/600]
Discriminator Loss: 0.2244, Generator Loss: 4.2260
D(x): 0.9310, D(G(z)): 0.0901
Epoch: [9/20], Batch Num: [157/600]
Discriminator Loss: 0.3810, Generator Loss: 4.2596
D(x): 0.8478, D(G(z)): 0.0298
Epoch: [9/20], Batch Num: [158/600]
Discriminator Loss: 0.3359, Generator Loss: 3.9147
D(x): 0.8845, D(G(z)): 0.0824
Epoch: [9/20], Batch Num: [159/600]
Discriminator Loss: 0.2934, Generator Loss: 3.7946
D(x): 0.9054, D(G(z)): 0.1009
Epoch: [9/20], Batch Num: [160/600]
Discriminator Loss: 0.2957, Generator Loss: 3.8362
D(x): 0.9168, D(G(z)): 0.0913
Epoch: [9/20], Batch Num: [161/600]
Discriminator Loss: 0.2888, Generator Loss: 3.5515
D(x): 0.9275, D(G(z)): 0.0948
Epoch: [9/20], Batch Num: [162/600]
Discriminator Loss: 0.2917, Generator Loss: 3.9498
D(x): 0.9405, D(G(z)): 0.1147
Epoch: [9/20], Batch Num: [163/600]
Discriminator Loss: 0.2165, Generator Loss: 4.3360
D(x): 0.9470, D(G(z)): 0.0819
Epoch: [9/20], Batch Num: [164/600]
Discriminator Loss: 0.2730, Generator Loss: 4.4611
D(x): 0.9033, D(G(z)): 0.0496
Epoch: [9/20], Batch Num: [165/600]
Discriminator Loss: 0.2817, Generator Loss: 3.9577
D(x): 0.8718, D(G(z)): 0.0336
Epoch: [9/20], Batch Num: [166/600]
Discriminator Loss: 0.2815, Generator Loss: 3.5549
D(x): 0.9056, D(G(z)): 0.0734
Epoch: [9/20], Batch Num: [167/600]
Discriminator Loss: 0.3295, Generator Loss: 3.3679
D(x): 0.9306, D(G(z)): 0.1211
Epoch: [9/20], Batch Num: [168/600]
Discriminator Loss: 0.2506, Generator Loss: 3.4669
D(x): 0.9422, D(G(z)): 0.1143
Epoch: [9/20], Batch Num: [169/600]
Discriminator Loss: 0.1924, Generator Loss: 4.2085
D(x): 0.9500, D(G(z)): 0.0874
Epoch: [9/20], Batch Num: [170/600]
Discriminator Loss: 0.3110, Generator Loss: 4.1633
D(x): 0.9017, D(G(z)): 0.0533
Epoch: [9/20], Batch Num: [171/600]
Discriminator Loss: 0.2292, Generator Loss: 4.1720
D(x): 0.9337, D(G(z)): 0.0691
Epoch: [9/20], Batch Num: [172/600]
Discriminator Loss: 0.2789, Generator Loss: 4.4384
D(x): 0.9181, D(G(z)): 0.0755
Epoch: [9/20], Batch Num: [173/600]
Discriminator Loss: 0.3051, Generator Loss: 3.9856
D(x): 0.9050, D(G(z)): 0.0838
Epoch: [9/20], Batch Num: [174/600]
Discriminator Loss: 0.3224, Generator Loss: 3.9275
D(x): 0.9157, D(G(z)): 0.1016
Epoch: [9/20], Batch Num: [175/600]
Discriminator Loss: 0.3888, Generator Loss: 3.9394
D(x): 0.8837, D(G(z)): 0.1141
Epoch: [9/20], Batch Num: [176/600]
Discriminator Loss: 0.2821, Generator Loss: 4.0498
D(x): 0.9203, D(G(z)): 0.0802
Epoch: [9/20], Batch Num: [177/600]
Discriminator Loss: 0.2870, Generator Loss: 3.9688
D(x): 0.8994, D(G(z)): 0.0883
Epoch: [9/20], Batch Num: [178/600]
Discriminator Loss: 0.2160, Generator Loss: 4.3948
D(x): 0.9541, D(G(z)): 0.1044
Epoch: [9/20], Batch Num: [179/600]
Discriminator Loss: 0.2693, Generator Loss: 3.9082
D(x): 0.8893, D(G(z)): 0.0639
Epoch: [9/20], Batch Num: [180/600]
Discriminator Loss: 0.2731, Generator Loss: 3.5378
D(x): 0.9317, D(G(z)): 0.0972
Epoch: [9/20], Batch Num: [181/600]
Discriminator Loss: 0.2643, Generator Loss: 4.0293
D(x): 0.9541, D(G(z)): 0.1044
Epoch: [9/20], Batch Num: [182/600]
Discriminator Loss: 0.3522, Generator Loss: 4.4055
D(x): 0.9099, D(G(z)): 0.1142
Epoch: [9/20], Batch Num: [183/600]
Discriminator Loss: 0.2439, Generator Loss: 5.7970
D(x): 0.9438, D(G(z)): 0.0916
Epoch: [9/20], Batch Num: [184/600]
Discriminator Loss: 0.3635, Generator Loss: 5.3108
D(x): 0.8701, D(G(z)): 0.0654
Epoch: [9/20], Batch Num: [185/600]
Discriminator Loss: 0.4320, Generator Loss: 3.8794
D(x): 0.8470, D(G(z)): 0.0619
Epoch: [9/20], Batch Num: [186/600]
Discriminator Loss: 0.3514, Generator Loss: 3.8271
D(x): 0.9075, D(G(z)): 0.1041
Epoch: [9/20], Batch Num: [187/600]
Discriminator Loss: 0.5123, Generator Loss: 3.6901
D(x): 0.9189, D(G(z)): 0.1595
Epoch: [9/20], Batch Num: [188/600]
Discriminator Loss: 0.4235, Generator Loss: 3.7378
D(x): 0.9000, D(G(z)): 0.1292
Epoch: [9/20], Batch Num: [189/600]
Discriminator Loss: 0.5133, Generator Loss: 4.1119
D(x): 0.8693, D(G(z)): 0.1108
Epoch: [9/20], Batch Num: [190/600]
Discriminator Loss: 0.3576, Generator Loss: 4.0139
D(x): 0.8942, D(G(z)): 0.0941
Epoch: [9/20], Batch Num: [191/600]
Discriminator Loss: 0.4840, Generator Loss: 3.7037
D(x): 0.8447, D(G(z)): 0.0821
Epoch: [9/20], Batch Num: [192/600]
Discriminator Loss: 0.3183, Generator Loss: 3.1978
D(x): 0.9208, D(G(z)): 0.1138
Epoch: [9/20], Batch Num: [193/600]
Discriminator Loss: 0.4319, Generator Loss: 3.0272
D(x): 0.8778, D(G(z)): 0.1301
Epoch: [9/20], Batch Num: [194/600]
Discriminator Loss: 0.5029, Generator Loss: 4.0122
D(x): 0.9006, D(G(z)): 0.1873
Epoch: [9/20], Batch Num: [195/600]
Discriminator Loss: 0.3782, Generator Loss: 4.2169
D(x): 0.9043, D(G(z)): 0.1040
Epoch: [9/20], Batch Num: [196/600]
Discriminator Loss: 0.3964, Generator Loss: 3.8744
D(x): 0.8518, D(G(z)): 0.0664
Epoch: [9/20], Batch Num: [197/600]
Discriminator Loss: 0.3491, Generator Loss: 3.6362
D(x): 0.8834, D(G(z)): 0.0920
Epoch: [9/20], Batch Num: [198/600]
Discriminator Loss: 0.3916, Generator Loss: 3.2871
D(x): 0.8893, D(G(z)): 0.1172
Epoch: [9/20], Batch Num: [199/600]
Discriminator Loss: 0.5556, Generator Loss: 2.8628
D(x): 0.8569, D(G(z)): 0.1666
Epoch: 9, Batch Num: [200/600]

Epoch: [9/20], Batch Num: [200/600]
Discriminator Loss: 0.5310, Generator Loss: 3.5367
D(x): 0.9092, D(G(z)): 0.2217
Epoch: [9/20], Batch Num: [201/600]
Discriminator Loss: 0.3794, Generator Loss: 3.2373
D(x): 0.8508, D(G(z)): 0.1011
Epoch: [9/20], Batch Num: [202/600]
Discriminator Loss: 0.4562, Generator Loss: 3.4699
D(x): 0.8236, D(G(z)): 0.0876
Epoch: [9/20], Batch Num: [203/600]
Discriminator Loss: 0.4829, Generator Loss: 2.7094
D(x): 0.8477, D(G(z)): 0.1111
Epoch: [9/20], Batch Num: [204/600]
Discriminator Loss: 0.5008, Generator Loss: 2.6291
D(x): 0.8394, D(G(z)): 0.1711
Epoch: [9/20], Batch Num: [205/600]
Discriminator Loss: 0.4705, Generator Loss: 2.4735
D(x): 0.8718, D(G(z)): 0.1795
Epoch: [9/20], Batch Num: [206/600]
Discriminator Loss: 0.5881, Generator Loss: 3.0682
D(x): 0.8771, D(G(z)): 0.2368
Epoch: [9/20], Batch Num: [207/600]
Discriminator Loss: 0.4302, Generator Loss: 3.7418
D(x): 0.8831, D(G(z)): 0.1423
Epoch: [9/20], Batch Num: [208/600]
Discriminator Loss: 0.5393, Generator Loss: 3.5569
D(x): 0.7978, D(G(z)): 0.0874
Epoch: [9/20], Batch Num: [209/600]
Discriminator Loss: 0.5626, Generator Loss: 2.8727
D(x): 0.7876, D(G(z)): 0.1001
Epoch: [9/20], Batch Num: [210/600]
Discriminator Loss: 0.4362, Generator Loss: 2.6110
D(x): 0.8584, D(G(z)): 0.1364
Epoch: [9/20], Batch Num: [211/600]
Discriminator Loss: 0.3415, Generator Loss: 2.7262
D(x): 0.9050, D(G(z)): 0.1669
Epoch: [9/20], Batch Num: [212/600]
Discriminator Loss: 0.4953, Generator Loss: 2.7789
D(x): 0.8967, D(G(z)): 0.2095
Epoch: [9/20], Batch Num: [213/600]
Discriminator Loss: 0.2793, Generator Loss: 3.3787
D(x): 0.9231, D(G(z)): 0.1289
Epoch: [9/20], Batch Num: [214/600]
Discriminator Loss: 0.3492, Generator Loss: 3.7912
D(x): 0.8695, D(G(z)): 0.0872
Epoch: [9/20], Batch Num: [215/600]
Discriminator Loss: 0.3375, Generator Loss: 4.3490
D(x): 0.8809, D(G(z)): 0.0870
Epoch: [9/20], Batch Num: [216/600]
Discriminator Loss: 0.2772, Generator Loss: 4.0804
D(x): 0.8737, D(G(z)): 0.0511
Epoch: [9/20], Batch Num: [217/600]
Discriminator Loss: 0.2279, Generator Loss: 3.6589
D(x): 0.9045, D(G(z)): 0.0699
Epoch: [9/20], Batch Num: [218/600]
Discriminator Loss: 0.2937, Generator Loss: 3.2570
D(x): 0.8784, D(G(z)): 0.0805
Epoch: [9/20], Batch Num: [219/600]
Discriminator Loss: 0.3923, Generator Loss: 3.2752
D(x): 0.9141, D(G(z)): 0.1512
Epoch: [9/20], Batch Num: [220/600]
Discriminator Loss: 0.2724, Generator Loss: 3.2006
D(x): 0.9252, D(G(z)): 0.1122
Epoch: [9/20], Batch Num: [221/600]
Discriminator Loss: 0.2473, Generator Loss: 3.8759
D(x): 0.9393, D(G(z)): 0.1232
Epoch: [9/20], Batch Num: [222/600]
Discriminator Loss: 0.2439, Generator Loss: 4.3889
D(x): 0.9207, D(G(z)): 0.0921
Epoch: [9/20], Batch Num: [223/600]
Discriminator Loss: 0.3274, Generator Loss: 4.2000
D(x): 0.8748, D(G(z)): 0.0784
Epoch: [9/20], Batch Num: [224/600]
Discriminator Loss: 0.2777, Generator Loss: 3.8280
D(x): 0.8769, D(G(z)): 0.0608
Epoch: [9/20], Batch Num: [225/600]
Discriminator Loss: 0.3235, Generator Loss: 3.5058
D(x): 0.9084, D(G(z)): 0.1138
Epoch: [9/20], Batch Num: [226/600]
Discriminator Loss: 0.3735, Generator Loss: 3.2777
D(x): 0.9120, D(G(z)): 0.1419
Epoch: [9/20], Batch Num: [227/600]
Discriminator Loss: 0.3898, Generator Loss: 4.0296
D(x): 0.9202, D(G(z)): 0.1444
Epoch: [9/20], Batch Num: [228/600]
Discriminator Loss: 0.3126, Generator Loss: 4.7868
D(x): 0.9297, D(G(z)): 0.1153
Epoch: [9/20], Batch Num: [229/600]
Discriminator Loss: 0.2309, Generator Loss: 5.2423
D(x): 0.9067, D(G(z)): 0.0422
Epoch: [9/20], Batch Num: [230/600]
Discriminator Loss: 0.4585, Generator Loss: 4.1948
D(x): 0.8051, D(G(z)): 0.0582
Epoch: [9/20], Batch Num: [231/600]
Discriminator Loss: 0.2498, Generator Loss: 3.3190
D(x): 0.9341, D(G(z)): 0.1145
Epoch: [9/20], Batch Num: [232/600]
Discriminator Loss: 0.3324, Generator Loss: 2.8692
D(x): 0.9012, D(G(z)): 0.1120
Epoch: [9/20], Batch Num: [233/600]
Discriminator Loss: 0.4334, Generator Loss: 3.4823
D(x): 0.9279, D(G(z)): 0.1913
Epoch: [9/20], Batch Num: [234/600]
Discriminator Loss: 0.3679, Generator Loss: 3.9451
D(x): 0.8944, D(G(z)): 0.1442
Epoch: [9/20], Batch Num: [235/600]
Discriminator Loss: 0.5706, Generator Loss: 4.8734
D(x): 0.8643, D(G(z)): 0.1560
Epoch: [9/20], Batch Num: [236/600]
Discriminator Loss: 0.4950, Generator Loss: 3.9699
D(x): 0.8103, D(G(z)): 0.0479
Epoch: [9/20], Batch Num: [237/600]
Discriminator Loss: 0.4007, Generator Loss: 3.2788
D(x): 0.8364, D(G(z)): 0.0568
Epoch: [9/20], Batch Num: [238/600]
Discriminator Loss: 0.4041, Generator Loss: 2.7569
D(x): 0.8938, D(G(z)): 0.1406
Epoch: [9/20], Batch Num: [239/600]
Discriminator Loss: 0.5532, Generator Loss: 2.8718
D(x): 0.9001, D(G(z)): 0.2031
Epoch: [9/20], Batch Num: [240/600]
Discriminator Loss: 0.4317, Generator Loss: 3.5358
D(x): 0.8967, D(G(z)): 0.1493
Epoch: [9/20], Batch Num: [241/600]
Discriminator Loss: 0.3376, Generator Loss: 3.6767
D(x): 0.9078, D(G(z)): 0.1200
Epoch: [9/20], Batch Num: [242/600]
Discriminator Loss: 0.4064, Generator Loss: 3.6220
D(x): 0.8608, D(G(z)): 0.1069
Epoch: [9/20], Batch Num: [243/600]
Discriminator Loss: 0.3471, Generator Loss: 3.7534
D(x): 0.8764, D(G(z)): 0.0854
Epoch: [9/20], Batch Num: [244/600]
Discriminator Loss: 0.3613, Generator Loss: 3.3420
D(x): 0.8652, D(G(z)): 0.0830
Epoch: [9/20], Batch Num: [245/600]
Discriminator Loss: 0.4491, Generator Loss: 3.2593
D(x): 0.8555, D(G(z)): 0.1045
Epoch: [9/20], Batch Num: [246/600]
Discriminator Loss: 0.4582, Generator Loss: 2.8293
D(x): 0.8594, D(G(z)): 0.1519
Epoch: [9/20], Batch Num: [247/600]
Discriminator Loss: 0.3748, Generator Loss: 2.8413
D(x): 0.9245, D(G(z)): 0.1764
Epoch: [9/20], Batch Num: [248/600]
Discriminator Loss: 0.4162, Generator Loss: 3.2412
D(x): 0.9163, D(G(z)): 0.1945
Epoch: [9/20], Batch Num: [249/600]
Discriminator Loss: 0.3977, Generator Loss: 4.0782
D(x): 0.8716, D(G(z)): 0.1233
Epoch: [9/20], Batch Num: [250/600]
Discriminator Loss: 0.2911, Generator Loss: 4.6551
D(x): 0.8833, D(G(z)): 0.0903
Epoch: [9/20], Batch Num: [251/600]
Discriminator Loss: 0.3879, Generator Loss: 3.9615
D(x): 0.8097, D(G(z)): 0.0388
Epoch: [9/20], Batch Num: [252/600]
Discriminator Loss: 0.3506, Generator Loss: 3.4907
D(x): 0.8783, D(G(z)): 0.0846
Epoch: [9/20], Batch Num: [253/600]
Discriminator Loss: 0.3118, Generator Loss: 2.9197
D(x): 0.9105, D(G(z)): 0.1026
Epoch: [9/20], Batch Num: [254/600]
Discriminator Loss: 0.4301, Generator Loss: 3.0323
D(x): 0.8896, D(G(z)): 0.1639
Epoch: [9/20], Batch Num: [255/600]
Discriminator Loss: 0.4047, Generator Loss: 3.2710
D(x): 0.9091, D(G(z)): 0.1766
Epoch: [9/20], Batch Num: [256/600]
Discriminator Loss: 0.3580, Generator Loss: 3.7418
D(x): 0.8920, D(G(z)): 0.1332
Epoch: [9/20], Batch Num: [257/600]
Discriminator Loss: 0.3005, Generator Loss: 3.8976
D(x): 0.8917, D(G(z)): 0.0691
Epoch: [9/20], Batch Num: [258/600]
Discriminator Loss: 0.3192, Generator Loss: 4.2418
D(x): 0.9006, D(G(z)): 0.0772
Epoch: [9/20], Batch Num: [259/600]
Discriminator Loss: 0.4424, Generator Loss: 3.8618
D(x): 0.8695, D(G(z)): 0.0884
Epoch: [9/20], Batch Num: [260/600]
Discriminator Loss: 0.2673, Generator Loss: 3.9080
D(x): 0.9478, D(G(z)): 0.1164
Epoch: [9/20], Batch Num: [261/600]
Discriminator Loss: 0.2983, Generator Loss: 4.1425
D(x): 0.9043, D(G(z)): 0.0857
Epoch: [9/20], Batch Num: [262/600]
Discriminator Loss: 0.3616, Generator Loss: 4.0380
D(x): 0.8783, D(G(z)): 0.0905
Epoch: [9/20], Batch Num: [263/600]
Discriminator Loss: 0.4276, Generator Loss: 3.6107
D(x): 0.8395, D(G(z)): 0.0749
Epoch: [9/20], Batch Num: [264/600]
Discriminator Loss: 0.3160, Generator Loss: 2.9027
D(x): 0.9261, D(G(z)): 0.1237
Epoch: [9/20], Batch Num: [265/600]
Discriminator Loss: 0.4086, Generator Loss: 3.2825
D(x): 0.9078, D(G(z)): 0.1538
Epoch: [9/20], Batch Num: [266/600]
Discriminator Loss: 0.3995, Generator Loss: 4.3840
D(x): 0.9373, D(G(z)): 0.1817
Epoch: [9/20], Batch Num: [267/600]
Discriminator Loss: 0.4086, Generator Loss: 4.7297
D(x): 0.8322, D(G(z)): 0.0611
Epoch: [9/20], Batch Num: [268/600]
Discriminator Loss: 0.4950, Generator Loss: 4.1450
D(x): 0.8391, D(G(z)): 0.0839
Epoch: [9/20], Batch Num: [269/600]
Discriminator Loss: 0.4512, Generator Loss: 2.5072
D(x): 0.8319, D(G(z)): 0.0756
Epoch: [9/20], Batch Num: [270/600]
Discriminator Loss: 0.4905, Generator Loss: 2.4351
D(x): 0.9252, D(G(z)): 0.2133
Epoch: [9/20], Batch Num: [271/600]
Discriminator Loss: 0.6704, Generator Loss: 3.6996
D(x): 0.9268, D(G(z)): 0.2815
Epoch: [9/20], Batch Num: [272/600]
Discriminator Loss: 0.3134, Generator Loss: 4.5786
D(x): 0.9089, D(G(z)): 0.1110
Epoch: [9/20], Batch Num: [273/600]
Discriminator Loss: 0.8305, Generator Loss: 4.2707
D(x): 0.7125, D(G(z)): 0.0456
Epoch: [9/20], Batch Num: [274/600]
Discriminator Loss: 0.5196, Generator Loss: 2.8882
D(x): 0.8146, D(G(z)): 0.0464
Epoch: [9/20], Batch Num: [275/600]
Discriminator Loss: 0.4032, Generator Loss: 2.1805
D(x): 0.8852, D(G(z)): 0.1268
Epoch: [9/20], Batch Num: [276/600]
Discriminator Loss: 0.3903, Generator Loss: 2.2335
D(x): 0.9535, D(G(z)): 0.2311
Epoch: [9/20], Batch Num: [277/600]
Discriminator Loss: 0.4377, Generator Loss: 2.8914
D(x): 0.9294, D(G(z)): 0.2326
Epoch: [9/20], Batch Num: [278/600]
Discriminator Loss: 0.4798, Generator Loss: 3.7551
D(x): 0.8324, D(G(z)): 0.1147
Epoch: [9/20], Batch Num: [279/600]
Discriminator Loss: 0.4976, Generator Loss: 3.5531
D(x): 0.8207, D(G(z)): 0.0795
Epoch: [9/20], Batch Num: [280/600]
Discriminator Loss: 0.3927, Generator Loss: 3.1770
D(x): 0.8441, D(G(z)): 0.0817
Epoch: [9/20], Batch Num: [281/600]
Discriminator Loss: 0.4016, Generator Loss: 2.5587
D(x): 0.8375, D(G(z)): 0.0759
Epoch: [9/20], Batch Num: [282/600]
Discriminator Loss: 0.4383, Generator Loss: 2.4461
D(x): 0.8829, D(G(z)): 0.1670
Epoch: [9/20], Batch Num: [283/600]
Discriminator Loss: 0.4349, Generator Loss: 2.4892
D(x): 0.8951, D(G(z)): 0.1760
Epoch: [9/20], Batch Num: [284/600]
Discriminator Loss: 0.4310, Generator Loss: 2.9526
D(x): 0.8948, D(G(z)): 0.1813
Epoch: [9/20], Batch Num: [285/600]
Discriminator Loss: 0.3466, Generator Loss: 3.2879
D(x): 0.8902, D(G(z)): 0.1307
Epoch: [9/20], Batch Num: [286/600]
Discriminator Loss: 0.5472, Generator Loss: 3.3911
D(x): 0.8221, D(G(z)): 0.1001
Epoch: [9/20], Batch Num: [287/600]
Discriminator Loss: 0.4509, Generator Loss: 3.0256
D(x): 0.8354, D(G(z)): 0.0802
Epoch: [9/20], Batch Num: [288/600]
Discriminator Loss: 0.4200, Generator Loss: 2.6538
D(x): 0.8566, D(G(z)): 0.1315
Epoch: [9/20], Batch Num: [289/600]
Discriminator Loss: 0.3395, Generator Loss: 2.7283
D(x): 0.9205, D(G(z)): 0.1442
Epoch: [9/20], Batch Num: [290/600]
Discriminator Loss: 0.4839, Generator Loss: 2.7340
D(x): 0.8725, D(G(z)): 0.1728
Epoch: [9/20], Batch Num: [291/600]
Discriminator Loss: 0.3132, Generator Loss: 2.9634
D(x): 0.9038, D(G(z)): 0.1429
Epoch: [9/20], Batch Num: [292/600]
Discriminator Loss: 0.2993, Generator Loss: 3.0958
D(x): 0.8942, D(G(z)): 0.1029
Epoch: [9/20], Batch Num: [293/600]
Discriminator Loss: 0.3210, Generator Loss: 3.6999
D(x): 0.8924, D(G(z)): 0.1184
Epoch: [9/20], Batch Num: [294/600]
Discriminator Loss: 0.4909, Generator Loss: 3.1671
D(x): 0.8195, D(G(z)): 0.0862
Epoch: [9/20], Batch Num: [295/600]
Discriminator Loss: 0.4579, Generator Loss: 2.7299
D(x): 0.8402, D(G(z)): 0.0978
Epoch: [9/20], Batch Num: [296/600]
Discriminator Loss: 0.3822, Generator Loss: 2.8295
D(x): 0.9362, D(G(z)): 0.1624
Epoch: [9/20], Batch Num: [297/600]
Discriminator Loss: 0.3258, Generator Loss: 3.2637
D(x): 0.9053, D(G(z)): 0.1417
Epoch: [9/20], Batch Num: [298/600]
Discriminator Loss: 0.3533, Generator Loss: 3.4891
D(x): 0.9315, D(G(z)): 0.1439
Epoch: [9/20], Batch Num: [299/600]
Discriminator Loss: 0.4552, Generator Loss: 3.9649
D(x): 0.8509, D(G(z)): 0.1021
Epoch: 9, Batch Num: [300/600]

Epoch: [9/20], Batch Num: [300/600]
Discriminator Loss: 0.3940, Generator Loss: 4.2236
D(x): 0.8761, D(G(z)): 0.0762
Epoch: [9/20], Batch Num: [301/600]
Discriminator Loss: 0.3604, Generator Loss: 3.9288
D(x): 0.8839, D(G(z)): 0.0767
Epoch: [9/20], Batch Num: [302/600]
Discriminator Loss: 0.4135, Generator Loss: 2.8205
D(x): 0.8390, D(G(z)): 0.0857
Epoch: [9/20], Batch Num: [303/600]
Discriminator Loss: 0.3193, Generator Loss: 2.7999
D(x): 0.9344, D(G(z)): 0.1566
Epoch: [9/20], Batch Num: [304/600]
Discriminator Loss: 0.3513, Generator Loss: 3.4015
D(x): 0.9371, D(G(z)): 0.1703
Epoch: [9/20], Batch Num: [305/600]
Discriminator Loss: 0.3914, Generator Loss: 3.4090
D(x): 0.8613, D(G(z)): 0.0805
Epoch: [9/20], Batch Num: [306/600]
Discriminator Loss: 0.3141, Generator Loss: 3.4729
D(x): 0.9102, D(G(z)): 0.0999
Epoch: [9/20], Batch Num: [307/600]
Discriminator Loss: 0.3414, Generator Loss: 3.3746
D(x): 0.8789, D(G(z)): 0.0792
Epoch: [9/20], Batch Num: [308/600]
Discriminator Loss: 0.2451, Generator Loss: 3.5599
D(x): 0.9110, D(G(z)): 0.0845
Epoch: [9/20], Batch Num: [309/600]
Discriminator Loss: 0.6341, Generator Loss: 3.2061
D(x): 0.8323, D(G(z)): 0.1127
Epoch: [9/20], Batch Num: [310/600]
Discriminator Loss: 0.3747, Generator Loss: 3.0902
D(x): 0.9058, D(G(z)): 0.1448
Epoch: [9/20], Batch Num: [311/600]
Discriminator Loss: 0.2998, Generator Loss: 2.9569
D(x): 0.9005, D(G(z)): 0.0833
Epoch: [9/20], Batch Num: [312/600]
Discriminator Loss: 0.3796, Generator Loss: 3.3445
D(x): 0.9061, D(G(z)): 0.1250
Epoch: [9/20], Batch Num: [313/600]
Discriminator Loss: 0.4504, Generator Loss: 3.1998
D(x): 0.8633, D(G(z)): 0.1257
Epoch: [9/20], Batch Num: [314/600]
Discriminator Loss: 0.3209, Generator Loss: 3.0867
D(x): 0.8860, D(G(z)): 0.0983
Epoch: [9/20], Batch Num: [315/600]
Discriminator Loss: 0.3664, Generator Loss: 2.8921
D(x): 0.8905, D(G(z)): 0.1179
Epoch: [9/20], Batch Num: [316/600]
Discriminator Loss: 0.3689, Generator Loss: 3.3628
D(x): 0.9079, D(G(z)): 0.1377
Epoch: [9/20], Batch Num: [317/600]
Discriminator Loss: 0.3229, Generator Loss: 3.6226
D(x): 0.9058, D(G(z)): 0.0903
Epoch: [9/20], Batch Num: [318/600]
Discriminator Loss: 0.5354, Generator Loss: 3.5207
D(x): 0.8184, D(G(z)): 0.0769
Epoch: [9/20], Batch Num: [319/600]
Discriminator Loss: 0.3028, Generator Loss: 3.1040
D(x): 0.9057, D(G(z)): 0.0782
Epoch: [9/20], Batch Num: [320/600]
Discriminator Loss: 0.3062, Generator Loss: 2.9200
D(x): 0.9134, D(G(z)): 0.1268
Epoch: [9/20], Batch Num: [321/600]
Discriminator Loss: 0.4567, Generator Loss: 3.2950
D(x): 0.9084, D(G(z)): 0.1704
Epoch: [9/20], Batch Num: [322/600]
Discriminator Loss: 0.3821, Generator Loss: 4.0077
D(x): 0.9034, D(G(z)): 0.1168
Epoch: [9/20], Batch Num: [323/600]
Discriminator Loss: 0.2437, Generator Loss: 4.3267
D(x): 0.9031, D(G(z)): 0.0516
Epoch: [9/20], Batch Num: [324/600]
Discriminator Loss: 0.2845, Generator Loss: 4.1574
D(x): 0.8890, D(G(z)): 0.0614
Epoch: [9/20], Batch Num: [325/600]
Discriminator Loss: 0.4140, Generator Loss: 3.4510
D(x): 0.8523, D(G(z)): 0.0656
Epoch: [9/20], Batch Num: [326/600]
Discriminator Loss: 0.3004, Generator Loss: 2.9256
D(x): 0.9235, D(G(z)): 0.0915
Epoch: [9/20], Batch Num: [327/600]
Discriminator Loss: 0.4806, Generator Loss: 2.6905
D(x): 0.9140, D(G(z)): 0.1777
Epoch: [9/20], Batch Num: [328/600]
Discriminator Loss: 0.3444, Generator Loss: 3.8350
D(x): 0.9263, D(G(z)): 0.1425
Epoch: [9/20], Batch Num: [329/600]
Discriminator Loss: 0.3668, Generator Loss: 4.4601
D(x): 0.8836, D(G(z)): 0.1045
Epoch: [9/20], Batch Num: [330/600]
Discriminator Loss: 0.4307, Generator Loss: 4.4475
D(x): 0.8434, D(G(z)): 0.0538
Epoch: [9/20], Batch Num: [331/600]
Discriminator Loss: 0.2564, Generator Loss: 3.9240
D(x): 0.8824, D(G(z)): 0.0471
Epoch: [9/20], Batch Num: [332/600]
Discriminator Loss: 0.2273, Generator Loss: 3.0396
D(x): 0.9162, D(G(z)): 0.0814
Epoch: [9/20], Batch Num: [333/600]
Discriminator Loss: 0.2798, Generator Loss: 2.8025
D(x): 0.9480, D(G(z)): 0.1367
Epoch: [9/20], Batch Num: [334/600]
Discriminator Loss: 0.3593, Generator Loss: 3.2328
D(x): 0.9260, D(G(z)): 0.1490
Epoch: [9/20], Batch Num: [335/600]
Discriminator Loss: 0.2959, Generator Loss: 4.1394
D(x): 0.9048, D(G(z)): 0.0922
Epoch: [9/20], Batch Num: [336/600]
Discriminator Loss: 0.2720, Generator Loss: 4.0725
D(x): 0.8879, D(G(z)): 0.0474
Epoch: [9/20], Batch Num: [337/600]
Discriminator Loss: 0.2812, Generator Loss: 4.0756
D(x): 0.9252, D(G(z)): 0.0781
Epoch: [9/20], Batch Num: [338/600]
Discriminator Loss: 0.3151, Generator Loss: 3.8728
D(x): 0.8891, D(G(z)): 0.0717
Epoch: [9/20], Batch Num: [339/600]
Discriminator Loss: 0.2349, Generator Loss: 3.4670
D(x): 0.9294, D(G(z)): 0.0830
Epoch: [9/20], Batch Num: [340/600]
Discriminator Loss: 0.2991, Generator Loss: 3.1832
D(x): 0.9136, D(G(z)): 0.0998
Epoch: [9/20], Batch Num: [341/600]
Discriminator Loss: 0.2807, Generator Loss: 3.6289
D(x): 0.9440, D(G(z)): 0.1152
Epoch: [9/20], Batch Num: [342/600]
Discriminator Loss: 0.2374, Generator Loss: 4.1029
D(x): 0.9596, D(G(z)): 0.1222
Epoch: [9/20], Batch Num: [343/600]
Discriminator Loss: 0.3532, Generator Loss: 4.2537
D(x): 0.8520, D(G(z)): 0.0539
Epoch: [9/20], Batch Num: [344/600]
Discriminator Loss: 0.4009, Generator Loss: 3.5912
D(x): 0.8685, D(G(z)): 0.0734
Epoch: [9/20], Batch Num: [345/600]
Discriminator Loss: 0.2852, Generator Loss: 3.3926
D(x): 0.9211, D(G(z)): 0.1088
Epoch: [9/20], Batch Num: [346/600]
Discriminator Loss: 0.3826, Generator Loss: 3.2305
D(x): 0.9108, D(G(z)): 0.1390
Epoch: [9/20], Batch Num: [347/600]
Discriminator Loss: 0.2424, Generator Loss: 3.5894
D(x): 0.9214, D(G(z)): 0.1010
Epoch: [9/20], Batch Num: [348/600]
Discriminator Loss: 0.3725, Generator Loss: 4.3892
D(x): 0.8942, D(G(z)): 0.1172
Epoch: [9/20], Batch Num: [349/600]
Discriminator Loss: 0.4120, Generator Loss: 4.2837
D(x): 0.8500, D(G(z)): 0.0661
Epoch: [9/20], Batch Num: [350/600]
Discriminator Loss: 0.2807, Generator Loss: 3.6445
D(x): 0.8972, D(G(z)): 0.0703
Epoch: [9/20], Batch Num: [351/600]
Discriminator Loss: 0.4526, Generator Loss: 2.8013
D(x): 0.8593, D(G(z)): 0.1007
Epoch: [9/20], Batch Num: [352/600]
Discriminator Loss: 0.4954, Generator Loss: 3.1117
D(x): 0.9149, D(G(z)): 0.2091
Epoch: [9/20], Batch Num: [353/600]
Discriminator Loss: 0.3856, Generator Loss: 4.1029
D(x): 0.9208, D(G(z)): 0.1429
Epoch: [9/20], Batch Num: [354/600]
Discriminator Loss: 0.5517, Generator Loss: 4.0907
D(x): 0.8474, D(G(z)): 0.0805
Epoch: [9/20], Batch Num: [355/600]
Discriminator Loss: 0.5379, Generator Loss: 3.2600
D(x): 0.8123, D(G(z)): 0.0840
Epoch: [9/20], Batch Num: [356/600]
Discriminator Loss: 0.3724, Generator Loss: 2.3194
D(x): 0.8603, D(G(z)): 0.0844
Epoch: [9/20], Batch Num: [357/600]
Discriminator Loss: 0.5923, Generator Loss: 2.8660
D(x): 0.9337, D(G(z)): 0.2283
Epoch: [9/20], Batch Num: [358/600]
Discriminator Loss: 0.5098, Generator Loss: 3.5081
D(x): 0.8973, D(G(z)): 0.1863
Epoch: [9/20], Batch Num: [359/600]
Discriminator Loss: 0.3666, Generator Loss: 4.4417
D(x): 0.8730, D(G(z)): 0.0979
Epoch: [9/20], Batch Num: [360/600]
Discriminator Loss: 0.6540, Generator Loss: 3.7660
D(x): 0.7876, D(G(z)): 0.0899
Epoch: [9/20], Batch Num: [361/600]
Discriminator Loss: 0.4306, Generator Loss: 3.0351
D(x): 0.8616, D(G(z)): 0.1047
Epoch: [9/20], Batch Num: [362/600]
Discriminator Loss: 0.4405, Generator Loss: 2.8150
D(x): 0.8659, D(G(z)): 0.1133
Epoch: [9/20], Batch Num: [363/600]
Discriminator Loss: 0.3604, Generator Loss: 2.4183
D(x): 0.8945, D(G(z)): 0.1433
Epoch: [9/20], Batch Num: [364/600]
Discriminator Loss: 0.3809, Generator Loss: 2.9028
D(x): 0.9316, D(G(z)): 0.1775
Epoch: [9/20], Batch Num: [365/600]
Discriminator Loss: 0.3295, Generator Loss: 3.1562
D(x): 0.9161, D(G(z)): 0.1311
Epoch: [9/20], Batch Num: [366/600]
Discriminator Loss: 0.5111, Generator Loss: 3.5836
D(x): 0.8556, D(G(z)): 0.1166
Epoch: [9/20], Batch Num: [367/600]
Discriminator Loss: 0.5622, Generator Loss: 3.3172
D(x): 0.8105, D(G(z)): 0.0557
Epoch: [9/20], Batch Num: [368/600]
Discriminator Loss: 0.3644, Generator Loss: 3.0951
D(x): 0.8900, D(G(z)): 0.0940
Epoch: [9/20], Batch Num: [369/600]
Discriminator Loss: 0.4768, Generator Loss: 2.8859
D(x): 0.8885, D(G(z)): 0.1523
Epoch: [9/20], Batch Num: [370/600]
Discriminator Loss: 0.4357, Generator Loss: 2.5296
D(x): 0.8700, D(G(z)): 0.1185
Epoch: [9/20], Batch Num: [371/600]
Discriminator Loss: 0.4178, Generator Loss: 2.7144
D(x): 0.8974, D(G(z)): 0.1620
Epoch: [9/20], Batch Num: [372/600]
Discriminator Loss: 0.4323, Generator Loss: 3.1514
D(x): 0.8830, D(G(z)): 0.1464
Epoch: [9/20], Batch Num: [373/600]
Discriminator Loss: 0.3578, Generator Loss: 3.5458
D(x): 0.8930, D(G(z)): 0.0958
Epoch: [9/20], Batch Num: [374/600]
Discriminator Loss: 0.5628, Generator Loss: 3.1047
D(x): 0.7978, D(G(z)): 0.1074
Epoch: [9/20], Batch Num: [375/600]
Discriminator Loss: 0.5734, Generator Loss: 2.8812
D(x): 0.8093, D(G(z)): 0.1295
Epoch: [9/20], Batch Num: [376/600]
Discriminator Loss: 0.3801, Generator Loss: 2.2483
D(x): 0.8697, D(G(z)): 0.1275
Epoch: [9/20], Batch Num: [377/600]
Discriminator Loss: 0.5393, Generator Loss: 2.1116
D(x): 0.9105, D(G(z)): 0.2220
Epoch: [9/20], Batch Num: [378/600]
Discriminator Loss: 0.3341, Generator Loss: 3.1205
D(x): 0.9165, D(G(z)): 0.1470
Epoch: [9/20], Batch Num: [379/600]
Discriminator Loss: 0.4679, Generator Loss: 3.2771
D(x): 0.8345, D(G(z)): 0.1280
Epoch: [9/20], Batch Num: [380/600]
Discriminator Loss: 0.4078, Generator Loss: 3.3425
D(x): 0.8567, D(G(z)): 0.1117
Epoch: [9/20], Batch Num: [381/600]
Discriminator Loss: 0.3797, Generator Loss: 3.0387
D(x): 0.8764, D(G(z)): 0.1149
Epoch: [9/20], Batch Num: [382/600]
Discriminator Loss: 0.4590, Generator Loss: 2.6623
D(x): 0.8187, D(G(z)): 0.1026
Epoch: [9/20], Batch Num: [383/600]
Discriminator Loss: 0.5074, Generator Loss: 2.4495
D(x): 0.8645, D(G(z)): 0.1737
Epoch: [9/20], Batch Num: [384/600]
Discriminator Loss: 0.5114, Generator Loss: 2.4646
D(x): 0.8607, D(G(z)): 0.1875
Epoch: [9/20], Batch Num: [385/600]
Discriminator Loss: 0.4104, Generator Loss: 2.8380
D(x): 0.8965, D(G(z)): 0.1751
Epoch: [9/20], Batch Num: [386/600]
Discriminator Loss: 0.4240, Generator Loss: 2.6846
D(x): 0.8481, D(G(z)): 0.1281
Epoch: [9/20], Batch Num: [387/600]
Discriminator Loss: 0.4439, Generator Loss: 2.9160
D(x): 0.8740, D(G(z)): 0.1291
Epoch: [9/20], Batch Num: [388/600]
Discriminator Loss: 0.4867, Generator Loss: 2.7797
D(x): 0.8344, D(G(z)): 0.1269
Epoch: [9/20], Batch Num: [389/600]
Discriminator Loss: 0.3474, Generator Loss: 2.5952
D(x): 0.8665, D(G(z)): 0.0942
Epoch: [9/20], Batch Num: [390/600]
Discriminator Loss: 0.5040, Generator Loss: 3.0012
D(x): 0.8702, D(G(z)): 0.1724
Epoch: [9/20], Batch Num: [391/600]
Discriminator Loss: 0.4465, Generator Loss: 2.7617
D(x): 0.8668, D(G(z)): 0.1611
Epoch: [9/20], Batch Num: [392/600]
Discriminator Loss: 0.3930, Generator Loss: 2.6423
D(x): 0.8606, D(G(z)): 0.1206
Epoch: [9/20], Batch Num: [393/600]
Discriminator Loss: 0.5158, Generator Loss: 2.9115
D(x): 0.8650, D(G(z)): 0.1633
Epoch: [9/20], Batch Num: [394/600]
Discriminator Loss: 0.4464, Generator Loss: 3.3192
D(x): 0.8774, D(G(z)): 0.1561
Epoch: [9/20], Batch Num: [395/600]
Discriminator Loss: 0.4983, Generator Loss: 3.4180
D(x): 0.8379, D(G(z)): 0.1176
Epoch: [9/20], Batch Num: [396/600]
Discriminator Loss: 0.3722, Generator Loss: 3.6555
D(x): 0.8877, D(G(z)): 0.1044
Epoch: [9/20], Batch Num: [397/600]
Discriminator Loss: 0.3974, Generator Loss: 3.5213
D(x): 0.9006, D(G(z)): 0.1068
Epoch: [9/20], Batch Num: [398/600]
Discriminator Loss: 0.3944, Generator Loss: 3.3712
D(x): 0.8553, D(G(z)): 0.0988
Epoch: [9/20], Batch Num: [399/600]
Discriminator Loss: 0.4171, Generator Loss: 3.0218
D(x): 0.8657, D(G(z)): 0.1002
Epoch: 9, Batch Num: [400/600]

Epoch: [9/20], Batch Num: [400/600]
Discriminator Loss: 0.3512, Generator Loss: 3.0446
D(x): 0.9121, D(G(z)): 0.1480
Epoch: [9/20], Batch Num: [401/600]
Discriminator Loss: 0.3768, Generator Loss: 3.0711
D(x): 0.8966, D(G(z)): 0.1479
Epoch: [9/20], Batch Num: [402/600]
Discriminator Loss: 0.3988, Generator Loss: 2.9503
D(x): 0.8516, D(G(z)): 0.0889
Epoch: [9/20], Batch Num: [403/600]
Discriminator Loss: 0.3831, Generator Loss: 2.9522
D(x): 0.8789, D(G(z)): 0.1188
Epoch: [9/20], Batch Num: [404/600]
Discriminator Loss: 0.4854, Generator Loss: 3.3627
D(x): 0.9357, D(G(z)): 0.2336
Epoch: [9/20], Batch Num: [405/600]
Discriminator Loss: 0.3403, Generator Loss: 4.4308
D(x): 0.8992, D(G(z)): 0.0970
Epoch: [9/20], Batch Num: [406/600]
Discriminator Loss: 0.4626, Generator Loss: 3.9880
D(x): 0.8117, D(G(z)): 0.0735
Epoch: [9/20], Batch Num: [407/600]
Discriminator Loss: 0.5454, Generator Loss: 3.1016
D(x): 0.8238, D(G(z)): 0.0777
Epoch: [9/20], Batch Num: [408/600]
Discriminator Loss: 0.3913, Generator Loss: 2.5139
D(x): 0.8559, D(G(z)): 0.1037
Epoch: [9/20], Batch Num: [409/600]
Discriminator Loss: 0.5707, Generator Loss: 2.2503
D(x): 0.8674, D(G(z)): 0.1882
Epoch: [9/20], Batch Num: [410/600]
Discriminator Loss: 0.6224, Generator Loss: 2.7191
D(x): 0.9045, D(G(z)): 0.2754
Epoch: [9/20], Batch Num: [411/600]
Discriminator Loss: 0.4595, Generator Loss: 3.5632
D(x): 0.8731, D(G(z)): 0.1616
Epoch: [9/20], Batch Num: [412/600]
Discriminator Loss: 0.6204, Generator Loss: 3.9286
D(x): 0.7819, D(G(z)): 0.1036
Epoch: [9/20], Batch Num: [413/600]
Discriminator Loss: 0.5707, Generator Loss: 3.3223
D(x): 0.7847, D(G(z)): 0.0886
Epoch: [9/20], Batch Num: [414/600]
Discriminator Loss: 0.3704, Generator Loss: 2.9035
D(x): 0.8554, D(G(z)): 0.0942
Epoch: [9/20], Batch Num: [415/600]
Discriminator Loss: 0.4054, Generator Loss: 2.2404
D(x): 0.8568, D(G(z)): 0.1426
Epoch: [9/20], Batch Num: [416/600]
Discriminator Loss: 0.5144, Generator Loss: 2.5979
D(x): 0.9131, D(G(z)): 0.2206
Epoch: [9/20], Batch Num: [417/600]
Discriminator Loss: 0.5231, Generator Loss: 3.1133
D(x): 0.8646, D(G(z)): 0.1611
Epoch: [9/20], Batch Num: [418/600]
Discriminator Loss: 0.3747, Generator Loss: 3.5316
D(x): 0.8796, D(G(z)): 0.1445
Epoch: [9/20], Batch Num: [419/600]
Discriminator Loss: 0.3767, Generator Loss: 3.3722
D(x): 0.8407, D(G(z)): 0.0726
Epoch: [9/20], Batch Num: [420/600]
Discriminator Loss: 0.4166, Generator Loss: 3.5080
D(x): 0.8566, D(G(z)): 0.0727
Epoch: [9/20], Batch Num: [421/600]
Discriminator Loss: 0.4332, Generator Loss: 2.5393
D(x): 0.8381, D(G(z)): 0.0978
Epoch: [9/20], Batch Num: [422/600]
Discriminator Loss: 0.4577, Generator Loss: 2.3406
D(x): 0.8491, D(G(z)): 0.1519
Epoch: [9/20], Batch Num: [423/600]
Discriminator Loss: 0.4189, Generator Loss: 2.4570
D(x): 0.9509, D(G(z)): 0.2312
Epoch: [9/20], Batch Num: [424/600]
Discriminator Loss: 0.3922, Generator Loss: 3.1061
D(x): 0.9047, D(G(z)): 0.1784
Epoch: [9/20], Batch Num: [425/600]
Discriminator Loss: 0.4333, Generator Loss: 3.6558
D(x): 0.8480, D(G(z)): 0.1231
Epoch: [9/20], Batch Num: [426/600]
Discriminator Loss: 0.3679, Generator Loss: 3.8960
D(x): 0.8561, D(G(z)): 0.0738
Epoch: [9/20], Batch Num: [427/600]
Discriminator Loss: 0.3756, Generator Loss: 3.2699
D(x): 0.8646, D(G(z)): 0.0805
Epoch: [9/20], Batch Num: [428/600]
Discriminator Loss: 0.3909, Generator Loss: 2.9065
D(x): 0.8605, D(G(z)): 0.0954
Epoch: [9/20], Batch Num: [429/600]
Discriminator Loss: 0.3302, Generator Loss: 2.4671
D(x): 0.9064, D(G(z)): 0.1058
Epoch: [9/20], Batch Num: [430/600]
Discriminator Loss: 0.4725, Generator Loss: 2.8321
D(x): 0.9265, D(G(z)): 0.2115
Epoch: [9/20], Batch Num: [431/600]
Discriminator Loss: 0.2419, Generator Loss: 3.3646
D(x): 0.9394, D(G(z)): 0.1121
Epoch: [9/20], Batch Num: [432/600]
Discriminator Loss: 0.2653, Generator Loss: 4.0459
D(x): 0.9307, D(G(z)): 0.1094
Epoch: [9/20], Batch Num: [433/600]
Discriminator Loss: 0.4885, Generator Loss: 3.7731
D(x): 0.8153, D(G(z)): 0.0519
Epoch: [9/20], Batch Num: [434/600]
Discriminator Loss: 0.4256, Generator Loss: 3.4922
D(x): 0.8343, D(G(z)): 0.0658
Epoch: [9/20], Batch Num: [435/600]
Discriminator Loss: 0.2476, Generator Loss: 2.5211
D(x): 0.9050, D(G(z)): 0.0893
Epoch: [9/20], Batch Num: [436/600]
Discriminator Loss: 0.3648, Generator Loss: 2.3974
D(x): 0.9142, D(G(z)): 0.1713
Epoch: [9/20], Batch Num: [437/600]
Discriminator Loss: 0.3630, Generator Loss: 2.6266
D(x): 0.9108, D(G(z)): 0.1693
Epoch: [9/20], Batch Num: [438/600]
Discriminator Loss: 0.3937, Generator Loss: 3.2093
D(x): 0.9162, D(G(z)): 0.1697
Epoch: [9/20], Batch Num: [439/600]
Discriminator Loss: 0.4202, Generator Loss: 3.8996
D(x): 0.8488, D(G(z)): 0.0992
Epoch: [9/20], Batch Num: [440/600]
Discriminator Loss: 0.2890, Generator Loss: 3.8559
D(x): 0.8853, D(G(z)): 0.0723
Epoch: [9/20], Batch Num: [441/600]
Discriminator Loss: 0.3291, Generator Loss: 4.0245
D(x): 0.8679, D(G(z)): 0.0702
Epoch: [9/20], Batch Num: [442/600]
Discriminator Loss: 0.4425, Generator Loss: 3.2999
D(x): 0.8527, D(G(z)): 0.1032
Epoch: [9/20], Batch Num: [443/600]
Discriminator Loss: 0.4487, Generator Loss: 2.9163
D(x): 0.8545, D(G(z)): 0.1154
Epoch: [9/20], Batch Num: [444/600]
Discriminator Loss: 0.3600, Generator Loss: 2.7695
D(x): 0.9473, D(G(z)): 0.1632
Epoch: [9/20], Batch Num: [445/600]
Discriminator Loss: 0.4721, Generator Loss: 2.8108
D(x): 0.8708, D(G(z)): 0.1460
Epoch: [9/20], Batch Num: [446/600]
Discriminator Loss: 0.3668, Generator Loss: 3.6687
D(x): 0.9111, D(G(z)): 0.1341
Epoch: [9/20], Batch Num: [447/600]
Discriminator Loss: 0.4070, Generator Loss: 3.7128
D(x): 0.8748, D(G(z)): 0.1160
Epoch: [9/20], Batch Num: [448/600]
Discriminator Loss: 0.6196, Generator Loss: 3.6680
D(x): 0.8088, D(G(z)): 0.1100
Epoch: [9/20], Batch Num: [449/600]
Discriminator Loss: 0.3870, Generator Loss: 3.1416
D(x): 0.8696, D(G(z)): 0.1001
Epoch: [9/20], Batch Num: [450/600]
Discriminator Loss: 0.3186, Generator Loss: 2.7937
D(x): 0.9110, D(G(z)): 0.1259
Epoch: [9/20], Batch Num: [451/600]
Discriminator Loss: 0.4837, Generator Loss: 2.5884
D(x): 0.8769, D(G(z)): 0.1668
Epoch: [9/20], Batch Num: [452/600]
Discriminator Loss: 0.3527, Generator Loss: 3.3642
D(x): 0.9041, D(G(z)): 0.1398
Epoch: [9/20], Batch Num: [453/600]
Discriminator Loss: 0.2894, Generator Loss: 3.7353
D(x): 0.9207, D(G(z)): 0.1172
Epoch: [9/20], Batch Num: [454/600]
Discriminator Loss: 0.4172, Generator Loss: 3.9723
D(x): 0.8476, D(G(z)): 0.0972
Epoch: [9/20], Batch Num: [455/600]
Discriminator Loss: 0.5847, Generator Loss: 3.0342
D(x): 0.8114, D(G(z)): 0.1243
Epoch: [9/20], Batch Num: [456/600]
Discriminator Loss: 0.6246, Generator Loss: 2.6500
D(x): 0.8347, D(G(z)): 0.1528
Epoch: [9/20], Batch Num: [457/600]
Discriminator Loss: 0.4032, Generator Loss: 2.4542
D(x): 0.8806, D(G(z)): 0.1623
Epoch: [9/20], Batch Num: [458/600]
Discriminator Loss: 0.5115, Generator Loss: 2.9223
D(x): 0.8851, D(G(z)): 0.1756
Epoch: [9/20], Batch Num: [459/600]
Discriminator Loss: 0.4171, Generator Loss: 3.2867
D(x): 0.8825, D(G(z)): 0.1451
Epoch: [9/20], Batch Num: [460/600]
Discriminator Loss: 0.3849, Generator Loss: 3.4031
D(x): 0.8882, D(G(z)): 0.0990
Epoch: [9/20], Batch Num: [461/600]
Discriminator Loss: 0.4084, Generator Loss: 3.1536
D(x): 0.8764, D(G(z)): 0.1001
Epoch: [9/20], Batch Num: [462/600]
Discriminator Loss: 0.3409, Generator Loss: 2.9161
D(x): 0.8727, D(G(z)): 0.0869
Epoch: [9/20], Batch Num: [463/600]
Discriminator Loss: 0.4784, Generator Loss: 2.8049
D(x): 0.8782, D(G(z)): 0.1249
Epoch: [9/20], Batch Num: [464/600]
Discriminator Loss: 0.4524, Generator Loss: 2.5045
D(x): 0.8948, D(G(z)): 0.1339
Epoch: [9/20], Batch Num: [465/600]
Discriminator Loss: 0.3758, Generator Loss: 2.7239
D(x): 0.8958, D(G(z)): 0.1400
Epoch: [9/20], Batch Num: [466/600]
Discriminator Loss: 0.4796, Generator Loss: 3.0505
D(x): 0.8846, D(G(z)): 0.1750
Epoch: [9/20], Batch Num: [467/600]
Discriminator Loss: 0.3932, Generator Loss: 2.9004
D(x): 0.8935, D(G(z)): 0.1308
Epoch: [9/20], Batch Num: [468/600]
Discriminator Loss: 0.3973, Generator Loss: 3.0754
D(x): 0.8662, D(G(z)): 0.0908
Epoch: [9/20], Batch Num: [469/600]
Discriminator Loss: 0.3628, Generator Loss: 3.3175
D(x): 0.8877, D(G(z)): 0.1046
Epoch: [9/20], Batch Num: [470/600]
Discriminator Loss: 0.3055, Generator Loss: 3.4986
D(x): 0.9051, D(G(z)): 0.1045
Epoch: [9/20], Batch Num: [471/600]
Discriminator Loss: 0.4038, Generator Loss: 3.1067
D(x): 0.8632, D(G(z)): 0.1009
Epoch: [9/20], Batch Num: [472/600]
Discriminator Loss: 0.4735, Generator Loss: 2.9977
D(x): 0.8345, D(G(z)): 0.1076
Epoch: [9/20], Batch Num: [473/600]
Discriminator Loss: 0.3751, Generator Loss: 2.6044
D(x): 0.9022, D(G(z)): 0.1487
Epoch: [9/20], Batch Num: [474/600]
Discriminator Loss: 0.3940, Generator Loss: 2.3020
D(x): 0.8905, D(G(z)): 0.1408
Epoch: [9/20], Batch Num: [475/600]
Discriminator Loss: 0.4478, Generator Loss: 3.4196
D(x): 0.9394, D(G(z)): 0.1947
Epoch: [9/20], Batch Num: [476/600]
Discriminator Loss: 0.4270, Generator Loss: 4.0033
D(x): 0.8683, D(G(z)): 0.0911
Epoch: [9/20], Batch Num: [477/600]
Discriminator Loss: 0.3302, Generator Loss: 3.9053
D(x): 0.8578, D(G(z)): 0.0564
Epoch: [9/20], Batch Num: [478/600]
Discriminator Loss: 0.4585, Generator Loss: 2.8636
D(x): 0.8525, D(G(z)): 0.0697
Epoch: [9/20], Batch Num: [479/600]
Discriminator Loss: 0.3283, Generator Loss: 2.8630
D(x): 0.8929, D(G(z)): 0.1064
Epoch: [9/20], Batch Num: [480/600]
Discriminator Loss: 0.3658, Generator Loss: 2.2749
D(x): 0.9123, D(G(z)): 0.1432
Epoch: [9/20], Batch Num: [481/600]
Discriminator Loss: 0.4208, Generator Loss: 2.4804
D(x): 0.8958, D(G(z)): 0.1659
Epoch: [9/20], Batch Num: [482/600]
Discriminator Loss: 0.3836, Generator Loss: 3.1155
D(x): 0.9243, D(G(z)): 0.1790
Epoch: [9/20], Batch Num: [483/600]
Discriminator Loss: 0.3692, Generator Loss: 3.5584
D(x): 0.8851, D(G(z)): 0.0849
Epoch: [9/20], Batch Num: [484/600]
Discriminator Loss: 0.3148, Generator Loss: 3.3026
D(x): 0.8790, D(G(z)): 0.0623
Epoch: [9/20], Batch Num: [485/600]
Discriminator Loss: 0.3986, Generator Loss: 2.8186
D(x): 0.8317, D(G(z)): 0.0779
Epoch: [9/20], Batch Num: [486/600]
Discriminator Loss: 0.3901, Generator Loss: 2.3892
D(x): 0.8925, D(G(z)): 0.1216
Epoch: [9/20], Batch Num: [487/600]
Discriminator Loss: 0.4309, Generator Loss: 2.7970
D(x): 0.9158, D(G(z)): 0.1592
Epoch: [9/20], Batch Num: [488/600]
Discriminator Loss: 0.3832, Generator Loss: 2.9367
D(x): 0.9208, D(G(z)): 0.1443
Epoch: [9/20], Batch Num: [489/600]
Discriminator Loss: 0.6131, Generator Loss: 3.1980
D(x): 0.8107, D(G(z)): 0.1360
Epoch: [9/20], Batch Num: [490/600]
Discriminator Loss: 0.4112, Generator Loss: 3.2142
D(x): 0.8865, D(G(z)): 0.1118
Epoch: [9/20], Batch Num: [491/600]
Discriminator Loss: 0.3533, Generator Loss: 3.4251
D(x): 0.8689, D(G(z)): 0.0756
Epoch: [9/20], Batch Num: [492/600]
Discriminator Loss: 0.4985, Generator Loss: 2.7882
D(x): 0.8488, D(G(z)): 0.1016
Epoch: [9/20], Batch Num: [493/600]
Discriminator Loss: 0.4044, Generator Loss: 2.4504
D(x): 0.9042, D(G(z)): 0.1575
Epoch: [9/20], Batch Num: [494/600]
Discriminator Loss: 0.4810, Generator Loss: 2.4644
D(x): 0.8882, D(G(z)): 0.1867
Epoch: [9/20], Batch Num: [495/600]
Discriminator Loss: 0.4571, Generator Loss: 2.9435
D(x): 0.9154, D(G(z)): 0.2016
Epoch: [9/20], Batch Num: [496/600]
Discriminator Loss: 0.4717, Generator Loss: 3.5284
D(x): 0.8309, D(G(z)): 0.1103
Epoch: [9/20], Batch Num: [497/600]
Discriminator Loss: 0.3586, Generator Loss: 3.3775
D(x): 0.8537, D(G(z)): 0.0825
Epoch: [9/20], Batch Num: [498/600]
Discriminator Loss: 0.4369, Generator Loss: 2.9083
D(x): 0.8384, D(G(z)): 0.0975
Epoch: [9/20], Batch Num: [499/600]
Discriminator Loss: 0.3753, Generator Loss: 2.4723
D(x): 0.8587, D(G(z)): 0.1152
Epoch: 9, Batch Num: [500/600]

Epoch: [9/20], Batch Num: [500/600]
Discriminator Loss: 0.4349, Generator Loss: 2.4174
D(x): 0.9023, D(G(z)): 0.2105
Epoch: [9/20], Batch Num: [501/600]
Discriminator Loss: 0.4463, Generator Loss: 2.8266
D(x): 0.9184, D(G(z)): 0.1783
Epoch: [9/20], Batch Num: [502/600]
Discriminator Loss: 0.4452, Generator Loss: 3.1550
D(x): 0.8614, D(G(z)): 0.1360
Epoch: [9/20], Batch Num: [503/600]
Discriminator Loss: 0.3105, Generator Loss: 3.4217
D(x): 0.8803, D(G(z)): 0.0763
Epoch: [9/20], Batch Num: [504/600]
Discriminator Loss: 0.4907, Generator Loss: 2.9629
D(x): 0.8243, D(G(z)): 0.0873
Epoch: [9/20], Batch Num: [505/600]
Discriminator Loss: 0.2993, Generator Loss: 2.6686
D(x): 0.8846, D(G(z)): 0.0929
Epoch: [9/20], Batch Num: [506/600]
Discriminator Loss: 0.3809, Generator Loss: 2.6846
D(x): 0.9134, D(G(z)): 0.1484
Epoch: [9/20], Batch Num: [507/600]
Discriminator Loss: 0.4916, Generator Loss: 2.7414
D(x): 0.8613, D(G(z)): 0.1570
Epoch: [9/20], Batch Num: [508/600]
Discriminator Loss: 0.4126, Generator Loss: 2.9100
D(x): 0.8923, D(G(z)): 0.1587
Epoch: [9/20], Batch Num: [509/600]
Discriminator Loss: 0.3696, Generator Loss: 3.4323
D(x): 0.9137, D(G(z)): 0.1309
Epoch: [9/20], Batch Num: [510/600]
Discriminator Loss: 0.4234, Generator Loss: 3.4060
D(x): 0.8361, D(G(z)): 0.0714
Epoch: [9/20], Batch Num: [511/600]
Discriminator Loss: 0.2862, Generator Loss: 3.5169
D(x): 0.8842, D(G(z)): 0.0892
Epoch: [9/20], Batch Num: [512/600]
Discriminator Loss: 0.4559, Generator Loss: 3.1473
D(x): 0.8369, D(G(z)): 0.1143
Epoch: [9/20], Batch Num: [513/600]
Discriminator Loss: 0.4283, Generator Loss: 2.6777
D(x): 0.8681, D(G(z)): 0.1180
Epoch: [9/20], Batch Num: [514/600]
Discriminator Loss: 0.4652, Generator Loss: 2.7006
D(x): 0.8864, D(G(z)): 0.1669
Epoch: [9/20], Batch Num: [515/600]
Discriminator Loss: 0.3462, Generator Loss: 3.4279
D(x): 0.9332, D(G(z)): 0.1763
Epoch: [9/20], Batch Num: [516/600]
Discriminator Loss: 0.4337, Generator Loss: 3.6441
D(x): 0.8537, D(G(z)): 0.0995
Epoch: [9/20], Batch Num: [517/600]
Discriminator Loss: 0.5208, Generator Loss: 3.0943
D(x): 0.7985, D(G(z)): 0.0747
Epoch: [9/20], Batch Num: [518/600]
Discriminator Loss: 0.4316, Generator Loss: 2.6329
D(x): 0.8694, D(G(z)): 0.1137
Epoch: [9/20], Batch Num: [519/600]
Discriminator Loss: 0.3247, Generator Loss: 2.5324
D(x): 0.9215, D(G(z)): 0.1588
Epoch: [9/20], Batch Num: [520/600]
Discriminator Loss: 0.5902, Generator Loss: 3.0367
D(x): 0.8819, D(G(z)): 0.2180
Epoch: [9/20], Batch Num: [521/600]
Discriminator Loss: 0.3932, Generator Loss: 3.2166
D(x): 0.8799, D(G(z)): 0.1289
Epoch: [9/20], Batch Num: [522/600]
Discriminator Loss: 0.3577, Generator Loss: 3.4319
D(x): 0.8549, D(G(z)): 0.0957
Epoch: [9/20], Batch Num: [523/600]
Discriminator Loss: 0.3657, Generator Loss: 2.8343
D(x): 0.8680, D(G(z)): 0.0933
Epoch: [9/20], Batch Num: [524/600]
Discriminator Loss: 0.5898, Generator Loss: 2.4852
D(x): 0.7987, D(G(z)): 0.1090
Epoch: [9/20], Batch Num: [525/600]
Discriminator Loss: 0.4409, Generator Loss: 2.6005
D(x): 0.9081, D(G(z)): 0.1967
Epoch: [9/20], Batch Num: [526/600]
Discriminator Loss: 0.4366, Generator Loss: 2.7511
D(x): 0.8701, D(G(z)): 0.1593
Epoch: [9/20], Batch Num: [527/600]
Discriminator Loss: 0.4378, Generator Loss: 2.9531
D(x): 0.8887, D(G(z)): 0.1439
Epoch: [9/20], Batch Num: [528/600]
Discriminator Loss: 0.3679, Generator Loss: 3.6633
D(x): 0.8864, D(G(z)): 0.1201
Epoch: [9/20], Batch Num: [529/600]
Discriminator Loss: 0.6193, Generator Loss: 3.6057
D(x): 0.7755, D(G(z)): 0.1005
Epoch: [9/20], Batch Num: [530/600]
Discriminator Loss: 0.6003, Generator Loss: 2.4451
D(x): 0.7911, D(G(z)): 0.1125
Epoch: [9/20], Batch Num: [531/600]
Discriminator Loss: 0.4451, Generator Loss: 2.2147
D(x): 0.9158, D(G(z)): 0.2041
Epoch: [9/20], Batch Num: [532/600]
Discriminator Loss: 0.5048, Generator Loss: 2.6770
D(x): 0.8899, D(G(z)): 0.2133
Epoch: [9/20], Batch Num: [533/600]
Discriminator Loss: 0.3598, Generator Loss: 3.2253
D(x): 0.9080, D(G(z)): 0.1500
Epoch: [9/20], Batch Num: [534/600]
Discriminator Loss: 0.5008, Generator Loss: 3.0099
D(x): 0.7970, D(G(z)): 0.0617
Epoch: [9/20], Batch Num: [535/600]
Discriminator Loss: 0.3751, Generator Loss: 3.0907
D(x): 0.8779, D(G(z)): 0.1064
Epoch: [9/20], Batch Num: [536/600]
Discriminator Loss: 0.3647, Generator Loss: 2.4383
D(x): 0.9005, D(G(z)): 0.1499
Epoch: [9/20], Batch Num: [537/600]
Discriminator Loss: 0.4066, Generator Loss: 2.5584
D(x): 0.8625, D(G(z)): 0.1245
Epoch: [9/20], Batch Num: [538/600]
Discriminator Loss: 0.3534, Generator Loss: 2.6253
D(x): 0.9071, D(G(z)): 0.1476
Epoch: [9/20], Batch Num: [539/600]
Discriminator Loss: 0.3409, Generator Loss: 2.8714
D(x): 0.8832, D(G(z)): 0.1303
Epoch: [9/20], Batch Num: [540/600]
Discriminator Loss: 0.3562, Generator Loss: 2.9903
D(x): 0.8793, D(G(z)): 0.1067
Epoch: [9/20], Batch Num: [541/600]
Discriminator Loss: 0.3927, Generator Loss: 3.6745
D(x): 0.9127, D(G(z)): 0.1576
Epoch: [9/20], Batch Num: [542/600]
Discriminator Loss: 0.3412, Generator Loss: 3.4820
D(x): 0.8840, D(G(z)): 0.1013
Epoch: [9/20], Batch Num: [543/600]
Discriminator Loss: 0.3166, Generator Loss: 3.7129
D(x): 0.8920, D(G(z)): 0.0772
Epoch: [9/20], Batch Num: [544/600]
Discriminator Loss: 0.4865, Generator Loss: 2.8730
D(x): 0.8310, D(G(z)): 0.0731
Epoch: [9/20], Batch Num: [545/600]
Discriminator Loss: 0.4668, Generator Loss: 2.2512
D(x): 0.8497, D(G(z)): 0.1370
Epoch: [9/20], Batch Num: [546/600]
Discriminator Loss: 0.5578, Generator Loss: 2.7939
D(x): 0.9113, D(G(z)): 0.2606
Epoch: [9/20], Batch Num: [547/600]
Discriminator Loss: 0.4208, Generator Loss: 3.2189
D(x): 0.8854, D(G(z)): 0.1781
Epoch: [9/20], Batch Num: [548/600]
Discriminator Loss: 0.5042, Generator Loss: 3.5910
D(x): 0.8580, D(G(z)): 0.1414
Epoch: [9/20], Batch Num: [549/600]
Discriminator Loss: 0.4514, Generator Loss: 3.7081
D(x): 0.8408, D(G(z)): 0.0667
Epoch: [9/20], Batch Num: [550/600]
Discriminator Loss: 0.3919, Generator Loss: 3.0591
D(x): 0.8397, D(G(z)): 0.0771
Epoch: [9/20], Batch Num: [551/600]
Discriminator Loss: 0.2554, Generator Loss: 2.7997
D(x): 0.9364, D(G(z)): 0.1278
Epoch: [9/20], Batch Num: [552/600]
Discriminator Loss: 0.3975, Generator Loss: 2.6916
D(x): 0.8664, D(G(z)): 0.1142
Epoch: [9/20], Batch Num: [553/600]
Discriminator Loss: 0.5710, Generator Loss: 2.4958
D(x): 0.8789, D(G(z)): 0.1901
Epoch: [9/20], Batch Num: [554/600]
Discriminator Loss: 0.4536, Generator Loss: 2.9694
D(x): 0.8834, D(G(z)): 0.1791
Epoch: [9/20], Batch Num: [555/600]
Discriminator Loss: 0.4483, Generator Loss: 3.4784
D(x): 0.8593, D(G(z)): 0.1283
Epoch: [9/20], Batch Num: [556/600]
Discriminator Loss: 0.4174, Generator Loss: 3.4878
D(x): 0.8431, D(G(z)): 0.0906
Epoch: [9/20], Batch Num: [557/600]
Discriminator Loss: 0.4518, Generator Loss: 3.2345
D(x): 0.8651, D(G(z)): 0.1062
Epoch: [9/20], Batch Num: [558/600]
Discriminator Loss: 0.4306, Generator Loss: 3.0656
D(x): 0.8881, D(G(z)): 0.1306
Epoch: [9/20], Batch Num: [559/600]
Discriminator Loss: 0.3857, Generator Loss: 3.0047
D(x): 0.9025, D(G(z)): 0.1519
Epoch: [9/20], Batch Num: [560/600]
Discriminator Loss: 0.4631, Generator Loss: 2.8272
D(x): 0.8446, D(G(z)): 0.1033
Epoch: [9/20], Batch Num: [561/600]
Discriminator Loss: 0.5690, Generator Loss: 3.1467
D(x): 0.8410, D(G(z)): 0.1801
Epoch: [9/20], Batch Num: [562/600]
Discriminator Loss: 0.3491, Generator Loss: 3.2540
D(x): 0.8865, D(G(z)): 0.1147
Epoch: [9/20], Batch Num: [563/600]
Discriminator Loss: 0.4531, Generator Loss: 3.0388
D(x): 0.8528, D(G(z)): 0.1169
Epoch: [9/20], Batch Num: [564/600]
Discriminator Loss: 0.5116, Generator Loss: 2.8713
D(x): 0.8462, D(G(z)): 0.1304
Epoch: [9/20], Batch Num: [565/600]
Discriminator Loss: 0.3754, Generator Loss: 2.4554
D(x): 0.8966, D(G(z)): 0.1494
Epoch: [9/20], Batch Num: [566/600]
Discriminator Loss: 0.3919, Generator Loss: 3.1565
D(x): 0.9174, D(G(z)): 0.1817
Epoch: [9/20], Batch Num: [567/600]
Discriminator Loss: 0.4734, Generator Loss: 3.5038
D(x): 0.8527, D(G(z)): 0.1511
Epoch: [9/20], Batch Num: [568/600]
Discriminator Loss: 0.5483, Generator Loss: 3.6767
D(x): 0.8174, D(G(z)): 0.1032
Epoch: [9/20], Batch Num: [569/600]
Discriminator Loss: 0.4656, Generator Loss: 3.0996
D(x): 0.8251, D(G(z)): 0.0997
Epoch: [9/20], Batch Num: [570/600]
Discriminator Loss: 0.4696, Generator Loss: 2.6739
D(x): 0.8344, D(G(z)): 0.0984
Epoch: [9/20], Batch Num: [571/600]
Discriminator Loss: 0.5156, Generator Loss: 2.4012
D(x): 0.9124, D(G(z)): 0.2214
Epoch: [9/20], Batch Num: [572/600]
Discriminator Loss: 0.5537, Generator Loss: 2.7577
D(x): 0.8378, D(G(z)): 0.1827
Epoch: [9/20], Batch Num: [573/600]
Discriminator Loss: 0.4169, Generator Loss: 2.8909
D(x): 0.8823, D(G(z)): 0.1489
Epoch: [9/20], Batch Num: [574/600]
Discriminator Loss: 0.4328, Generator Loss: 2.9590
D(x): 0.8468, D(G(z)): 0.1009
Epoch: [9/20], Batch Num: [575/600]
Discriminator Loss: 0.3631, Generator Loss: 2.7395
D(x): 0.8808, D(G(z)): 0.1159
Epoch: [9/20], Batch Num: [576/600]
Discriminator Loss: 0.4065, Generator Loss: 2.7050
D(x): 0.8616, D(G(z)): 0.1172
Epoch: [9/20], Batch Num: [577/600]
Discriminator Loss: 0.3724, Generator Loss: 2.7490
D(x): 0.8995, D(G(z)): 0.1425
Epoch: [9/20], Batch Num: [578/600]
Discriminator Loss: 0.4042, Generator Loss: 2.8155
D(x): 0.9242, D(G(z)): 0.1827
Epoch: [9/20], Batch Num: [579/600]
Discriminator Loss: 0.2709, Generator Loss: 3.7018
D(x): 0.9185, D(G(z)): 0.0913
Epoch: [9/20], Batch Num: [580/600]
Discriminator Loss: 0.5223, Generator Loss: 4.1290
D(x): 0.7986, D(G(z)): 0.0934
Epoch: [9/20], Batch Num: [581/600]
Discriminator Loss: 0.4127, Generator Loss: 3.2232
D(x): 0.8477, D(G(z)): 0.0734
Epoch: [9/20], Batch Num: [582/600]
Discriminator Loss: 0.6226, Generator Loss: 2.8544
D(x): 0.8428, D(G(z)): 0.1617
Epoch: [9/20], Batch Num: [583/600]
Discriminator Loss: 0.3960, Generator Loss: 2.8501
D(x): 0.9215, D(G(z)): 0.1605
Epoch: [9/20], Batch Num: [584/600]
Discriminator Loss: 0.4621, Generator Loss: 3.2177
D(x): 0.9073, D(G(z)): 0.1834
Epoch: [9/20], Batch Num: [585/600]
Discriminator Loss: 0.3759, Generator Loss: 3.5957
D(x): 0.9035, D(G(z)): 0.1308
Epoch: [9/20], Batch Num: [586/600]
Discriminator Loss: 0.5196, Generator Loss: 3.4915
D(x): 0.8248, D(G(z)): 0.0883
Epoch: [9/20], Batch Num: [587/600]
Discriminator Loss: 0.3691, Generator Loss: 3.2539
D(x): 0.8559, D(G(z)): 0.0692
Epoch: [9/20], Batch Num: [588/600]
Discriminator Loss: 0.4435, Generator Loss: 2.9900
D(x): 0.8552, D(G(z)): 0.1121
Epoch: [9/20], Batch Num: [589/600]
Discriminator Loss: 0.4041, Generator Loss: 2.6549
D(x): 0.9019, D(G(z)): 0.1606
Epoch: [9/20], Batch Num: [590/600]
Discriminator Loss: 0.4486, Generator Loss: 2.6686
D(x): 0.8685, D(G(z)): 0.1716
Epoch: [9/20], Batch Num: [591/600]
Discriminator Loss: 0.4793, Generator Loss: 3.0201
D(x): 0.9113, D(G(z)): 0.2099
Epoch: [9/20], Batch Num: [592/600]
Discriminator Loss: 0.3888, Generator Loss: 3.1348
D(x): 0.8814, D(G(z)): 0.1097
Epoch: [9/20], Batch Num: [593/600]
Discriminator Loss: 0.3471, Generator Loss: 3.5281
D(x): 0.8746, D(G(z)): 0.0893
Epoch: [9/20], Batch Num: [594/600]
Discriminator Loss: 0.4341, Generator Loss: 3.4325
D(x): 0.8360, D(G(z)): 0.0880
Epoch: [9/20], Batch Num: [595/600]
Discriminator Loss: 0.5408, Generator Loss: 3.1213
D(x): 0.8219, D(G(z)): 0.1018
Epoch: [9/20], Batch Num: [596/600]
Discriminator Loss: 0.4401, Generator Loss: 2.2883
D(x): 0.8538, D(G(z)): 0.1014
Epoch: [9/20], Batch Num: [597/600]
Discriminator Loss: 0.3491, Generator Loss: 2.1124
D(x): 0.9263, D(G(z)): 0.1655
Epoch: [9/20], Batch Num: [598/600]
Discriminator Loss: 0.4868, Generator Loss: 2.6661
D(x): 0.9043, D(G(z)): 0.2012
Epoch: [9/20], Batch Num: [599/600]
Discriminator Loss: 0.4465, Generator Loss: 3.3973
D(x): 0.8924, D(G(z)): 0.1674
Epoch: 10, Batch Num: [0/600]

Epoch: [10/20], Batch Num: [0/600]
Discriminator Loss: 0.3456, Generator Loss: 4.0177
D(x): 0.8807, D(G(z)): 0.0801
Epoch: [10/20], Batch Num: [1/600]
Discriminator Loss: 0.4137, Generator Loss: 3.9956
D(x): 0.8621, D(G(z)): 0.0619
Epoch: [10/20], Batch Num: [2/600]
Discriminator Loss: 0.4791, Generator Loss: 3.4735
D(x): 0.8281, D(G(z)): 0.0690
Epoch: [10/20], Batch Num: [3/600]
Discriminator Loss: 0.4044, Generator Loss: 2.4612
D(x): 0.8297, D(G(z)): 0.0716
Epoch: [10/20], Batch Num: [4/600]
Discriminator Loss: 0.4209, Generator Loss: 1.9787
D(x): 0.9106, D(G(z)): 0.1881
Epoch: [10/20], Batch Num: [5/600]
Discriminator Loss: 0.5817, Generator Loss: 2.5971
D(x): 0.9049, D(G(z)): 0.2651
Epoch: [10/20], Batch Num: [6/600]
Discriminator Loss: 0.5024, Generator Loss: 2.8562
D(x): 0.8464, D(G(z)): 0.1390
Epoch: [10/20], Batch Num: [7/600]
Discriminator Loss: 0.3403, Generator Loss: 3.5267
D(x): 0.9038, D(G(z)): 0.1174
Epoch: [10/20], Batch Num: [8/600]
Discriminator Loss: 0.3471, Generator Loss: 3.3139
D(x): 0.8543, D(G(z)): 0.0730
Epoch: [10/20], Batch Num: [9/600]
Discriminator Loss: 0.3375, Generator Loss: 3.1673
D(x): 0.8634, D(G(z)): 0.0764
Epoch: [10/20], Batch Num: [10/600]
Discriminator Loss: 0.4080, Generator Loss: 3.1668
D(x): 0.8739, D(G(z)): 0.1124
Epoch: [10/20], Batch Num: [11/600]
Discriminator Loss: 0.4912, Generator Loss: 2.6075
D(x): 0.8585, D(G(z)): 0.1519
Epoch: [10/20], Batch Num: [12/600]
Discriminator Loss: 0.5226, Generator Loss: 2.7056
D(x): 0.8545, D(G(z)): 0.1644
Epoch: [10/20], Batch Num: [13/600]
Discriminator Loss: 0.3394, Generator Loss: 2.9652
D(x): 0.9266, D(G(z)): 0.1409
Epoch: [10/20], Batch Num: [14/600]
Discriminator Loss: 0.3476, Generator Loss: 3.2123
D(x): 0.8927, D(G(z)): 0.1309
Epoch: [10/20], Batch Num: [15/600]
Discriminator Loss: 0.4090, Generator Loss: 3.4524
D(x): 0.8725, D(G(z)): 0.0984
Epoch: [10/20], Batch Num: [16/600]
Discriminator Loss: 0.3809, Generator Loss: 3.6667
D(x): 0.8752, D(G(z)): 0.0727
Epoch: [10/20], Batch Num: [17/600]
Discriminator Loss: 0.3151, Generator Loss: 3.2165
D(x): 0.8847, D(G(z)): 0.0925
Epoch: [10/20], Batch Num: [18/600]
Discriminator Loss: 0.5131, Generator Loss: 2.5505
D(x): 0.8137, D(G(z)): 0.1026
Epoch: [10/20], Batch Num: [19/600]
Discriminator Loss: 0.4683, Generator Loss: 2.5786
D(x): 0.8930, D(G(z)): 0.1781
Epoch: [10/20], Batch Num: [20/600]
Discriminator Loss: 0.5982, Generator Loss: 3.0559
D(x): 0.9097, D(G(z)): 0.2348
Epoch: [10/20], Batch Num: [21/600]
Discriminator Loss: 0.3545, Generator Loss: 3.6778
D(x): 0.8811, D(G(z)): 0.1108
Epoch: [10/20], Batch Num: [22/600]
Discriminator Loss: 0.4676, Generator Loss: 3.4096
D(x): 0.8242, D(G(z)): 0.0926
Epoch: [10/20], Batch Num: [23/600]
Discriminator Loss: 0.3846, Generator Loss: 2.7501
D(x): 0.8275, D(G(z)): 0.0654
Epoch: [10/20], Batch Num: [24/600]
Discriminator Loss: 0.4632, Generator Loss: 2.1726
D(x): 0.8290, D(G(z)): 0.0986
Epoch: [10/20], Batch Num: [25/600]
Discriminator Loss: 0.4511, Generator Loss: 2.0811
D(x): 0.9164, D(G(z)): 0.2126
Epoch: [10/20], Batch Num: [26/600]
Discriminator Loss: 0.4924, Generator Loss: 3.0073
D(x): 0.9456, D(G(z)): 0.2549
Epoch: [10/20], Batch Num: [27/600]
Discriminator Loss: 0.2867, Generator Loss: 3.7208
D(x): 0.9038, D(G(z)): 0.1095
Epoch: [10/20], Batch Num: [28/600]
Discriminator Loss: 0.3752, Generator Loss: 4.2000
D(x): 0.8511, D(G(z)): 0.0769
Epoch: [10/20], Batch Num: [29/600]
Discriminator Loss: 0.4915, Generator Loss: 3.9318
D(x): 0.8300, D(G(z)): 0.0614
Epoch: [10/20], Batch Num: [30/600]
Discriminator Loss: 0.3216, Generator Loss: 3.8251
D(x): 0.8921, D(G(z)): 0.0546
Epoch: [10/20], Batch Num: [31/600]
Discriminator Loss: 0.2587, Generator Loss: 3.5324
D(x): 0.9112, D(G(z)): 0.0505
Epoch: [10/20], Batch Num: [32/600]
Discriminator Loss: 0.4520, Generator Loss: 2.5145
D(x): 0.8718, D(G(z)): 0.1054
Epoch: [10/20], Batch Num: [33/600]
Discriminator Loss: 0.4085, Generator Loss: 2.6325
D(x): 0.9365, D(G(z)): 0.1792
Epoch: [10/20], Batch Num: [34/600]
Discriminator Loss: 0.3464, Generator Loss: 2.8397
D(x): 0.9182, D(G(z)): 0.1505
Epoch: [10/20], Batch Num: [35/600]
Discriminator Loss: 0.3853, Generator Loss: 2.7406
D(x): 0.8692, D(G(z)): 0.1100
Epoch: [10/20], Batch Num: [36/600]
Discriminator Loss: 0.3566, Generator Loss: 2.8493
D(x): 0.8740, D(G(z)): 0.1097
Epoch: [10/20], Batch Num: [37/600]
Discriminator Loss: 0.2566, Generator Loss: 3.1529
D(x): 0.9303, D(G(z)): 0.1099
Epoch: [10/20], Batch Num: [38/600]
Discriminator Loss: 0.4071, Generator Loss: 3.1898
D(x): 0.8606, D(G(z)): 0.1108
Epoch: [10/20], Batch Num: [39/600]
Discriminator Loss: 0.2873, Generator Loss: 3.1850
D(x): 0.8966, D(G(z)): 0.0869
Epoch: [10/20], Batch Num: [40/600]
Discriminator Loss: 0.3437, Generator Loss: 3.1330
D(x): 0.8915, D(G(z)): 0.1039
Epoch: [10/20], Batch Num: [41/600]
Discriminator Loss: 0.4036, Generator Loss: 3.4664
D(x): 0.9099, D(G(z)): 0.1289
Epoch: [10/20], Batch Num: [42/600]
Discriminator Loss: 0.2836, Generator Loss: 3.3866
D(x): 0.8977, D(G(z)): 0.0694
Epoch: [10/20], Batch Num: [43/600]
Discriminator Loss: 0.2987, Generator Loss: 3.3140
D(x): 0.8857, D(G(z)): 0.0834
Epoch: [10/20], Batch Num: [44/600]
Discriminator Loss: 0.2018, Generator Loss: 3.1707
D(x): 0.9453, D(G(z)): 0.0836
Epoch: [10/20], Batch Num: [45/600]
Discriminator Loss: 0.2803, Generator Loss: 3.2399
D(x): 0.8864, D(G(z)): 0.0780
Epoch: [10/20], Batch Num: [46/600]
Discriminator Loss: 0.3993, Generator Loss: 2.9101
D(x): 0.8802, D(G(z)): 0.1047
Epoch: [10/20], Batch Num: [47/600]
Discriminator Loss: 0.4463, Generator Loss: 2.7619
D(x): 0.8840, D(G(z)): 0.1449
Epoch: [10/20], Batch Num: [48/600]
Discriminator Loss: 0.2914, Generator Loss: 2.8784
D(x): 0.9121, D(G(z)): 0.1203
Epoch: [10/20], Batch Num: [49/600]
Discriminator Loss: 0.2617, Generator Loss: 3.4105
D(x): 0.9247, D(G(z)): 0.1049
Epoch: [10/20], Batch Num: [50/600]
Discriminator Loss: 0.2485, Generator Loss: 3.6668
D(x): 0.9303, D(G(z)): 0.0872
Epoch: [10/20], Batch Num: [51/600]
Discriminator Loss: 0.3892, Generator Loss: 3.8682
D(x): 0.8816, D(G(z)): 0.1106
Epoch: [10/20], Batch Num: [52/600]
Discriminator Loss: 0.2789, Generator Loss: 3.9183
D(x): 0.8974, D(G(z)): 0.0512
Epoch: [10/20], Batch Num: [53/600]
Discriminator Loss: 0.1734, Generator Loss: 3.5936
D(x): 0.9226, D(G(z)): 0.0356
Epoch: [10/20], Batch Num: [54/600]
Discriminator Loss: 0.2301, Generator Loss: 3.2485
D(x): 0.9194, D(G(z)): 0.0662
Epoch: [10/20], Batch Num: [55/600]
Discriminator Loss: 0.2889, Generator Loss: 3.1042
D(x): 0.8948, D(G(z)): 0.0878
Epoch: [10/20], Batch Num: [56/600]
Discriminator Loss: 0.3041, Generator Loss: 2.7081
D(x): 0.9131, D(G(z)): 0.1299
Epoch: [10/20], Batch Num: [57/600]
Discriminator Loss: 0.3689, Generator Loss: 3.3307
D(x): 0.9387, D(G(z)): 0.1690
Epoch: [10/20], Batch Num: [58/600]
Discriminator Loss: 0.3029, Generator Loss: 4.0800
D(x): 0.9478, D(G(z)): 0.1552
Epoch: [10/20], Batch Num: [59/600]
Discriminator Loss: 0.4230, Generator Loss: 4.5371
D(x): 0.8508, D(G(z)): 0.0486
Epoch: [10/20], Batch Num: [60/600]
Discriminator Loss: 0.3590, Generator Loss: 4.2077
D(x): 0.8838, D(G(z)): 0.0715
Epoch: [10/20], Batch Num: [61/600]
Discriminator Loss: 0.4061, Generator Loss: 4.1780
D(x): 0.8744, D(G(z)): 0.0604
Epoch: [10/20], Batch Num: [62/600]
Discriminator Loss: 0.2184, Generator Loss: 3.9300
D(x): 0.9422, D(G(z)): 0.0834
Epoch: [10/20], Batch Num: [63/600]
Discriminator Loss: 0.2164, Generator Loss: 3.2535
D(x): 0.9506, D(G(z)): 0.0838
Epoch: [10/20], Batch Num: [64/600]
Discriminator Loss: 0.4643, Generator Loss: 3.2108
D(x): 0.8914, D(G(z)): 0.1177
Epoch: [10/20], Batch Num: [65/600]
Discriminator Loss: 0.4238, Generator Loss: 3.3706
D(x): 0.9167, D(G(z)): 0.1108
Epoch: [10/20], Batch Num: [66/600]
Discriminator Loss: 0.2282, Generator Loss: 3.6120
D(x): 0.9514, D(G(z)): 0.0966
Epoch: [10/20], Batch Num: [67/600]
Discriminator Loss: 0.2158, Generator Loss: 4.1038
D(x): 0.9453, D(G(z)): 0.0908
Epoch: [10/20], Batch Num: [68/600]
Discriminator Loss: 0.4010, Generator Loss: 4.2656
D(x): 0.8553, D(G(z)): 0.0541
Epoch: [10/20], Batch Num: [69/600]
Discriminator Loss: 0.4525, Generator Loss: 3.4704
D(x): 0.8477, D(G(z)): 0.0590
Epoch: [10/20], Batch Num: [70/600]
Discriminator Loss: 0.3847, Generator Loss: 3.2527
D(x): 0.9116, D(G(z)): 0.1299
Epoch: [10/20], Batch Num: [71/600]
Discriminator Loss: 0.4554, Generator Loss: 2.8731
D(x): 0.8642, D(G(z)): 0.1423
Epoch: [10/20], Batch Num: [72/600]
Discriminator Loss: 0.3474, Generator Loss: 3.1145
D(x): 0.9416, D(G(z)): 0.1570
Epoch: [10/20], Batch Num: [73/600]
Discriminator Loss: 0.4783, Generator Loss: 3.2346
D(x): 0.8507, D(G(z)): 0.1102
Epoch: [10/20], Batch Num: [74/600]
Discriminator Loss: 0.3969, Generator Loss: 3.2665
D(x): 0.8875, D(G(z)): 0.1093
Epoch: [10/20], Batch Num: [75/600]
Discriminator Loss: 0.3497, Generator Loss: 3.2378
D(x): 0.9109, D(G(z)): 0.0998
Epoch: [10/20], Batch Num: [76/600]
Discriminator Loss: 0.4281, Generator Loss: 3.0817
D(x): 0.8758, D(G(z)): 0.1247
Epoch: [10/20], Batch Num: [77/600]
Discriminator Loss: 0.3132, Generator Loss: 3.1236
D(x): 0.8851, D(G(z)): 0.0904
Epoch: [10/20], Batch Num: [78/600]
Discriminator Loss: 0.4766, Generator Loss: 2.7518
D(x): 0.8684, D(G(z)): 0.1313
Epoch: [10/20], Batch Num: [79/600]
Discriminator Loss: 0.3669, Generator Loss: 3.0239
D(x): 0.9278, D(G(z)): 0.1471
Epoch: [10/20], Batch Num: [80/600]
Discriminator Loss: 0.4182, Generator Loss: 3.5725
D(x): 0.9145, D(G(z)): 0.1482
Epoch: [10/20], Batch Num: [81/600]
Discriminator Loss: 0.3157, Generator Loss: 3.4730
D(x): 0.9148, D(G(z)): 0.1050
Epoch: [10/20], Batch Num: [82/600]
Discriminator Loss: 0.3158, Generator Loss: 3.7440
D(x): 0.8713, D(G(z)): 0.0648
Epoch: [10/20], Batch Num: [83/600]
Discriminator Loss: 0.3558, Generator Loss: 3.3196
D(x): 0.8532, D(G(z)): 0.0580
Epoch: [10/20], Batch Num: [84/600]
Discriminator Loss: 0.3813, Generator Loss: 2.8235
D(x): 0.8665, D(G(z)): 0.0881
Epoch: [10/20], Batch Num: [85/600]
Discriminator Loss: 0.3256, Generator Loss: 2.3521
D(x): 0.9066, D(G(z)): 0.1316
Epoch: [10/20], Batch Num: [86/600]
Discriminator Loss: 0.3828, Generator Loss: 2.8607
D(x): 0.9429, D(G(z)): 0.1873
Epoch: [10/20], Batch Num: [87/600]
Discriminator Loss: 0.3026, Generator Loss: 3.4304
D(x): 0.9145, D(G(z)): 0.1250
Epoch: [10/20], Batch Num: [88/600]
Discriminator Loss: 0.3953, Generator Loss: 4.0178
D(x): 0.8866, D(G(z)): 0.1140
Epoch: [10/20], Batch Num: [89/600]
Discriminator Loss: 0.3837, Generator Loss: 3.9770
D(x): 0.8339, D(G(z)): 0.0469
Epoch: [10/20], Batch Num: [90/600]
Discriminator Loss: 0.5797, Generator Loss: 3.5010
D(x): 0.8582, D(G(z)): 0.1128
Epoch: [10/20], Batch Num: [91/600]
Discriminator Loss: 0.4454, Generator Loss: 2.7053
D(x): 0.8665, D(G(z)): 0.0887
Epoch: [10/20], Batch Num: [92/600]
Discriminator Loss: 0.2538, Generator Loss: 2.5654
D(x): 0.9394, D(G(z)): 0.1213
Epoch: [10/20], Batch Num: [93/600]
Discriminator Loss: 0.6640, Generator Loss: 3.3036
D(x): 0.8994, D(G(z)): 0.2190
Epoch: [10/20], Batch Num: [94/600]
Discriminator Loss: 0.4458, Generator Loss: 3.7199
D(x): 0.9087, D(G(z)): 0.1381
Epoch: [10/20], Batch Num: [95/600]
Discriminator Loss: 0.3623, Generator Loss: 3.9326
D(x): 0.8894, D(G(z)): 0.1124
Epoch: [10/20], Batch Num: [96/600]
Discriminator Loss: 0.3376, Generator Loss: 4.0826
D(x): 0.8659, D(G(z)): 0.0619
Epoch: [10/20], Batch Num: [97/600]
Discriminator Loss: 0.4860, Generator Loss: 3.2731
D(x): 0.8167, D(G(z)): 0.0535
Epoch: [10/20], Batch Num: [98/600]
Discriminator Loss: 0.4236, Generator Loss: 2.8010
D(x): 0.8461, D(G(z)): 0.1106
Epoch: [10/20], Batch Num: [99/600]
Discriminator Loss: 0.4961, Generator Loss: 2.3600
D(x): 0.8926, D(G(z)): 0.1632
Epoch: 10, Batch Num: [100/600]

Epoch: [10/20], Batch Num: [100/600]
Discriminator Loss: 0.4745, Generator Loss: 2.1539
D(x): 0.8831, D(G(z)): 0.1694
Epoch: [10/20], Batch Num: [101/600]
Discriminator Loss: 0.4436, Generator Loss: 2.6617
D(x): 0.9198, D(G(z)): 0.1919
Epoch: [10/20], Batch Num: [102/600]
Discriminator Loss: 0.4305, Generator Loss: 3.3416
D(x): 0.9404, D(G(z)): 0.1984
Epoch: [10/20], Batch Num: [103/600]
Discriminator Loss: 0.4111, Generator Loss: 4.0255
D(x): 0.8419, D(G(z)): 0.0741
Epoch: [10/20], Batch Num: [104/600]
Discriminator Loss: 0.4534, Generator Loss: 3.6290
D(x): 0.8513, D(G(z)): 0.0764
Epoch: [10/20], Batch Num: [105/600]
Discriminator Loss: 0.6216, Generator Loss: 3.1157
D(x): 0.7651, D(G(z)): 0.0767
Epoch: [10/20], Batch Num: [106/600]
Discriminator Loss: 0.3625, Generator Loss: 2.5731
D(x): 0.8793, D(G(z)): 0.1130
Epoch: [10/20], Batch Num: [107/600]
Discriminator Loss: 0.4512, Generator Loss: 2.3683
D(x): 0.9070, D(G(z)): 0.1802
Epoch: [10/20], Batch Num: [108/600]
Discriminator Loss: 0.5720, Generator Loss: 2.6108
D(x): 0.9048, D(G(z)): 0.2209
Epoch: [10/20], Batch Num: [109/600]
Discriminator Loss: 0.3856, Generator Loss: 2.9994
D(x): 0.9454, D(G(z)): 0.2038
Epoch: [10/20], Batch Num: [110/600]
Discriminator Loss: 0.4455, Generator Loss: 3.4168
D(x): 0.8846, D(G(z)): 0.1668
Epoch: [10/20], Batch Num: [111/600]
Discriminator Loss: 0.3127, Generator Loss: 3.8265
D(x): 0.8835, D(G(z)): 0.0797
Epoch: [10/20], Batch Num: [112/600]
Discriminator Loss: 0.4597, Generator Loss: 3.6902
D(x): 0.8057, D(G(z)): 0.0551
Epoch: [10/20], Batch Num: [113/600]
Discriminator Loss: 0.2840, Generator Loss: 3.1975
D(x): 0.8498, D(G(z)): 0.0452
Epoch: [10/20], Batch Num: [114/600]
Discriminator Loss: 0.4433, Generator Loss: 2.6881
D(x): 0.8363, D(G(z)): 0.0915
Epoch: [10/20], Batch Num: [115/600]
Discriminator Loss: 0.5031, Generator Loss: 1.8821
D(x): 0.8644, D(G(z)): 0.1816
Epoch: [10/20], Batch Num: [116/600]
Discriminator Loss: 0.4083, Generator Loss: 2.3523
D(x): 0.9525, D(G(z)): 0.2369
Epoch: [10/20], Batch Num: [117/600]
Discriminator Loss: 0.3971, Generator Loss: 2.7798
D(x): 0.9199, D(G(z)): 0.1723
Epoch: [10/20], Batch Num: [118/600]
Discriminator Loss: 0.3218, Generator Loss: 3.7752
D(x): 0.9187, D(G(z)): 0.1400
Epoch: [10/20], Batch Num: [119/600]
Discriminator Loss: 0.2861, Generator Loss: 4.2801
D(x): 0.8690, D(G(z)): 0.0491
Epoch: [10/20], Batch Num: [120/600]
Discriminator Loss: 0.3210, Generator Loss: 4.0056
D(x): 0.8497, D(G(z)): 0.0426
Epoch: [10/20], Batch Num: [121/600]
Discriminator Loss: 0.4781, Generator Loss: 3.4447
D(x): 0.8117, D(G(z)): 0.0590
Epoch: [10/20], Batch Num: [122/600]
Discriminator Loss: 0.4583, Generator Loss: 2.7374
D(x): 0.8219, D(G(z)): 0.0747
Epoch: [10/20], Batch Num: [123/600]
Discriminator Loss: 0.4262, Generator Loss: 2.2939
D(x): 0.8908, D(G(z)): 0.1688
Epoch: [10/20], Batch Num: [124/600]
Discriminator Loss: 0.5243, Generator Loss: 2.8364
D(x): 0.9733, D(G(z)): 0.2572
Epoch: [10/20], Batch Num: [125/600]
Discriminator Loss: 0.3220, Generator Loss: 3.3415
D(x): 0.9391, D(G(z)): 0.1593
Epoch: [10/20], Batch Num: [126/600]
Discriminator Loss: 0.2826, Generator Loss: 3.6439
D(x): 0.8912, D(G(z)): 0.0845
Epoch: [10/20], Batch Num: [127/600]
Discriminator Loss: 0.3449, Generator Loss: 3.9670
D(x): 0.8566, D(G(z)): 0.0594
Epoch: [10/20], Batch Num: [128/600]
Discriminator Loss: 0.4484, Generator Loss: 3.7669
D(x): 0.8380, D(G(z)): 0.0516
Epoch: [10/20], Batch Num: [129/600]
Discriminator Loss: 0.4842, Generator Loss: 2.9875
D(x): 0.8045, D(G(z)): 0.0684
Epoch: [10/20], Batch Num: [130/600]
Discriminator Loss: 0.3250, Generator Loss: 2.4993
D(x): 0.8761, D(G(z)): 0.0981
Epoch: [10/20], Batch Num: [131/600]
Discriminator Loss: 0.4181, Generator Loss: 2.1038
D(x): 0.9146, D(G(z)): 0.1802
Epoch: [10/20], Batch Num: [132/600]
Discriminator Loss: 0.3910, Generator Loss: 2.4363
D(x): 0.9478, D(G(z)): 0.2082
Epoch: [10/20], Batch Num: [133/600]
Discriminator Loss: 0.3423, Generator Loss: 3.2904
D(x): 0.9223, D(G(z)): 0.1616
Epoch: [10/20], Batch Num: [134/600]
Discriminator Loss: 0.2981, Generator Loss: 3.7555
D(x): 0.9194, D(G(z)): 0.1142
Epoch: [10/20], Batch Num: [135/600]
Discriminator Loss: 0.4404, Generator Loss: 4.2074
D(x): 0.8131, D(G(z)): 0.0570
Epoch: [10/20], Batch Num: [136/600]
Discriminator Loss: 0.3765, Generator Loss: 3.3573
D(x): 0.8495, D(G(z)): 0.0524
Epoch: [10/20], Batch Num: [137/600]
Discriminator Loss: 0.3305, Generator Loss: 3.2027
D(x): 0.8778, D(G(z)): 0.0684
Epoch: [10/20], Batch Num: [138/600]
Discriminator Loss: 0.2941, Generator Loss: 3.0160
D(x): 0.9041, D(G(z)): 0.0975
Epoch: [10/20], Batch Num: [139/600]
Discriminator Loss: 0.4795, Generator Loss: 2.7551
D(x): 0.8892, D(G(z)): 0.1503
Epoch: [10/20], Batch Num: [140/600]
Discriminator Loss: 0.2470, Generator Loss: 2.8526
D(x): 0.9459, D(G(z)): 0.1243
Epoch: [10/20], Batch Num: [141/600]
Discriminator Loss: 0.3959, Generator Loss: 3.0591
D(x): 0.9438, D(G(z)): 0.1769
Epoch: [10/20], Batch Num: [142/600]
Discriminator Loss: 0.3293, Generator Loss: 3.3187
D(x): 0.8760, D(G(z)): 0.0832
Epoch: [10/20], Batch Num: [143/600]
Discriminator Loss: 0.2918, Generator Loss: 3.5853
D(x): 0.8911, D(G(z)): 0.0794
Epoch: [10/20], Batch Num: [144/600]
Discriminator Loss: 0.3132, Generator Loss: 3.1916
D(x): 0.8806, D(G(z)): 0.0810
Epoch: [10/20], Batch Num: [145/600]
Discriminator Loss: 0.3633, Generator Loss: 3.1416
D(x): 0.8647, D(G(z)): 0.1000
Epoch: [10/20], Batch Num: [146/600]
Discriminator Loss: 0.2505, Generator Loss: 2.9207
D(x): 0.9158, D(G(z)): 0.1009
Epoch: [10/20], Batch Num: [147/600]
Discriminator Loss: 0.3147, Generator Loss: 2.5450
D(x): 0.9443, D(G(z)): 0.1476
Epoch: [10/20], Batch Num: [148/600]
Discriminator Loss: 0.4431, Generator Loss: 3.3077
D(x): 0.8973, D(G(z)): 0.1738
Epoch: [10/20], Batch Num: [149/600]
Discriminator Loss: 0.2983, Generator Loss: 3.5206
D(x): 0.9231, D(G(z)): 0.1332
Epoch: [10/20], Batch Num: [150/600]
Discriminator Loss: 0.2174, Generator Loss: 3.9170
D(x): 0.9173, D(G(z)): 0.0595
Epoch: [10/20], Batch Num: [151/600]
Discriminator Loss: 0.7173, Generator Loss: 3.6316
D(x): 0.7466, D(G(z)): 0.0439
Epoch: [10/20], Batch Num: [152/600]
Discriminator Loss: 0.3135, Generator Loss: 2.6612
D(x): 0.8588, D(G(z)): 0.0646
Epoch: [10/20], Batch Num: [153/600]
Discriminator Loss: 0.3484, Generator Loss: 2.6538
D(x): 0.9435, D(G(z)): 0.1665
Epoch: [10/20], Batch Num: [154/600]
Discriminator Loss: 0.4185, Generator Loss: 3.2766
D(x): 0.9460, D(G(z)): 0.2090
Epoch: [10/20], Batch Num: [155/600]
Discriminator Loss: 0.2049, Generator Loss: 3.7030
D(x): 0.9321, D(G(z)): 0.0826
Epoch: [10/20], Batch Num: [156/600]
Discriminator Loss: 0.3718, Generator Loss: 4.0539
D(x): 0.8568, D(G(z)): 0.0961
Epoch: [10/20], Batch Num: [157/600]
Discriminator Loss: 0.2955, Generator Loss: 3.9248
D(x): 0.8887, D(G(z)): 0.0544
Epoch: [10/20], Batch Num: [158/600]
Discriminator Loss: 0.3706, Generator Loss: 3.5399
D(x): 0.8580, D(G(z)): 0.0641
Epoch: [10/20], Batch Num: [159/600]
Discriminator Loss: 0.2629, Generator Loss: 3.5099
D(x): 0.9290, D(G(z)): 0.0913
Epoch: [10/20], Batch Num: [160/600]
Discriminator Loss: 0.4417, Generator Loss: 3.2588
D(x): 0.8487, D(G(z)): 0.0884
Epoch: [10/20], Batch Num: [161/600]
Discriminator Loss: 0.2635, Generator Loss: 2.9632
D(x): 0.9195, D(G(z)): 0.1119
Epoch: [10/20], Batch Num: [162/600]
Discriminator Loss: 0.3779, Generator Loss: 2.8066
D(x): 0.9287, D(G(z)): 0.1530
Epoch: [10/20], Batch Num: [163/600]
Discriminator Loss: 0.3129, Generator Loss: 3.2930
D(x): 0.9292, D(G(z)): 0.1336
Epoch: [10/20], Batch Num: [164/600]
Discriminator Loss: 0.3912, Generator Loss: 3.5327
D(x): 0.8924, D(G(z)): 0.1277
Epoch: [10/20], Batch Num: [165/600]
Discriminator Loss: 0.4011, Generator Loss: 3.8762
D(x): 0.8719, D(G(z)): 0.0684
Epoch: [10/20], Batch Num: [166/600]
Discriminator Loss: 0.3006, Generator Loss: 3.9766
D(x): 0.8822, D(G(z)): 0.0704
Epoch: [10/20], Batch Num: [167/600]
Discriminator Loss: 0.3077, Generator Loss: 3.2354
D(x): 0.8809, D(G(z)): 0.0593
Epoch: [10/20], Batch Num: [168/600]
Discriminator Loss: 0.2469, Generator Loss: 3.0441
D(x): 0.9321, D(G(z)): 0.0982
Epoch: [10/20], Batch Num: [169/600]
Discriminator Loss: 0.3803, Generator Loss: 2.8539
D(x): 0.8842, D(G(z)): 0.1256
Epoch: [10/20], Batch Num: [170/600]
Discriminator Loss: 0.2811, Generator Loss: 2.6652
D(x): 0.9332, D(G(z)): 0.1425
Epoch: [10/20], Batch Num: [171/600]
Discriminator Loss: 0.3736, Generator Loss: 3.8111
D(x): 0.9370, D(G(z)): 0.1755
Epoch: [10/20], Batch Num: [172/600]
Discriminator Loss: 0.3634, Generator Loss: 4.1687
D(x): 0.8601, D(G(z)): 0.0712
Epoch: [10/20], Batch Num: [173/600]
Discriminator Loss: 0.3155, Generator Loss: 4.1185
D(x): 0.8802, D(G(z)): 0.0506
Epoch: [10/20], Batch Num: [174/600]
Discriminator Loss: 0.4110, Generator Loss: 3.7737
D(x): 0.8839, D(G(z)): 0.0878
Epoch: [10/20], Batch Num: [175/600]
Discriminator Loss: 0.3321, Generator Loss: 3.3417
D(x): 0.8972, D(G(z)): 0.0975
Epoch: [10/20], Batch Num: [176/600]
Discriminator Loss: 0.3330, Generator Loss: 3.0259
D(x): 0.8938, D(G(z)): 0.1086
Epoch: [10/20], Batch Num: [177/600]
Discriminator Loss: 0.3433, Generator Loss: 2.9892
D(x): 0.9052, D(G(z)): 0.1306
Epoch: [10/20], Batch Num: [178/600]
Discriminator Loss: 0.2891, Generator Loss: 3.0991
D(x): 0.9032, D(G(z)): 0.1059
Epoch: [10/20], Batch Num: [179/600]
Discriminator Loss: 0.4475, Generator Loss: 3.2675
D(x): 0.8714, D(G(z)): 0.1421
Epoch: [10/20], Batch Num: [180/600]
Discriminator Loss: 0.4133, Generator Loss: 3.1542
D(x): 0.8965, D(G(z)): 0.1382
Epoch: [10/20], Batch Num: [181/600]
Discriminator Loss: 0.2699, Generator Loss: 3.5470
D(x): 0.9062, D(G(z)): 0.0987
Epoch: [10/20], Batch Num: [182/600]
Discriminator Loss: 0.3816, Generator Loss: 3.7392
D(x): 0.9009, D(G(z)): 0.1237
Epoch: [10/20], Batch Num: [183/600]
Discriminator Loss: 0.4086, Generator Loss: 3.8177
D(x): 0.8559, D(G(z)): 0.0864
Epoch: [10/20], Batch Num: [184/600]
Discriminator Loss: 0.3209, Generator Loss: 3.5542
D(x): 0.9029, D(G(z)): 0.0694
Epoch: [10/20], Batch Num: [185/600]
Discriminator Loss: 0.4792, Generator Loss: 3.2342
D(x): 0.8694, D(G(z)): 0.1224
Epoch: [10/20], Batch Num: [186/600]
Discriminator Loss: 0.3648, Generator Loss: 3.5847
D(x): 0.9020, D(G(z)): 0.1280
Epoch: [10/20], Batch Num: [187/600]
Discriminator Loss: 0.3741, Generator Loss: 3.3560
D(x): 0.8915, D(G(z)): 0.1010
Epoch: [10/20], Batch Num: [188/600]
Discriminator Loss: 0.3696, Generator Loss: 3.6342
D(x): 0.8856, D(G(z)): 0.1092
Epoch: [10/20], Batch Num: [189/600]
Discriminator Loss: 0.4165, Generator Loss: 3.1428
D(x): 0.8577, D(G(z)): 0.0771
Epoch: [10/20], Batch Num: [190/600]
Discriminator Loss: 0.3336, Generator Loss: 2.5678
D(x): 0.8710, D(G(z)): 0.0744
Epoch: [10/20], Batch Num: [191/600]
Discriminator Loss: 0.4879, Generator Loss: 2.6566
D(x): 0.8958, D(G(z)): 0.2000
Epoch: [10/20], Batch Num: [192/600]
Discriminator Loss: 0.3443, Generator Loss: 3.2128
D(x): 0.9133, D(G(z)): 0.1352
Epoch: [10/20], Batch Num: [193/600]
Discriminator Loss: 0.3285, Generator Loss: 3.7609
D(x): 0.9047, D(G(z)): 0.1160
Epoch: [10/20], Batch Num: [194/600]
Discriminator Loss: 0.4035, Generator Loss: 3.9926
D(x): 0.8668, D(G(z)): 0.0779
Epoch: [10/20], Batch Num: [195/600]
Discriminator Loss: 0.3298, Generator Loss: 3.3834
D(x): 0.8977, D(G(z)): 0.0826
Epoch: [10/20], Batch Num: [196/600]
Discriminator Loss: 0.4149, Generator Loss: 3.4930
D(x): 0.8601, D(G(z)): 0.0661
Epoch: [10/20], Batch Num: [197/600]
Discriminator Loss: 0.5944, Generator Loss: 2.6324
D(x): 0.8068, D(G(z)): 0.1233
Epoch: [10/20], Batch Num: [198/600]
Discriminator Loss: 0.5057, Generator Loss: 2.6083
D(x): 0.9301, D(G(z)): 0.2441
Epoch: [10/20], Batch Num: [199/600]
Discriminator Loss: 0.4581, Generator Loss: 3.5832
D(x): 0.9318, D(G(z)): 0.2093
Epoch: 10, Batch Num: [200/600]

Epoch: [10/20], Batch Num: [200/600]
Discriminator Loss: 0.4303, Generator Loss: 4.0928
D(x): 0.8454, D(G(z)): 0.0830
Epoch: [10/20], Batch Num: [201/600]
Discriminator Loss: 0.3642, Generator Loss: 4.4875
D(x): 0.8764, D(G(z)): 0.0536
Epoch: [10/20], Batch Num: [202/600]
Discriminator Loss: 0.6185, Generator Loss: 3.7153
D(x): 0.7904, D(G(z)): 0.0624
Epoch: [10/20], Batch Num: [203/600]
Discriminator Loss: 0.4037, Generator Loss: 2.2937
D(x): 0.8375, D(G(z)): 0.0725
Epoch: [10/20], Batch Num: [204/600]
Discriminator Loss: 0.4648, Generator Loss: 2.1868
D(x): 0.9460, D(G(z)): 0.2374
Epoch: [10/20], Batch Num: [205/600]
Discriminator Loss: 0.4555, Generator Loss: 2.7937
D(x): 0.9246, D(G(z)): 0.1997
Epoch: [10/20], Batch Num: [206/600]
Discriminator Loss: 0.3809, Generator Loss: 3.5656
D(x): 0.8765, D(G(z)): 0.1342
Epoch: [10/20], Batch Num: [207/600]
Discriminator Loss: 0.5331, Generator Loss: 3.6617
D(x): 0.8466, D(G(z)): 0.1022
Epoch: [10/20], Batch Num: [208/600]
Discriminator Loss: 0.3863, Generator Loss: 3.6891
D(x): 0.8753, D(G(z)): 0.0791
Epoch: [10/20], Batch Num: [209/600]
Discriminator Loss: 0.3701, Generator Loss: 2.8577
D(x): 0.8511, D(G(z)): 0.0516
Epoch: [10/20], Batch Num: [210/600]
Discriminator Loss: 0.5199, Generator Loss: 2.4227
D(x): 0.8541, D(G(z)): 0.1645
Epoch: [10/20], Batch Num: [211/600]
Discriminator Loss: 0.3705, Generator Loss: 2.1142
D(x): 0.9029, D(G(z)): 0.1520
Epoch: [10/20], Batch Num: [212/600]
Discriminator Loss: 0.4430, Generator Loss: 2.4577
D(x): 0.8897, D(G(z)): 0.1826
Epoch: [10/20], Batch Num: [213/600]
Discriminator Loss: 0.4170, Generator Loss: 2.6723
D(x): 0.8993, D(G(z)): 0.1661
Epoch: [10/20], Batch Num: [214/600]
Discriminator Loss: 0.2487, Generator Loss: 3.3690
D(x): 0.9387, D(G(z)): 0.1200
Epoch: [10/20], Batch Num: [215/600]
Discriminator Loss: 0.5157, Generator Loss: 3.5915
D(x): 0.8402, D(G(z)): 0.1173
Epoch: [10/20], Batch Num: [216/600]
Discriminator Loss: 0.5370, Generator Loss: 3.2358
D(x): 0.7978, D(G(z)): 0.0542
Epoch: [10/20], Batch Num: [217/600]
Discriminator Loss: 0.3632, Generator Loss: 2.7674
D(x): 0.8488, D(G(z)): 0.0698
Epoch: [10/20], Batch Num: [218/600]
Discriminator Loss: 0.4349, Generator Loss: 2.4259
D(x): 0.8682, D(G(z)): 0.1636
Epoch: [10/20], Batch Num: [219/600]
Discriminator Loss: 0.3430, Generator Loss: 2.3903
D(x): 0.9369, D(G(z)): 0.1701
Epoch: [10/20], Batch Num: [220/600]
Discriminator Loss: 0.5505, Generator Loss: 2.7303
D(x): 0.8817, D(G(z)): 0.2168
Epoch: [10/20], Batch Num: [221/600]
Discriminator Loss: 0.3707, Generator Loss: 2.9590
D(x): 0.8646, D(G(z)): 0.1124
Epoch: [10/20], Batch Num: [222/600]
Discriminator Loss: 0.5022, Generator Loss: 3.1930
D(x): 0.8258, D(G(z)): 0.1096
Epoch: [10/20], Batch Num: [223/600]
Discriminator Loss: 0.4109, Generator Loss: 2.9838
D(x): 0.8501, D(G(z)): 0.0950
Epoch: [10/20], Batch Num: [224/600]
Discriminator Loss: 0.3325, Generator Loss: 2.6734
D(x): 0.8945, D(G(z)): 0.1198
Epoch: [10/20], Batch Num: [225/600]
Discriminator Loss: 0.3546, Generator Loss: 2.8751
D(x): 0.8915, D(G(z)): 0.1258
Epoch: [10/20], Batch Num: [226/600]
Discriminator Loss: 0.3829, Generator Loss: 2.6510
D(x): 0.8864, D(G(z)): 0.1264
Epoch: [10/20], Batch Num: [227/600]
Discriminator Loss: 0.3691, Generator Loss: 3.0950
D(x): 0.9149, D(G(z)): 0.1514
Epoch: [10/20], Batch Num: [228/600]
Discriminator Loss: 0.3313, Generator Loss: 3.1802
D(x): 0.9402, D(G(z)): 0.1493
Epoch: [10/20], Batch Num: [229/600]
Discriminator Loss: 0.3629, Generator Loss: 3.5440
D(x): 0.8844, D(G(z)): 0.0983
Epoch: [10/20], Batch Num: [230/600]
Discriminator Loss: 0.5030, Generator Loss: 3.5853
D(x): 0.8026, D(G(z)): 0.0538
Epoch: [10/20], Batch Num: [231/600]
Discriminator Loss: 0.4397, Generator Loss: 3.1336
D(x): 0.8451, D(G(z)): 0.0911
Epoch: [10/20], Batch Num: [232/600]
Discriminator Loss: 0.4172, Generator Loss: 2.7078
D(x): 0.8836, D(G(z)): 0.1271
Epoch: [10/20], Batch Num: [233/600]
Discriminator Loss: 0.4101, Generator Loss: 2.5962
D(x): 0.9085, D(G(z)): 0.1724
Epoch: [10/20], Batch Num: [234/600]
Discriminator Loss: 0.3722, Generator Loss: 2.7423
D(x): 0.9151, D(G(z)): 0.1800
Epoch: [10/20], Batch Num: [235/600]
Discriminator Loss: 0.3615, Generator Loss: 3.4657
D(x): 0.8790, D(G(z)): 0.1158
Epoch: [10/20], Batch Num: [236/600]
Discriminator Loss: 0.5654, Generator Loss: 3.1865
D(x): 0.7937, D(G(z)): 0.0991
Epoch: [10/20], Batch Num: [237/600]
Discriminator Loss: 0.3929, Generator Loss: 2.8792
D(x): 0.8802, D(G(z)): 0.1263
Epoch: [10/20], Batch Num: [238/600]
Discriminator Loss: 0.4026, Generator Loss: 2.8817
D(x): 0.9108, D(G(z)): 0.1316
Epoch: [10/20], Batch Num: [239/600]
Discriminator Loss: 0.5571, Generator Loss: 2.6682
D(x): 0.8594, D(G(z)): 0.1629
Epoch: [10/20], Batch Num: [240/600]
Discriminator Loss: 0.4008, Generator Loss: 2.9807
D(x): 0.9027, D(G(z)): 0.1295
Epoch: [10/20], Batch Num: [241/600]
Discriminator Loss: 0.4819, Generator Loss: 2.9991
D(x): 0.8419, D(G(z)): 0.1322
Epoch: [10/20], Batch Num: [242/600]
Discriminator Loss: 0.3658, Generator Loss: 3.0603
D(x): 0.8803, D(G(z)): 0.1171
Epoch: [10/20], Batch Num: [243/600]
Discriminator Loss: 0.4644, Generator Loss: 2.9644
D(x): 0.8559, D(G(z)): 0.1264
Epoch: [10/20], Batch Num: [244/600]
Discriminator Loss: 0.4501, Generator Loss: 2.8358
D(x): 0.8585, D(G(z)): 0.1339
Epoch: [10/20], Batch Num: [245/600]
Discriminator Loss: 0.4234, Generator Loss: 2.7322
D(x): 0.8695, D(G(z)): 0.1230
Epoch: [10/20], Batch Num: [246/600]
Discriminator Loss: 0.4958, Generator Loss: 2.7507
D(x): 0.8420, D(G(z)): 0.1323
Epoch: [10/20], Batch Num: [247/600]
Discriminator Loss: 0.5499, Generator Loss: 2.4771
D(x): 0.8378, D(G(z)): 0.1455
Epoch: [10/20], Batch Num: [248/600]
Discriminator Loss: 0.4618, Generator Loss: 2.8427
D(x): 0.8889, D(G(z)): 0.2037
Epoch: [10/20], Batch Num: [249/600]
Discriminator Loss: 0.4512, Generator Loss: 2.9206
D(x): 0.8706, D(G(z)): 0.1429
Epoch: [10/20], Batch Num: [250/600]
Discriminator Loss: 0.5350, Generator Loss: 2.8780
D(x): 0.8412, D(G(z)): 0.1482
Epoch: [10/20], Batch Num: [251/600]
Discriminator Loss: 0.4231, Generator Loss: 2.8143
D(x): 0.8753, D(G(z)): 0.1253
Epoch: [10/20], Batch Num: [252/600]
Discriminator Loss: 0.4590, Generator Loss: 2.7923
D(x): 0.8565, D(G(z)): 0.1331
Epoch: [10/20], Batch Num: [253/600]
Discriminator Loss: 0.5377, Generator Loss: 2.7542
D(x): 0.8297, D(G(z)): 0.1247
Epoch: [10/20], Batch Num: [254/600]
Discriminator Loss: 0.4627, Generator Loss: 2.6405
D(x): 0.8659, D(G(z)): 0.1565
Epoch: [10/20], Batch Num: [255/600]
Discriminator Loss: 0.5375, Generator Loss: 2.6600
D(x): 0.8498, D(G(z)): 0.1446
Epoch: [10/20], Batch Num: [256/600]
Discriminator Loss: 0.3372, Generator Loss: 2.9632
D(x): 0.9065, D(G(z)): 0.1323
Epoch: [10/20], Batch Num: [257/600]
Discriminator Loss: 0.3354, Generator Loss: 2.7525
D(x): 0.8857, D(G(z)): 0.1099
Epoch: [10/20], Batch Num: [258/600]
Discriminator Loss: 0.4461, Generator Loss: 2.8898
D(x): 0.8755, D(G(z)): 0.1254
Epoch: [10/20], Batch Num: [259/600]
Discriminator Loss: 0.4244, Generator Loss: 3.1881
D(x): 0.8580, D(G(z)): 0.1127
Epoch: [10/20], Batch Num: [260/600]
Discriminator Loss: 0.3985, Generator Loss: 2.8943
D(x): 0.8934, D(G(z)): 0.1480
Epoch: [10/20], Batch Num: [261/600]
Discriminator Loss: 0.4654, Generator Loss: 3.0671
D(x): 0.8601, D(G(z)): 0.1275
Epoch: [10/20], Batch Num: [262/600]
Discriminator Loss: 0.4828, Generator Loss: 3.0362
D(x): 0.8499, D(G(z)): 0.1322
Epoch: [10/20], Batch Num: [263/600]
Discriminator Loss: 0.5109, Generator Loss: 2.7565
D(x): 0.8283, D(G(z)): 0.1511
Epoch: [10/20], Batch Num: [264/600]
Discriminator Loss: 0.4755, Generator Loss: 2.9255
D(x): 0.8795, D(G(z)): 0.1458
Epoch: [10/20], Batch Num: [265/600]
Discriminator Loss: 0.4795, Generator Loss: 2.8036
D(x): 0.8550, D(G(z)): 0.1541
Epoch: [10/20], Batch Num: [266/600]
Discriminator Loss: 0.3558, Generator Loss: 3.0684
D(x): 0.8781, D(G(z)): 0.1032
Epoch: [10/20], Batch Num: [267/600]
Discriminator Loss: 0.4757, Generator Loss: 2.7221
D(x): 0.8446, D(G(z)): 0.1245
Epoch: [10/20], Batch Num: [268/600]
Discriminator Loss: 0.5017, Generator Loss: 2.5172
D(x): 0.8363, D(G(z)): 0.1472
Epoch: [10/20], Batch Num: [269/600]
Discriminator Loss: 0.4731, Generator Loss: 2.5803
D(x): 0.8508, D(G(z)): 0.1552
Epoch: [10/20], Batch Num: [270/600]
Discriminator Loss: 0.3951, Generator Loss: 2.5606
D(x): 0.9036, D(G(z)): 0.1663
Epoch: [10/20], Batch Num: [271/600]
Discriminator Loss: 0.3195, Generator Loss: 2.7581
D(x): 0.8779, D(G(z)): 0.1047
Epoch: [10/20], Batch Num: [272/600]
Discriminator Loss: 0.4078, Generator Loss: 3.1166
D(x): 0.9157, D(G(z)): 0.1645
Epoch: [10/20], Batch Num: [273/600]
Discriminator Loss: 0.3227, Generator Loss: 3.2745
D(x): 0.8797, D(G(z)): 0.0840
Epoch: [10/20], Batch Num: [274/600]
Discriminator Loss: 0.3153, Generator Loss: 3.5871
D(x): 0.8925, D(G(z)): 0.0932
Epoch: [10/20], Batch Num: [275/600]
Discriminator Loss: 0.5250, Generator Loss: 3.0418
D(x): 0.8272, D(G(z)): 0.0883
Epoch: [10/20], Batch Num: [276/600]
Discriminator Loss: 0.4021, Generator Loss: 2.7603
D(x): 0.8743, D(G(z)): 0.1146
Epoch: [10/20], Batch Num: [277/600]
Discriminator Loss: 0.4429, Generator Loss: 2.4862
D(x): 0.9141, D(G(z)): 0.1784
Epoch: [10/20], Batch Num: [278/600]
Discriminator Loss: 0.3351, Generator Loss: 2.9813
D(x): 0.9166, D(G(z)): 0.1462
Epoch: [10/20], Batch Num: [279/600]
Discriminator Loss: 0.4226, Generator Loss: 3.4019
D(x): 0.8930, D(G(z)): 0.1360
Epoch: [10/20], Batch Num: [280/600]
Discriminator Loss: 0.4180, Generator Loss: 3.9597
D(x): 0.8748, D(G(z)): 0.1214
Epoch: [10/20], Batch Num: [281/600]
Discriminator Loss: 0.5283, Generator Loss: 3.4264
D(x): 0.8156, D(G(z)): 0.0823
Epoch: [10/20], Batch Num: [282/600]
Discriminator Loss: 0.4835, Generator Loss: 2.9084
D(x): 0.8329, D(G(z)): 0.0900
Epoch: [10/20], Batch Num: [283/600]
Discriminator Loss: 0.4646, Generator Loss: 2.5079
D(x): 0.8582, D(G(z)): 0.1463
Epoch: [10/20], Batch Num: [284/600]
Discriminator Loss: 0.5489, Generator Loss: 2.1129
D(x): 0.8826, D(G(z)): 0.1950
Epoch: [10/20], Batch Num: [285/600]
Discriminator Loss: 0.4180, Generator Loss: 2.7254
D(x): 0.9158, D(G(z)): 0.1953
Epoch: [10/20], Batch Num: [286/600]
Discriminator Loss: 0.4552, Generator Loss: 3.1721
D(x): 0.8593, D(G(z)): 0.1525
Epoch: [10/20], Batch Num: [287/600]
Discriminator Loss: 0.4847, Generator Loss: 3.3167
D(x): 0.8389, D(G(z)): 0.1128
Epoch: [10/20], Batch Num: [288/600]
Discriminator Loss: 0.6427, Generator Loss: 3.3758
D(x): 0.8240, D(G(z)): 0.1222
Epoch: [10/20], Batch Num: [289/600]
Discriminator Loss: 0.4773, Generator Loss: 3.1341
D(x): 0.8201, D(G(z)): 0.0935
Epoch: [10/20], Batch Num: [290/600]
Discriminator Loss: 0.6514, Generator Loss: 2.5069
D(x): 0.7817, D(G(z)): 0.1305
Epoch: [10/20], Batch Num: [291/600]
Discriminator Loss: 0.4875, Generator Loss: 2.4867
D(x): 0.9058, D(G(z)): 0.1821
Epoch: [10/20], Batch Num: [292/600]
Discriminator Loss: 0.4490, Generator Loss: 2.5819
D(x): 0.9066, D(G(z)): 0.1928
Epoch: [10/20], Batch Num: [293/600]
Discriminator Loss: 0.3874, Generator Loss: 2.7303
D(x): 0.9192, D(G(z)): 0.1726
Epoch: [10/20], Batch Num: [294/600]
Discriminator Loss: 0.4581, Generator Loss: 3.0940
D(x): 0.8307, D(G(z)): 0.0970
Epoch: [10/20], Batch Num: [295/600]
Discriminator Loss: 0.6975, Generator Loss: 2.7945
D(x): 0.7783, D(G(z)): 0.1296
Epoch: [10/20], Batch Num: [296/600]
Discriminator Loss: 0.4942, Generator Loss: 2.4156
D(x): 0.8182, D(G(z)): 0.1075
Epoch: [10/20], Batch Num: [297/600]
Discriminator Loss: 0.4518, Generator Loss: 2.0990
D(x): 0.9035, D(G(z)): 0.1710
Epoch: [10/20], Batch Num: [298/600]
Discriminator Loss: 0.4381, Generator Loss: 2.2768
D(x): 0.8791, D(G(z)): 0.1742
Epoch: [10/20], Batch Num: [299/600]
Discriminator Loss: 0.3980, Generator Loss: 2.5321
D(x): 0.8767, D(G(z)): 0.1406
Epoch: 10, Batch Num: [300/600]

Epoch: [10/20], Batch Num: [300/600]
Discriminator Loss: 0.5488, Generator Loss: 2.6681
D(x): 0.8475, D(G(z)): 0.1815
Epoch: [10/20], Batch Num: [301/600]
Discriminator Loss: 0.6204, Generator Loss: 2.8882
D(x): 0.8009, D(G(z)): 0.1370
Epoch: [10/20], Batch Num: [302/600]
Discriminator Loss: 0.4070, Generator Loss: 2.8068
D(x): 0.8429, D(G(z)): 0.0912
Epoch: [10/20], Batch Num: [303/600]
Discriminator Loss: 0.5599, Generator Loss: 2.4524
D(x): 0.8074, D(G(z)): 0.1423
Epoch: [10/20], Batch Num: [304/600]
Discriminator Loss: 0.3935, Generator Loss: 2.3582
D(x): 0.8585, D(G(z)): 0.1119
Epoch: [10/20], Batch Num: [305/600]
Discriminator Loss: 0.4326, Generator Loss: 1.9233
D(x): 0.8832, D(G(z)): 0.1846
Epoch: [10/20], Batch Num: [306/600]
Discriminator Loss: 0.3877, Generator Loss: 1.9766
D(x): 0.8957, D(G(z)): 0.1824
Epoch: [10/20], Batch Num: [307/600]
Discriminator Loss: 0.5829, Generator Loss: 2.7961
D(x): 0.8896, D(G(z)): 0.2533
Epoch: [10/20], Batch Num: [308/600]
Discriminator Loss: 0.4086, Generator Loss: 3.2434
D(x): 0.8476, D(G(z)): 0.1293
Epoch: [10/20], Batch Num: [309/600]
Discriminator Loss: 0.4642, Generator Loss: 3.1093
D(x): 0.8256, D(G(z)): 0.0918
Epoch: [10/20], Batch Num: [310/600]
Discriminator Loss: 0.4535, Generator Loss: 3.1925
D(x): 0.8181, D(G(z)): 0.0934
Epoch: [10/20], Batch Num: [311/600]
Discriminator Loss: 0.3774, Generator Loss: 2.9025
D(x): 0.8806, D(G(z)): 0.1181
Epoch: [10/20], Batch Num: [312/600]
Discriminator Loss: 0.4212, Generator Loss: 2.7679
D(x): 0.9029, D(G(z)): 0.1512
Epoch: [10/20], Batch Num: [313/600]
Discriminator Loss: 0.3345, Generator Loss: 2.8384
D(x): 0.9014, D(G(z)): 0.1268
Epoch: [10/20], Batch Num: [314/600]
Discriminator Loss: 0.4529, Generator Loss: 2.8133
D(x): 0.8930, D(G(z)): 0.1466
Epoch: [10/20], Batch Num: [315/600]
Discriminator Loss: 0.3731, Generator Loss: 2.6501
D(x): 0.8412, D(G(z)): 0.0828
Epoch: [10/20], Batch Num: [316/600]
Discriminator Loss: 0.4453, Generator Loss: 2.7783
D(x): 0.8688, D(G(z)): 0.1162
Epoch: [10/20], Batch Num: [317/600]
Discriminator Loss: 0.3714, Generator Loss: 2.8502
D(x): 0.9086, D(G(z)): 0.1202
Epoch: [10/20], Batch Num: [318/600]
Discriminator Loss: 0.3842, Generator Loss: 2.6848
D(x): 0.8965, D(G(z)): 0.1598
Epoch: [10/20], Batch Num: [319/600]
Discriminator Loss: 0.4171, Generator Loss: 3.0655
D(x): 0.8870, D(G(z)): 0.1582
Epoch: [10/20], Batch Num: [320/600]
Discriminator Loss: 0.3914, Generator Loss: 3.0835
D(x): 0.8695, D(G(z)): 0.1115
Epoch: [10/20], Batch Num: [321/600]
Discriminator Loss: 0.4435, Generator Loss: 2.8740
D(x): 0.8405, D(G(z)): 0.0884
Epoch: [10/20], Batch Num: [322/600]
Discriminator Loss: 0.3696, Generator Loss: 2.5402
D(x): 0.8744, D(G(z)): 0.1295
Epoch: [10/20], Batch Num: [323/600]
Discriminator Loss: 0.4042, Generator Loss: 2.6703
D(x): 0.8877, D(G(z)): 0.1518
Epoch: [10/20], Batch Num: [324/600]
Discriminator Loss: 0.4798, Generator Loss: 2.4817
D(x): 0.8661, D(G(z)): 0.1626
Epoch: [10/20], Batch Num: [325/600]
Discriminator Loss: 0.4032, Generator Loss: 2.7490
D(x): 0.9027, D(G(z)): 0.1704
Epoch: [10/20], Batch Num: [326/600]
Discriminator Loss: 0.2800, Generator Loss: 3.3691
D(x): 0.9271, D(G(z)): 0.1332
Epoch: [10/20], Batch Num: [327/600]
Discriminator Loss: 0.3867, Generator Loss: 3.3612
D(x): 0.8554, D(G(z)): 0.0939
Epoch: [10/20], Batch Num: [328/600]
Discriminator Loss: 0.4202, Generator Loss: 2.7957
D(x): 0.8126, D(G(z)): 0.0681
Epoch: [10/20], Batch Num: [329/600]
Discriminator Loss: 0.3567, Generator Loss: 2.7178
D(x): 0.8865, D(G(z)): 0.1182
Epoch: [10/20], Batch Num: [330/600]
Discriminator Loss: 0.4038, Generator Loss: 2.3930
D(x): 0.8917, D(G(z)): 0.1715
Epoch: [10/20], Batch Num: [331/600]
Discriminator Loss: 0.5936, Generator Loss: 2.5276
D(x): 0.8792, D(G(z)): 0.2051
Epoch: [10/20], Batch Num: [332/600]
Discriminator Loss: 0.4943, Generator Loss: 3.2562
D(x): 0.8805, D(G(z)): 0.1898
Epoch: [10/20], Batch Num: [333/600]
Discriminator Loss: 0.4584, Generator Loss: 3.5178
D(x): 0.8442, D(G(z)): 0.0898
Epoch: [10/20], Batch Num: [334/600]
Discriminator Loss: 0.3342, Generator Loss: 3.4804
D(x): 0.8901, D(G(z)): 0.0744
Epoch: [10/20], Batch Num: [335/600]
Discriminator Loss: 0.3967, Generator Loss: 3.1855
D(x): 0.8486, D(G(z)): 0.0895
Epoch: [10/20], Batch Num: [336/600]
Discriminator Loss: 0.2648, Generator Loss: 2.6863
D(x): 0.9162, D(G(z)): 0.0980
Epoch: [10/20], Batch Num: [337/600]
Discriminator Loss: 0.4714, Generator Loss: 2.3565
D(x): 0.8920, D(G(z)): 0.1447
Epoch: [10/20], Batch Num: [338/600]
Discriminator Loss: 0.3332, Generator Loss: 2.3357
D(x): 0.9219, D(G(z)): 0.1603
Epoch: [10/20], Batch Num: [339/600]
Discriminator Loss: 0.5864, Generator Loss: 2.9254
D(x): 0.8683, D(G(z)): 0.2111
Epoch: [10/20], Batch Num: [340/600]
Discriminator Loss: 0.4570, Generator Loss: 3.3139
D(x): 0.8702, D(G(z)): 0.1310
Epoch: [10/20], Batch Num: [341/600]
Discriminator Loss: 0.4301, Generator Loss: 2.9520
D(x): 0.8284, D(G(z)): 0.0755
Epoch: [10/20], Batch Num: [342/600]
Discriminator Loss: 0.6149, Generator Loss: 2.7376
D(x): 0.8058, D(G(z)): 0.1413
Epoch: [10/20], Batch Num: [343/600]
Discriminator Loss: 0.4632, Generator Loss: 2.6417
D(x): 0.9004, D(G(z)): 0.1972
Epoch: [10/20], Batch Num: [344/600]
Discriminator Loss: 0.3529, Generator Loss: 3.1912
D(x): 0.8984, D(G(z)): 0.1375
Epoch: [10/20], Batch Num: [345/600]
Discriminator Loss: 0.4484, Generator Loss: 2.9107
D(x): 0.8314, D(G(z)): 0.1116
Epoch: [10/20], Batch Num: [346/600]
Discriminator Loss: 0.3840, Generator Loss: 3.0277
D(x): 0.8814, D(G(z)): 0.1250
Epoch: [10/20], Batch Num: [347/600]
Discriminator Loss: 0.3784, Generator Loss: 3.0123
D(x): 0.8860, D(G(z)): 0.1251
Epoch: [10/20], Batch Num: [348/600]
Discriminator Loss: 0.4981, Generator Loss: 2.8419
D(x): 0.8520, D(G(z)): 0.1287
Epoch: [10/20], Batch Num: [349/600]
Discriminator Loss: 0.5377, Generator Loss: 2.4210
D(x): 0.8324, D(G(z)): 0.1390
Epoch: [10/20], Batch Num: [350/600]
Discriminator Loss: 0.6035, Generator Loss: 2.4653
D(x): 0.8430, D(G(z)): 0.1821
Epoch: [10/20], Batch Num: [351/600]
Discriminator Loss: 0.6356, Generator Loss: 2.5186
D(x): 0.8330, D(G(z)): 0.2088
Epoch: [10/20], Batch Num: [352/600]
Discriminator Loss: 0.5020, Generator Loss: 2.8710
D(x): 0.8861, D(G(z)): 0.1752
Epoch: [10/20], Batch Num: [353/600]
Discriminator Loss: 0.4685, Generator Loss: 2.9734
D(x): 0.8254, D(G(z)): 0.1164
Epoch: [10/20], Batch Num: [354/600]
Discriminator Loss: 0.4516, Generator Loss: 3.0649
D(x): 0.8617, D(G(z)): 0.1299
Epoch: [10/20], Batch Num: [355/600]
Discriminator Loss: 0.4332, Generator Loss: 2.9719
D(x): 0.8448, D(G(z)): 0.1166
Epoch: [10/20], Batch Num: [356/600]
Discriminator Loss: 0.6063, Generator Loss: 2.4294
D(x): 0.8176, D(G(z)): 0.1751
Epoch: [10/20], Batch Num: [357/600]
Discriminator Loss: 0.5354, Generator Loss: 2.6228
D(x): 0.8544, D(G(z)): 0.1952
Epoch: [10/20], Batch Num: [358/600]
Discriminator Loss: 0.5053, Generator Loss: 2.7673
D(x): 0.8804, D(G(z)): 0.1963
Epoch: [10/20], Batch Num: [359/600]
Discriminator Loss: 0.5321, Generator Loss: 2.9019
D(x): 0.8048, D(G(z)): 0.1168
Epoch: [10/20], Batch Num: [360/600]
Discriminator Loss: 0.5261, Generator Loss: 2.7818
D(x): 0.8615, D(G(z)): 0.1566
Epoch: [10/20], Batch Num: [361/600]
Discriminator Loss: 0.6214, Generator Loss: 2.8591
D(x): 0.8250, D(G(z)): 0.1399
Epoch: [10/20], Batch Num: [362/600]
Discriminator Loss: 0.4992, Generator Loss: 2.8253
D(x): 0.8568, D(G(z)): 0.1618
Epoch: [10/20], Batch Num: [363/600]
Discriminator Loss: 0.3547, Generator Loss: 2.9063
D(x): 0.8748, D(G(z)): 0.1124
Epoch: [10/20], Batch Num: [364/600]
Discriminator Loss: 0.4268, Generator Loss: 2.6988
D(x): 0.8802, D(G(z)): 0.1374
Epoch: [10/20], Batch Num: [365/600]
Discriminator Loss: 0.4852, Generator Loss: 2.7788
D(x): 0.8320, D(G(z)): 0.1269
Epoch: [10/20], Batch Num: [366/600]
Discriminator Loss: 0.4943, Generator Loss: 2.8965
D(x): 0.8519, D(G(z)): 0.1615
Epoch: [10/20], Batch Num: [367/600]
Discriminator Loss: 0.4498, Generator Loss: 2.7446
D(x): 0.8759, D(G(z)): 0.1429
Epoch: [10/20], Batch Num: [368/600]
Discriminator Loss: 0.5440, Generator Loss: 2.7343
D(x): 0.8203, D(G(z)): 0.1516
Epoch: [10/20], Batch Num: [369/600]
Discriminator Loss: 0.4432, Generator Loss: 3.1645
D(x): 0.8715, D(G(z)): 0.1556
Epoch: [10/20], Batch Num: [370/600]
Discriminator Loss: 0.5157, Generator Loss: 2.8242
D(x): 0.7984, D(G(z)): 0.1143
Epoch: [10/20], Batch Num: [371/600]
Discriminator Loss: 0.4022, Generator Loss: 2.7873
D(x): 0.8866, D(G(z)): 0.1558
Epoch: [10/20], Batch Num: [372/600]
Discriminator Loss: 0.5417, Generator Loss: 2.8283
D(x): 0.8575, D(G(z)): 0.1924
Epoch: [10/20], Batch Num: [373/600]
Discriminator Loss: 0.4804, Generator Loss: 3.3553
D(x): 0.8787, D(G(z)): 0.1651
Epoch: [10/20], Batch Num: [374/600]
Discriminator Loss: 0.4828, Generator Loss: 3.1064
D(x): 0.8224, D(G(z)): 0.0962
Epoch: [10/20], Batch Num: [375/600]
Discriminator Loss: 0.4742, Generator Loss: 2.8471
D(x): 0.8325, D(G(z)): 0.1137
Epoch: [10/20], Batch Num: [376/600]
Discriminator Loss: 0.3541, Generator Loss: 2.5068
D(x): 0.8826, D(G(z)): 0.0972
Epoch: [10/20], Batch Num: [377/600]
Discriminator Loss: 0.4465, Generator Loss: 2.4788
D(x): 0.8764, D(G(z)): 0.1797
Epoch: [10/20], Batch Num: [378/600]
Discriminator Loss: 0.5416, Generator Loss: 2.8263
D(x): 0.8747, D(G(z)): 0.2092
Epoch: [10/20], Batch Num: [379/600]
Discriminator Loss: 0.3763, Generator Loss: 3.2925
D(x): 0.9073, D(G(z)): 0.1618
Epoch: [10/20], Batch Num: [380/600]
Discriminator Loss: 0.3389, Generator Loss: 3.8317
D(x): 0.8605, D(G(z)): 0.0844
Epoch: [10/20], Batch Num: [381/600]
Discriminator Loss: 0.4344, Generator Loss: 3.4853
D(x): 0.8573, D(G(z)): 0.0915
Epoch: [10/20], Batch Num: [382/600]
Discriminator Loss: 0.4191, Generator Loss: 3.1274
D(x): 0.8482, D(G(z)): 0.0870
Epoch: [10/20], Batch Num: [383/600]
Discriminator Loss: 0.4655, Generator Loss: 2.9089
D(x): 0.8590, D(G(z)): 0.1350
Epoch: [10/20], Batch Num: [384/600]
Discriminator Loss: 0.5210, Generator Loss: 3.0643
D(x): 0.8758, D(G(z)): 0.1643
Epoch: [10/20], Batch Num: [385/600]
Discriminator Loss: 0.5109, Generator Loss: 3.2161
D(x): 0.8493, D(G(z)): 0.1499
Epoch: [10/20], Batch Num: [386/600]
Discriminator Loss: 0.4489, Generator Loss: 2.9499
D(x): 0.8571, D(G(z)): 0.1186
Epoch: [10/20], Batch Num: [387/600]
Discriminator Loss: 0.3853, Generator Loss: 3.2078
D(x): 0.8992, D(G(z)): 0.1241
Epoch: [10/20], Batch Num: [388/600]
Discriminator Loss: 0.3863, Generator Loss: 3.7743
D(x): 0.9135, D(G(z)): 0.1491
Epoch: [10/20], Batch Num: [389/600]
Discriminator Loss: 0.3755, Generator Loss: 3.9790
D(x): 0.8563, D(G(z)): 0.0826
Epoch: [10/20], Batch Num: [390/600]
Discriminator Loss: 0.4404, Generator Loss: 3.5613
D(x): 0.8487, D(G(z)): 0.0790
Epoch: [10/20], Batch Num: [391/600]
Discriminator Loss: 0.3470, Generator Loss: 3.0550
D(x): 0.8811, D(G(z)): 0.0792
Epoch: [10/20], Batch Num: [392/600]
Discriminator Loss: 0.3485, Generator Loss: 2.9632
D(x): 0.9215, D(G(z)): 0.1374
Epoch: [10/20], Batch Num: [393/600]
Discriminator Loss: 0.3818, Generator Loss: 2.9769
D(x): 0.8854, D(G(z)): 0.1199
Epoch: [10/20], Batch Num: [394/600]
Discriminator Loss: 0.3785, Generator Loss: 3.3837
D(x): 0.8999, D(G(z)): 0.1329
Epoch: [10/20], Batch Num: [395/600]
Discriminator Loss: 0.2784, Generator Loss: 3.5462
D(x): 0.9254, D(G(z)): 0.1169
Epoch: [10/20], Batch Num: [396/600]
Discriminator Loss: 0.4305, Generator Loss: 3.6894
D(x): 0.8464, D(G(z)): 0.0821
Epoch: [10/20], Batch Num: [397/600]
Discriminator Loss: 0.4209, Generator Loss: 3.6547
D(x): 0.8789, D(G(z)): 0.1347
Epoch: [10/20], Batch Num: [398/600]
Discriminator Loss: 0.5320, Generator Loss: 3.1062
D(x): 0.8383, D(G(z)): 0.1124
Epoch: [10/20], Batch Num: [399/600]
Discriminator Loss: 0.3916, Generator Loss: 2.7102
D(x): 0.8592, D(G(z)): 0.0884
Epoch: 10, Batch Num: [400/600]

Epoch: [10/20], Batch Num: [400/600]
Discriminator Loss: 0.6273, Generator Loss: 3.3396
D(x): 0.8991, D(G(z)): 0.2415
Epoch: [10/20], Batch Num: [401/600]
Discriminator Loss: 0.3870, Generator Loss: 3.8688
D(x): 0.9005, D(G(z)): 0.1410
Epoch: [10/20], Batch Num: [402/600]
Discriminator Loss: 0.3022, Generator Loss: 3.8936
D(x): 0.8913, D(G(z)): 0.0633
Epoch: [10/20], Batch Num: [403/600]
Discriminator Loss: 0.4720, Generator Loss: 3.8352
D(x): 0.8188, D(G(z)): 0.0692
Epoch: [10/20], Batch Num: [404/600]
Discriminator Loss: 0.4633, Generator Loss: 2.7841
D(x): 0.8577, D(G(z)): 0.1060
Epoch: [10/20], Batch Num: [405/600]
Discriminator Loss: 0.4259, Generator Loss: 2.5922
D(x): 0.9162, D(G(z)): 0.1521
Epoch: [10/20], Batch Num: [406/600]
Discriminator Loss: 0.4761, Generator Loss: 3.3296
D(x): 0.9076, D(G(z)): 0.1979
Epoch: [10/20], Batch Num: [407/600]
Discriminator Loss: 0.3427, Generator Loss: 3.9179
D(x): 0.8891, D(G(z)): 0.1175
Epoch: [10/20], Batch Num: [408/600]
Discriminator Loss: 0.4280, Generator Loss: 3.9023
D(x): 0.8540, D(G(z)): 0.1030
Epoch: [10/20], Batch Num: [409/600]
Discriminator Loss: 0.3813, Generator Loss: 3.8866
D(x): 0.8390, D(G(z)): 0.0639
Epoch: [10/20], Batch Num: [410/600]
Discriminator Loss: 0.3434, Generator Loss: 2.9166
D(x): 0.8703, D(G(z)): 0.0870
Epoch: [10/20], Batch Num: [411/600]
Discriminator Loss: 0.5001, Generator Loss: 2.6400
D(x): 0.8373, D(G(z)): 0.1106
Epoch: [10/20], Batch Num: [412/600]
Discriminator Loss: 0.4381, Generator Loss: 2.2298
D(x): 0.9008, D(G(z)): 0.1666
Epoch: [10/20], Batch Num: [413/600]
Discriminator Loss: 0.4841, Generator Loss: 2.7160
D(x): 0.9272, D(G(z)): 0.2298
Epoch: [10/20], Batch Num: [414/600]
Discriminator Loss: 0.4578, Generator Loss: 3.8909
D(x): 0.9123, D(G(z)): 0.2005
Epoch: [10/20], Batch Num: [415/600]
Discriminator Loss: 0.3886, Generator Loss: 4.0991
D(x): 0.8466, D(G(z)): 0.0767
Epoch: [10/20], Batch Num: [416/600]
Discriminator Loss: 0.4749, Generator Loss: 4.1034
D(x): 0.8362, D(G(z)): 0.0521
Epoch: [10/20], Batch Num: [417/600]
Discriminator Loss: 0.3251, Generator Loss: 3.4337
D(x): 0.8781, D(G(z)): 0.0626
Epoch: [10/20], Batch Num: [418/600]
Discriminator Loss: 0.4618, Generator Loss: 2.8182
D(x): 0.8700, D(G(z)): 0.1244
Epoch: [10/20], Batch Num: [419/600]
Discriminator Loss: 0.5098, Generator Loss: 2.2173
D(x): 0.8726, D(G(z)): 0.1843
Epoch: [10/20], Batch Num: [420/600]
Discriminator Loss: 0.3726, Generator Loss: 2.5866
D(x): 0.9252, D(G(z)): 0.1883
Epoch: [10/20], Batch Num: [421/600]
Discriminator Loss: 0.5282, Generator Loss: 2.9198
D(x): 0.8622, D(G(z)): 0.1709
Epoch: [10/20], Batch Num: [422/600]
Discriminator Loss: 0.3058, Generator Loss: 3.3081
D(x): 0.9078, D(G(z)): 0.1348
Epoch: [10/20], Batch Num: [423/600]
Discriminator Loss: 0.3241, Generator Loss: 3.3787
D(x): 0.8740, D(G(z)): 0.0787
Epoch: [10/20], Batch Num: [424/600]
Discriminator Loss: 0.5567, Generator Loss: 2.9086
D(x): 0.7915, D(G(z)): 0.0913
Epoch: [10/20], Batch Num: [425/600]
Discriminator Loss: 0.4596, Generator Loss: 2.9729
D(x): 0.8822, D(G(z)): 0.1529
Epoch: [10/20], Batch Num: [426/600]
Discriminator Loss: 0.4178, Generator Loss: 3.0715
D(x): 0.9051, D(G(z)): 0.1799
Epoch: [10/20], Batch Num: [427/600]
Discriminator Loss: 0.4363, Generator Loss: 3.1882
D(x): 0.8583, D(G(z)): 0.1181
Epoch: [10/20], Batch Num: [428/600]
Discriminator Loss: 0.3753, Generator Loss: 3.3597
D(x): 0.8967, D(G(z)): 0.1175
Epoch: [10/20], Batch Num: [429/600]
Discriminator Loss: 0.3782, Generator Loss: 3.2014
D(x): 0.8549, D(G(z)): 0.0836
Epoch: [10/20], Batch Num: [430/600]
Discriminator Loss: 0.5838, Generator Loss: 2.4601
D(x): 0.8375, D(G(z)): 0.1421
Epoch: [10/20], Batch Num: [431/600]
Discriminator Loss: 0.4984, Generator Loss: 2.7000
D(x): 0.8755, D(G(z)): 0.1916
Epoch: [10/20], Batch Num: [432/600]
Discriminator Loss: 0.5479, Generator Loss: 2.9564
D(x): 0.8716, D(G(z)): 0.2200
Epoch: [10/20], Batch Num: [433/600]
Discriminator Loss: 0.5215, Generator Loss: 2.8331
D(x): 0.8285, D(G(z)): 0.1619
Epoch: [10/20], Batch Num: [434/600]
Discriminator Loss: 0.5698, Generator Loss: 2.6763
D(x): 0.8035, D(G(z)): 0.1200
Epoch: [10/20], Batch Num: [435/600]
Discriminator Loss: 0.4464, Generator Loss: 2.5043
D(x): 0.8715, D(G(z)): 0.1478
Epoch: [10/20], Batch Num: [436/600]
Discriminator Loss: 0.3623, Generator Loss: 2.8889
D(x): 0.8988, D(G(z)): 0.1715
Epoch: [10/20], Batch Num: [437/600]
Discriminator Loss: 0.3517, Generator Loss: 3.2384
D(x): 0.8924, D(G(z)): 0.1479
Epoch: [10/20], Batch Num: [438/600]
Discriminator Loss: 0.4409, Generator Loss: 2.8969
D(x): 0.8276, D(G(z)): 0.1075
Epoch: [10/20], Batch Num: [439/600]
Discriminator Loss: 0.5168, Generator Loss: 2.7048
D(x): 0.8191, D(G(z)): 0.1480
Epoch: [10/20], Batch Num: [440/600]
Discriminator Loss: 0.5318, Generator Loss: 2.4245
D(x): 0.8188, D(G(z)): 0.1596
Epoch: [10/20], Batch Num: [441/600]
Discriminator Loss: 0.4755, Generator Loss: 2.4742
D(x): 0.8839, D(G(z)): 0.1962
Epoch: [10/20], Batch Num: [442/600]
Discriminator Loss: 0.4280, Generator Loss: 2.9683
D(x): 0.8953, D(G(z)): 0.1909
Epoch: [10/20], Batch Num: [443/600]
Discriminator Loss: 0.3904, Generator Loss: 3.4802
D(x): 0.8752, D(G(z)): 0.1448
Epoch: [10/20], Batch Num: [444/600]
Discriminator Loss: 0.6371, Generator Loss: 3.0722
D(x): 0.7991, D(G(z)): 0.1294
Epoch: [10/20], Batch Num: [445/600]
Discriminator Loss: 0.5438, Generator Loss: 2.8852
D(x): 0.8189, D(G(z)): 0.1289
Epoch: [10/20], Batch Num: [446/600]
Discriminator Loss: 0.4686, Generator Loss: 2.7632
D(x): 0.8634, D(G(z)): 0.1465
Epoch: [10/20], Batch Num: [447/600]
Discriminator Loss: 0.5067, Generator Loss: 2.2435
D(x): 0.8419, D(G(z)): 0.1325
Epoch: [10/20], Batch Num: [448/600]
Discriminator Loss: 0.4290, Generator Loss: 2.5981
D(x): 0.9127, D(G(z)): 0.1887
Epoch: [10/20], Batch Num: [449/600]
Discriminator Loss: 0.4022, Generator Loss: 3.3957
D(x): 0.9074, D(G(z)): 0.1783
Epoch: [10/20], Batch Num: [450/600]
Discriminator Loss: 0.5473, Generator Loss: 3.3772
D(x): 0.8322, D(G(z)): 0.1101
Epoch: [10/20], Batch Num: [451/600]
Discriminator Loss: 0.7047, Generator Loss: 2.9260
D(x): 0.7612, D(G(z)): 0.1087
Epoch: [10/20], Batch Num: [452/600]
Discriminator Loss: 0.4274, Generator Loss: 2.4812
D(x): 0.8524, D(G(z)): 0.0952
Epoch: [10/20], Batch Num: [453/600]
Discriminator Loss: 0.5235, Generator Loss: 2.2516
D(x): 0.9091, D(G(z)): 0.2128
Epoch: [10/20], Batch Num: [454/600]
Discriminator Loss: 0.5234, Generator Loss: 2.6152
D(x): 0.8821, D(G(z)): 0.2080
Epoch: [10/20], Batch Num: [455/600]
Discriminator Loss: 0.4286, Generator Loss: 3.2297
D(x): 0.8679, D(G(z)): 0.1323
Epoch: [10/20], Batch Num: [456/600]
Discriminator Loss: 0.4875, Generator Loss: 3.3338
D(x): 0.8159, D(G(z)): 0.0948
Epoch: [10/20], Batch Num: [457/600]
Discriminator Loss: 0.4002, Generator Loss: 3.1396
D(x): 0.8621, D(G(z)): 0.0836
Epoch: [10/20], Batch Num: [458/600]
Discriminator Loss: 0.3621, Generator Loss: 2.5289
D(x): 0.8632, D(G(z)): 0.0894
Epoch: [10/20], Batch Num: [459/600]
Discriminator Loss: 0.3317, Generator Loss: 2.1222
D(x): 0.8870, D(G(z)): 0.1317
Epoch: [10/20], Batch Num: [460/600]
Discriminator Loss: 0.4563, Generator Loss: 2.4681
D(x): 0.9039, D(G(z)): 0.2045
Epoch: [10/20], Batch Num: [461/600]
Discriminator Loss: 0.5143, Generator Loss: 2.8681
D(x): 0.8564, D(G(z)): 0.1901
Epoch: [10/20], Batch Num: [462/600]
Discriminator Loss: 0.4182, Generator Loss: 3.0490
D(x): 0.8579, D(G(z)): 0.1344
Epoch: [10/20], Batch Num: [463/600]
Discriminator Loss: 0.4270, Generator Loss: 3.6424
D(x): 0.8584, D(G(z)): 0.0917
Epoch: [10/20], Batch Num: [464/600]
Discriminator Loss: 0.3630, Generator Loss: 3.3017
D(x): 0.8502, D(G(z)): 0.0725
Epoch: [10/20], Batch Num: [465/600]
Discriminator Loss: 0.3298, Generator Loss: 3.0457
D(x): 0.8831, D(G(z)): 0.0971
Epoch: [10/20], Batch Num: [466/600]
Discriminator Loss: 0.3527, Generator Loss: 2.7479
D(x): 0.8666, D(G(z)): 0.0917
Epoch: [10/20], Batch Num: [467/600]
Discriminator Loss: 0.3458, Generator Loss: 2.8101
D(x): 0.9378, D(G(z)): 0.1652
Epoch: [10/20], Batch Num: [468/600]
Discriminator Loss: 0.3865, Generator Loss: 2.9627
D(x): 0.9047, D(G(z)): 0.1467
Epoch: [10/20], Batch Num: [469/600]
Discriminator Loss: 0.2927, Generator Loss: 3.3644
D(x): 0.9214, D(G(z)): 0.1253
Epoch: [10/20], Batch Num: [470/600]
Discriminator Loss: 0.3088, Generator Loss: 3.9814
D(x): 0.9000, D(G(z)): 0.0683
Epoch: [10/20], Batch Num: [471/600]
Discriminator Loss: 0.4988, Generator Loss: 3.2573
D(x): 0.8237, D(G(z)): 0.0568
Epoch: [10/20], Batch Num: [472/600]
Discriminator Loss: 0.3095, Generator Loss: 2.9249
D(x): 0.9040, D(G(z)): 0.1106
Epoch: [10/20], Batch Num: [473/600]
Discriminator Loss: 0.3764, Generator Loss: 3.1511
D(x): 0.9004, D(G(z)): 0.1234
Epoch: [10/20], Batch Num: [474/600]
Discriminator Loss: 0.3771, Generator Loss: 3.1958
D(x): 0.8997, D(G(z)): 0.1242
Epoch: [10/20], Batch Num: [475/600]
Discriminator Loss: 0.3323, Generator Loss: 3.4922
D(x): 0.9052, D(G(z)): 0.1150
Epoch: [10/20], Batch Num: [476/600]
Discriminator Loss: 0.2767, Generator Loss: 3.6087
D(x): 0.9062, D(G(z)): 0.0862
Epoch: [10/20], Batch Num: [477/600]
Discriminator Loss: 0.3462, Generator Loss: 3.6164
D(x): 0.8604, D(G(z)): 0.0691
Epoch: [10/20], Batch Num: [478/600]
Discriminator Loss: 0.4657, Generator Loss: 2.7024
D(x): 0.8458, D(G(z)): 0.0848
Epoch: [10/20], Batch Num: [479/600]
Discriminator Loss: 0.4163, Generator Loss: 2.8619
D(x): 0.9203, D(G(z)): 0.1446
Epoch: [10/20], Batch Num: [480/600]
Discriminator Loss: 0.3869, Generator Loss: 2.9619
D(x): 0.9330, D(G(z)): 0.1715
Epoch: [10/20], Batch Num: [481/600]
Discriminator Loss: 0.5558, Generator Loss: 3.7713
D(x): 0.8933, D(G(z)): 0.1605
Epoch: [10/20], Batch Num: [482/600]
Discriminator Loss: 0.5138, Generator Loss: 4.1842
D(x): 0.8201, D(G(z)): 0.0537
Epoch: [10/20], Batch Num: [483/600]
Discriminator Loss: 0.6488, Generator Loss: 3.2338
D(x): 0.8120, D(G(z)): 0.0929
Epoch: [10/20], Batch Num: [484/600]
Discriminator Loss: 0.4060, Generator Loss: 2.1814
D(x): 0.8731, D(G(z)): 0.1179
Epoch: [10/20], Batch Num: [485/600]
Discriminator Loss: 0.4235, Generator Loss: 2.6618
D(x): 0.9312, D(G(z)): 0.2104
Epoch: [10/20], Batch Num: [486/600]
Discriminator Loss: 0.5407, Generator Loss: 3.2453
D(x): 0.8827, D(G(z)): 0.2049
Epoch: [10/20], Batch Num: [487/600]
Discriminator Loss: 0.3713, Generator Loss: 3.5899
D(x): 0.8625, D(G(z)): 0.0818
Epoch: [10/20], Batch Num: [488/600]
Discriminator Loss: 0.4512, Generator Loss: 2.8993
D(x): 0.8316, D(G(z)): 0.0842
Epoch: [10/20], Batch Num: [489/600]
Discriminator Loss: 0.4296, Generator Loss: 2.9825
D(x): 0.8525, D(G(z)): 0.1025
Epoch: [10/20], Batch Num: [490/600]
Discriminator Loss: 0.4046, Generator Loss: 2.6368
D(x): 0.8729, D(G(z)): 0.1259
Epoch: [10/20], Batch Num: [491/600]
Discriminator Loss: 0.6222, Generator Loss: 2.5567
D(x): 0.8455, D(G(z)): 0.1746
Epoch: [10/20], Batch Num: [492/600]
Discriminator Loss: 0.5962, Generator Loss: 2.3642
D(x): 0.8256, D(G(z)): 0.1694
Epoch: [10/20], Batch Num: [493/600]
Discriminator Loss: 0.5674, Generator Loss: 2.3846
D(x): 0.8360, D(G(z)): 0.1693
Epoch: [10/20], Batch Num: [494/600]
Discriminator Loss: 0.5333, Generator Loss: 2.6911
D(x): 0.8918, D(G(z)): 0.2071
Epoch: [10/20], Batch Num: [495/600]
Discriminator Loss: 0.4911, Generator Loss: 3.2285
D(x): 0.8561, D(G(z)): 0.1608
Epoch: [10/20], Batch Num: [496/600]
Discriminator Loss: 0.4272, Generator Loss: 3.1249
D(x): 0.8412, D(G(z)): 0.0919
Epoch: [10/20], Batch Num: [497/600]
Discriminator Loss: 0.4840, Generator Loss: 2.5778
D(x): 0.7869, D(G(z)): 0.0838
Epoch: [10/20], Batch Num: [498/600]
Discriminator Loss: 0.4551, Generator Loss: 2.3962
D(x): 0.8520, D(G(z)): 0.1233
Epoch: [10/20], Batch Num: [499/600]
Discriminator Loss: 0.4383, Generator Loss: 2.1737
D(x): 0.8776, D(G(z)): 0.1626
Epoch: 10, Batch Num: [500/600]

Epoch: [10/20], Batch Num: [500/600]
Discriminator Loss: 0.6153, Generator Loss: 2.0950
D(x): 0.8448, D(G(z)): 0.2131
Epoch: [10/20], Batch Num: [501/600]
Discriminator Loss: 0.4250, Generator Loss: 2.2784
D(x): 0.8932, D(G(z)): 0.1892
Epoch: [10/20], Batch Num: [502/600]
Discriminator Loss: 0.5050, Generator Loss: 2.6096
D(x): 0.8633, D(G(z)): 0.1946
Epoch: [10/20], Batch Num: [503/600]
Discriminator Loss: 0.3811, Generator Loss: 2.7976
D(x): 0.8607, D(G(z)): 0.1143
Epoch: [10/20], Batch Num: [504/600]
Discriminator Loss: 0.4021, Generator Loss: 2.6738
D(x): 0.8426, D(G(z)): 0.1043
Epoch: [10/20], Batch Num: [505/600]
Discriminator Loss: 0.5294, Generator Loss: 2.7526
D(x): 0.8040, D(G(z)): 0.1348
Epoch: [10/20], Batch Num: [506/600]
Discriminator Loss: 0.4828, Generator Loss: 2.5366
D(x): 0.8277, D(G(z)): 0.1383
Epoch: [10/20], Batch Num: [507/600]
Discriminator Loss: 0.4655, Generator Loss: 2.2651
D(x): 0.8649, D(G(z)): 0.1734
Epoch: [10/20], Batch Num: [508/600]
Discriminator Loss: 0.4391, Generator Loss: 2.5281
D(x): 0.8618, D(G(z)): 0.1299
Epoch: [10/20], Batch Num: [509/600]
Discriminator Loss: 0.4147, Generator Loss: 2.5571
D(x): 0.8868, D(G(z)): 0.1707
Epoch: [10/20], Batch Num: [510/600]
Discriminator Loss: 0.2614, Generator Loss: 2.9292
D(x): 0.9108, D(G(z)): 0.1024
Epoch: [10/20], Batch Num: [511/600]
Discriminator Loss: 0.3663, Generator Loss: 3.0702
D(x): 0.8932, D(G(z)): 0.1313
Epoch: [10/20], Batch Num: [512/600]
Discriminator Loss: 0.4219, Generator Loss: 3.4198
D(x): 0.8669, D(G(z)): 0.1169
Epoch: [10/20], Batch Num: [513/600]
Discriminator Loss: 0.3306, Generator Loss: 3.0688
D(x): 0.8751, D(G(z)): 0.0858
Epoch: [10/20], Batch Num: [514/600]
Discriminator Loss: 0.3492, Generator Loss: 2.9412
D(x): 0.8983, D(G(z)): 0.1040
Epoch: [10/20], Batch Num: [515/600]
Discriminator Loss: 0.4926, Generator Loss: 2.7226
D(x): 0.8319, D(G(z)): 0.1076
Epoch: [10/20], Batch Num: [516/600]
Discriminator Loss: 0.4882, Generator Loss: 2.2931
D(x): 0.8390, D(G(z)): 0.1428
Epoch: [10/20], Batch Num: [517/600]
Discriminator Loss: 0.4284, Generator Loss: 2.3959
D(x): 0.9174, D(G(z)): 0.1990
Epoch: [10/20], Batch Num: [518/600]
Discriminator Loss: 0.4061, Generator Loss: 2.9648
D(x): 0.9118, D(G(z)): 0.1783
Epoch: [10/20], Batch Num: [519/600]
Discriminator Loss: 0.4276, Generator Loss: 2.8834
D(x): 0.8578, D(G(z)): 0.1113
Epoch: [10/20], Batch Num: [520/600]
Discriminator Loss: 0.3891, Generator Loss: 2.9709
D(x): 0.8543, D(G(z)): 0.0888
Epoch: [10/20], Batch Num: [521/600]
Discriminator Loss: 0.4899, Generator Loss: 3.0844
D(x): 0.8730, D(G(z)): 0.1615
Epoch: [10/20], Batch Num: [522/600]
Discriminator Loss: 0.4214, Generator Loss: 2.9130
D(x): 0.8436, D(G(z)): 0.0985
Epoch: [10/20], Batch Num: [523/600]
Discriminator Loss: 0.5530, Generator Loss: 2.5170
D(x): 0.8364, D(G(z)): 0.1373
Epoch: [10/20], Batch Num: [524/600]
Discriminator Loss: 0.4270, Generator Loss: 2.4156
D(x): 0.8693, D(G(z)): 0.1302
Epoch: [10/20], Batch Num: [525/600]
Discriminator Loss: 0.3627, Generator Loss: 2.7434
D(x): 0.9327, D(G(z)): 0.1849
Epoch: [10/20], Batch Num: [526/600]
Discriminator Loss: 0.3585, Generator Loss: 3.3382
D(x): 0.9268, D(G(z)): 0.1515
Epoch: [10/20], Batch Num: [527/600]
Discriminator Loss: 0.4835, Generator Loss: 3.6982
D(x): 0.8444, D(G(z)): 0.0937
Epoch: [10/20], Batch Num: [528/600]
Discriminator Loss: 0.3200, Generator Loss: 3.4691
D(x): 0.8969, D(G(z)): 0.1077
Epoch: [10/20], Batch Num: [529/600]
Discriminator Loss: 0.5285, Generator Loss: 3.1502
D(x): 0.7873, D(G(z)): 0.0700
Epoch: [10/20], Batch Num: [530/600]
Discriminator Loss: 0.5252, Generator Loss: 2.3589
D(x): 0.8367, D(G(z)): 0.1238
Epoch: [10/20], Batch Num: [531/600]
Discriminator Loss: 0.4372, Generator Loss: 2.1543
D(x): 0.8802, D(G(z)): 0.1548
Epoch: [10/20], Batch Num: [532/600]
Discriminator Loss: 0.5101, Generator Loss: 2.5801
D(x): 0.9292, D(G(z)): 0.2514
Epoch: [10/20], Batch Num: [533/600]
Discriminator Loss: 0.4564, Generator Loss: 2.7646
D(x): 0.8535, D(G(z)): 0.1460
Epoch: [10/20], Batch Num: [534/600]
Discriminator Loss: 0.4225, Generator Loss: 3.0637
D(x): 0.8549, D(G(z)): 0.1123
Epoch: [10/20], Batch Num: [535/600]
Discriminator Loss: 0.4881, Generator Loss: 3.1423
D(x): 0.8202, D(G(z)): 0.1273
Epoch: [10/20], Batch Num: [536/600]
Discriminator Loss: 0.3653, Generator Loss: 2.8640
D(x): 0.8641, D(G(z)): 0.1062
Epoch: [10/20], Batch Num: [537/600]
Discriminator Loss: 0.4648, Generator Loss: 2.5912
D(x): 0.8604, D(G(z)): 0.1311
Epoch: [10/20], Batch Num: [538/600]
Discriminator Loss: 0.4637, Generator Loss: 2.9119
D(x): 0.9168, D(G(z)): 0.1746
Epoch: [10/20], Batch Num: [539/600]
Discriminator Loss: 0.4316, Generator Loss: 3.5152
D(x): 0.8980, D(G(z)): 0.1471
Epoch: [10/20], Batch Num: [540/600]
Discriminator Loss: 0.5486, Generator Loss: 3.4432
D(x): 0.8198, D(G(z)): 0.0957
Epoch: [10/20], Batch Num: [541/600]
Discriminator Loss: 0.4242, Generator Loss: 3.3132
D(x): 0.8618, D(G(z)): 0.1177
Epoch: [10/20], Batch Num: [542/600]
Discriminator Loss: 0.4859, Generator Loss: 3.1224
D(x): 0.8181, D(G(z)): 0.1009
Epoch: [10/20], Batch Num: [543/600]
Discriminator Loss: 0.5686, Generator Loss: 2.3863
D(x): 0.8032, D(G(z)): 0.1302
Epoch: [10/20], Batch Num: [544/600]
Discriminator Loss: 0.4335, Generator Loss: 2.4341
D(x): 0.8877, D(G(z)): 0.1636
Epoch: [10/20], Batch Num: [545/600]
Discriminator Loss: 0.4505, Generator Loss: 2.3992
D(x): 0.9223, D(G(z)): 0.2285
Epoch: [10/20], Batch Num: [546/600]
Discriminator Loss: 0.4922, Generator Loss: 2.9146
D(x): 0.8875, D(G(z)): 0.2048
Epoch: [10/20], Batch Num: [547/600]
Discriminator Loss: 0.5244, Generator Loss: 3.6019
D(x): 0.8417, D(G(z)): 0.1358
Epoch: [10/20], Batch Num: [548/600]
Discriminator Loss: 0.3680, Generator Loss: 3.5600
D(x): 0.8583, D(G(z)): 0.0677
Epoch: [10/20], Batch Num: [549/600]
Discriminator Loss: 0.5610, Generator Loss: 3.1475
D(x): 0.7761, D(G(z)): 0.0608
Epoch: [10/20], Batch Num: [550/600]
Discriminator Loss: 0.5499, Generator Loss: 2.4227
D(x): 0.8328, D(G(z)): 0.1428
Epoch: [10/20], Batch Num: [551/600]
Discriminator Loss: 0.5671, Generator Loss: 2.3166
D(x): 0.8581, D(G(z)): 0.1982
Epoch: [10/20], Batch Num: [552/600]
Discriminator Loss: 0.4300, Generator Loss: 2.0293
D(x): 0.8989, D(G(z)): 0.1971
Epoch: [10/20], Batch Num: [553/600]
Discriminator Loss: 0.4117, Generator Loss: 2.4256
D(x): 0.9096, D(G(z)): 0.1915
Epoch: [10/20], Batch Num: [554/600]
Discriminator Loss: 0.4614, Generator Loss: 2.7346
D(x): 0.8778, D(G(z)): 0.1953
Epoch: [10/20], Batch Num: [555/600]
Discriminator Loss: 0.3779, Generator Loss: 3.2588
D(x): 0.9073, D(G(z)): 0.1467
Epoch: [10/20], Batch Num: [556/600]
Discriminator Loss: 0.3902, Generator Loss: 3.3653
D(x): 0.8440, D(G(z)): 0.0686
Epoch: [10/20], Batch Num: [557/600]
Discriminator Loss: 0.4560, Generator Loss: 3.1233
D(x): 0.8293, D(G(z)): 0.0585
Epoch: [10/20], Batch Num: [558/600]
Discriminator Loss: 0.4762, Generator Loss: 3.0143
D(x): 0.8438, D(G(z)): 0.1073
Epoch: [10/20], Batch Num: [559/600]
Discriminator Loss: 0.5134, Generator Loss: 2.6077
D(x): 0.8571, D(G(z)): 0.1716
Epoch: [10/20], Batch Num: [560/600]
Discriminator Loss: 0.4156, Generator Loss: 2.6925
D(x): 0.9015, D(G(z)): 0.1760
Epoch: [10/20], Batch Num: [561/600]
Discriminator Loss: 0.5003, Generator Loss: 3.1334
D(x): 0.8801, D(G(z)): 0.1877
Epoch: [10/20], Batch Num: [562/600]
Discriminator Loss: 0.4877, Generator Loss: 2.7888
D(x): 0.8057, D(G(z)): 0.1017
Epoch: [10/20], Batch Num: [563/600]
Discriminator Loss: 0.3068, Generator Loss: 2.6433
D(x): 0.8969, D(G(z)): 0.1222
Epoch: [10/20], Batch Num: [564/600]
Discriminator Loss: 0.4569, Generator Loss: 2.9410
D(x): 0.8813, D(G(z)): 0.1656
Epoch: [10/20], Batch Num: [565/600]
Discriminator Loss: 0.4175, Generator Loss: 2.6495
D(x): 0.8502, D(G(z)): 0.1259
Epoch: [10/20], Batch Num: [566/600]
Discriminator Loss: 0.4008, Generator Loss: 2.9919
D(x): 0.8793, D(G(z)): 0.1232
Epoch: [10/20], Batch Num: [567/600]
Discriminator Loss: 0.3474, Generator Loss: 3.0554
D(x): 0.8955, D(G(z)): 0.1229
Epoch: [10/20], Batch Num: [568/600]
Discriminator Loss: 0.2934, Generator Loss: 3.2511
D(x): 0.9149, D(G(z)): 0.1134
Epoch: [10/20], Batch Num: [569/600]
Discriminator Loss: 0.4873, Generator Loss: 3.1022
D(x): 0.8362, D(G(z)): 0.1071
Epoch: [10/20], Batch Num: [570/600]
Discriminator Loss: 0.3420, Generator Loss: 2.8288
D(x): 0.8735, D(G(z)): 0.1053
Epoch: [10/20], Batch Num: [571/600]
Discriminator Loss: 0.3709, Generator Loss: 2.5217
D(x): 0.9062, D(G(z)): 0.1454
Epoch: [10/20], Batch Num: [572/600]
Discriminator Loss: 0.4116, Generator Loss: 3.1907
D(x): 0.9092, D(G(z)): 0.1530
Epoch: [10/20], Batch Num: [573/600]
Discriminator Loss: 0.4019, Generator Loss: 3.2203
D(x): 0.8950, D(G(z)): 0.1135
Epoch: [10/20], Batch Num: [574/600]
Discriminator Loss: 0.4667, Generator Loss: 3.5899
D(x): 0.9107, D(G(z)): 0.1400
Epoch: [10/20], Batch Num: [575/600]
Discriminator Loss: 0.4222, Generator Loss: 3.6144
D(x): 0.8453, D(G(z)): 0.0823
Epoch: [10/20], Batch Num: [576/600]
Discriminator Loss: 0.3774, Generator Loss: 3.5247
D(x): 0.8817, D(G(z)): 0.0826
Epoch: [10/20], Batch Num: [577/600]
Discriminator Loss: 0.6125, Generator Loss: 2.9803
D(x): 0.8072, D(G(z)): 0.1238
Epoch: [10/20], Batch Num: [578/600]
Discriminator Loss: 0.3380, Generator Loss: 2.6894
D(x): 0.9269, D(G(z)): 0.1320
Epoch: [10/20], Batch Num: [579/600]
Discriminator Loss: 0.3498, Generator Loss: 2.7774
D(x): 0.9232, D(G(z)): 0.1528
Epoch: [10/20], Batch Num: [580/600]
Discriminator Loss: 0.2903, Generator Loss: 3.0878
D(x): 0.9364, D(G(z)): 0.1417
Epoch: [10/20], Batch Num: [581/600]
Discriminator Loss: 0.3624, Generator Loss: 3.3900
D(x): 0.8779, D(G(z)): 0.1027
Epoch: [10/20], Batch Num: [582/600]
Discriminator Loss: 0.4121, Generator Loss: 3.3243
D(x): 0.8599, D(G(z)): 0.0909
Epoch: [10/20], Batch Num: [583/600]
Discriminator Loss: 0.5905, Generator Loss: 2.9457
D(x): 0.8270, D(G(z)): 0.0888
Epoch: [10/20], Batch Num: [584/600]
Discriminator Loss: 0.3864, Generator Loss: 2.5843
D(x): 0.8744, D(G(z)): 0.1210
Epoch: [10/20], Batch Num: [585/600]
Discriminator Loss: 0.5431, Generator Loss: 2.3393
D(x): 0.8574, D(G(z)): 0.1729
Epoch: [10/20], Batch Num: [586/600]
Discriminator Loss: 0.6237, Generator Loss: 2.7136
D(x): 0.8670, D(G(z)): 0.2300
Epoch: [10/20], Batch Num: [587/600]
Discriminator Loss: 0.3772, Generator Loss: 3.1479
D(x): 0.9050, D(G(z)): 0.1633
Epoch: [10/20], Batch Num: [588/600]
Discriminator Loss: 0.4484, Generator Loss: 3.1278
D(x): 0.8403, D(G(z)): 0.0854
Epoch: [10/20], Batch Num: [589/600]
Discriminator Loss: 0.4114, Generator Loss: 2.8639
D(x): 0.8488, D(G(z)): 0.0877
Epoch: [10/20], Batch Num: [590/600]
Discriminator Loss: 0.4989, Generator Loss: 2.6529
D(x): 0.8310, D(G(z)): 0.1069
Epoch: [10/20], Batch Num: [591/600]
Discriminator Loss: 0.4862, Generator Loss: 2.4244
D(x): 0.8467, D(G(z)): 0.1413
Epoch: [10/20], Batch Num: [592/600]
Discriminator Loss: 0.5207, Generator Loss: 2.4539
D(x): 0.8864, D(G(z)): 0.2131
Epoch: [10/20], Batch Num: [593/600]
Discriminator Loss: 0.4509, Generator Loss: 3.0649
D(x): 0.9261, D(G(z)): 0.2064
Epoch: [10/20], Batch Num: [594/600]
Discriminator Loss: 0.5122, Generator Loss: 3.4127
D(x): 0.8611, D(G(z)): 0.1622
Epoch: [10/20], Batch Num: [595/600]
Discriminator Loss: 0.5255, Generator Loss: 3.0715
D(x): 0.7929, D(G(z)): 0.0754
Epoch: [10/20], Batch Num: [596/600]
Discriminator Loss: 0.2401, Generator Loss: 3.2859
D(x): 0.9227, D(G(z)): 0.0831
Epoch: [10/20], Batch Num: [597/600]
Discriminator Loss: 0.4282, Generator Loss: 3.4073
D(x): 0.9008, D(G(z)): 0.1370
Epoch: [10/20], Batch Num: [598/600]
Discriminator Loss: 0.5373, Generator Loss: 3.0487
D(x): 0.8504, D(G(z)): 0.1159
Epoch: [10/20], Batch Num: [599/600]
Discriminator Loss: 0.5597, Generator Loss: 2.7493
D(x): 0.8373, D(G(z)): 0.1360
Epoch: 11, Batch Num: [0/600]

Epoch: [11/20], Batch Num: [0/600]
Discriminator Loss: 0.5101, Generator Loss: 2.4329
D(x): 0.8479, D(G(z)): 0.1392
Epoch: [11/20], Batch Num: [1/600]
Discriminator Loss: 0.4859, Generator Loss: 2.4931
D(x): 0.9022, D(G(z)): 0.1576
Epoch: [11/20], Batch Num: [2/600]
Discriminator Loss: 0.4880, Generator Loss: 2.9613
D(x): 0.9011, D(G(z)): 0.1675
Epoch: [11/20], Batch Num: [3/600]
Discriminator Loss: 0.5126, Generator Loss: 3.1807
D(x): 0.8374, D(G(z)): 0.1248
Epoch: [11/20], Batch Num: [4/600]
Discriminator Loss: 0.4746, Generator Loss: 3.0375
D(x): 0.8632, D(G(z)): 0.1263
Epoch: [11/20], Batch Num: [5/600]
Discriminator Loss: 0.2841, Generator Loss: 3.0748
D(x): 0.8822, D(G(z)): 0.0894
Epoch: [11/20], Batch Num: [6/600]
Discriminator Loss: 0.4070, Generator Loss: 2.9893
D(x): 0.8676, D(G(z)): 0.1181
Epoch: [11/20], Batch Num: [7/600]
Discriminator Loss: 0.5682, Generator Loss: 2.8170
D(x): 0.8123, D(G(z)): 0.1138
Epoch: [11/20], Batch Num: [8/600]
Discriminator Loss: 0.4678, Generator Loss: 2.3399
D(x): 0.8485, D(G(z)): 0.1394
Epoch: [11/20], Batch Num: [9/600]
Discriminator Loss: 0.4876, Generator Loss: 2.3997
D(x): 0.8748, D(G(z)): 0.1746
Epoch: [11/20], Batch Num: [10/600]
Discriminator Loss: 0.4104, Generator Loss: 2.4747
D(x): 0.9000, D(G(z)): 0.1865
Epoch: [11/20], Batch Num: [11/600]
Discriminator Loss: 0.5617, Generator Loss: 2.9884
D(x): 0.8246, D(G(z)): 0.1771
Epoch: [11/20], Batch Num: [12/600]
Discriminator Loss: 0.5009, Generator Loss: 3.5519
D(x): 0.8384, D(G(z)): 0.1476
Epoch: [11/20], Batch Num: [13/600]
Discriminator Loss: 0.4266, Generator Loss: 2.9398
D(x): 0.8645, D(G(z)): 0.1123
Epoch: [11/20], Batch Num: [14/600]
Discriminator Loss: 0.3960, Generator Loss: 3.0444
D(x): 0.8456, D(G(z)): 0.1067
Epoch: [11/20], Batch Num: [15/600]
Discriminator Loss: 0.4352, Generator Loss: 2.9405
D(x): 0.8638, D(G(z)): 0.1076
Epoch: [11/20], Batch Num: [16/600]
Discriminator Loss: 0.4716, Generator Loss: 2.4451
D(x): 0.8492, D(G(z)): 0.1179
Epoch: [11/20], Batch Num: [17/600]
Discriminator Loss: 0.3261, Generator Loss: 2.9231
D(x): 0.9201, D(G(z)): 0.1540
Epoch: [11/20], Batch Num: [18/600]
Discriminator Loss: 0.4826, Generator Loss: 2.7666
D(x): 0.8727, D(G(z)): 0.1643
Epoch: [11/20], Batch Num: [19/600]
Discriminator Loss: 0.4514, Generator Loss: 2.8822
D(x): 0.8661, D(G(z)): 0.1242
Epoch: [11/20], Batch Num: [20/600]
Discriminator Loss: 0.3659, Generator Loss: 2.8564
D(x): 0.8988, D(G(z)): 0.1312
Epoch: [11/20], Batch Num: [21/600]
Discriminator Loss: 0.4127, Generator Loss: 3.1468
D(x): 0.8796, D(G(z)): 0.1264
Epoch: [11/20], Batch Num: [22/600]
Discriminator Loss: 0.4271, Generator Loss: 2.9231
D(x): 0.8682, D(G(z)): 0.1361
Epoch: [11/20], Batch Num: [23/600]
Discriminator Loss: 0.3475, Generator Loss: 3.0096
D(x): 0.8879, D(G(z)): 0.1064
Epoch: [11/20], Batch Num: [24/600]
Discriminator Loss: 0.4710, Generator Loss: 2.5563
D(x): 0.8305, D(G(z)): 0.1024
Epoch: [11/20], Batch Num: [25/600]
Discriminator Loss: 0.5559, Generator Loss: 2.1897
D(x): 0.8610, D(G(z)): 0.1810
Epoch: [11/20], Batch Num: [26/600]
Discriminator Loss: 0.4374, Generator Loss: 2.2798
D(x): 0.8865, D(G(z)): 0.1634
Epoch: [11/20], Batch Num: [27/600]
Discriminator Loss: 0.4172, Generator Loss: 2.3733
D(x): 0.8755, D(G(z)): 0.1609
Epoch: [11/20], Batch Num: [28/600]
Discriminator Loss: 0.4103, Generator Loss: 2.7891
D(x): 0.9205, D(G(z)): 0.1670
Epoch: [11/20], Batch Num: [29/600]
Discriminator Loss: 0.4968, Generator Loss: 2.9391
D(x): 0.8102, D(G(z)): 0.1099
Epoch: [11/20], Batch Num: [30/600]
Discriminator Loss: 0.4282, Generator Loss: 3.1126
D(x): 0.8694, D(G(z)): 0.1345
Epoch: [11/20], Batch Num: [31/600]
Discriminator Loss: 0.4588, Generator Loss: 3.1498
D(x): 0.8077, D(G(z)): 0.0902
Epoch: [11/20], Batch Num: [32/600]
Discriminator Loss: 0.4887, Generator Loss: 2.4864
D(x): 0.8397, D(G(z)): 0.1123
Epoch: [11/20], Batch Num: [33/600]
Discriminator Loss: 0.4513, Generator Loss: 2.3927
D(x): 0.9003, D(G(z)): 0.2036
Epoch: [11/20], Batch Num: [34/600]
Discriminator Loss: 0.4648, Generator Loss: 2.9973
D(x): 0.8893, D(G(z)): 0.2019
Epoch: [11/20], Batch Num: [35/600]
Discriminator Loss: 0.5035, Generator Loss: 3.2661
D(x): 0.8307, D(G(z)): 0.1432
Epoch: [11/20], Batch Num: [36/600]
Discriminator Loss: 0.5981, Generator Loss: 2.7702
D(x): 0.7840, D(G(z)): 0.1225
Epoch: [11/20], Batch Num: [37/600]
Discriminator Loss: 0.3849, Generator Loss: 2.7551
D(x): 0.8777, D(G(z)): 0.1052
Epoch: [11/20], Batch Num: [38/600]
Discriminator Loss: 0.3202, Generator Loss: 2.8775
D(x): 0.9056, D(G(z)): 0.1254
Epoch: [11/20], Batch Num: [39/600]
Discriminator Loss: 0.4221, Generator Loss: 2.7584
D(x): 0.8812, D(G(z)): 0.1578
Epoch: [11/20], Batch Num: [40/600]
Discriminator Loss: 0.4609, Generator Loss: 3.2745
D(x): 0.8927, D(G(z)): 0.1669
Epoch: [11/20], Batch Num: [41/600]
Discriminator Loss: 0.5289, Generator Loss: 3.3829
D(x): 0.8331, D(G(z)): 0.1503
Epoch: [11/20], Batch Num: [42/600]
Discriminator Loss: 0.5785, Generator Loss: 3.0321
D(x): 0.8013, D(G(z)): 0.0809
Epoch: [11/20], Batch Num: [43/600]
Discriminator Loss: 0.4668, Generator Loss: 2.5108
D(x): 0.8715, D(G(z)): 0.1322
Epoch: [11/20], Batch Num: [44/600]
Discriminator Loss: 0.5360, Generator Loss: 2.7057
D(x): 0.8588, D(G(z)): 0.1667
Epoch: [11/20], Batch Num: [45/600]
Discriminator Loss: 0.4043, Generator Loss: 2.8068
D(x): 0.9248, D(G(z)): 0.1916
Epoch: [11/20], Batch Num: [46/600]
Discriminator Loss: 0.4785, Generator Loss: 3.2238
D(x): 0.8689, D(G(z)): 0.1669
Epoch: [11/20], Batch Num: [47/600]
Discriminator Loss: 0.5240, Generator Loss: 3.3217
D(x): 0.8377, D(G(z)): 0.1137
Epoch: [11/20], Batch Num: [48/600]
Discriminator Loss: 0.5526, Generator Loss: 3.1481
D(x): 0.7878, D(G(z)): 0.0875
Epoch: [11/20], Batch Num: [49/600]
Discriminator Loss: 0.3789, Generator Loss: 2.5091
D(x): 0.8644, D(G(z)): 0.1102
Epoch: [11/20], Batch Num: [50/600]
Discriminator Loss: 0.4881, Generator Loss: 2.4425
D(x): 0.8807, D(G(z)): 0.1791
Epoch: [11/20], Batch Num: [51/600]
Discriminator Loss: 0.6029, Generator Loss: 2.3943
D(x): 0.8417, D(G(z)): 0.1744
Epoch: [11/20], Batch Num: [52/600]
Discriminator Loss: 0.5105, Generator Loss: 2.7248
D(x): 0.8776, D(G(z)): 0.1830
Epoch: [11/20], Batch Num: [53/600]
Discriminator Loss: 0.3973, Generator Loss: 2.7474
D(x): 0.8804, D(G(z)): 0.1269
Epoch: [11/20], Batch Num: [54/600]
Discriminator Loss: 0.4103, Generator Loss: 2.9776
D(x): 0.8917, D(G(z)): 0.1351
Epoch: [11/20], Batch Num: [55/600]
Discriminator Loss: 0.4603, Generator Loss: 3.0522
D(x): 0.8572, D(G(z)): 0.1404
Epoch: [11/20], Batch Num: [56/600]
Discriminator Loss: 0.3901, Generator Loss: 2.9254
D(x): 0.8614, D(G(z)): 0.0915
Epoch: [11/20], Batch Num: [57/600]
Discriminator Loss: 0.3835, Generator Loss: 2.6454
D(x): 0.8681, D(G(z)): 0.0967
Epoch: [11/20], Batch Num: [58/600]
Discriminator Loss: 0.3554, Generator Loss: 2.5110
D(x): 0.8765, D(G(z)): 0.1172
Epoch: [11/20], Batch Num: [59/600]
Discriminator Loss: 0.4256, Generator Loss: 2.7293
D(x): 0.9101, D(G(z)): 0.1794
Epoch: [11/20], Batch Num: [60/600]
Discriminator Loss: 0.4673, Generator Loss: 3.3200
D(x): 0.8868, D(G(z)): 0.1773
Epoch: [11/20], Batch Num: [61/600]
Discriminator Loss: 0.3605, Generator Loss: 3.4256
D(x): 0.8690, D(G(z)): 0.0820
Epoch: [11/20], Batch Num: [62/600]
Discriminator Loss: 0.4721, Generator Loss: 3.0477
D(x): 0.8199, D(G(z)): 0.0842
Epoch: [11/20], Batch Num: [63/600]
Discriminator Loss: 0.3042, Generator Loss: 2.7992
D(x): 0.8882, D(G(z)): 0.0877
Epoch: [11/20], Batch Num: [64/600]
Discriminator Loss: 0.4272, Generator Loss: 2.2770
D(x): 0.8568, D(G(z)): 0.1432
Epoch: [11/20], Batch Num: [65/600]
Discriminator Loss: 0.4861, Generator Loss: 2.6141
D(x): 0.9011, D(G(z)): 0.2075
Epoch: [11/20], Batch Num: [66/600]
Discriminator Loss: 0.4316, Generator Loss: 3.3139
D(x): 0.9187, D(G(z)): 0.2062
Epoch: [11/20], Batch Num: [67/600]
Discriminator Loss: 0.5661, Generator Loss: 3.3500
D(x): 0.8292, D(G(z)): 0.1286
Epoch: [11/20], Batch Num: [68/600]
Discriminator Loss: 0.5408, Generator Loss: 3.4583
D(x): 0.8231, D(G(z)): 0.1317
Epoch: [11/20], Batch Num: [69/600]
Discriminator Loss: 0.4944, Generator Loss: 3.1072
D(x): 0.8071, D(G(z)): 0.1027
Epoch: [11/20], Batch Num: [70/600]
Discriminator Loss: 0.4085, Generator Loss: 2.3807
D(x): 0.8364, D(G(z)): 0.1029
Epoch: [11/20], Batch Num: [71/600]
Discriminator Loss: 0.3909, Generator Loss: 2.2267
D(x): 0.8955, D(G(z)): 0.1624
Epoch: [11/20], Batch Num: [72/600]
Discriminator Loss: 0.5890, Generator Loss: 2.6159
D(x): 0.8802, D(G(z)): 0.2312
Epoch: [11/20], Batch Num: [73/600]
Discriminator Loss: 0.4318, Generator Loss: 2.8595
D(x): 0.8505, D(G(z)): 0.1426
Epoch: [11/20], Batch Num: [74/600]
Discriminator Loss: 0.4452, Generator Loss: 3.0807
D(x): 0.8693, D(G(z)): 0.1453
Epoch: [11/20], Batch Num: [75/600]
Discriminator Loss: 0.4439, Generator Loss: 3.1375
D(x): 0.8539, D(G(z)): 0.1286
Epoch: [11/20], Batch Num: [76/600]
Discriminator Loss: 0.7085, Generator Loss: 2.8351
D(x): 0.7551, D(G(z)): 0.1289
Epoch: [11/20], Batch Num: [77/600]
Discriminator Loss: 0.4594, Generator Loss: 2.6440
D(x): 0.8285, D(G(z)): 0.1179
Epoch: [11/20], Batch Num: [78/600]
Discriminator Loss: 0.4211, Generator Loss: 2.2418
D(x): 0.9319, D(G(z)): 0.2156
Epoch: [11/20], Batch Num: [79/600]
Discriminator Loss: 0.4907, Generator Loss: 2.6290
D(x): 0.8704, D(G(z)): 0.1856
Epoch: [11/20], Batch Num: [80/600]
Discriminator Loss: 0.4672, Generator Loss: 3.1509
D(x): 0.8735, D(G(z)): 0.1613
Epoch: [11/20], Batch Num: [81/600]
Discriminator Loss: 0.5072, Generator Loss: 3.1711
D(x): 0.8122, D(G(z)): 0.1030
Epoch: [11/20], Batch Num: [82/600]
Discriminator Loss: 0.5307, Generator Loss: 2.8176
D(x): 0.8085, D(G(z)): 0.1014
Epoch: [11/20], Batch Num: [83/600]
Discriminator Loss: 0.6329, Generator Loss: 2.6048
D(x): 0.8268, D(G(z)): 0.1696
Epoch: [11/20], Batch Num: [84/600]
Discriminator Loss: 0.5555, Generator Loss: 2.5291
D(x): 0.8465, D(G(z)): 0.1768
Epoch: [11/20], Batch Num: [85/600]
Discriminator Loss: 0.5613, Generator Loss: 2.5089
D(x): 0.8374, D(G(z)): 0.1988
Epoch: [11/20], Batch Num: [86/600]
Discriminator Loss: 0.5683, Generator Loss: 2.9362
D(x): 0.8352, D(G(z)): 0.1642
Epoch: [11/20], Batch Num: [87/600]
Discriminator Loss: 0.5061, Generator Loss: 2.9200
D(x): 0.8432, D(G(z)): 0.1622
Epoch: [11/20], Batch Num: [88/600]
Discriminator Loss: 0.5770, Generator Loss: 2.6323
D(x): 0.7722, D(G(z)): 0.1120
Epoch: [11/20], Batch Num: [89/600]
Discriminator Loss: 0.4076, Generator Loss: 2.1407
D(x): 0.8594, D(G(z)): 0.1275
Epoch: [11/20], Batch Num: [90/600]
Discriminator Loss: 0.5579, Generator Loss: 2.6047
D(x): 0.8681, D(G(z)): 0.2041
Epoch: [11/20], Batch Num: [91/600]
Discriminator Loss: 0.4934, Generator Loss: 2.6016
D(x): 0.8516, D(G(z)): 0.1702
Epoch: [11/20], Batch Num: [92/600]
Discriminator Loss: 0.4280, Generator Loss: 3.1314
D(x): 0.8995, D(G(z)): 0.1598
Epoch: [11/20], Batch Num: [93/600]
Discriminator Loss: 0.4819, Generator Loss: 3.6564
D(x): 0.8392, D(G(z)): 0.1077
Epoch: [11/20], Batch Num: [94/600]
Discriminator Loss: 0.4494, Generator Loss: 3.4355
D(x): 0.8447, D(G(z)): 0.0987
Epoch: [11/20], Batch Num: [95/600]
Discriminator Loss: 0.5507, Generator Loss: 2.6407
D(x): 0.7938, D(G(z)): 0.0825
Epoch: [11/20], Batch Num: [96/600]
Discriminator Loss: 0.4751, Generator Loss: 2.1593
D(x): 0.8556, D(G(z)): 0.1351
Epoch: [11/20], Batch Num: [97/600]
Discriminator Loss: 0.4452, Generator Loss: 2.1261
D(x): 0.9080, D(G(z)): 0.1924
Epoch: [11/20], Batch Num: [98/600]
Discriminator Loss: 0.4284, Generator Loss: 2.5426
D(x): 0.9068, D(G(z)): 0.1800
Epoch: [11/20], Batch Num: [99/600]
Discriminator Loss: 0.4986, Generator Loss: 2.9618
D(x): 0.9152, D(G(z)): 0.2184
Epoch: 11, Batch Num: [100/600]

Epoch: [11/20], Batch Num: [100/600]
Discriminator Loss: 0.5956, Generator Loss: 3.5031
D(x): 0.8189, D(G(z)): 0.1317
Epoch: [11/20], Batch Num: [101/600]
Discriminator Loss: 0.6186, Generator Loss: 3.3343
D(x): 0.7489, D(G(z)): 0.0613
Epoch: [11/20], Batch Num: [102/600]
Discriminator Loss: 0.7832, Generator Loss: 2.2201
D(x): 0.7690, D(G(z)): 0.1855
Epoch: [11/20], Batch Num: [103/600]
Discriminator Loss: 0.4288, Generator Loss: 2.1741
D(x): 0.9298, D(G(z)): 0.2087
Epoch: [11/20], Batch Num: [104/600]
Discriminator Loss: 0.5239, Generator Loss: 2.5725
D(x): 0.8909, D(G(z)): 0.2268
Epoch: [11/20], Batch Num: [105/600]
Discriminator Loss: 0.4993, Generator Loss: 2.9009
D(x): 0.8052, D(G(z)): 0.1485
Epoch: [11/20], Batch Num: [106/600]
Discriminator Loss: 0.6142, Generator Loss: 2.8894
D(x): 0.7760, D(G(z)): 0.1563
Epoch: [11/20], Batch Num: [107/600]
Discriminator Loss: 0.4353, Generator Loss: 2.4053
D(x): 0.8581, D(G(z)): 0.1510
Epoch: [11/20], Batch Num: [108/600]
Discriminator Loss: 0.4669, Generator Loss: 2.4919
D(x): 0.8294, D(G(z)): 0.1351
Epoch: [11/20], Batch Num: [109/600]
Discriminator Loss: 0.4824, Generator Loss: 2.2372
D(x): 0.8332, D(G(z)): 0.1553
Epoch: [11/20], Batch Num: [110/600]
Discriminator Loss: 0.6392, Generator Loss: 2.6649
D(x): 0.8479, D(G(z)): 0.2257
Epoch: [11/20], Batch Num: [111/600]
Discriminator Loss: 0.5554, Generator Loss: 2.5442
D(x): 0.8270, D(G(z)): 0.1622
Epoch: [11/20], Batch Num: [112/600]
Discriminator Loss: 0.4305, Generator Loss: 2.4962
D(x): 0.8443, D(G(z)): 0.1171
Epoch: [11/20], Batch Num: [113/600]
Discriminator Loss: 0.4979, Generator Loss: 2.5888
D(x): 0.8351, D(G(z)): 0.1353
Epoch: [11/20], Batch Num: [114/600]
Discriminator Loss: 0.5013, Generator Loss: 2.8236
D(x): 0.8964, D(G(z)): 0.1848
Epoch: [11/20], Batch Num: [115/600]
Discriminator Loss: 0.4169, Generator Loss: 2.9487
D(x): 0.8648, D(G(z)): 0.1183
Epoch: [11/20], Batch Num: [116/600]
Discriminator Loss: 0.5083, Generator Loss: 2.7218
D(x): 0.8418, D(G(z)): 0.1164
Epoch: [11/20], Batch Num: [117/600]
Discriminator Loss: 0.3519, Generator Loss: 2.9875
D(x): 0.9036, D(G(z)): 0.1482
Epoch: [11/20], Batch Num: [118/600]
Discriminator Loss: 0.6197, Generator Loss: 2.5141
D(x): 0.7970, D(G(z)): 0.1216
Epoch: [11/20], Batch Num: [119/600]
Discriminator Loss: 0.4112, Generator Loss: 2.8246
D(x): 0.8911, D(G(z)): 0.1490
Epoch: [11/20], Batch Num: [120/600]
Discriminator Loss: 0.4329, Generator Loss: 2.8681
D(x): 0.8652, D(G(z)): 0.1533
Epoch: [11/20], Batch Num: [121/600]
Discriminator Loss: 0.3833, Generator Loss: 2.9661
D(x): 0.8873, D(G(z)): 0.1491
Epoch: [11/20], Batch Num: [122/600]
Discriminator Loss: 0.4034, Generator Loss: 2.9965
D(x): 0.8743, D(G(z)): 0.1379
Epoch: [11/20], Batch Num: [123/600]
Discriminator Loss: 0.4286, Generator Loss: 3.3148
D(x): 0.8674, D(G(z)): 0.1397
Epoch: [11/20], Batch Num: [124/600]
Discriminator Loss: 0.6301, Generator Loss: 2.4701
D(x): 0.7608, D(G(z)): 0.0966
Epoch: [11/20], Batch Num: [125/600]
Discriminator Loss: 0.5341, Generator Loss: 2.1881
D(x): 0.8454, D(G(z)): 0.1701
Epoch: [11/20], Batch Num: [126/600]
Discriminator Loss: 0.5502, Generator Loss: 2.3253
D(x): 0.9379, D(G(z)): 0.2882
Epoch: [11/20], Batch Num: [127/600]
Discriminator Loss: 0.4306, Generator Loss: 2.9763
D(x): 0.8901, D(G(z)): 0.1876
Epoch: [11/20], Batch Num: [128/600]
Discriminator Loss: 0.5100, Generator Loss: 3.2889
D(x): 0.8181, D(G(z)): 0.1063
Epoch: [11/20], Batch Num: [129/600]
Discriminator Loss: 0.5248, Generator Loss: 3.0575
D(x): 0.7873, D(G(z)): 0.0983
Epoch: [11/20], Batch Num: [130/600]
Discriminator Loss: 0.4037, Generator Loss: 2.7677
D(x): 0.8568, D(G(z)): 0.1216
Epoch: [11/20], Batch Num: [131/600]
Discriminator Loss: 0.4878, Generator Loss: 2.3358
D(x): 0.8386, D(G(z)): 0.1436
Epoch: [11/20], Batch Num: [132/600]
Discriminator Loss: 0.5428, Generator Loss: 2.4817
D(x): 0.8650, D(G(z)): 0.1977
Epoch: [11/20], Batch Num: [133/600]
Discriminator Loss: 0.4577, Generator Loss: 2.3391
D(x): 0.8872, D(G(z)): 0.1771
Epoch: [11/20], Batch Num: [134/600]
Discriminator Loss: 0.4911, Generator Loss: 2.7738
D(x): 0.8765, D(G(z)): 0.1881
Epoch: [11/20], Batch Num: [135/600]
Discriminator Loss: 0.5040, Generator Loss: 2.8694
D(x): 0.8569, D(G(z)): 0.1589
Epoch: [11/20], Batch Num: [136/600]
Discriminator Loss: 0.4340, Generator Loss: 3.3025
D(x): 0.8621, D(G(z)): 0.1276
Epoch: [11/20], Batch Num: [137/600]
Discriminator Loss: 0.7390, Generator Loss: 2.9220
D(x): 0.7635, D(G(z)): 0.1294
Epoch: [11/20], Batch Num: [138/600]
Discriminator Loss: 0.4688, Generator Loss: 2.6834
D(x): 0.8518, D(G(z)): 0.1273
Epoch: [11/20], Batch Num: [139/600]
Discriminator Loss: 0.3750, Generator Loss: 1.8570
D(x): 0.8745, D(G(z)): 0.1286
Epoch: [11/20], Batch Num: [140/600]
Discriminator Loss: 0.6317, Generator Loss: 2.4286
D(x): 0.9070, D(G(z)): 0.2550
Epoch: [11/20], Batch Num: [141/600]
Discriminator Loss: 0.6465, Generator Loss: 3.2564
D(x): 0.8615, D(G(z)): 0.2225
Epoch: [11/20], Batch Num: [142/600]
Discriminator Loss: 0.4271, Generator Loss: 3.4254
D(x): 0.8318, D(G(z)): 0.0766
Epoch: [11/20], Batch Num: [143/600]
Discriminator Loss: 0.5289, Generator Loss: 2.7650
D(x): 0.7668, D(G(z)): 0.0609
Epoch: [11/20], Batch Num: [144/600]
Discriminator Loss: 0.5457, Generator Loss: 2.2233
D(x): 0.7998, D(G(z)): 0.1183
Epoch: [11/20], Batch Num: [145/600]
Discriminator Loss: 0.5388, Generator Loss: 2.0547
D(x): 0.8722, D(G(z)): 0.2249
Epoch: [11/20], Batch Num: [146/600]
Discriminator Loss: 0.5551, Generator Loss: 2.2498
D(x): 0.9063, D(G(z)): 0.2714
Epoch: [11/20], Batch Num: [147/600]
Discriminator Loss: 0.4864, Generator Loss: 2.3761
D(x): 0.8256, D(G(z)): 0.1556
Epoch: [11/20], Batch Num: [148/600]
Discriminator Loss: 0.5133, Generator Loss: 2.7015
D(x): 0.8709, D(G(z)): 0.1931
Epoch: [11/20], Batch Num: [149/600]
Discriminator Loss: 0.4622, Generator Loss: 2.8590
D(x): 0.8283, D(G(z)): 0.1222
Epoch: [11/20], Batch Num: [150/600]
Discriminator Loss: 0.5711, Generator Loss: 2.8426
D(x): 0.8011, D(G(z)): 0.1142
Epoch: [11/20], Batch Num: [151/600]
Discriminator Loss: 0.5575, Generator Loss: 2.6424
D(x): 0.8243, D(G(z)): 0.1487
Epoch: [11/20], Batch Num: [152/600]
Discriminator Loss: 0.4528, Generator Loss: 2.2835
D(x): 0.8611, D(G(z)): 0.1465
Epoch: [11/20], Batch Num: [153/600]
Discriminator Loss: 0.4180, Generator Loss: 2.2979
D(x): 0.8481, D(G(z)): 0.1426
Epoch: [11/20], Batch Num: [154/600]
Discriminator Loss: 0.4745, Generator Loss: 2.2802
D(x): 0.8948, D(G(z)): 0.2167
Epoch: [11/20], Batch Num: [155/600]
Discriminator Loss: 0.5258, Generator Loss: 2.5851
D(x): 0.8746, D(G(z)): 0.1943
Epoch: [11/20], Batch Num: [156/600]
Discriminator Loss: 0.4292, Generator Loss: 3.1593
D(x): 0.8720, D(G(z)): 0.1621
Epoch: [11/20], Batch Num: [157/600]
Discriminator Loss: 0.4676, Generator Loss: 2.9126
D(x): 0.8047, D(G(z)): 0.1032
Epoch: [11/20], Batch Num: [158/600]
Discriminator Loss: 0.4142, Generator Loss: 3.0628
D(x): 0.8698, D(G(z)): 0.1182
Epoch: [11/20], Batch Num: [159/600]
Discriminator Loss: 0.2848, Generator Loss: 2.8796
D(x): 0.9017, D(G(z)): 0.1096
Epoch: [11/20], Batch Num: [160/600]
Discriminator Loss: 0.4293, Generator Loss: 2.4993
D(x): 0.8437, D(G(z)): 0.0961
Epoch: [11/20], Batch Num: [161/600]
Discriminator Loss: 0.3826, Generator Loss: 2.7290
D(x): 0.8942, D(G(z)): 0.1402
Epoch: [11/20], Batch Num: [162/600]
Discriminator Loss: 0.4006, Generator Loss: 2.6521
D(x): 0.8752, D(G(z)): 0.1197
Epoch: [11/20], Batch Num: [163/600]
Discriminator Loss: 0.3301, Generator Loss: 2.9541
D(x): 0.8955, D(G(z)): 0.1182
Epoch: [11/20], Batch Num: [164/600]
Discriminator Loss: 0.5251, Generator Loss: 2.7050
D(x): 0.8633, D(G(z)): 0.1623
Epoch: [11/20], Batch Num: [165/600]
Discriminator Loss: 0.3627, Generator Loss: 3.2397
D(x): 0.9123, D(G(z)): 0.1361
Epoch: [11/20], Batch Num: [166/600]
Discriminator Loss: 0.4834, Generator Loss: 3.2202
D(x): 0.8384, D(G(z)): 0.0841
Epoch: [11/20], Batch Num: [167/600]
Discriminator Loss: 0.3702, Generator Loss: 2.8499
D(x): 0.8618, D(G(z)): 0.0887
Epoch: [11/20], Batch Num: [168/600]
Discriminator Loss: 0.3246, Generator Loss: 2.5079
D(x): 0.9134, D(G(z)): 0.1228
Epoch: [11/20], Batch Num: [169/600]
Discriminator Loss: 0.6291, Generator Loss: 2.6306
D(x): 0.8270, D(G(z)): 0.1802
Epoch: [11/20], Batch Num: [170/600]
Discriminator Loss: 0.4885, Generator Loss: 2.8860
D(x): 0.8897, D(G(z)): 0.2166
Epoch: [11/20], Batch Num: [171/600]
Discriminator Loss: 0.4636, Generator Loss: 3.2744
D(x): 0.8439, D(G(z)): 0.1231
Epoch: [11/20], Batch Num: [172/600]
Discriminator Loss: 0.4149, Generator Loss: 3.2275
D(x): 0.8203, D(G(z)): 0.0723
Epoch: [11/20], Batch Num: [173/600]
Discriminator Loss: 0.3398, Generator Loss: 2.7340
D(x): 0.8825, D(G(z)): 0.1048
Epoch: [11/20], Batch Num: [174/600]
Discriminator Loss: 0.3267, Generator Loss: 2.6578
D(x): 0.9042, D(G(z)): 0.1228
Epoch: [11/20], Batch Num: [175/600]
Discriminator Loss: 0.3377, Generator Loss: 2.7841
D(x): 0.9051, D(G(z)): 0.1515
Epoch: [11/20], Batch Num: [176/600]
Discriminator Loss: 0.4757, Generator Loss: 2.7650
D(x): 0.8309, D(G(z)): 0.1394
Epoch: [11/20], Batch Num: [177/600]
Discriminator Loss: 0.4874, Generator Loss: 2.9828
D(x): 0.8548, D(G(z)): 0.1464
Epoch: [11/20], Batch Num: [178/600]
Discriminator Loss: 0.5345, Generator Loss: 3.2504
D(x): 0.8631, D(G(z)): 0.1720
Epoch: [11/20], Batch Num: [179/600]
Discriminator Loss: 0.4683, Generator Loss: 3.4434
D(x): 0.8788, D(G(z)): 0.1242
Epoch: [11/20], Batch Num: [180/600]
Discriminator Loss: 0.4740, Generator Loss: 3.3142
D(x): 0.8567, D(G(z)): 0.1210
Epoch: [11/20], Batch Num: [181/600]
Discriminator Loss: 0.4261, Generator Loss: 3.0659
D(x): 0.8478, D(G(z)): 0.0891
Epoch: [11/20], Batch Num: [182/600]
Discriminator Loss: 0.5825, Generator Loss: 2.8102
D(x): 0.8191, D(G(z)): 0.1247
Epoch: [11/20], Batch Num: [183/600]
Discriminator Loss: 0.4850, Generator Loss: 2.2147
D(x): 0.8501, D(G(z)): 0.1483
Epoch: [11/20], Batch Num: [184/600]
Discriminator Loss: 0.4476, Generator Loss: 2.2612
D(x): 0.9368, D(G(z)): 0.2158
Epoch: [11/20], Batch Num: [185/600]
Discriminator Loss: 0.5873, Generator Loss: 3.4271
D(x): 0.8878, D(G(z)): 0.2227
Epoch: [11/20], Batch Num: [186/600]
Discriminator Loss: 0.4785, Generator Loss: 3.6788
D(x): 0.8148, D(G(z)): 0.0885
Epoch: [11/20], Batch Num: [187/600]
Discriminator Loss: 0.4584, Generator Loss: 3.1080
D(x): 0.8140, D(G(z)): 0.0862
Epoch: [11/20], Batch Num: [188/600]
Discriminator Loss: 0.6392, Generator Loss: 2.3534
D(x): 0.7653, D(G(z)): 0.1329
Epoch: [11/20], Batch Num: [189/600]
Discriminator Loss: 0.3665, Generator Loss: 1.9933
D(x): 0.9156, D(G(z)): 0.1556
Epoch: [11/20], Batch Num: [190/600]
Discriminator Loss: 0.5662, Generator Loss: 2.6590
D(x): 0.9539, D(G(z)): 0.2440
Epoch: [11/20], Batch Num: [191/600]
Discriminator Loss: 0.5355, Generator Loss: 3.6239
D(x): 0.8865, D(G(z)): 0.2082
Epoch: [11/20], Batch Num: [192/600]
Discriminator Loss: 0.4041, Generator Loss: 4.0876
D(x): 0.8571, D(G(z)): 0.0875
Epoch: [11/20], Batch Num: [193/600]
Discriminator Loss: 0.6306, Generator Loss: 3.9263
D(x): 0.7619, D(G(z)): 0.0506
Epoch: [11/20], Batch Num: [194/600]
Discriminator Loss: 0.5209, Generator Loss: 2.6216
D(x): 0.7942, D(G(z)): 0.0581
Epoch: [11/20], Batch Num: [195/600]
Discriminator Loss: 0.5189, Generator Loss: 2.1719
D(x): 0.8553, D(G(z)): 0.1461
Epoch: [11/20], Batch Num: [196/600]
Discriminator Loss: 0.5264, Generator Loss: 2.1353
D(x): 0.8906, D(G(z)): 0.2257
Epoch: [11/20], Batch Num: [197/600]
Discriminator Loss: 0.6275, Generator Loss: 2.4758
D(x): 0.8994, D(G(z)): 0.2733
Epoch: [11/20], Batch Num: [198/600]
Discriminator Loss: 0.4045, Generator Loss: 3.1330
D(x): 0.8735, D(G(z)): 0.1188
Epoch: [11/20], Batch Num: [199/600]
Discriminator Loss: 0.4353, Generator Loss: 2.8795
D(x): 0.8286, D(G(z)): 0.1127
Epoch: 11, Batch Num: [200/600]

Epoch: [11/20], Batch Num: [200/600]
Discriminator Loss: 0.3939, Generator Loss: 2.8265
D(x): 0.8497, D(G(z)): 0.1009
Epoch: [11/20], Batch Num: [201/600]
Discriminator Loss: 0.5465, Generator Loss: 2.3468
D(x): 0.7818, D(G(z)): 0.1020
Epoch: [11/20], Batch Num: [202/600]
Discriminator Loss: 0.3578, Generator Loss: 2.4662
D(x): 0.9252, D(G(z)): 0.1769
Epoch: [11/20], Batch Num: [203/600]
Discriminator Loss: 0.5194, Generator Loss: 2.6544
D(x): 0.8893, D(G(z)): 0.2019
Epoch: [11/20], Batch Num: [204/600]
Discriminator Loss: 0.4271, Generator Loss: 2.8919
D(x): 0.8695, D(G(z)): 0.1314
Epoch: [11/20], Batch Num: [205/600]
Discriminator Loss: 0.5411, Generator Loss: 2.6674
D(x): 0.8065, D(G(z)): 0.1528
Epoch: [11/20], Batch Num: [206/600]
Discriminator Loss: 0.4339, Generator Loss: 2.5747
D(x): 0.8230, D(G(z)): 0.1047
Epoch: [11/20], Batch Num: [207/600]
Discriminator Loss: 0.4550, Generator Loss: 2.2800
D(x): 0.8436, D(G(z)): 0.1437
Epoch: [11/20], Batch Num: [208/600]
Discriminator Loss: 0.4775, Generator Loss: 2.2706
D(x): 0.8586, D(G(z)): 0.1771
Epoch: [11/20], Batch Num: [209/600]
Discriminator Loss: 0.5504, Generator Loss: 2.3813
D(x): 0.8659, D(G(z)): 0.1967
Epoch: [11/20], Batch Num: [210/600]
Discriminator Loss: 0.3994, Generator Loss: 2.9676
D(x): 0.9208, D(G(z)): 0.1920
Epoch: [11/20], Batch Num: [211/600]
Discriminator Loss: 0.5200, Generator Loss: 3.2071
D(x): 0.8460, D(G(z)): 0.1486
Epoch: [11/20], Batch Num: [212/600]
Discriminator Loss: 0.4911, Generator Loss: 3.2652
D(x): 0.8240, D(G(z)): 0.0923
Epoch: [11/20], Batch Num: [213/600]
Discriminator Loss: 0.5422, Generator Loss: 2.5341
D(x): 0.8146, D(G(z)): 0.0882
Epoch: [11/20], Batch Num: [214/600]
Discriminator Loss: 0.6733, Generator Loss: 2.0137
D(x): 0.7905, D(G(z)): 0.1318
Epoch: [11/20], Batch Num: [215/600]
Discriminator Loss: 0.5208, Generator Loss: 1.9407
D(x): 0.8897, D(G(z)): 0.2263
Epoch: [11/20], Batch Num: [216/600]
Discriminator Loss: 0.4990, Generator Loss: 2.5745
D(x): 0.8924, D(G(z)): 0.2255
Epoch: [11/20], Batch Num: [217/600]
Discriminator Loss: 0.4410, Generator Loss: 3.1388
D(x): 0.9004, D(G(z)): 0.1581
Epoch: [11/20], Batch Num: [218/600]
Discriminator Loss: 0.4066, Generator Loss: 3.8103
D(x): 0.8301, D(G(z)): 0.0904
Epoch: [11/20], Batch Num: [219/600]
Discriminator Loss: 0.6770, Generator Loss: 2.9675
D(x): 0.7496, D(G(z)): 0.0834
Epoch: [11/20], Batch Num: [220/600]
Discriminator Loss: 0.5232, Generator Loss: 2.4954
D(x): 0.8131, D(G(z)): 0.0913
Epoch: [11/20], Batch Num: [221/600]
Discriminator Loss: 0.5493, Generator Loss: 1.9668
D(x): 0.8410, D(G(z)): 0.1739
Epoch: [11/20], Batch Num: [222/600]
Discriminator Loss: 0.7572, Generator Loss: 1.9794
D(x): 0.8911, D(G(z)): 0.3154
Epoch: [11/20], Batch Num: [223/600]
Discriminator Loss: 0.7358, Generator Loss: 2.5990
D(x): 0.9176, D(G(z)): 0.3090
Epoch: [11/20], Batch Num: [224/600]
Discriminator Loss: 0.6493, Generator Loss: 3.3132
D(x): 0.8258, D(G(z)): 0.1860
Epoch: [11/20], Batch Num: [225/600]
Discriminator Loss: 0.6229, Generator Loss: 3.3548
D(x): 0.7832, D(G(z)): 0.1081
Epoch: [11/20], Batch Num: [226/600]
Discriminator Loss: 0.5726, Generator Loss: 3.3691
D(x): 0.7584, D(G(z)): 0.0569
Epoch: [11/20], Batch Num: [227/600]
Discriminator Loss: 0.6643, Generator Loss: 2.4924
D(x): 0.7467, D(G(z)): 0.0892
Epoch: [11/20], Batch Num: [228/600]
Discriminator Loss: 0.4753, Generator Loss: 2.0391
D(x): 0.8583, D(G(z)): 0.1617
Epoch: [11/20], Batch Num: [229/600]
Discriminator Loss: 0.5372, Generator Loss: 1.5694
D(x): 0.8566, D(G(z)): 0.1971
Epoch: [11/20], Batch Num: [230/600]
Discriminator Loss: 0.6192, Generator Loss: 1.8057
D(x): 0.9304, D(G(z)): 0.2964
Epoch: [11/20], Batch Num: [231/600]
Discriminator Loss: 0.5205, Generator Loss: 2.5202
D(x): 0.8940, D(G(z)): 0.2401
Epoch: [11/20], Batch Num: [232/600]
Discriminator Loss: 0.5785, Generator Loss: 2.9393
D(x): 0.8077, D(G(z)): 0.1565
Epoch: [11/20], Batch Num: [233/600]
Discriminator Loss: 0.5182, Generator Loss: 3.0178
D(x): 0.7805, D(G(z)): 0.1233
Epoch: [11/20], Batch Num: [234/600]
Discriminator Loss: 0.6167, Generator Loss: 2.8055
D(x): 0.7390, D(G(z)): 0.0884
Epoch: [11/20], Batch Num: [235/600]
Discriminator Loss: 0.5836, Generator Loss: 2.5833
D(x): 0.7770, D(G(z)): 0.1321
Epoch: [11/20], Batch Num: [236/600]
Discriminator Loss: 0.4598, Generator Loss: 2.1873
D(x): 0.8432, D(G(z)): 0.1416
Epoch: [11/20], Batch Num: [237/600]
Discriminator Loss: 0.5890, Generator Loss: 2.0028
D(x): 0.8323, D(G(z)): 0.2205
Epoch: [11/20], Batch Num: [238/600]
Discriminator Loss: 0.4788, Generator Loss: 1.9814
D(x): 0.8836, D(G(z)): 0.2374
Epoch: [11/20], Batch Num: [239/600]
Discriminator Loss: 0.4744, Generator Loss: 2.0308
D(x): 0.8723, D(G(z)): 0.2249
Epoch: [11/20], Batch Num: [240/600]
Discriminator Loss: 0.5370, Generator Loss: 2.4857
D(x): 0.8435, D(G(z)): 0.2103
Epoch: [11/20], Batch Num: [241/600]
Discriminator Loss: 0.4977, Generator Loss: 2.6365
D(x): 0.8456, D(G(z)): 0.1633
Epoch: [11/20], Batch Num: [242/600]
Discriminator Loss: 0.4659, Generator Loss: 2.6930
D(x): 0.7964, D(G(z)): 0.1078
Epoch: [11/20], Batch Num: [243/600]
Discriminator Loss: 0.6388, Generator Loss: 2.4287
D(x): 0.7348, D(G(z)): 0.1264
Epoch: [11/20], Batch Num: [244/600]
Discriminator Loss: 0.6095, Generator Loss: 2.1950
D(x): 0.7946, D(G(z)): 0.1791
Epoch: [11/20], Batch Num: [245/600]
Discriminator Loss: 0.4806, Generator Loss: 1.9620
D(x): 0.8638, D(G(z)): 0.1954
Epoch: [11/20], Batch Num: [246/600]
Discriminator Loss: 0.4394, Generator Loss: 2.1800
D(x): 0.9037, D(G(z)): 0.2161
Epoch: [11/20], Batch Num: [247/600]
Discriminator Loss: 0.5538, Generator Loss: 2.4349
D(x): 0.8391, D(G(z)): 0.1857
Epoch: [11/20], Batch Num: [248/600]
Discriminator Loss: 0.4421, Generator Loss: 2.9122
D(x): 0.8714, D(G(z)): 0.1711
Epoch: [11/20], Batch Num: [249/600]
Discriminator Loss: 0.4032, Generator Loss: 2.8600
D(x): 0.8512, D(G(z)): 0.1158
Epoch: [11/20], Batch Num: [250/600]
Discriminator Loss: 0.5154, Generator Loss: 3.0374
D(x): 0.7764, D(G(z)): 0.1067
Epoch: [11/20], Batch Num: [251/600]
Discriminator Loss: 0.4662, Generator Loss: 2.1124
D(x): 0.8174, D(G(z)): 0.1170
Epoch: [11/20], Batch Num: [252/600]
Discriminator Loss: 0.3893, Generator Loss: 2.1008
D(x): 0.9213, D(G(z)): 0.1902
Epoch: [11/20], Batch Num: [253/600]
Discriminator Loss: 0.5134, Generator Loss: 2.5052
D(x): 0.8767, D(G(z)): 0.2089
Epoch: [11/20], Batch Num: [254/600]
Discriminator Loss: 0.4190, Generator Loss: 2.5967
D(x): 0.8876, D(G(z)): 0.1818
Epoch: [11/20], Batch Num: [255/600]
Discriminator Loss: 0.5597, Generator Loss: 2.7558
D(x): 0.7764, D(G(z)): 0.1193
Epoch: [11/20], Batch Num: [256/600]
Discriminator Loss: 0.3981, Generator Loss: 2.5481
D(x): 0.8736, D(G(z)): 0.1451
Epoch: [11/20], Batch Num: [257/600]
Discriminator Loss: 0.3490, Generator Loss: 2.8511
D(x): 0.9224, D(G(z)): 0.1493
Epoch: [11/20], Batch Num: [258/600]
Discriminator Loss: 0.4284, Generator Loss: 3.0183
D(x): 0.8858, D(G(z)): 0.1453
Epoch: [11/20], Batch Num: [259/600]
Discriminator Loss: 0.6038, Generator Loss: 3.0011
D(x): 0.7998, D(G(z)): 0.1739
Epoch: [11/20], Batch Num: [260/600]
Discriminator Loss: 0.4910, Generator Loss: 3.1470
D(x): 0.8438, D(G(z)): 0.1622
Epoch: [11/20], Batch Num: [261/600]
Discriminator Loss: 0.4515, Generator Loss: 2.6105
D(x): 0.8377, D(G(z)): 0.1011
Epoch: [11/20], Batch Num: [262/600]
Discriminator Loss: 0.4244, Generator Loss: 2.5497
D(x): 0.8495, D(G(z)): 0.1270
Epoch: [11/20], Batch Num: [263/600]
Discriminator Loss: 0.4653, Generator Loss: 2.4804
D(x): 0.8698, D(G(z)): 0.1507
Epoch: [11/20], Batch Num: [264/600]
Discriminator Loss: 0.4878, Generator Loss: 2.7653
D(x): 0.8713, D(G(z)): 0.1539
Epoch: [11/20], Batch Num: [265/600]
Discriminator Loss: 0.5812, Generator Loss: 2.6753
D(x): 0.8503, D(G(z)): 0.2097
Epoch: [11/20], Batch Num: [266/600]
Discriminator Loss: 0.4612, Generator Loss: 3.3371
D(x): 0.8771, D(G(z)): 0.1655
Epoch: [11/20], Batch Num: [267/600]
Discriminator Loss: 0.5203, Generator Loss: 3.1582
D(x): 0.8006, D(G(z)): 0.0968
Epoch: [11/20], Batch Num: [268/600]
Discriminator Loss: 0.4086, Generator Loss: 3.2643
D(x): 0.8406, D(G(z)): 0.0738
Epoch: [11/20], Batch Num: [269/600]
Discriminator Loss: 0.3844, Generator Loss: 2.7293
D(x): 0.8658, D(G(z)): 0.0862
Epoch: [11/20], Batch Num: [270/600]
Discriminator Loss: 0.5953, Generator Loss: 2.5664
D(x): 0.8801, D(G(z)): 0.1540
Epoch: [11/20], Batch Num: [271/600]
Discriminator Loss: 0.4893, Generator Loss: 2.7312
D(x): 0.8877, D(G(z)): 0.1912
Epoch: [11/20], Batch Num: [272/600]
Discriminator Loss: 0.4516, Generator Loss: 2.9464
D(x): 0.9205, D(G(z)): 0.1840
Epoch: [11/20], Batch Num: [273/600]
Discriminator Loss: 0.5245, Generator Loss: 3.3386
D(x): 0.8304, D(G(z)): 0.1320
Epoch: [11/20], Batch Num: [274/600]
Discriminator Loss: 0.3588, Generator Loss: 3.2692
D(x): 0.8910, D(G(z)): 0.0975
Epoch: [11/20], Batch Num: [275/600]
Discriminator Loss: 0.4068, Generator Loss: 3.4841
D(x): 0.8651, D(G(z)): 0.1010
Epoch: [11/20], Batch Num: [276/600]
Discriminator Loss: 0.6495, Generator Loss: 2.8940
D(x): 0.7671, D(G(z)): 0.0890
Epoch: [11/20], Batch Num: [277/600]
Discriminator Loss: 0.4965, Generator Loss: 2.2889
D(x): 0.8377, D(G(z)): 0.1337
Epoch: [11/20], Batch Num: [278/600]
Discriminator Loss: 0.4830, Generator Loss: 2.5273
D(x): 0.8979, D(G(z)): 0.2320
Epoch: [11/20], Batch Num: [279/600]
Discriminator Loss: 0.4847, Generator Loss: 2.7743
D(x): 0.8899, D(G(z)): 0.1873
Epoch: [11/20], Batch Num: [280/600]
Discriminator Loss: 0.5964, Generator Loss: 2.9751
D(x): 0.8207, D(G(z)): 0.1486
Epoch: [11/20], Batch Num: [281/600]
Discriminator Loss: 0.5439, Generator Loss: 2.7222
D(x): 0.8068, D(G(z)): 0.1100
Epoch: [11/20], Batch Num: [282/600]
Discriminator Loss: 0.4660, Generator Loss: 2.7908
D(x): 0.8564, D(G(z)): 0.1511
Epoch: [11/20], Batch Num: [283/600]
Discriminator Loss: 0.4640, Generator Loss: 2.9379
D(x): 0.8702, D(G(z)): 0.1525
Epoch: [11/20], Batch Num: [284/600]
Discriminator Loss: 0.4092, Generator Loss: 2.7043
D(x): 0.8608, D(G(z)): 0.1339
Epoch: [11/20], Batch Num: [285/600]
Discriminator Loss: 0.5373, Generator Loss: 2.4557
D(x): 0.8294, D(G(z)): 0.1536
Epoch: [11/20], Batch Num: [286/600]
Discriminator Loss: 0.4042, Generator Loss: 2.3609
D(x): 0.8752, D(G(z)): 0.1595
Epoch: [11/20], Batch Num: [287/600]
Discriminator Loss: 0.5872, Generator Loss: 2.3562
D(x): 0.8723, D(G(z)): 0.2256
Epoch: [11/20], Batch Num: [288/600]
Discriminator Loss: 0.5004, Generator Loss: 3.1599
D(x): 0.8737, D(G(z)): 0.1850
Epoch: [11/20], Batch Num: [289/600]
Discriminator Loss: 0.3536, Generator Loss: 3.1874
D(x): 0.8795, D(G(z)): 0.1075
Epoch: [11/20], Batch Num: [290/600]
Discriminator Loss: 0.3895, Generator Loss: 3.5000
D(x): 0.8422, D(G(z)): 0.0956
Epoch: [11/20], Batch Num: [291/600]
Discriminator Loss: 0.6879, Generator Loss: 3.0900
D(x): 0.7655, D(G(z)): 0.1053
Epoch: [11/20], Batch Num: [292/600]
Discriminator Loss: 0.4704, Generator Loss: 2.4099
D(x): 0.8583, D(G(z)): 0.1212
Epoch: [11/20], Batch Num: [293/600]
Discriminator Loss: 0.4535, Generator Loss: 2.0677
D(x): 0.8735, D(G(z)): 0.1844
Epoch: [11/20], Batch Num: [294/600]
Discriminator Loss: 0.6004, Generator Loss: 2.3249
D(x): 0.8844, D(G(z)): 0.2430
Epoch: [11/20], Batch Num: [295/600]
Discriminator Loss: 0.4784, Generator Loss: 2.7360
D(x): 0.8915, D(G(z)): 0.2015
Epoch: [11/20], Batch Num: [296/600]
Discriminator Loss: 0.5539, Generator Loss: 3.7221
D(x): 0.8107, D(G(z)): 0.1444
Epoch: [11/20], Batch Num: [297/600]
Discriminator Loss: 0.4069, Generator Loss: 3.2957
D(x): 0.8511, D(G(z)): 0.1021
Epoch: [11/20], Batch Num: [298/600]
Discriminator Loss: 0.4342, Generator Loss: 3.1397
D(x): 0.8195, D(G(z)): 0.0913
Epoch: [11/20], Batch Num: [299/600]
Discriminator Loss: 0.5016, Generator Loss: 2.5357
D(x): 0.8446, D(G(z)): 0.1232
Epoch: 11, Batch Num: [300/600]

Epoch: [11/20], Batch Num: [300/600]
Discriminator Loss: 0.3995, Generator Loss: 2.4323
D(x): 0.8927, D(G(z)): 0.1737
Epoch: [11/20], Batch Num: [301/600]
Discriminator Loss: 0.6121, Generator Loss: 2.8335
D(x): 0.8397, D(G(z)): 0.2296
Epoch: [11/20], Batch Num: [302/600]
Discriminator Loss: 0.5118, Generator Loss: 3.3118
D(x): 0.8341, D(G(z)): 0.1742
Epoch: [11/20], Batch Num: [303/600]
Discriminator Loss: 0.5437, Generator Loss: 2.6116
D(x): 0.7931, D(G(z)): 0.1224
Epoch: [11/20], Batch Num: [304/600]
Discriminator Loss: 0.3773, Generator Loss: 2.9898
D(x): 0.8719, D(G(z)): 0.1300
Epoch: [11/20], Batch Num: [305/600]
Discriminator Loss: 0.4152, Generator Loss: 2.8062
D(x): 0.9045, D(G(z)): 0.1786
Epoch: [11/20], Batch Num: [306/600]
Discriminator Loss: 0.3943, Generator Loss: 3.0035
D(x): 0.9057, D(G(z)): 0.1557
Epoch: [11/20], Batch Num: [307/600]
Discriminator Loss: 0.6730, Generator Loss: 3.3034
D(x): 0.7666, D(G(z)): 0.1383
Epoch: [11/20], Batch Num: [308/600]
Discriminator Loss: 0.6425, Generator Loss: 2.9439
D(x): 0.8062, D(G(z)): 0.1339
Epoch: [11/20], Batch Num: [309/600]
Discriminator Loss: 0.4758, Generator Loss: 2.8208
D(x): 0.8802, D(G(z)): 0.1552
Epoch: [11/20], Batch Num: [310/600]
Discriminator Loss: 0.5913, Generator Loss: 2.5038
D(x): 0.8355, D(G(z)): 0.1863
Epoch: [11/20], Batch Num: [311/600]
Discriminator Loss: 0.5133, Generator Loss: 2.7275
D(x): 0.8675, D(G(z)): 0.1939
Epoch: [11/20], Batch Num: [312/600]
Discriminator Loss: 0.5690, Generator Loss: 2.6468
D(x): 0.8178, D(G(z)): 0.1713
Epoch: [11/20], Batch Num: [313/600]
Discriminator Loss: 0.5678, Generator Loss: 3.1176
D(x): 0.8230, D(G(z)): 0.1512
Epoch: [11/20], Batch Num: [314/600]
Discriminator Loss: 0.5434, Generator Loss: 2.6740
D(x): 0.8065, D(G(z)): 0.1039
Epoch: [11/20], Batch Num: [315/600]
Discriminator Loss: 0.3810, Generator Loss: 2.3816
D(x): 0.8834, D(G(z)): 0.1443
Epoch: [11/20], Batch Num: [316/600]
Discriminator Loss: 0.5303, Generator Loss: 2.3612
D(x): 0.8475, D(G(z)): 0.1776
Epoch: [11/20], Batch Num: [317/600]
Discriminator Loss: 0.4720, Generator Loss: 2.5670
D(x): 0.8952, D(G(z)): 0.2088
Epoch: [11/20], Batch Num: [318/600]
Discriminator Loss: 0.6429, Generator Loss: 3.1040
D(x): 0.8578, D(G(z)): 0.2077
Epoch: [11/20], Batch Num: [319/600]
Discriminator Loss: 0.5506, Generator Loss: 2.8905
D(x): 0.8118, D(G(z)): 0.0856
Epoch: [11/20], Batch Num: [320/600]
Discriminator Loss: 0.6225, Generator Loss: 2.9240
D(x): 0.7835, D(G(z)): 0.1142
Epoch: [11/20], Batch Num: [321/600]
Discriminator Loss: 0.5039, Generator Loss: 2.8350
D(x): 0.8389, D(G(z)): 0.1537
Epoch: [11/20], Batch Num: [322/600]
Discriminator Loss: 0.5054, Generator Loss: 1.9255
D(x): 0.8022, D(G(z)): 0.1055
Epoch: [11/20], Batch Num: [323/600]
Discriminator Loss: 0.7840, Generator Loss: 2.0987
D(x): 0.8496, D(G(z)): 0.2963
Epoch: [11/20], Batch Num: [324/600]
Discriminator Loss: 0.5570, Generator Loss: 2.9125
D(x): 0.9069, D(G(z)): 0.2417
Epoch: [11/20], Batch Num: [325/600]
Discriminator Loss: 0.5040, Generator Loss: 3.3294
D(x): 0.8022, D(G(z)): 0.1226
Epoch: [11/20], Batch Num: [326/600]
Discriminator Loss: 0.5892, Generator Loss: 2.7724
D(x): 0.7624, D(G(z)): 0.0832
Epoch: [11/20], Batch Num: [327/600]
Discriminator Loss: 0.5745, Generator Loss: 2.5752
D(x): 0.8223, D(G(z)): 0.1319
Epoch: [11/20], Batch Num: [328/600]
Discriminator Loss: 0.6191, Generator Loss: 2.4368
D(x): 0.8464, D(G(z)): 0.1963
Epoch: [11/20], Batch Num: [329/600]
Discriminator Loss: 0.4827, Generator Loss: 2.5785
D(x): 0.8825, D(G(z)): 0.2047
Epoch: [11/20], Batch Num: [330/600]
Discriminator Loss: 0.5474, Generator Loss: 2.3811
D(x): 0.8234, D(G(z)): 0.1833
Epoch: [11/20], Batch Num: [331/600]
Discriminator Loss: 0.5076, Generator Loss: 2.5936
D(x): 0.8680, D(G(z)): 0.1554
Epoch: [11/20], Batch Num: [332/600]
Discriminator Loss: 0.8323, Generator Loss: 2.4503
D(x): 0.7557, D(G(z)): 0.1649
Epoch: [11/20], Batch Num: [333/600]
Discriminator Loss: 0.5586, Generator Loss: 2.0840
D(x): 0.7760, D(G(z)): 0.1442
Epoch: [11/20], Batch Num: [334/600]
Discriminator Loss: 0.6463, Generator Loss: 2.0446
D(x): 0.8508, D(G(z)): 0.2279
Epoch: [11/20], Batch Num: [335/600]
Discriminator Loss: 0.7351, Generator Loss: 2.4137
D(x): 0.8294, D(G(z)): 0.2474
Epoch: [11/20], Batch Num: [336/600]
Discriminator Loss: 0.4610, Generator Loss: 2.2375
D(x): 0.8591, D(G(z)): 0.1786
Epoch: [11/20], Batch Num: [337/600]
Discriminator Loss: 0.7024, Generator Loss: 2.7993
D(x): 0.8043, D(G(z)): 0.2100
Epoch: [11/20], Batch Num: [338/600]
Discriminator Loss: 0.5635, Generator Loss: 2.7237
D(x): 0.7916, D(G(z)): 0.1492
Epoch: [11/20], Batch Num: [339/600]
Discriminator Loss: 0.5663, Generator Loss: 2.2315
D(x): 0.7638, D(G(z)): 0.1346
Epoch: [11/20], Batch Num: [340/600]
Discriminator Loss: 0.5944, Generator Loss: 1.9537
D(x): 0.7638, D(G(z)): 0.1476
Epoch: [11/20], Batch Num: [341/600]
Discriminator Loss: 0.6616, Generator Loss: 1.7316
D(x): 0.8191, D(G(z)): 0.2496
Epoch: [11/20], Batch Num: [342/600]
Discriminator Loss: 0.6989, Generator Loss: 1.8722
D(x): 0.8344, D(G(z)): 0.2823
Epoch: [11/20], Batch Num: [343/600]
Discriminator Loss: 0.5199, Generator Loss: 1.9797
D(x): 0.8647, D(G(z)): 0.2244
Epoch: [11/20], Batch Num: [344/600]
Discriminator Loss: 0.4512, Generator Loss: 2.3425
D(x): 0.8488, D(G(z)): 0.1669
Epoch: [11/20], Batch Num: [345/600]
Discriminator Loss: 0.5641, Generator Loss: 2.6236
D(x): 0.8351, D(G(z)): 0.1696
Epoch: [11/20], Batch Num: [346/600]
Discriminator Loss: 0.5163, Generator Loss: 2.6944
D(x): 0.8162, D(G(z)): 0.1553
Epoch: [11/20], Batch Num: [347/600]
Discriminator Loss: 0.4815, Generator Loss: 2.6547
D(x): 0.7987, D(G(z)): 0.1149
Epoch: [11/20], Batch Num: [348/600]
Discriminator Loss: 0.5029, Generator Loss: 2.5529
D(x): 0.8008, D(G(z)): 0.1335
Epoch: [11/20], Batch Num: [349/600]
Discriminator Loss: 0.5742, Generator Loss: 2.2340
D(x): 0.7639, D(G(z)): 0.1376
Epoch: [11/20], Batch Num: [350/600]
Discriminator Loss: 0.6229, Generator Loss: 1.8806
D(x): 0.8632, D(G(z)): 0.2421
Epoch: [11/20], Batch Num: [351/600]
Discriminator Loss: 0.6049, Generator Loss: 1.9339
D(x): 0.8784, D(G(z)): 0.2669
Epoch: [11/20], Batch Num: [352/600]
Discriminator Loss: 0.5464, Generator Loss: 2.5663
D(x): 0.8406, D(G(z)): 0.1988
Epoch: [11/20], Batch Num: [353/600]
Discriminator Loss: 0.5801, Generator Loss: 2.9291
D(x): 0.7872, D(G(z)): 0.1272
Epoch: [11/20], Batch Num: [354/600]
Discriminator Loss: 0.5198, Generator Loss: 2.7146
D(x): 0.8037, D(G(z)): 0.1177
Epoch: [11/20], Batch Num: [355/600]
Discriminator Loss: 0.6145, Generator Loss: 2.3835
D(x): 0.7822, D(G(z)): 0.1614
Epoch: [11/20], Batch Num: [356/600]
Discriminator Loss: 0.4326, Generator Loss: 2.3572
D(x): 0.8633, D(G(z)): 0.1553
Epoch: [11/20], Batch Num: [357/600]
Discriminator Loss: 0.4404, Generator Loss: 2.3577
D(x): 0.9026, D(G(z)): 0.2193
Epoch: [11/20], Batch Num: [358/600]
Discriminator Loss: 0.5564, Generator Loss: 2.5842
D(x): 0.8758, D(G(z)): 0.2251
Epoch: [11/20], Batch Num: [359/600]
Discriminator Loss: 0.5225, Generator Loss: 3.2618
D(x): 0.8789, D(G(z)): 0.1954
Epoch: [11/20], Batch Num: [360/600]
Discriminator Loss: 0.5728, Generator Loss: 3.7163
D(x): 0.7521, D(G(z)): 0.1018
Epoch: [11/20], Batch Num: [361/600]
Discriminator Loss: 0.5507, Generator Loss: 3.0250
D(x): 0.7889, D(G(z)): 0.1290
Epoch: [11/20], Batch Num: [362/600]
Discriminator Loss: 0.4604, Generator Loss: 2.3414
D(x): 0.8172, D(G(z)): 0.1002
Epoch: [11/20], Batch Num: [363/600]
Discriminator Loss: 0.4487, Generator Loss: 2.3434
D(x): 0.8780, D(G(z)): 0.1818
Epoch: [11/20], Batch Num: [364/600]
Discriminator Loss: 0.5010, Generator Loss: 2.1688
D(x): 0.8928, D(G(z)): 0.2242
Epoch: [11/20], Batch Num: [365/600]
Discriminator Loss: 0.5663, Generator Loss: 2.7553
D(x): 0.8814, D(G(z)): 0.2372
Epoch: [11/20], Batch Num: [366/600]
Discriminator Loss: 0.4812, Generator Loss: 2.9753
D(x): 0.8530, D(G(z)): 0.1682
Epoch: [11/20], Batch Num: [367/600]
Discriminator Loss: 0.4841, Generator Loss: 2.8368
D(x): 0.7962, D(G(z)): 0.0977
Epoch: [11/20], Batch Num: [368/600]
Discriminator Loss: 0.4900, Generator Loss: 2.6768
D(x): 0.8231, D(G(z)): 0.1302
Epoch: [11/20], Batch Num: [369/600]
Discriminator Loss: 0.4880, Generator Loss: 2.6035
D(x): 0.8339, D(G(z)): 0.1628
Epoch: [11/20], Batch Num: [370/600]
Discriminator Loss: 0.5337, Generator Loss: 2.5248
D(x): 0.8429, D(G(z)): 0.1960
Epoch: [11/20], Batch Num: [371/600]
Discriminator Loss: 0.5244, Generator Loss: 2.6101
D(x): 0.8444, D(G(z)): 0.1828
Epoch: [11/20], Batch Num: [372/600]
Discriminator Loss: 0.5475, Generator Loss: 2.6625
D(x): 0.8430, D(G(z)): 0.1849
Epoch: [11/20], Batch Num: [373/600]
Discriminator Loss: 0.3923, Generator Loss: 2.7815
D(x): 0.8656, D(G(z)): 0.1168
Epoch: [11/20], Batch Num: [374/600]
Discriminator Loss: 0.4719, Generator Loss: 2.5436
D(x): 0.8508, D(G(z)): 0.1468
Epoch: [11/20], Batch Num: [375/600]
Discriminator Loss: 0.5388, Generator Loss: 2.5719
D(x): 0.8160, D(G(z)): 0.1381
Epoch: [11/20], Batch Num: [376/600]
Discriminator Loss: 0.5077, Generator Loss: 2.6940
D(x): 0.8585, D(G(z)): 0.1561
Epoch: [11/20], Batch Num: [377/600]
Discriminator Loss: 0.3699, Generator Loss: 2.5298
D(x): 0.8909, D(G(z)): 0.1561
Epoch: [11/20], Batch Num: [378/600]
Discriminator Loss: 0.4037, Generator Loss: 2.8566
D(x): 0.8768, D(G(z)): 0.1387
Epoch: [11/20], Batch Num: [379/600]
Discriminator Loss: 0.5902, Generator Loss: 2.4855
D(x): 0.8084, D(G(z)): 0.1604
Epoch: [11/20], Batch Num: [380/600]
Discriminator Loss: 0.4025, Generator Loss: 2.8760
D(x): 0.8931, D(G(z)): 0.1669
Epoch: [11/20], Batch Num: [381/600]
Discriminator Loss: 0.4684, Generator Loss: 3.0714
D(x): 0.8695, D(G(z)): 0.1748
Epoch: [11/20], Batch Num: [382/600]
Discriminator Loss: 0.5987, Generator Loss: 3.1356
D(x): 0.7971, D(G(z)): 0.1339
Epoch: [11/20], Batch Num: [383/600]
Discriminator Loss: 0.4775, Generator Loss: 2.9993
D(x): 0.8338, D(G(z)): 0.0919
Epoch: [11/20], Batch Num: [384/600]
Discriminator Loss: 0.6695, Generator Loss: 2.7925
D(x): 0.7909, D(G(z)): 0.1432
Epoch: [11/20], Batch Num: [385/600]
Discriminator Loss: 0.5356, Generator Loss: 2.3849
D(x): 0.8835, D(G(z)): 0.1764
Epoch: [11/20], Batch Num: [386/600]
Discriminator Loss: 0.5297, Generator Loss: 2.5874
D(x): 0.8689, D(G(z)): 0.1959
Epoch: [11/20], Batch Num: [387/600]
Discriminator Loss: 0.5409, Generator Loss: 2.1969
D(x): 0.8350, D(G(z)): 0.1687
Epoch: [11/20], Batch Num: [388/600]
Discriminator Loss: 0.5042, Generator Loss: 2.7776
D(x): 0.8819, D(G(z)): 0.1996
Epoch: [11/20], Batch Num: [389/600]
Discriminator Loss: 0.5098, Generator Loss: 3.1509
D(x): 0.8456, D(G(z)): 0.1550
Epoch: [11/20], Batch Num: [390/600]
Discriminator Loss: 0.6047, Generator Loss: 2.5689
D(x): 0.7415, D(G(z)): 0.1087
Epoch: [11/20], Batch Num: [391/600]
Discriminator Loss: 0.6374, Generator Loss: 2.3234
D(x): 0.8076, D(G(z)): 0.1621
Epoch: [11/20], Batch Num: [392/600]
Discriminator Loss: 0.4378, Generator Loss: 2.3529
D(x): 0.9214, D(G(z)): 0.2255
Epoch: [11/20], Batch Num: [393/600]
Discriminator Loss: 0.6166, Generator Loss: 2.7330
D(x): 0.8166, D(G(z)): 0.1858
Epoch: [11/20], Batch Num: [394/600]
Discriminator Loss: 0.7089, Generator Loss: 2.8772
D(x): 0.7825, D(G(z)): 0.1340
Epoch: [11/20], Batch Num: [395/600]
Discriminator Loss: 0.6790, Generator Loss: 2.3333
D(x): 0.7289, D(G(z)): 0.1279
Epoch: [11/20], Batch Num: [396/600]
Discriminator Loss: 0.4407, Generator Loss: 2.3539
D(x): 0.8749, D(G(z)): 0.1717
Epoch: [11/20], Batch Num: [397/600]
Discriminator Loss: 0.4862, Generator Loss: 2.5500
D(x): 0.9164, D(G(z)): 0.2200
Epoch: [11/20], Batch Num: [398/600]
Discriminator Loss: 0.5841, Generator Loss: 2.9453
D(x): 0.8427, D(G(z)): 0.2137
Epoch: [11/20], Batch Num: [399/600]
Discriminator Loss: 0.6098, Generator Loss: 2.9918
D(x): 0.8022, D(G(z)): 0.1578
Epoch: 11, Batch Num: [400/600]

Epoch: [11/20], Batch Num: [400/600]
Discriminator Loss: 0.5843, Generator Loss: 2.6940
D(x): 0.7468, D(G(z)): 0.0895
Epoch: [11/20], Batch Num: [401/600]
Discriminator Loss: 0.5665, Generator Loss: 2.2003
D(x): 0.8318, D(G(z)): 0.1571
Epoch: [11/20], Batch Num: [402/600]
Discriminator Loss: 0.5087, Generator Loss: 2.0213
D(x): 0.8533, D(G(z)): 0.1994
Epoch: [11/20], Batch Num: [403/600]
Discriminator Loss: 0.6708, Generator Loss: 2.6061
D(x): 0.9110, D(G(z)): 0.2982
Epoch: [11/20], Batch Num: [404/600]
Discriminator Loss: 0.6468, Generator Loss: 3.3355
D(x): 0.8004, D(G(z)): 0.1599
Epoch: [11/20], Batch Num: [405/600]
Discriminator Loss: 0.5679, Generator Loss: 3.0273
D(x): 0.8047, D(G(z)): 0.1133
Epoch: [11/20], Batch Num: [406/600]
Discriminator Loss: 0.5723, Generator Loss: 3.1584
D(x): 0.8044, D(G(z)): 0.1082
Epoch: [11/20], Batch Num: [407/600]
Discriminator Loss: 0.4225, Generator Loss: 2.2592
D(x): 0.8164, D(G(z)): 0.0922
Epoch: [11/20], Batch Num: [408/600]
Discriminator Loss: 0.4114, Generator Loss: 2.1764
D(x): 0.9221, D(G(z)): 0.2073
Epoch: [11/20], Batch Num: [409/600]
Discriminator Loss: 0.5155, Generator Loss: 2.0931
D(x): 0.8629, D(G(z)): 0.2233
Epoch: [11/20], Batch Num: [410/600]
Discriminator Loss: 0.4527, Generator Loss: 2.5318
D(x): 0.9050, D(G(z)): 0.2166
Epoch: [11/20], Batch Num: [411/600]
Discriminator Loss: 0.4199, Generator Loss: 3.0630
D(x): 0.8785, D(G(z)): 0.1607
Epoch: [11/20], Batch Num: [412/600]
Discriminator Loss: 0.4528, Generator Loss: 3.2505
D(x): 0.8375, D(G(z)): 0.1236
Epoch: [11/20], Batch Num: [413/600]
Discriminator Loss: 0.5760, Generator Loss: 3.0850
D(x): 0.7567, D(G(z)): 0.0757
Epoch: [11/20], Batch Num: [414/600]
Discriminator Loss: 0.5448, Generator Loss: 2.4707
D(x): 0.7978, D(G(z)): 0.1270
Epoch: [11/20], Batch Num: [415/600]
Discriminator Loss: 0.5799, Generator Loss: 1.9547
D(x): 0.7899, D(G(z)): 0.1433
Epoch: [11/20], Batch Num: [416/600]
Discriminator Loss: 0.5428, Generator Loss: 1.6766
D(x): 0.8745, D(G(z)): 0.2263
Epoch: [11/20], Batch Num: [417/600]
Discriminator Loss: 0.6286, Generator Loss: 2.1073
D(x): 0.9267, D(G(z)): 0.3042
Epoch: [11/20], Batch Num: [418/600]
Discriminator Loss: 0.5759, Generator Loss: 2.9356
D(x): 0.8708, D(G(z)): 0.2095
Epoch: [11/20], Batch Num: [419/600]
Discriminator Loss: 0.6363, Generator Loss: 3.0330
D(x): 0.7538, D(G(z)): 0.1283
Epoch: [11/20], Batch Num: [420/600]
Discriminator Loss: 0.6089, Generator Loss: 2.8197
D(x): 0.7630, D(G(z)): 0.1079
Epoch: [11/20], Batch Num: [421/600]
Discriminator Loss: 0.6444, Generator Loss: 2.5411
D(x): 0.7899, D(G(z)): 0.1393
Epoch: [11/20], Batch Num: [422/600]
Discriminator Loss: 0.5319, Generator Loss: 2.3038
D(x): 0.8442, D(G(z)): 0.1706
Epoch: [11/20], Batch Num: [423/600]
Discriminator Loss: 0.5375, Generator Loss: 1.9691
D(x): 0.8442, D(G(z)): 0.1788
Epoch: [11/20], Batch Num: [424/600]
Discriminator Loss: 0.6473, Generator Loss: 2.1654
D(x): 0.8587, D(G(z)): 0.2450
Epoch: [11/20], Batch Num: [425/600]
Discriminator Loss: 0.4914, Generator Loss: 2.5886
D(x): 0.8543, D(G(z)): 0.1697
Epoch: [11/20], Batch Num: [426/600]
Discriminator Loss: 0.6084, Generator Loss: 2.4472
D(x): 0.8196, D(G(z)): 0.1751
Epoch: [11/20], Batch Num: [427/600]
Discriminator Loss: 0.5237, Generator Loss: 2.6583
D(x): 0.8136, D(G(z)): 0.1542
Epoch: [11/20], Batch Num: [428/600]
Discriminator Loss: 0.5784, Generator Loss: 2.6200
D(x): 0.7890, D(G(z)): 0.1563
Epoch: [11/20], Batch Num: [429/600]
Discriminator Loss: 0.4575, Generator Loss: 2.5504
D(x): 0.8498, D(G(z)): 0.1617
Epoch: [11/20], Batch Num: [430/600]
Discriminator Loss: 0.5007, Generator Loss: 2.2884
D(x): 0.8040, D(G(z)): 0.1211
Epoch: [11/20], Batch Num: [431/600]
Discriminator Loss: 0.5151, Generator Loss: 2.2582
D(x): 0.8464, D(G(z)): 0.1990
Epoch: [11/20], Batch Num: [432/600]
Discriminator Loss: 0.5073, Generator Loss: 2.2267
D(x): 0.8737, D(G(z)): 0.1938
Epoch: [11/20], Batch Num: [433/600]
Discriminator Loss: 0.4500, Generator Loss: 2.5961
D(x): 0.8838, D(G(z)): 0.1831
Epoch: [11/20], Batch Num: [434/600]
Discriminator Loss: 0.6643, Generator Loss: 2.5610
D(x): 0.7938, D(G(z)): 0.1610
Epoch: [11/20], Batch Num: [435/600]
Discriminator Loss: 0.4237, Generator Loss: 2.5102
D(x): 0.8654, D(G(z)): 0.1434
Epoch: [11/20], Batch Num: [436/600]
Discriminator Loss: 0.6788, Generator Loss: 2.3758
D(x): 0.7900, D(G(z)): 0.1617
Epoch: [11/20], Batch Num: [437/600]
Discriminator Loss: 0.4563, Generator Loss: 2.5123
D(x): 0.8595, D(G(z)): 0.1426
Epoch: [11/20], Batch Num: [438/600]
Discriminator Loss: 0.7062, Generator Loss: 2.3529
D(x): 0.7741, D(G(z)): 0.1777
Epoch: [11/20], Batch Num: [439/600]
Discriminator Loss: 0.5857, Generator Loss: 1.8815
D(x): 0.8401, D(G(z)): 0.2027
Epoch: [11/20], Batch Num: [440/600]
Discriminator Loss: 0.5975, Generator Loss: 2.5308
D(x): 0.8663, D(G(z)): 0.2193
Epoch: [11/20], Batch Num: [441/600]
Discriminator Loss: 0.4840, Generator Loss: 2.8503
D(x): 0.8581, D(G(z)): 0.1810
Epoch: [11/20], Batch Num: [442/600]
Discriminator Loss: 0.6436, Generator Loss: 3.0230
D(x): 0.7797, D(G(z)): 0.1276
Epoch: [11/20], Batch Num: [443/600]
Discriminator Loss: 0.5168, Generator Loss: 2.6099
D(x): 0.7990, D(G(z)): 0.1080
Epoch: [11/20], Batch Num: [444/600]
Discriminator Loss: 0.5989, Generator Loss: 2.2980
D(x): 0.7766, D(G(z)): 0.1299
Epoch: [11/20], Batch Num: [445/600]
Discriminator Loss: 0.4501, Generator Loss: 2.0398
D(x): 0.8706, D(G(z)): 0.1825
Epoch: [11/20], Batch Num: [446/600]
Discriminator Loss: 0.5465, Generator Loss: 2.2258
D(x): 0.8738, D(G(z)): 0.2121
Epoch: [11/20], Batch Num: [447/600]
Discriminator Loss: 0.4908, Generator Loss: 2.3865
D(x): 0.8470, D(G(z)): 0.1766
Epoch: [11/20], Batch Num: [448/600]
Discriminator Loss: 0.4540, Generator Loss: 2.9200
D(x): 0.8831, D(G(z)): 0.1735
Epoch: [11/20], Batch Num: [449/600]
Discriminator Loss: 0.6181, Generator Loss: 2.9850
D(x): 0.7859, D(G(z)): 0.1487
Epoch: [11/20], Batch Num: [450/600]
Discriminator Loss: 0.3855, Generator Loss: 2.6436
D(x): 0.8531, D(G(z)): 0.1167
Epoch: [11/20], Batch Num: [451/600]
Discriminator Loss: 0.5282, Generator Loss: 2.7395
D(x): 0.8484, D(G(z)): 0.1737
Epoch: [11/20], Batch Num: [452/600]
Discriminator Loss: 0.6030, Generator Loss: 2.7249
D(x): 0.8153, D(G(z)): 0.1886
Epoch: [11/20], Batch Num: [453/600]
Discriminator Loss: 0.6513, Generator Loss: 2.7523
D(x): 0.8009, D(G(z)): 0.1612
Epoch: [11/20], Batch Num: [454/600]
Discriminator Loss: 0.6847, Generator Loss: 2.3129
D(x): 0.8124, D(G(z)): 0.2072
Epoch: [11/20], Batch Num: [455/600]
Discriminator Loss: 0.5295, Generator Loss: 2.3552
D(x): 0.8201, D(G(z)): 0.1723
Epoch: [11/20], Batch Num: [456/600]
Discriminator Loss: 0.4257, Generator Loss: 2.2997
D(x): 0.8632, D(G(z)): 0.1582
Epoch: [11/20], Batch Num: [457/600]
Discriminator Loss: 0.5696, Generator Loss: 2.7816
D(x): 0.8818, D(G(z)): 0.2139
Epoch: [11/20], Batch Num: [458/600]
Discriminator Loss: 0.5761, Generator Loss: 3.3035
D(x): 0.8140, D(G(z)): 0.1808
Epoch: [11/20], Batch Num: [459/600]
Discriminator Loss: 0.6145, Generator Loss: 3.1921
D(x): 0.8014, D(G(z)): 0.1600
Epoch: [11/20], Batch Num: [460/600]
Discriminator Loss: 0.5065, Generator Loss: 2.8123
D(x): 0.8093, D(G(z)): 0.1211
Epoch: [11/20], Batch Num: [461/600]
Discriminator Loss: 0.6660, Generator Loss: 2.4728
D(x): 0.7858, D(G(z)): 0.1689
Epoch: [11/20], Batch Num: [462/600]
Discriminator Loss: 0.5343, Generator Loss: 2.1815
D(x): 0.8190, D(G(z)): 0.1645
Epoch: [11/20], Batch Num: [463/600]
Discriminator Loss: 0.6182, Generator Loss: 2.4208
D(x): 0.8402, D(G(z)): 0.2268
Epoch: [11/20], Batch Num: [464/600]
Discriminator Loss: 0.4671, Generator Loss: 2.8214
D(x): 0.8619, D(G(z)): 0.1917
Epoch: [11/20], Batch Num: [465/600]
Discriminator Loss: 0.6715, Generator Loss: 3.3179
D(x): 0.7768, D(G(z)): 0.1496
Epoch: [11/20], Batch Num: [466/600]
Discriminator Loss: 0.5482, Generator Loss: 2.6770
D(x): 0.7731, D(G(z)): 0.1257
Epoch: [11/20], Batch Num: [467/600]
Discriminator Loss: 0.5002, Generator Loss: 2.4901
D(x): 0.8354, D(G(z)): 0.1554
Epoch: [11/20], Batch Num: [468/600]
Discriminator Loss: 0.6386, Generator Loss: 2.2629
D(x): 0.8302, D(G(z)): 0.1884
Epoch: [11/20], Batch Num: [469/600]
Discriminator Loss: 0.5838, Generator Loss: 2.2686
D(x): 0.8731, D(G(z)): 0.2362
Epoch: [11/20], Batch Num: [470/600]
Discriminator Loss: 0.5519, Generator Loss: 2.3969
D(x): 0.8226, D(G(z)): 0.1823
Epoch: [11/20], Batch Num: [471/600]
Discriminator Loss: 0.5598, Generator Loss: 2.1612
D(x): 0.8285, D(G(z)): 0.1646
Epoch: [11/20], Batch Num: [472/600]
Discriminator Loss: 0.5855, Generator Loss: 2.5566
D(x): 0.8342, D(G(z)): 0.1916
Epoch: [11/20], Batch Num: [473/600]
Discriminator Loss: 0.6138, Generator Loss: 2.4857
D(x): 0.8001, D(G(z)): 0.1830
Epoch: [11/20], Batch Num: [474/600]
Discriminator Loss: 0.5638, Generator Loss: 2.4293
D(x): 0.8500, D(G(z)): 0.2111
Epoch: [11/20], Batch Num: [475/600]
Discriminator Loss: 0.6948, Generator Loss: 2.4824
D(x): 0.7920, D(G(z)): 0.1679
Epoch: [11/20], Batch Num: [476/600]
Discriminator Loss: 0.4855, Generator Loss: 2.6746
D(x): 0.8584, D(G(z)): 0.1740
Epoch: [11/20], Batch Num: [477/600]
Discriminator Loss: 0.5180, Generator Loss: 2.5880
D(x): 0.8365, D(G(z)): 0.1640
Epoch: [11/20], Batch Num: [478/600]
Discriminator Loss: 0.4875, Generator Loss: 2.4630
D(x): 0.8346, D(G(z)): 0.1765
Epoch: [11/20], Batch Num: [479/600]
Discriminator Loss: 0.4431, Generator Loss: 2.6931
D(x): 0.8835, D(G(z)): 0.1873
Epoch: [11/20], Batch Num: [480/600]
Discriminator Loss: 0.4310, Generator Loss: 3.1150
D(x): 0.8663, D(G(z)): 0.1675
Epoch: [11/20], Batch Num: [481/600]
Discriminator Loss: 0.5420, Generator Loss: 2.8708
D(x): 0.8067, D(G(z)): 0.1307
Epoch: [11/20], Batch Num: [482/600]
Discriminator Loss: 0.5228, Generator Loss: 2.7572
D(x): 0.8011, D(G(z)): 0.1136
Epoch: [11/20], Batch Num: [483/600]
Discriminator Loss: 0.5673, Generator Loss: 2.4100
D(x): 0.8435, D(G(z)): 0.1878
Epoch: [11/20], Batch Num: [484/600]
Discriminator Loss: 0.4936, Generator Loss: 2.7130
D(x): 0.8512, D(G(z)): 0.1607
Epoch: [11/20], Batch Num: [485/600]
Discriminator Loss: 0.4901, Generator Loss: 2.6128
D(x): 0.8804, D(G(z)): 0.1914
Epoch: [11/20], Batch Num: [486/600]
Discriminator Loss: 0.4387, Generator Loss: 3.1870
D(x): 0.8600, D(G(z)): 0.1419
Epoch: [11/20], Batch Num: [487/600]
Discriminator Loss: 0.4311, Generator Loss: 3.2217
D(x): 0.8713, D(G(z)): 0.1281
Epoch: [11/20], Batch Num: [488/600]
Discriminator Loss: 0.4731, Generator Loss: 3.0457
D(x): 0.8576, D(G(z)): 0.1215
Epoch: [11/20], Batch Num: [489/600]
Discriminator Loss: 0.3981, Generator Loss: 2.7088
D(x): 0.8434, D(G(z)): 0.1093
Epoch: [11/20], Batch Num: [490/600]
Discriminator Loss: 0.4583, Generator Loss: 2.8368
D(x): 0.9020, D(G(z)): 0.1817
Epoch: [11/20], Batch Num: [491/600]
Discriminator Loss: 0.4971, Generator Loss: 3.2585
D(x): 0.8783, D(G(z)): 0.1929
Epoch: [11/20], Batch Num: [492/600]
Discriminator Loss: 0.5474, Generator Loss: 3.3982
D(x): 0.8386, D(G(z)): 0.1437
Epoch: [11/20], Batch Num: [493/600]
Discriminator Loss: 0.4743, Generator Loss: 3.0044
D(x): 0.8523, D(G(z)): 0.1398
Epoch: [11/20], Batch Num: [494/600]
Discriminator Loss: 0.3959, Generator Loss: 2.7681
D(x): 0.8664, D(G(z)): 0.0969
Epoch: [11/20], Batch Num: [495/600]
Discriminator Loss: 0.4986, Generator Loss: 2.9148
D(x): 0.8941, D(G(z)): 0.1546
Epoch: [11/20], Batch Num: [496/600]
Discriminator Loss: 0.4658, Generator Loss: 2.6556
D(x): 0.8635, D(G(z)): 0.1435
Epoch: [11/20], Batch Num: [497/600]
Discriminator Loss: 0.5167, Generator Loss: 2.6995
D(x): 0.8442, D(G(z)): 0.1688
Epoch: [11/20], Batch Num: [498/600]
Discriminator Loss: 0.4725, Generator Loss: 2.9432
D(x): 0.8943, D(G(z)): 0.1903
Epoch: [11/20], Batch Num: [499/600]
Discriminator Loss: 0.4001, Generator Loss: 3.4381
D(x): 0.8667, D(G(z)): 0.1211
Epoch: 11, Batch Num: [500/600]

Epoch: [11/20], Batch Num: [500/600]
Discriminator Loss: 0.6439, Generator Loss: 2.9388
D(x): 0.7670, D(G(z)): 0.1201
Epoch: [11/20], Batch Num: [501/600]
Discriminator Loss: 0.6854, Generator Loss: 2.6282
D(x): 0.7922, D(G(z)): 0.1716
Epoch: [11/20], Batch Num: [502/600]
Discriminator Loss: 0.5794, Generator Loss: 1.9663
D(x): 0.8484, D(G(z)): 0.1929
Epoch: [11/20], Batch Num: [503/600]
Discriminator Loss: 0.6553, Generator Loss: 2.2467
D(x): 0.8371, D(G(z)): 0.2266
Epoch: [11/20], Batch Num: [504/600]
Discriminator Loss: 0.4896, Generator Loss: 2.7806
D(x): 0.8456, D(G(z)): 0.1704
Epoch: [11/20], Batch Num: [505/600]
Discriminator Loss: 0.5678, Generator Loss: 2.7947
D(x): 0.8417, D(G(z)): 0.1899
Epoch: [11/20], Batch Num: [506/600]
Discriminator Loss: 0.3875, Generator Loss: 3.1256
D(x): 0.8516, D(G(z)): 0.1237
Epoch: [11/20], Batch Num: [507/600]
Discriminator Loss: 0.5424, Generator Loss: 2.8619
D(x): 0.7907, D(G(z)): 0.1092
Epoch: [11/20], Batch Num: [508/600]
Discriminator Loss: 0.5824, Generator Loss: 2.0854
D(x): 0.7981, D(G(z)): 0.1119
Epoch: [11/20], Batch Num: [509/600]
Discriminator Loss: 0.5004, Generator Loss: 2.0417
D(x): 0.8718, D(G(z)): 0.2103
Epoch: [11/20], Batch Num: [510/600]
Discriminator Loss: 0.5537, Generator Loss: 2.3533
D(x): 0.8925, D(G(z)): 0.2433
Epoch: [11/20], Batch Num: [511/600]
Discriminator Loss: 0.5344, Generator Loss: 2.9387
D(x): 0.8588, D(G(z)): 0.1935
Epoch: [11/20], Batch Num: [512/600]
Discriminator Loss: 0.6294, Generator Loss: 3.0906
D(x): 0.7896, D(G(z)): 0.1494
Epoch: [11/20], Batch Num: [513/600]
Discriminator Loss: 0.4426, Generator Loss: 3.0220
D(x): 0.8499, D(G(z)): 0.1242
Epoch: [11/20], Batch Num: [514/600]
Discriminator Loss: 0.5252, Generator Loss: 2.9096
D(x): 0.8122, D(G(z)): 0.1365
Epoch: [11/20], Batch Num: [515/600]
Discriminator Loss: 0.4855, Generator Loss: 2.7681
D(x): 0.8205, D(G(z)): 0.1396
Epoch: [11/20], Batch Num: [516/600]
Discriminator Loss: 0.5422, Generator Loss: 2.4776
D(x): 0.8556, D(G(z)): 0.1907
Epoch: [11/20], Batch Num: [517/600]
Discriminator Loss: 0.4952, Generator Loss: 2.8727
D(x): 0.8725, D(G(z)): 0.2006
Epoch: [11/20], Batch Num: [518/600]
Discriminator Loss: 0.6079, Generator Loss: 2.9455
D(x): 0.8324, D(G(z)): 0.2005
Epoch: [11/20], Batch Num: [519/600]
Discriminator Loss: 0.5447, Generator Loss: 3.3014
D(x): 0.8212, D(G(z)): 0.1602
Epoch: [11/20], Batch Num: [520/600]
Discriminator Loss: 0.5437, Generator Loss: 3.2873
D(x): 0.7804, D(G(z)): 0.0949
Epoch: [11/20], Batch Num: [521/600]
Discriminator Loss: 0.4628, Generator Loss: 2.5430
D(x): 0.8151, D(G(z)): 0.1093
Epoch: [11/20], Batch Num: [522/600]
Discriminator Loss: 0.5497, Generator Loss: 1.8203
D(x): 0.8295, D(G(z)): 0.1796
Epoch: [11/20], Batch Num: [523/600]
Discriminator Loss: 0.5336, Generator Loss: 1.9692
D(x): 0.9011, D(G(z)): 0.2466
Epoch: [11/20], Batch Num: [524/600]
Discriminator Loss: 0.4791, Generator Loss: 2.8581
D(x): 0.9279, D(G(z)): 0.2351
Epoch: [11/20], Batch Num: [525/600]
Discriminator Loss: 0.4693, Generator Loss: 3.6090
D(x): 0.8348, D(G(z)): 0.1518
Epoch: [11/20], Batch Num: [526/600]
Discriminator Loss: 0.4890, Generator Loss: 3.5506
D(x): 0.8120, D(G(z)): 0.1105
Epoch: [11/20], Batch Num: [527/600]
Discriminator Loss: 0.5732, Generator Loss: 2.8014
D(x): 0.7566, D(G(z)): 0.0948
Epoch: [11/20], Batch Num: [528/600]
Discriminator Loss: 0.5798, Generator Loss: 2.4224
D(x): 0.8254, D(G(z)): 0.1661
Epoch: [11/20], Batch Num: [529/600]
Discriminator Loss: 0.6899, Generator Loss: 2.2568
D(x): 0.8690, D(G(z)): 0.2610
Epoch: [11/20], Batch Num: [530/600]
Discriminator Loss: 0.5359, Generator Loss: 2.7592
D(x): 0.8557, D(G(z)): 0.2181
Epoch: [11/20], Batch Num: [531/600]
Discriminator Loss: 0.4450, Generator Loss: 3.2824
D(x): 0.8683, D(G(z)): 0.1576
Epoch: [11/20], Batch Num: [532/600]
Discriminator Loss: 0.3927, Generator Loss: 3.3677
D(x): 0.8354, D(G(z)): 0.0938
Epoch: [11/20], Batch Num: [533/600]
Discriminator Loss: 0.5548, Generator Loss: 2.7569
D(x): 0.7386, D(G(z)): 0.0890
Epoch: [11/20], Batch Num: [534/600]
Discriminator Loss: 0.3960, Generator Loss: 2.2558
D(x): 0.8765, D(G(z)): 0.1562
Epoch: [11/20], Batch Num: [535/600]
Discriminator Loss: 0.6066, Generator Loss: 1.9396
D(x): 0.8730, D(G(z)): 0.2311
Epoch: [11/20], Batch Num: [536/600]
Discriminator Loss: 0.6837, Generator Loss: 3.1243
D(x): 0.9082, D(G(z)): 0.3012
Epoch: [11/20], Batch Num: [537/600]
Discriminator Loss: 0.5965, Generator Loss: 3.8851
D(x): 0.8085, D(G(z)): 0.1436
Epoch: [11/20], Batch Num: [538/600]
Discriminator Loss: 0.6508, Generator Loss: 3.1099
D(x): 0.7354, D(G(z)): 0.0630
Epoch: [11/20], Batch Num: [539/600]
Discriminator Loss: 0.5157, Generator Loss: 2.8183
D(x): 0.8288, D(G(z)): 0.1281
Epoch: [11/20], Batch Num: [540/600]
Discriminator Loss: 0.5785, Generator Loss: 1.8552
D(x): 0.8647, D(G(z)): 0.2070
Epoch: [11/20], Batch Num: [541/600]
Discriminator Loss: 0.6626, Generator Loss: 2.4297
D(x): 0.8619, D(G(z)): 0.2520
Epoch: [11/20], Batch Num: [542/600]
Discriminator Loss: 0.6677, Generator Loss: 3.0001
D(x): 0.8522, D(G(z)): 0.2428
Epoch: [11/20], Batch Num: [543/600]
Discriminator Loss: 0.6310, Generator Loss: 3.1118
D(x): 0.7638, D(G(z)): 0.1249
Epoch: [11/20], Batch Num: [544/600]
Discriminator Loss: 0.6046, Generator Loss: 2.7511
D(x): 0.7835, D(G(z)): 0.1102
Epoch: [11/20], Batch Num: [545/600]
Discriminator Loss: 0.5832, Generator Loss: 2.4772
D(x): 0.7992, D(G(z)): 0.1343
Epoch: [11/20], Batch Num: [546/600]
Discriminator Loss: 0.5742, Generator Loss: 2.0931
D(x): 0.7949, D(G(z)): 0.1670
Epoch: [11/20], Batch Num: [547/600]
Discriminator Loss: 0.6700, Generator Loss: 1.9511
D(x): 0.8525, D(G(z)): 0.2480
Epoch: [11/20], Batch Num: [548/600]
Discriminator Loss: 0.5081, Generator Loss: 2.2506
D(x): 0.8623, D(G(z)): 0.2229
Epoch: [11/20], Batch Num: [549/600]
Discriminator Loss: 0.5737, Generator Loss: 2.4832
D(x): 0.8599, D(G(z)): 0.2146
Epoch: [11/20], Batch Num: [550/600]
Discriminator Loss: 0.5060, Generator Loss: 2.6520
D(x): 0.8358, D(G(z)): 0.1431
Epoch: [11/20], Batch Num: [551/600]
Discriminator Loss: 0.6441, Generator Loss: 3.0738
D(x): 0.7974, D(G(z)): 0.1611
Epoch: [11/20], Batch Num: [552/600]
Discriminator Loss: 0.6006, Generator Loss: 2.8695
D(x): 0.7722, D(G(z)): 0.1159
Epoch: [11/20], Batch Num: [553/600]
Discriminator Loss: 0.6609, Generator Loss: 2.4428
D(x): 0.7870, D(G(z)): 0.1334
Epoch: [11/20], Batch Num: [554/600]
Discriminator Loss: 0.5089, Generator Loss: 2.3455
D(x): 0.8932, D(G(z)): 0.2017
Epoch: [11/20], Batch Num: [555/600]
Discriminator Loss: 0.4296, Generator Loss: 2.4795
D(x): 0.8369, D(G(z)): 0.1529
Epoch: [11/20], Batch Num: [556/600]
Discriminator Loss: 0.4777, Generator Loss: 2.2396
D(x): 0.8161, D(G(z)): 0.1549
Epoch: [11/20], Batch Num: [557/600]
Discriminator Loss: 0.6590, Generator Loss: 2.5125
D(x): 0.8343, D(G(z)): 0.2500
Epoch: [11/20], Batch Num: [558/600]
Discriminator Loss: 0.5659, Generator Loss: 2.7058
D(x): 0.8376, D(G(z)): 0.1797
Epoch: [11/20], Batch Num: [559/600]
Discriminator Loss: 0.4614, Generator Loss: 2.8488
D(x): 0.8451, D(G(z)): 0.1547
Epoch: [11/20], Batch Num: [560/600]
Discriminator Loss: 0.4276, Generator Loss: 3.0231
D(x): 0.8693, D(G(z)): 0.1600
Epoch: [11/20], Batch Num: [561/600]
Discriminator Loss: 0.5293, Generator Loss: 3.2765
D(x): 0.8478, D(G(z)): 0.1494
Epoch: [11/20], Batch Num: [562/600]
Discriminator Loss: 0.7244, Generator Loss: 2.7208
D(x): 0.7161, D(G(z)): 0.1251
Epoch: [11/20], Batch Num: [563/600]
Discriminator Loss: 0.4641, Generator Loss: 2.7257
D(x): 0.8517, D(G(z)): 0.1369
Epoch: [11/20], Batch Num: [564/600]
Discriminator Loss: 0.5168, Generator Loss: 2.1241
D(x): 0.8158, D(G(z)): 0.1457
Epoch: [11/20], Batch Num: [565/600]
Discriminator Loss: 0.6535, Generator Loss: 2.0569
D(x): 0.8538, D(G(z)): 0.2227
Epoch: [11/20], Batch Num: [566/600]
Discriminator Loss: 0.5895, Generator Loss: 2.4097
D(x): 0.8528, D(G(z)): 0.1999
Epoch: [11/20], Batch Num: [567/600]
Discriminator Loss: 0.4627, Generator Loss: 2.2973
D(x): 0.8629, D(G(z)): 0.1930
Epoch: [11/20], Batch Num: [568/600]
Discriminator Loss: 0.5555, Generator Loss: 2.7382
D(x): 0.8452, D(G(z)): 0.1836
Epoch: [11/20], Batch Num: [569/600]
Discriminator Loss: 0.5963, Generator Loss: 2.9155
D(x): 0.7980, D(G(z)): 0.1209
Epoch: [11/20], Batch Num: [570/600]
Discriminator Loss: 0.5500, Generator Loss: 2.6017
D(x): 0.8032, D(G(z)): 0.1171
Epoch: [11/20], Batch Num: [571/600]
Discriminator Loss: 0.5776, Generator Loss: 2.6028
D(x): 0.8541, D(G(z)): 0.1905
Epoch: [11/20], Batch Num: [572/600]
Discriminator Loss: 0.4949, Generator Loss: 2.6191
D(x): 0.8671, D(G(z)): 0.1746
Epoch: [11/20], Batch Num: [573/600]
Discriminator Loss: 0.4964, Generator Loss: 2.6369
D(x): 0.8601, D(G(z)): 0.1662
Epoch: [11/20], Batch Num: [574/600]
Discriminator Loss: 0.4845, Generator Loss: 2.6051
D(x): 0.8564, D(G(z)): 0.1690
Epoch: [11/20], Batch Num: [575/600]
Discriminator Loss: 0.4629, Generator Loss: 2.9956
D(x): 0.8617, D(G(z)): 0.1659
Epoch: [11/20], Batch Num: [576/600]
Discriminator Loss: 0.5329, Generator Loss: 3.2159
D(x): 0.8352, D(G(z)): 0.1563
Epoch: [11/20], Batch Num: [577/600]
Discriminator Loss: 0.4902, Generator Loss: 2.6859
D(x): 0.8241, D(G(z)): 0.1153
Epoch: [11/20], Batch Num: [578/600]
Discriminator Loss: 0.5898, Generator Loss: 2.6888
D(x): 0.8249, D(G(z)): 0.1634
Epoch: [11/20], Batch Num: [579/600]
Discriminator Loss: 0.3854, Generator Loss: 2.7428
D(x): 0.8903, D(G(z)): 0.1616
Epoch: [11/20], Batch Num: [580/600]
Discriminator Loss: 0.5069, Generator Loss: 2.5877
D(x): 0.8385, D(G(z)): 0.1499
Epoch: [11/20], Batch Num: [581/600]
Discriminator Loss: 0.4666, Generator Loss: 2.8802
D(x): 0.8738, D(G(z)): 0.1518
Epoch: [11/20], Batch Num: [582/600]
Discriminator Loss: 0.3916, Generator Loss: 3.3873
D(x): 0.8834, D(G(z)): 0.1326
Epoch: [11/20], Batch Num: [583/600]
Discriminator Loss: 0.4278, Generator Loss: 3.0259
D(x): 0.8509, D(G(z)): 0.1322
Epoch: [11/20], Batch Num: [584/600]
Discriminator Loss: 0.3594, Generator Loss: 3.2609
D(x): 0.8892, D(G(z)): 0.1287
Epoch: [11/20], Batch Num: [585/600]
Discriminator Loss: 0.5861, Generator Loss: 2.6260
D(x): 0.7560, D(G(z)): 0.0993
Epoch: [11/20], Batch Num: [586/600]
Discriminator Loss: 0.4966, Generator Loss: 2.7062
D(x): 0.8814, D(G(z)): 0.1881
Epoch: [11/20], Batch Num: [587/600]
Discriminator Loss: 0.5349, Generator Loss: 2.7575
D(x): 0.8394, D(G(z)): 0.1754
Epoch: [11/20], Batch Num: [588/600]
Discriminator Loss: 0.5025, Generator Loss: 2.5737
D(x): 0.8750, D(G(z)): 0.1707
Epoch: [11/20], Batch Num: [589/600]
Discriminator Loss: 0.4030, Generator Loss: 2.8105
D(x): 0.8776, D(G(z)): 0.1470
Epoch: [11/20], Batch Num: [590/600]
Discriminator Loss: 0.6207, Generator Loss: 3.0973
D(x): 0.8292, D(G(z)): 0.1814
Epoch: [11/20], Batch Num: [591/600]
Discriminator Loss: 0.5318, Generator Loss: 3.1557
D(x): 0.8209, D(G(z)): 0.1172
Epoch: [11/20], Batch Num: [592/600]
Discriminator Loss: 0.5527, Generator Loss: 2.9599
D(x): 0.8088, D(G(z)): 0.1164
Epoch: [11/20], Batch Num: [593/600]
Discriminator Loss: 0.4838, Generator Loss: 2.7274
D(x): 0.8516, D(G(z)): 0.1403
Epoch: [11/20], Batch Num: [594/600]
Discriminator Loss: 0.5420, Generator Loss: 2.7220
D(x): 0.8556, D(G(z)): 0.1866
Epoch: [11/20], Batch Num: [595/600]
Discriminator Loss: 0.4908, Generator Loss: 2.4481
D(x): 0.8368, D(G(z)): 0.1486
Epoch: [11/20], Batch Num: [596/600]
Discriminator Loss: 0.4360, Generator Loss: 3.1092
D(x): 0.9164, D(G(z)): 0.1942
Epoch: [11/20], Batch Num: [597/600]
Discriminator Loss: 0.4754, Generator Loss: 3.6923
D(x): 0.8779, D(G(z)): 0.1636
Epoch: [11/20], Batch Num: [598/600]
Discriminator Loss: 0.6476, Generator Loss: 3.4709
D(x): 0.7724, D(G(z)): 0.0853
Epoch: [11/20], Batch Num: [599/600]
Discriminator Loss: 0.5557, Generator Loss: 3.0996
D(x): 0.7785, D(G(z)): 0.0901
Epoch: 12, Batch Num: [0/600]

Epoch: [12/20], Batch Num: [0/600]
Discriminator Loss: 0.4302, Generator Loss: 2.1562
D(x): 0.8771, D(G(z)): 0.1420
Epoch: [12/20], Batch Num: [1/600]
Discriminator Loss: 0.5484, Generator Loss: 2.4132
D(x): 0.8746, D(G(z)): 0.2003
Epoch: [12/20], Batch Num: [2/600]
Discriminator Loss: 0.6255, Generator Loss: 2.5167
D(x): 0.8399, D(G(z)): 0.2079
Epoch: [12/20], Batch Num: [3/600]
Discriminator Loss: 0.6188, Generator Loss: 2.7232
D(x): 0.8816, D(G(z)): 0.2169
Epoch: [12/20], Batch Num: [4/600]
Discriminator Loss: 0.6436, Generator Loss: 3.0095
D(x): 0.8169, D(G(z)): 0.1784
Epoch: [12/20], Batch Num: [5/600]
Discriminator Loss: 0.7232, Generator Loss: 3.2854
D(x): 0.8188, D(G(z)): 0.1458
Epoch: [12/20], Batch Num: [6/600]
Discriminator Loss: 0.6731, Generator Loss: 3.0502
D(x): 0.7764, D(G(z)): 0.1335
Epoch: [12/20], Batch Num: [7/600]
Discriminator Loss: 0.8505, Generator Loss: 2.5886
D(x): 0.7467, D(G(z)): 0.1662
Epoch: [12/20], Batch Num: [8/600]
Discriminator Loss: 0.5650, Generator Loss: 2.1657
D(x): 0.8616, D(G(z)): 0.1868
Epoch: [12/20], Batch Num: [9/600]
Discriminator Loss: 0.8004, Generator Loss: 2.0922
D(x): 0.8154, D(G(z)): 0.2233
Epoch: [12/20], Batch Num: [10/600]
Discriminator Loss: 0.4760, Generator Loss: 2.3247
D(x): 0.8736, D(G(z)): 0.1872
Epoch: [12/20], Batch Num: [11/600]
Discriminator Loss: 0.5461, Generator Loss: 2.5738
D(x): 0.8175, D(G(z)): 0.1738
Epoch: [12/20], Batch Num: [12/600]
Discriminator Loss: 0.6102, Generator Loss: 2.5713
D(x): 0.8262, D(G(z)): 0.1599
Epoch: [12/20], Batch Num: [13/600]
Discriminator Loss: 0.6583, Generator Loss: 2.4571
D(x): 0.7515, D(G(z)): 0.1402
Epoch: [12/20], Batch Num: [14/600]
Discriminator Loss: 0.6005, Generator Loss: 2.0781
D(x): 0.7835, D(G(z)): 0.1752
Epoch: [12/20], Batch Num: [15/600]
Discriminator Loss: 0.6511, Generator Loss: 2.2365
D(x): 0.8416, D(G(z)): 0.2487
Epoch: [12/20], Batch Num: [16/600]
Discriminator Loss: 0.4757, Generator Loss: 2.7221
D(x): 0.8993, D(G(z)): 0.2338
Epoch: [12/20], Batch Num: [17/600]
Discriminator Loss: 0.5514, Generator Loss: 2.5745
D(x): 0.8263, D(G(z)): 0.1615
Epoch: [12/20], Batch Num: [18/600]
Discriminator Loss: 0.5200, Generator Loss: 2.9667
D(x): 0.7968, D(G(z)): 0.1359
Epoch: [12/20], Batch Num: [19/600]
Discriminator Loss: 0.6461, Generator Loss: 2.3976
D(x): 0.7291, D(G(z)): 0.1058
Epoch: [12/20], Batch Num: [20/600]
Discriminator Loss: 0.5018, Generator Loss: 2.1198
D(x): 0.8103, D(G(z)): 0.1442
Epoch: [12/20], Batch Num: [21/600]
Discriminator Loss: 0.4864, Generator Loss: 1.9516
D(x): 0.8830, D(G(z)): 0.2249
Epoch: [12/20], Batch Num: [22/600]
Discriminator Loss: 0.5417, Generator Loss: 2.1600
D(x): 0.8664, D(G(z)): 0.2238
Epoch: [12/20], Batch Num: [23/600]
Discriminator Loss: 0.8553, Generator Loss: 2.4196
D(x): 0.8286, D(G(z)): 0.2878
Epoch: [12/20], Batch Num: [24/600]
Discriminator Loss: 0.5751, Generator Loss: 3.0211
D(x): 0.8301, D(G(z)): 0.1767
Epoch: [12/20], Batch Num: [25/600]
Discriminator Loss: 0.5375, Generator Loss: 3.1413
D(x): 0.8070, D(G(z)): 0.1297
Epoch: [12/20], Batch Num: [26/600]
Discriminator Loss: 0.6622, Generator Loss: 2.6852
D(x): 0.7400, D(G(z)): 0.1135
Epoch: [12/20], Batch Num: [27/600]
Discriminator Loss: 0.4765, Generator Loss: 2.3803
D(x): 0.8079, D(G(z)): 0.1099
Epoch: [12/20], Batch Num: [28/600]
Discriminator Loss: 0.4214, Generator Loss: 2.2014
D(x): 0.8994, D(G(z)): 0.1997
Epoch: [12/20], Batch Num: [29/600]
Discriminator Loss: 0.6401, Generator Loss: 1.8866
D(x): 0.8296, D(G(z)): 0.2219
Epoch: [12/20], Batch Num: [30/600]
Discriminator Loss: 0.4758, Generator Loss: 2.0401
D(x): 0.8708, D(G(z)): 0.1943
Epoch: [12/20], Batch Num: [31/600]
Discriminator Loss: 0.5463, Generator Loss: 2.3356
D(x): 0.8702, D(G(z)): 0.2324
Epoch: [12/20], Batch Num: [32/600]
Discriminator Loss: 0.5427, Generator Loss: 2.7189
D(x): 0.8050, D(G(z)): 0.1615
Epoch: [12/20], Batch Num: [33/600]
Discriminator Loss: 0.5042, Generator Loss: 2.4812
D(x): 0.7992, D(G(z)): 0.1184
Epoch: [12/20], Batch Num: [34/600]
Discriminator Loss: 0.6474, Generator Loss: 2.7487
D(x): 0.8006, D(G(z)): 0.1580
Epoch: [12/20], Batch Num: [35/600]
Discriminator Loss: 0.4679, Generator Loss: 2.5942
D(x): 0.8456, D(G(z)): 0.1666
Epoch: [12/20], Batch Num: [36/600]
Discriminator Loss: 0.5193, Generator Loss: 2.2396
D(x): 0.8453, D(G(z)): 0.1692
Epoch: [12/20], Batch Num: [37/600]
Discriminator Loss: 0.6110, Generator Loss: 2.4799
D(x): 0.8676, D(G(z)): 0.2241
Epoch: [12/20], Batch Num: [38/600]
Discriminator Loss: 0.5855, Generator Loss: 2.2725
D(x): 0.8075, D(G(z)): 0.1821
Epoch: [12/20], Batch Num: [39/600]
Discriminator Loss: 0.6405, Generator Loss: 2.4457
D(x): 0.8233, D(G(z)): 0.2198
Epoch: [12/20], Batch Num: [40/600]
Discriminator Loss: 0.4990, Generator Loss: 2.6252
D(x): 0.8241, D(G(z)): 0.1645
Epoch: [12/20], Batch Num: [41/600]
Discriminator Loss: 0.4495, Generator Loss: 2.8504
D(x): 0.8786, D(G(z)): 0.2010
Epoch: [12/20], Batch Num: [42/600]
Discriminator Loss: 0.6091, Generator Loss: 2.7324
D(x): 0.7913, D(G(z)): 0.1438
Epoch: [12/20], Batch Num: [43/600]
Discriminator Loss: 0.6302, Generator Loss: 2.4906
D(x): 0.7992, D(G(z)): 0.1586
Epoch: [12/20], Batch Num: [44/600]
Discriminator Loss: 0.6254, Generator Loss: 2.3338
D(x): 0.8117, D(G(z)): 0.1866
Epoch: [12/20], Batch Num: [45/600]
Discriminator Loss: 0.6370, Generator Loss: 2.2359
D(x): 0.8209, D(G(z)): 0.2111
Epoch: [12/20], Batch Num: [46/600]
Discriminator Loss: 0.5970, Generator Loss: 2.4249
D(x): 0.8031, D(G(z)): 0.2031
Epoch: [12/20], Batch Num: [47/600]
Discriminator Loss: 0.6389, Generator Loss: 2.2169
D(x): 0.7927, D(G(z)): 0.1847
Epoch: [12/20], Batch Num: [48/600]
Discriminator Loss: 0.6321, Generator Loss: 2.2914
D(x): 0.7970, D(G(z)): 0.1982
Epoch: [12/20], Batch Num: [49/600]
Discriminator Loss: 0.5618, Generator Loss: 2.2732
D(x): 0.8741, D(G(z)): 0.2320
Epoch: [12/20], Batch Num: [50/600]
Discriminator Loss: 0.5681, Generator Loss: 2.6512
D(x): 0.8281, D(G(z)): 0.2095
Epoch: [12/20], Batch Num: [51/600]
Discriminator Loss: 0.4743, Generator Loss: 3.0695
D(x): 0.8440, D(G(z)): 0.1516
Epoch: [12/20], Batch Num: [52/600]
Discriminator Loss: 0.5614, Generator Loss: 3.1974
D(x): 0.7764, D(G(z)): 0.1279
Epoch: [12/20], Batch Num: [53/600]
Discriminator Loss: 0.6696, Generator Loss: 2.4750
D(x): 0.7517, D(G(z)): 0.1325
Epoch: [12/20], Batch Num: [54/600]
Discriminator Loss: 0.6193, Generator Loss: 2.6755
D(x): 0.8450, D(G(z)): 0.2093
Epoch: [12/20], Batch Num: [55/600]
Discriminator Loss: 0.6142, Generator Loss: 2.3218
D(x): 0.8586, D(G(z)): 0.2249
Epoch: [12/20], Batch Num: [56/600]
Discriminator Loss: 0.6843, Generator Loss: 2.3961
D(x): 0.8000, D(G(z)): 0.2009
Epoch: [12/20], Batch Num: [57/600]
Discriminator Loss: 0.6127, Generator Loss: 2.5235
D(x): 0.8413, D(G(z)): 0.2218
Epoch: [12/20], Batch Num: [58/600]
Discriminator Loss: 0.7097, Generator Loss: 2.6766
D(x): 0.7845, D(G(z)): 0.1693
Epoch: [12/20], Batch Num: [59/600]
Discriminator Loss: 0.5453, Generator Loss: 2.7555
D(x): 0.7995, D(G(z)): 0.1240
Epoch: [12/20], Batch Num: [60/600]
Discriminator Loss: 0.6969, Generator Loss: 2.2063
D(x): 0.7378, D(G(z)): 0.1498
Epoch: [12/20], Batch Num: [61/600]
Discriminator Loss: 0.4823, Generator Loss: 1.9592
D(x): 0.8112, D(G(z)): 0.1405
Epoch: [12/20], Batch Num: [62/600]
Discriminator Loss: 0.6476, Generator Loss: 1.7242
D(x): 0.8093, D(G(z)): 0.2379
Epoch: [12/20], Batch Num: [63/600]
Discriminator Loss: 0.7028, Generator Loss: 1.8263
D(x): 0.8268, D(G(z)): 0.2838
Epoch: [12/20], Batch Num: [64/600]
Discriminator Loss: 0.7278, Generator Loss: 1.7661
D(x): 0.8389, D(G(z)): 0.2946
Epoch: [12/20], Batch Num: [65/600]
Discriminator Loss: 0.6304, Generator Loss: 2.4944
D(x): 0.8592, D(G(z)): 0.2931
Epoch: [12/20], Batch Num: [66/600]
Discriminator Loss: 0.5996, Generator Loss: 2.8480
D(x): 0.8000, D(G(z)): 0.1747
Epoch: [12/20], Batch Num: [67/600]
Discriminator Loss: 0.6352, Generator Loss: 2.7814
D(x): 0.7493, D(G(z)): 0.0938
Epoch: [12/20], Batch Num: [68/600]
Discriminator Loss: 0.6816, Generator Loss: 2.3400
D(x): 0.7340, D(G(z)): 0.1518
Epoch: [12/20], Batch Num: [69/600]
Discriminator Loss: 0.6002, Generator Loss: 2.0635
D(x): 0.7637, D(G(z)): 0.1471
Epoch: [12/20], Batch Num: [70/600]
Discriminator Loss: 0.5368, Generator Loss: 1.7617
D(x): 0.8258, D(G(z)): 0.1958
Epoch: [12/20], Batch Num: [71/600]
Discriminator Loss: 0.6049, Generator Loss: 1.4954
D(x): 0.8860, D(G(z)): 0.2765
Epoch: [12/20], Batch Num: [72/600]
Discriminator Loss: 0.5980, Generator Loss: 1.9657
D(x): 0.8862, D(G(z)): 0.2767
Epoch: [12/20], Batch Num: [73/600]
Discriminator Loss: 0.5068, Generator Loss: 2.4095
D(x): 0.8714, D(G(z)): 0.2296
Epoch: [12/20], Batch Num: [74/600]
Discriminator Loss: 0.6631, Generator Loss: 2.7650
D(x): 0.7760, D(G(z)): 0.1542
Epoch: [12/20], Batch Num: [75/600]
Discriminator Loss: 0.6250, Generator Loss: 2.6920
D(x): 0.7713, D(G(z)): 0.1285
Epoch: [12/20], Batch Num: [76/600]
Discriminator Loss: 0.5162, Generator Loss: 2.5914
D(x): 0.7967, D(G(z)): 0.1087
Epoch: [12/20], Batch Num: [77/600]
Discriminator Loss: 0.6048, Generator Loss: 2.2092
D(x): 0.7667, D(G(z)): 0.1371
Epoch: [12/20], Batch Num: [78/600]
Discriminator Loss: 0.5960, Generator Loss: 1.8156
D(x): 0.7872, D(G(z)): 0.1584
Epoch: [12/20], Batch Num: [79/600]
Discriminator Loss: 0.6237, Generator Loss: 1.7746
D(x): 0.8706, D(G(z)): 0.2639
Epoch: [12/20], Batch Num: [80/600]
Discriminator Loss: 0.6011, Generator Loss: 1.8599
D(x): 0.8942, D(G(z)): 0.2840
Epoch: [12/20], Batch Num: [81/600]
Discriminator Loss: 0.5717, Generator Loss: 2.2852
D(x): 0.8365, D(G(z)): 0.2102
Epoch: [12/20], Batch Num: [82/600]
Discriminator Loss: 0.5696, Generator Loss: 2.4996
D(x): 0.8363, D(G(z)): 0.1853
Epoch: [12/20], Batch Num: [83/600]
Discriminator Loss: 0.6609, Generator Loss: 2.5002
D(x): 0.7261, D(G(z)): 0.1204
Epoch: [12/20], Batch Num: [84/600]
Discriminator Loss: 0.5452, Generator Loss: 2.3607
D(x): 0.7858, D(G(z)): 0.1394
Epoch: [12/20], Batch Num: [85/600]
Discriminator Loss: 0.6257, Generator Loss: 2.1163
D(x): 0.7772, D(G(z)): 0.1553
Epoch: [12/20], Batch Num: [86/600]
Discriminator Loss: 0.6972, Generator Loss: 1.9582
D(x): 0.7989, D(G(z)): 0.2324
Epoch: [12/20], Batch Num: [87/600]
Discriminator Loss: 0.6316, Generator Loss: 1.9881
D(x): 0.8484, D(G(z)): 0.2664
Epoch: [12/20], Batch Num: [88/600]
Discriminator Loss: 0.6196, Generator Loss: 2.1776
D(x): 0.8472, D(G(z)): 0.2414
Epoch: [12/20], Batch Num: [89/600]
Discriminator Loss: 0.5639, Generator Loss: 2.4856
D(x): 0.8435, D(G(z)): 0.2117
Epoch: [12/20], Batch Num: [90/600]
Discriminator Loss: 0.5138, Generator Loss: 2.5407
D(x): 0.8062, D(G(z)): 0.1426
Epoch: [12/20], Batch Num: [91/600]
Discriminator Loss: 0.5758, Generator Loss: 2.3265
D(x): 0.7702, D(G(z)): 0.1137
Epoch: [12/20], Batch Num: [92/600]
Discriminator Loss: 0.6934, Generator Loss: 2.1590
D(x): 0.7326, D(G(z)): 0.1580
Epoch: [12/20], Batch Num: [93/600]
Discriminator Loss: 0.6797, Generator Loss: 1.8205
D(x): 0.7749, D(G(z)): 0.1845
Epoch: [12/20], Batch Num: [94/600]
Discriminator Loss: 0.5802, Generator Loss: 1.7618
D(x): 0.8443, D(G(z)): 0.2436
Epoch: [12/20], Batch Num: [95/600]
Discriminator Loss: 0.4394, Generator Loss: 2.0726
D(x): 0.9073, D(G(z)): 0.2222
Epoch: [12/20], Batch Num: [96/600]
Discriminator Loss: 0.4878, Generator Loss: 2.5861
D(x): 0.8833, D(G(z)): 0.2066
Epoch: [12/20], Batch Num: [97/600]
Discriminator Loss: 0.4925, Generator Loss: 2.7682
D(x): 0.8112, D(G(z)): 0.1434
Epoch: [12/20], Batch Num: [98/600]
Discriminator Loss: 0.4920, Generator Loss: 2.8600
D(x): 0.8106, D(G(z)): 0.1436
Epoch: [12/20], Batch Num: [99/600]
Discriminator Loss: 0.6759, Generator Loss: 2.5253
D(x): 0.7150, D(G(z)): 0.1074
Epoch: 12, Batch Num: [100/600]

Epoch: [12/20], Batch Num: [100/600]
Discriminator Loss: 0.5416, Generator Loss: 1.9459
D(x): 0.8072, D(G(z)): 0.1531
Epoch: [12/20], Batch Num: [101/600]
Discriminator Loss: 0.5523, Generator Loss: 1.6780
D(x): 0.8789, D(G(z)): 0.2447
Epoch: [12/20], Batch Num: [102/600]
Discriminator Loss: 0.4744, Generator Loss: 1.9944
D(x): 0.8928, D(G(z)): 0.2384
Epoch: [12/20], Batch Num: [103/600]
Discriminator Loss: 0.5385, Generator Loss: 2.3871
D(x): 0.8632, D(G(z)): 0.2477
Epoch: [12/20], Batch Num: [104/600]
Discriminator Loss: 0.3668, Generator Loss: 2.8496
D(x): 0.8808, D(G(z)): 0.1285
Epoch: [12/20], Batch Num: [105/600]
Discriminator Loss: 0.4900, Generator Loss: 3.1071
D(x): 0.8277, D(G(z)): 0.1299
Epoch: [12/20], Batch Num: [106/600]
Discriminator Loss: 0.4690, Generator Loss: 3.2645
D(x): 0.8285, D(G(z)): 0.1147
Epoch: [12/20], Batch Num: [107/600]
Discriminator Loss: 0.6035, Generator Loss: 2.7695
D(x): 0.7738, D(G(z)): 0.1292
Epoch: [12/20], Batch Num: [108/600]
Discriminator Loss: 0.5499, Generator Loss: 2.7309
D(x): 0.8370, D(G(z)): 0.1773
Epoch: [12/20], Batch Num: [109/600]
Discriminator Loss: 0.5882, Generator Loss: 2.6265
D(x): 0.8235, D(G(z)): 0.1729
Epoch: [12/20], Batch Num: [110/600]
Discriminator Loss: 0.6472, Generator Loss: 2.5333
D(x): 0.7794, D(G(z)): 0.1605
Epoch: [12/20], Batch Num: [111/600]
Discriminator Loss: 0.5896, Generator Loss: 2.4774
D(x): 0.8299, D(G(z)): 0.2006
Epoch: [12/20], Batch Num: [112/600]
Discriminator Loss: 0.5254, Generator Loss: 2.4746
D(x): 0.8784, D(G(z)): 0.2168
Epoch: [12/20], Batch Num: [113/600]
Discriminator Loss: 0.4395, Generator Loss: 3.2429
D(x): 0.8709, D(G(z)): 0.1673
Epoch: [12/20], Batch Num: [114/600]
Discriminator Loss: 0.3836, Generator Loss: 3.0626
D(x): 0.8519, D(G(z)): 0.1041
Epoch: [12/20], Batch Num: [115/600]
Discriminator Loss: 0.6378, Generator Loss: 2.7108
D(x): 0.7858, D(G(z)): 0.1086
Epoch: [12/20], Batch Num: [116/600]
Discriminator Loss: 0.6559, Generator Loss: 2.5335
D(x): 0.7513, D(G(z)): 0.1607
Epoch: [12/20], Batch Num: [117/600]
Discriminator Loss: 0.4685, Generator Loss: 1.9361
D(x): 0.8707, D(G(z)): 0.1659
Epoch: [12/20], Batch Num: [118/600]
Discriminator Loss: 0.7562, Generator Loss: 1.9705
D(x): 0.8055, D(G(z)): 0.2631
Epoch: [12/20], Batch Num: [119/600]
Discriminator Loss: 0.5730, Generator Loss: 2.2375
D(x): 0.8673, D(G(z)): 0.2349
Epoch: [12/20], Batch Num: [120/600]
Discriminator Loss: 0.4759, Generator Loss: 2.8325
D(x): 0.8636, D(G(z)): 0.1964
Epoch: [12/20], Batch Num: [121/600]
Discriminator Loss: 0.6181, Generator Loss: 2.8434
D(x): 0.7711, D(G(z)): 0.1395
Epoch: [12/20], Batch Num: [122/600]
Discriminator Loss: 0.6598, Generator Loss: 2.9402
D(x): 0.7847, D(G(z)): 0.1712
Epoch: [12/20], Batch Num: [123/600]
Discriminator Loss: 0.5878, Generator Loss: 2.6773
D(x): 0.8448, D(G(z)): 0.1775
Epoch: [12/20], Batch Num: [124/600]
Discriminator Loss: 0.6387, Generator Loss: 2.5809
D(x): 0.8054, D(G(z)): 0.1570
Epoch: [12/20], Batch Num: [125/600]
Discriminator Loss: 0.5897, Generator Loss: 2.3558
D(x): 0.8200, D(G(z)): 0.2010
Epoch: [12/20], Batch Num: [126/600]
Discriminator Loss: 0.5381, Generator Loss: 2.4826
D(x): 0.8610, D(G(z)): 0.1968
Epoch: [12/20], Batch Num: [127/600]
Discriminator Loss: 0.5034, Generator Loss: 2.8230
D(x): 0.8575, D(G(z)): 0.1879
Epoch: [12/20], Batch Num: [128/600]
Discriminator Loss: 0.4815, Generator Loss: 2.8761
D(x): 0.8467, D(G(z)): 0.1511
Epoch: [12/20], Batch Num: [129/600]
Discriminator Loss: 0.4747, Generator Loss: 2.6278
D(x): 0.8032, D(G(z)): 0.1187
Epoch: [12/20], Batch Num: [130/600]
Discriminator Loss: 0.5745, Generator Loss: 2.7182
D(x): 0.8118, D(G(z)): 0.1620
Epoch: [12/20], Batch Num: [131/600]
Discriminator Loss: 0.5342, Generator Loss: 1.9917
D(x): 0.8070, D(G(z)): 0.1705
Epoch: [12/20], Batch Num: [132/600]
Discriminator Loss: 0.5339, Generator Loss: 2.3923
D(x): 0.8561, D(G(z)): 0.2073
Epoch: [12/20], Batch Num: [133/600]
Discriminator Loss: 0.7635, Generator Loss: 2.5339
D(x): 0.8395, D(G(z)): 0.2814
Epoch: [12/20], Batch Num: [134/600]
Discriminator Loss: 0.5866, Generator Loss: 3.2629
D(x): 0.8287, D(G(z)): 0.2059
Epoch: [12/20], Batch Num: [135/600]
Discriminator Loss: 0.8264, Generator Loss: 2.7767
D(x): 0.6695, D(G(z)): 0.1435
Epoch: [12/20], Batch Num: [136/600]
Discriminator Loss: 0.7061, Generator Loss: 2.4397
D(x): 0.7353, D(G(z)): 0.1678
Epoch: [12/20], Batch Num: [137/600]
Discriminator Loss: 0.7879, Generator Loss: 1.9232
D(x): 0.7267, D(G(z)): 0.2298
Epoch: [12/20], Batch Num: [138/600]
Discriminator Loss: 0.6643, Generator Loss: 1.8358
D(x): 0.8758, D(G(z)): 0.2943
Epoch: [12/20], Batch Num: [139/600]
Discriminator Loss: 0.5360, Generator Loss: 2.3898
D(x): 0.8442, D(G(z)): 0.2224
Epoch: [12/20], Batch Num: [140/600]
Discriminator Loss: 0.7735, Generator Loss: 2.5507
D(x): 0.7894, D(G(z)): 0.2522
Epoch: [12/20], Batch Num: [141/600]
Discriminator Loss: 0.7553, Generator Loss: 2.5895
D(x): 0.7257, D(G(z)): 0.1629
Epoch: [12/20], Batch Num: [142/600]
Discriminator Loss: 0.5984, Generator Loss: 2.3283
D(x): 0.7969, D(G(z)): 0.1575
Epoch: [12/20], Batch Num: [143/600]
Discriminator Loss: 0.8422, Generator Loss: 2.1799
D(x): 0.7753, D(G(z)): 0.2418
Epoch: [12/20], Batch Num: [144/600]
Discriminator Loss: 0.7226, Generator Loss: 1.8493
D(x): 0.7950, D(G(z)): 0.2072
Epoch: [12/20], Batch Num: [145/600]
Discriminator Loss: 0.7049, Generator Loss: 2.0198
D(x): 0.7994, D(G(z)): 0.2318
Epoch: [12/20], Batch Num: [146/600]
Discriminator Loss: 0.4875, Generator Loss: 2.1200
D(x): 0.8253, D(G(z)): 0.1805
Epoch: [12/20], Batch Num: [147/600]
Discriminator Loss: 0.5811, Generator Loss: 2.3159
D(x): 0.8249, D(G(z)): 0.2220
Epoch: [12/20], Batch Num: [148/600]
Discriminator Loss: 0.5992, Generator Loss: 2.4566
D(x): 0.8030, D(G(z)): 0.2123
Epoch: [12/20], Batch Num: [149/600]
Discriminator Loss: 0.7062, Generator Loss: 2.2648
D(x): 0.7695, D(G(z)): 0.1922
Epoch: [12/20], Batch Num: [150/600]
Discriminator Loss: 0.6903, Generator Loss: 2.0215
D(x): 0.7870, D(G(z)): 0.2075
Epoch: [12/20], Batch Num: [151/600]
Discriminator Loss: 0.4957, Generator Loss: 2.0649
D(x): 0.8529, D(G(z)): 0.1974
Epoch: [12/20], Batch Num: [152/600]
Discriminator Loss: 0.5676, Generator Loss: 2.4743
D(x): 0.8246, D(G(z)): 0.1848
Epoch: [12/20], Batch Num: [153/600]
Discriminator Loss: 0.4936, Generator Loss: 2.5798
D(x): 0.8402, D(G(z)): 0.1574
Epoch: [12/20], Batch Num: [154/600]
Discriminator Loss: 0.5931, Generator Loss: 2.2854
D(x): 0.8212, D(G(z)): 0.1737
Epoch: [12/20], Batch Num: [155/600]
Discriminator Loss: 0.4990, Generator Loss: 2.6952
D(x): 0.8365, D(G(z)): 0.1675
Epoch: [12/20], Batch Num: [156/600]
Discriminator Loss: 0.5380, Generator Loss: 2.6239
D(x): 0.8129, D(G(z)): 0.1451
Epoch: [12/20], Batch Num: [157/600]
Discriminator Loss: 0.6072, Generator Loss: 2.6423
D(x): 0.7996, D(G(z)): 0.1799
Epoch: [12/20], Batch Num: [158/600]
Discriminator Loss: 0.4767, Generator Loss: 2.6736
D(x): 0.8188, D(G(z)): 0.1492
Epoch: [12/20], Batch Num: [159/600]
Discriminator Loss: 0.6463, Generator Loss: 2.3332
D(x): 0.8061, D(G(z)): 0.1694
Epoch: [12/20], Batch Num: [160/600]
Discriminator Loss: 0.5350, Generator Loss: 2.5955
D(x): 0.8557, D(G(z)): 0.1571
Epoch: [12/20], Batch Num: [161/600]
Discriminator Loss: 0.7329, Generator Loss: 2.7191
D(x): 0.8491, D(G(z)): 0.2527
Epoch: [12/20], Batch Num: [162/600]
Discriminator Loss: 0.4432, Generator Loss: 3.0536
D(x): 0.8535, D(G(z)): 0.1242
Epoch: [12/20], Batch Num: [163/600]
Discriminator Loss: 0.6379, Generator Loss: 3.0663
D(x): 0.8062, D(G(z)): 0.1346
Epoch: [12/20], Batch Num: [164/600]
Discriminator Loss: 0.5033, Generator Loss: 2.6842
D(x): 0.8141, D(G(z)): 0.1138
Epoch: [12/20], Batch Num: [165/600]
Discriminator Loss: 0.6282, Generator Loss: 2.3961
D(x): 0.8087, D(G(z)): 0.1610
Epoch: [12/20], Batch Num: [166/600]
Discriminator Loss: 0.5717, Generator Loss: 1.9669
D(x): 0.8399, D(G(z)): 0.1864
Epoch: [12/20], Batch Num: [167/600]
Discriminator Loss: 0.7240, Generator Loss: 2.1179
D(x): 0.8314, D(G(z)): 0.2535
Epoch: [12/20], Batch Num: [168/600]
Discriminator Loss: 0.7253, Generator Loss: 2.5601
D(x): 0.8518, D(G(z)): 0.2700
Epoch: [12/20], Batch Num: [169/600]
Discriminator Loss: 0.4987, Generator Loss: 2.8630
D(x): 0.8387, D(G(z)): 0.1890
Epoch: [12/20], Batch Num: [170/600]
Discriminator Loss: 0.6304, Generator Loss: 3.1829
D(x): 0.7678, D(G(z)): 0.1224
Epoch: [12/20], Batch Num: [171/600]
Discriminator Loss: 0.7189, Generator Loss: 2.6924
D(x): 0.7232, D(G(z)): 0.1138
Epoch: [12/20], Batch Num: [172/600]
Discriminator Loss: 0.5931, Generator Loss: 2.3399
D(x): 0.7630, D(G(z)): 0.1333
Epoch: [12/20], Batch Num: [173/600]
Discriminator Loss: 0.5259, Generator Loss: 1.6180
D(x): 0.8582, D(G(z)): 0.2067
Epoch: [12/20], Batch Num: [174/600]
Discriminator Loss: 0.7105, Generator Loss: 1.5861
D(x): 0.8651, D(G(z)): 0.3018
Epoch: [12/20], Batch Num: [175/600]
Discriminator Loss: 0.5851, Generator Loss: 2.1379
D(x): 0.8707, D(G(z)): 0.2680
Epoch: [12/20], Batch Num: [176/600]
Discriminator Loss: 0.6862, Generator Loss: 2.5424
D(x): 0.8006, D(G(z)): 0.2206
Epoch: [12/20], Batch Num: [177/600]
Discriminator Loss: 0.6362, Generator Loss: 2.3184
D(x): 0.7496, D(G(z)): 0.1435
Epoch: [12/20], Batch Num: [178/600]
Discriminator Loss: 0.4827, Generator Loss: 2.7320
D(x): 0.8163, D(G(z)): 0.1225
Epoch: [12/20], Batch Num: [179/600]
Discriminator Loss: 0.6415, Generator Loss: 2.5642
D(x): 0.7792, D(G(z)): 0.1529
Epoch: [12/20], Batch Num: [180/600]
Discriminator Loss: 0.5199, Generator Loss: 2.1893
D(x): 0.8514, D(G(z)): 0.1745
Epoch: [12/20], Batch Num: [181/600]
Discriminator Loss: 0.6205, Generator Loss: 2.3677
D(x): 0.8531, D(G(z)): 0.2233
Epoch: [12/20], Batch Num: [182/600]
Discriminator Loss: 0.4935, Generator Loss: 2.4155
D(x): 0.8625, D(G(z)): 0.2040
Epoch: [12/20], Batch Num: [183/600]
Discriminator Loss: 0.4952, Generator Loss: 2.5896
D(x): 0.8448, D(G(z)): 0.1555
Epoch: [12/20], Batch Num: [184/600]
Discriminator Loss: 0.5000, Generator Loss: 2.8423
D(x): 0.8135, D(G(z)): 0.1290
Epoch: [12/20], Batch Num: [185/600]
Discriminator Loss: 0.5407, Generator Loss: 2.8539
D(x): 0.8021, D(G(z)): 0.1204
Epoch: [12/20], Batch Num: [186/600]
Discriminator Loss: 0.7480, Generator Loss: 2.2822
D(x): 0.7642, D(G(z)): 0.1739
Epoch: [12/20], Batch Num: [187/600]
Discriminator Loss: 0.5037, Generator Loss: 2.4211
D(x): 0.8787, D(G(z)): 0.2155
Epoch: [12/20], Batch Num: [188/600]
Discriminator Loss: 0.7626, Generator Loss: 2.4175
D(x): 0.7929, D(G(z)): 0.2321
Epoch: [12/20], Batch Num: [189/600]
Discriminator Loss: 0.7030, Generator Loss: 2.5747
D(x): 0.8000, D(G(z)): 0.2103
Epoch: [12/20], Batch Num: [190/600]
Discriminator Loss: 0.4720, Generator Loss: 2.7896
D(x): 0.8461, D(G(z)): 0.1610
Epoch: [12/20], Batch Num: [191/600]
Discriminator Loss: 0.5372, Generator Loss: 2.2875
D(x): 0.7980, D(G(z)): 0.1240
Epoch: [12/20], Batch Num: [192/600]
Discriminator Loss: 0.5722, Generator Loss: 2.1153
D(x): 0.7950, D(G(z)): 0.1618
Epoch: [12/20], Batch Num: [193/600]
Discriminator Loss: 0.5268, Generator Loss: 1.9827
D(x): 0.8783, D(G(z)): 0.2392
Epoch: [12/20], Batch Num: [194/600]
Discriminator Loss: 0.6120, Generator Loss: 2.1951
D(x): 0.8656, D(G(z)): 0.2419
Epoch: [12/20], Batch Num: [195/600]
Discriminator Loss: 0.5282, Generator Loss: 2.4495
D(x): 0.8320, D(G(z)): 0.1781
Epoch: [12/20], Batch Num: [196/600]
Discriminator Loss: 0.5516, Generator Loss: 2.7830
D(x): 0.8613, D(G(z)): 0.1988
Epoch: [12/20], Batch Num: [197/600]
Discriminator Loss: 0.3755, Generator Loss: 2.8264
D(x): 0.8483, D(G(z)): 0.0988
Epoch: [12/20], Batch Num: [198/600]
Discriminator Loss: 0.4784, Generator Loss: 2.8696
D(x): 0.8056, D(G(z)): 0.0909
Epoch: [12/20], Batch Num: [199/600]
Discriminator Loss: 0.5477, Generator Loss: 2.2354
D(x): 0.7872, D(G(z)): 0.1118
Epoch: 12, Batch Num: [200/600]

Epoch: [12/20], Batch Num: [200/600]
Discriminator Loss: 0.5028, Generator Loss: 2.0293
D(x): 0.8348, D(G(z)): 0.1793
Epoch: [12/20], Batch Num: [201/600]
Discriminator Loss: 0.6465, Generator Loss: 2.1532
D(x): 0.8525, D(G(z)): 0.2420
Epoch: [12/20], Batch Num: [202/600]
Discriminator Loss: 0.8501, Generator Loss: 2.3776
D(x): 0.8477, D(G(z)): 0.2920
Epoch: [12/20], Batch Num: [203/600]
Discriminator Loss: 0.6106, Generator Loss: 2.7935
D(x): 0.7841, D(G(z)): 0.1392
Epoch: [12/20], Batch Num: [204/600]
Discriminator Loss: 0.5223, Generator Loss: 2.5371
D(x): 0.8251, D(G(z)): 0.1263
Epoch: [12/20], Batch Num: [205/600]
Discriminator Loss: 0.5734, Generator Loss: 2.3727
D(x): 0.7941, D(G(z)): 0.1496
Epoch: [12/20], Batch Num: [206/600]
Discriminator Loss: 0.5125, Generator Loss: 2.4565
D(x): 0.8522, D(G(z)): 0.1633
Epoch: [12/20], Batch Num: [207/600]
Discriminator Loss: 0.4243, Generator Loss: 2.2022
D(x): 0.8687, D(G(z)): 0.1633
Epoch: [12/20], Batch Num: [208/600]
Discriminator Loss: 0.4460, Generator Loss: 2.1887
D(x): 0.8665, D(G(z)): 0.1716
Epoch: [12/20], Batch Num: [209/600]
Discriminator Loss: 0.6051, Generator Loss: 2.2951
D(x): 0.7826, D(G(z)): 0.1649
Epoch: [12/20], Batch Num: [210/600]
Discriminator Loss: 0.4590, Generator Loss: 2.1911
D(x): 0.8931, D(G(z)): 0.1937
Epoch: [12/20], Batch Num: [211/600]
Discriminator Loss: 0.4153, Generator Loss: 2.5383
D(x): 0.8629, D(G(z)): 0.1293
Epoch: [12/20], Batch Num: [212/600]
Discriminator Loss: 0.4763, Generator Loss: 2.5613
D(x): 0.8368, D(G(z)): 0.1451
Epoch: [12/20], Batch Num: [213/600]
Discriminator Loss: 0.6387, Generator Loss: 2.4498
D(x): 0.7708, D(G(z)): 0.1272
Epoch: [12/20], Batch Num: [214/600]
Discriminator Loss: 0.5164, Generator Loss: 2.4153
D(x): 0.8569, D(G(z)): 0.1762
Epoch: [12/20], Batch Num: [215/600]
Discriminator Loss: 0.5536, Generator Loss: 2.2843
D(x): 0.7765, D(G(z)): 0.1370
Epoch: [12/20], Batch Num: [216/600]
Discriminator Loss: 0.5263, Generator Loss: 2.2676
D(x): 0.8974, D(G(z)): 0.2241
Epoch: [12/20], Batch Num: [217/600]
Discriminator Loss: 0.5709, Generator Loss: 2.5185
D(x): 0.8537, D(G(z)): 0.1998
Epoch: [12/20], Batch Num: [218/600]
Discriminator Loss: 0.5269, Generator Loss: 2.8672
D(x): 0.8436, D(G(z)): 0.1737
Epoch: [12/20], Batch Num: [219/600]
Discriminator Loss: 0.4985, Generator Loss: 2.7323
D(x): 0.8124, D(G(z)): 0.1101
Epoch: [12/20], Batch Num: [220/600]
Discriminator Loss: 0.4609, Generator Loss: 2.6551
D(x): 0.8393, D(G(z)): 0.1123
Epoch: [12/20], Batch Num: [221/600]
Discriminator Loss: 0.5661, Generator Loss: 2.5531
D(x): 0.8568, D(G(z)): 0.1940
Epoch: [12/20], Batch Num: [222/600]
Discriminator Loss: 0.8006, Generator Loss: 2.7735
D(x): 0.8258, D(G(z)): 0.2292
Epoch: [12/20], Batch Num: [223/600]
Discriminator Loss: 0.6347, Generator Loss: 2.7091
D(x): 0.8115, D(G(z)): 0.1448
Epoch: [12/20], Batch Num: [224/600]
Discriminator Loss: 0.6024, Generator Loss: 2.6087
D(x): 0.7896, D(G(z)): 0.1461
Epoch: [12/20], Batch Num: [225/600]
Discriminator Loss: 0.6511, Generator Loss: 2.6078
D(x): 0.7958, D(G(z)): 0.1767
Epoch: [12/20], Batch Num: [226/600]
Discriminator Loss: 0.6023, Generator Loss: 2.2895
D(x): 0.7901, D(G(z)): 0.1643
Epoch: [12/20], Batch Num: [227/600]
Discriminator Loss: 0.4995, Generator Loss: 2.4181
D(x): 0.8753, D(G(z)): 0.2197
Epoch: [12/20], Batch Num: [228/600]
Discriminator Loss: 0.5723, Generator Loss: 2.4479
D(x): 0.8240, D(G(z)): 0.2022
Epoch: [12/20], Batch Num: [229/600]
Discriminator Loss: 0.7433, Generator Loss: 2.3508
D(x): 0.7208, D(G(z)): 0.1798
Epoch: [12/20], Batch Num: [230/600]
Discriminator Loss: 0.4398, Generator Loss: 2.1705
D(x): 0.8491, D(G(z)): 0.1528
Epoch: [12/20], Batch Num: [231/600]
Discriminator Loss: 0.6424, Generator Loss: 2.0023
D(x): 0.8078, D(G(z)): 0.2055
Epoch: [12/20], Batch Num: [232/600]
Discriminator Loss: 0.6542, Generator Loss: 2.0342
D(x): 0.8192, D(G(z)): 0.2259
Epoch: [12/20], Batch Num: [233/600]
Discriminator Loss: 0.7370, Generator Loss: 2.0994
D(x): 0.7880, D(G(z)): 0.2168
Epoch: [12/20], Batch Num: [234/600]
Discriminator Loss: 0.7225, Generator Loss: 2.1838
D(x): 0.8049, D(G(z)): 0.2208
Epoch: [12/20], Batch Num: [235/600]
Discriminator Loss: 0.5568, Generator Loss: 2.5631
D(x): 0.8220, D(G(z)): 0.1855
Epoch: [12/20], Batch Num: [236/600]
Discriminator Loss: 0.6515, Generator Loss: 2.4788
D(x): 0.7634, D(G(z)): 0.1676
Epoch: [12/20], Batch Num: [237/600]
Discriminator Loss: 0.6396, Generator Loss: 2.1609
D(x): 0.7518, D(G(z)): 0.1363
Epoch: [12/20], Batch Num: [238/600]
Discriminator Loss: 0.6219, Generator Loss: 1.8282
D(x): 0.7949, D(G(z)): 0.1946
Epoch: [12/20], Batch Num: [239/600]
Discriminator Loss: 0.5231, Generator Loss: 1.6259
D(x): 0.8447, D(G(z)): 0.2094
Epoch: [12/20], Batch Num: [240/600]
Discriminator Loss: 0.6908, Generator Loss: 1.8564
D(x): 0.8781, D(G(z)): 0.2945
Epoch: [12/20], Batch Num: [241/600]
Discriminator Loss: 0.6074, Generator Loss: 2.4108
D(x): 0.8603, D(G(z)): 0.2408
Epoch: [12/20], Batch Num: [242/600]
Discriminator Loss: 0.5503, Generator Loss: 2.7471
D(x): 0.7973, D(G(z)): 0.1460
Epoch: [12/20], Batch Num: [243/600]
Discriminator Loss: 0.5649, Generator Loss: 2.3096
D(x): 0.7769, D(G(z)): 0.1224
Epoch: [12/20], Batch Num: [244/600]
Discriminator Loss: 0.6378, Generator Loss: 2.2073
D(x): 0.7526, D(G(z)): 0.1419
Epoch: [12/20], Batch Num: [245/600]
Discriminator Loss: 0.4831, Generator Loss: 1.8846
D(x): 0.8511, D(G(z)): 0.1925
Epoch: [12/20], Batch Num: [246/600]
Discriminator Loss: 0.4691, Generator Loss: 1.9812
D(x): 0.8901, D(G(z)): 0.2122
Epoch: [12/20], Batch Num: [247/600]
Discriminator Loss: 0.6322, Generator Loss: 2.2849
D(x): 0.8258, D(G(z)): 0.2126
Epoch: [12/20], Batch Num: [248/600]
Discriminator Loss: 0.6038, Generator Loss: 2.4416
D(x): 0.8335, D(G(z)): 0.2058
Epoch: [12/20], Batch Num: [249/600]
Discriminator Loss: 0.5207, Generator Loss: 2.7421
D(x): 0.8595, D(G(z)): 0.1790
Epoch: [12/20], Batch Num: [250/600]
Discriminator Loss: 0.7337, Generator Loss: 2.6767
D(x): 0.7571, D(G(z)): 0.1101
Epoch: [12/20], Batch Num: [251/600]
Discriminator Loss: 0.6660, Generator Loss: 2.4105
D(x): 0.7915, D(G(z)): 0.1738
Epoch: [12/20], Batch Num: [252/600]
Discriminator Loss: 0.6276, Generator Loss: 2.0303
D(x): 0.7739, D(G(z)): 0.1636
Epoch: [12/20], Batch Num: [253/600]
Discriminator Loss: 0.6185, Generator Loss: 2.1398
D(x): 0.8564, D(G(z)): 0.2362
Epoch: [12/20], Batch Num: [254/600]
Discriminator Loss: 0.6211, Generator Loss: 2.3332
D(x): 0.8499, D(G(z)): 0.2521
Epoch: [12/20], Batch Num: [255/600]
Discriminator Loss: 0.4475, Generator Loss: 2.7470
D(x): 0.8544, D(G(z)): 0.1621
Epoch: [12/20], Batch Num: [256/600]
Discriminator Loss: 0.7188, Generator Loss: 2.7072
D(x): 0.7615, D(G(z)): 0.1727
Epoch: [12/20], Batch Num: [257/600]
Discriminator Loss: 0.5277, Generator Loss: 2.3483
D(x): 0.7991, D(G(z)): 0.1161
Epoch: [12/20], Batch Num: [258/600]
Discriminator Loss: 0.5129, Generator Loss: 2.0403
D(x): 0.8025, D(G(z)): 0.1539
Epoch: [12/20], Batch Num: [259/600]
Discriminator Loss: 0.4332, Generator Loss: 1.9061
D(x): 0.8675, D(G(z)): 0.1898
Epoch: [12/20], Batch Num: [260/600]
Discriminator Loss: 0.4489, Generator Loss: 2.0900
D(x): 0.8748, D(G(z)): 0.2100
Epoch: [12/20], Batch Num: [261/600]
Discriminator Loss: 0.5788, Generator Loss: 2.4238
D(x): 0.8551, D(G(z)): 0.2327
Epoch: [12/20], Batch Num: [262/600]
Discriminator Loss: 0.4869, Generator Loss: 2.6941
D(x): 0.8313, D(G(z)): 0.1536
Epoch: [12/20], Batch Num: [263/600]
Discriminator Loss: 0.5530, Generator Loss: 2.8150
D(x): 0.8168, D(G(z)): 0.1531
Epoch: [12/20], Batch Num: [264/600]
Discriminator Loss: 0.4817, Generator Loss: 2.9578
D(x): 0.8144, D(G(z)): 0.1362
Epoch: [12/20], Batch Num: [265/600]
Discriminator Loss: 0.5559, Generator Loss: 2.4345
D(x): 0.7967, D(G(z)): 0.1327
Epoch: [12/20], Batch Num: [266/600]
Discriminator Loss: 0.6778, Generator Loss: 2.1199
D(x): 0.8005, D(G(z)): 0.2139
Epoch: [12/20], Batch Num: [267/600]
Discriminator Loss: 0.5261, Generator Loss: 2.2244
D(x): 0.8931, D(G(z)): 0.2268
Epoch: [12/20], Batch Num: [268/600]
Discriminator Loss: 0.5513, Generator Loss: 2.4857
D(x): 0.8472, D(G(z)): 0.1905
Epoch: [12/20], Batch Num: [269/600]
Discriminator Loss: 0.5100, Generator Loss: 2.5163
D(x): 0.8250, D(G(z)): 0.1564
Epoch: [12/20], Batch Num: [270/600]
Discriminator Loss: 0.4885, Generator Loss: 2.7721
D(x): 0.8019, D(G(z)): 0.1222
Epoch: [12/20], Batch Num: [271/600]
Discriminator Loss: 0.7412, Generator Loss: 2.4422
D(x): 0.7863, D(G(z)): 0.1594
Epoch: [12/20], Batch Num: [272/600]
Discriminator Loss: 0.5840, Generator Loss: 2.2174
D(x): 0.8230, D(G(z)): 0.2053
Epoch: [12/20], Batch Num: [273/600]
Discriminator Loss: 0.6552, Generator Loss: 2.2694
D(x): 0.8083, D(G(z)): 0.1991
Epoch: [12/20], Batch Num: [274/600]
Discriminator Loss: 0.5417, Generator Loss: 2.2954
D(x): 0.8443, D(G(z)): 0.2217
Epoch: [12/20], Batch Num: [275/600]
Discriminator Loss: 0.5856, Generator Loss: 2.5651
D(x): 0.8356, D(G(z)): 0.1987
Epoch: [12/20], Batch Num: [276/600]
Discriminator Loss: 0.4741, Generator Loss: 3.2392
D(x): 0.8338, D(G(z)): 0.1501
Epoch: [12/20], Batch Num: [277/600]
Discriminator Loss: 0.4891, Generator Loss: 2.8062
D(x): 0.7900, D(G(z)): 0.1211
Epoch: [12/20], Batch Num: [278/600]
Discriminator Loss: 0.4925, Generator Loss: 2.2749
D(x): 0.7996, D(G(z)): 0.1003
Epoch: [12/20], Batch Num: [279/600]
Discriminator Loss: 0.6022, Generator Loss: 1.9204
D(x): 0.8271, D(G(z)): 0.1960
Epoch: [12/20], Batch Num: [280/600]
Discriminator Loss: 0.6028, Generator Loss: 2.1023
D(x): 0.8759, D(G(z)): 0.2498
Epoch: [12/20], Batch Num: [281/600]
Discriminator Loss: 0.5375, Generator Loss: 2.6085
D(x): 0.8982, D(G(z)): 0.2409
Epoch: [12/20], Batch Num: [282/600]
Discriminator Loss: 0.5848, Generator Loss: 3.1074
D(x): 0.7915, D(G(z)): 0.1432
Epoch: [12/20], Batch Num: [283/600]
Discriminator Loss: 0.5004, Generator Loss: 3.1152
D(x): 0.8474, D(G(z)): 0.1381
Epoch: [12/20], Batch Num: [284/600]
Discriminator Loss: 0.6818, Generator Loss: 2.7665
D(x): 0.7454, D(G(z)): 0.1022
Epoch: [12/20], Batch Num: [285/600]
Discriminator Loss: 0.5968, Generator Loss: 2.1316
D(x): 0.7774, D(G(z)): 0.1254
Epoch: [12/20], Batch Num: [286/600]
Discriminator Loss: 0.6320, Generator Loss: 2.1192
D(x): 0.8574, D(G(z)): 0.2584
Epoch: [12/20], Batch Num: [287/600]
Discriminator Loss: 0.6048, Generator Loss: 2.6987
D(x): 0.8613, D(G(z)): 0.2453
Epoch: [12/20], Batch Num: [288/600]
Discriminator Loss: 0.4947, Generator Loss: 3.0699
D(x): 0.8815, D(G(z)): 0.2046
Epoch: [12/20], Batch Num: [289/600]
Discriminator Loss: 0.6096, Generator Loss: 3.2831
D(x): 0.7595, D(G(z)): 0.0976
Epoch: [12/20], Batch Num: [290/600]
Discriminator Loss: 0.6726, Generator Loss: 3.0427
D(x): 0.7874, D(G(z)): 0.1474
Epoch: [12/20], Batch Num: [291/600]
Discriminator Loss: 0.5829, Generator Loss: 2.2304
D(x): 0.7462, D(G(z)): 0.0925
Epoch: [12/20], Batch Num: [292/600]
Discriminator Loss: 0.4914, Generator Loss: 2.0969
D(x): 0.8906, D(G(z)): 0.2088
Epoch: [12/20], Batch Num: [293/600]
Discriminator Loss: 0.7327, Generator Loss: 1.9954
D(x): 0.8719, D(G(z)): 0.2976
Epoch: [12/20], Batch Num: [294/600]
Discriminator Loss: 0.6324, Generator Loss: 2.5441
D(x): 0.8308, D(G(z)): 0.2241
Epoch: [12/20], Batch Num: [295/600]
Discriminator Loss: 0.5974, Generator Loss: 2.9111
D(x): 0.8130, D(G(z)): 0.1730
Epoch: [12/20], Batch Num: [296/600]
Discriminator Loss: 0.7882, Generator Loss: 2.5959
D(x): 0.7063, D(G(z)): 0.1119
Epoch: [12/20], Batch Num: [297/600]
Discriminator Loss: 0.5955, Generator Loss: 2.1023
D(x): 0.7839, D(G(z)): 0.1411
Epoch: [12/20], Batch Num: [298/600]
Discriminator Loss: 0.7624, Generator Loss: 1.6646
D(x): 0.8136, D(G(z)): 0.2268
Epoch: [12/20], Batch Num: [299/600]
Discriminator Loss: 0.6066, Generator Loss: 1.8659
D(x): 0.8888, D(G(z)): 0.2811
Epoch: 12, Batch Num: [300/600]

Epoch: [12/20], Batch Num: [300/600]
Discriminator Loss: 0.6422, Generator Loss: 2.6106
D(x): 0.8727, D(G(z)): 0.2772
Epoch: [12/20], Batch Num: [301/600]
Discriminator Loss: 0.5995, Generator Loss: 3.0624
D(x): 0.8183, D(G(z)): 0.1915
Epoch: [12/20], Batch Num: [302/600]
Discriminator Loss: 0.5426, Generator Loss: 3.2063
D(x): 0.7936, D(G(z)): 0.1127
Epoch: [12/20], Batch Num: [303/600]
Discriminator Loss: 0.6785, Generator Loss: 2.7433
D(x): 0.7330, D(G(z)): 0.1048
Epoch: [12/20], Batch Num: [304/600]
Discriminator Loss: 0.7127, Generator Loss: 2.1535
D(x): 0.7739, D(G(z)): 0.1525
Epoch: [12/20], Batch Num: [305/600]
Discriminator Loss: 0.5352, Generator Loss: 1.6882
D(x): 0.8321, D(G(z)): 0.2001
Epoch: [12/20], Batch Num: [306/600]
Discriminator Loss: 0.7548, Generator Loss: 1.5546
D(x): 0.8631, D(G(z)): 0.3095
Epoch: [12/20], Batch Num: [307/600]
Discriminator Loss: 0.5562, Generator Loss: 1.9218
D(x): 0.8870, D(G(z)): 0.2665
Epoch: [12/20], Batch Num: [308/600]
Discriminator Loss: 0.6333, Generator Loss: 2.2883
D(x): 0.8015, D(G(z)): 0.2152
Epoch: [12/20], Batch Num: [309/600]
Discriminator Loss: 0.7537, Generator Loss: 2.4295
D(x): 0.7610, D(G(z)): 0.1795
Epoch: [12/20], Batch Num: [310/600]
Discriminator Loss: 0.6440, Generator Loss: 2.2215
D(x): 0.7703, D(G(z)): 0.1574
Epoch: [12/20], Batch Num: [311/600]
Discriminator Loss: 0.6091, Generator Loss: 2.0106
D(x): 0.7959, D(G(z)): 0.1780
Epoch: [12/20], Batch Num: [312/600]
Discriminator Loss: 0.4826, Generator Loss: 2.2360
D(x): 0.8448, D(G(z)): 0.1680
Epoch: [12/20], Batch Num: [313/600]
Discriminator Loss: 0.7193, Generator Loss: 2.0442
D(x): 0.8003, D(G(z)): 0.2245
Epoch: [12/20], Batch Num: [314/600]
Discriminator Loss: 0.6282, Generator Loss: 2.1137
D(x): 0.7921, D(G(z)): 0.1902
Epoch: [12/20], Batch Num: [315/600]
Discriminator Loss: 0.5251, Generator Loss: 2.1353
D(x): 0.8238, D(G(z)): 0.1963
Epoch: [12/20], Batch Num: [316/600]
Discriminator Loss: 0.5461, Generator Loss: 2.1617
D(x): 0.8496, D(G(z)): 0.2202
Epoch: [12/20], Batch Num: [317/600]
Discriminator Loss: 0.6291, Generator Loss: 2.0980
D(x): 0.7921, D(G(z)): 0.1915
Epoch: [12/20], Batch Num: [318/600]
Discriminator Loss: 0.7386, Generator Loss: 2.0450
D(x): 0.7333, D(G(z)): 0.1794
Epoch: [12/20], Batch Num: [319/600]
Discriminator Loss: 0.4204, Generator Loss: 2.3046
D(x): 0.9023, D(G(z)): 0.2191
Epoch: [12/20], Batch Num: [320/600]
Discriminator Loss: 0.6182, Generator Loss: 2.4887
D(x): 0.8473, D(G(z)): 0.2096
Epoch: [12/20], Batch Num: [321/600]
Discriminator Loss: 0.5261, Generator Loss: 2.4378
D(x): 0.8111, D(G(z)): 0.1695
Epoch: [12/20], Batch Num: [322/600]
Discriminator Loss: 0.4970, Generator Loss: 2.6483
D(x): 0.8294, D(G(z)): 0.1590
Epoch: [12/20], Batch Num: [323/600]
Discriminator Loss: 0.7267, Generator Loss: 2.1646
D(x): 0.7372, D(G(z)): 0.1484
Epoch: [12/20], Batch Num: [324/600]
Discriminator Loss: 0.6806, Generator Loss: 1.8834
D(x): 0.7696, D(G(z)): 0.1834
Epoch: [12/20], Batch Num: [325/600]
Discriminator Loss: 0.7351, Generator Loss: 1.8305
D(x): 0.8282, D(G(z)): 0.2384
Epoch: [12/20], Batch Num: [326/600]
Discriminator Loss: 0.5234, Generator Loss: 2.1616
D(x): 0.8966, D(G(z)): 0.2347
Epoch: [12/20], Batch Num: [327/600]
Discriminator Loss: 0.6800, Generator Loss: 2.4945
D(x): 0.8325, D(G(z)): 0.2358
Epoch: [12/20], Batch Num: [328/600]
Discriminator Loss: 0.5766, Generator Loss: 2.7776
D(x): 0.7914, D(G(z)): 0.1543
Epoch: [12/20], Batch Num: [329/600]
Discriminator Loss: 0.6908, Generator Loss: 2.7038
D(x): 0.7888, D(G(z)): 0.1934
Epoch: [12/20], Batch Num: [330/600]
Discriminator Loss: 0.5599, Generator Loss: 2.5138
D(x): 0.8028, D(G(z)): 0.1488
Epoch: [12/20], Batch Num: [331/600]
Discriminator Loss: 0.6488, Generator Loss: 2.2419
D(x): 0.7656, D(G(z)): 0.1449
Epoch: [12/20], Batch Num: [332/600]
Discriminator Loss: 0.6056, Generator Loss: 2.2033
D(x): 0.8219, D(G(z)): 0.2049
Epoch: [12/20], Batch Num: [333/600]
Discriminator Loss: 0.7247, Generator Loss: 2.0053
D(x): 0.7731, D(G(z)): 0.1872
Epoch: [12/20], Batch Num: [334/600]
Discriminator Loss: 0.6819, Generator Loss: 1.8346
D(x): 0.8029, D(G(z)): 0.2332
Epoch: [12/20], Batch Num: [335/600]
Discriminator Loss: 0.6344, Generator Loss: 2.0267
D(x): 0.8437, D(G(z)): 0.2452
Epoch: [12/20], Batch Num: [336/600]
Discriminator Loss: 0.6329, Generator Loss: 2.2766
D(x): 0.8813, D(G(z)): 0.2632
Epoch: [12/20], Batch Num: [337/600]
Discriminator Loss: 0.5963, Generator Loss: 2.6105
D(x): 0.8002, D(G(z)): 0.1893
Epoch: [12/20], Batch Num: [338/600]
Discriminator Loss: 0.7589, Generator Loss: 2.6332
D(x): 0.7180, D(G(z)): 0.1463
Epoch: [12/20], Batch Num: [339/600]
Discriminator Loss: 0.7492, Generator Loss: 2.2683
D(x): 0.7115, D(G(z)): 0.1598
Epoch: [12/20], Batch Num: [340/600]
Discriminator Loss: 0.6609, Generator Loss: 2.2601
D(x): 0.8129, D(G(z)): 0.2315
Epoch: [12/20], Batch Num: [341/600]
Discriminator Loss: 0.8541, Generator Loss: 1.9380
D(x): 0.7357, D(G(z)): 0.2011
Epoch: [12/20], Batch Num: [342/600]
Discriminator Loss: 0.6223, Generator Loss: 2.0620
D(x): 0.8385, D(G(z)): 0.2561
Epoch: [12/20], Batch Num: [343/600]
Discriminator Loss: 0.7268, Generator Loss: 2.2327
D(x): 0.8319, D(G(z)): 0.2709
Epoch: [12/20], Batch Num: [344/600]
Discriminator Loss: 0.6764, Generator Loss: 2.4158
D(x): 0.7675, D(G(z)): 0.1835
Epoch: [12/20], Batch Num: [345/600]
Discriminator Loss: 0.5974, Generator Loss: 2.2496
D(x): 0.7767, D(G(z)): 0.1748
Epoch: [12/20], Batch Num: [346/600]
Discriminator Loss: 0.6533, Generator Loss: 2.0196
D(x): 0.7756, D(G(z)): 0.1765
Epoch: [12/20], Batch Num: [347/600]
Discriminator Loss: 0.6408, Generator Loss: 1.9676
D(x): 0.8390, D(G(z)): 0.2449
Epoch: [12/20], Batch Num: [348/600]
Discriminator Loss: 0.6986, Generator Loss: 2.4598
D(x): 0.8214, D(G(z)): 0.2530
Epoch: [12/20], Batch Num: [349/600]
Discriminator Loss: 0.6651, Generator Loss: 2.5255
D(x): 0.7861, D(G(z)): 0.1998
Epoch: [12/20], Batch Num: [350/600]
Discriminator Loss: 0.5373, Generator Loss: 2.3412
D(x): 0.7829, D(G(z)): 0.1281
Epoch: [12/20], Batch Num: [351/600]
Discriminator Loss: 0.6177, Generator Loss: 2.1128
D(x): 0.7971, D(G(z)): 0.1881
Epoch: [12/20], Batch Num: [352/600]
Discriminator Loss: 0.6306, Generator Loss: 1.8738
D(x): 0.7859, D(G(z)): 0.2189
Epoch: [12/20], Batch Num: [353/600]
Discriminator Loss: 0.4830, Generator Loss: 1.8932
D(x): 0.8585, D(G(z)): 0.2045
Epoch: [12/20], Batch Num: [354/600]
Discriminator Loss: 0.6004, Generator Loss: 2.1942
D(x): 0.8538, D(G(z)): 0.2544
Epoch: [12/20], Batch Num: [355/600]
Discriminator Loss: 0.6517, Generator Loss: 2.1867
D(x): 0.7958, D(G(z)): 0.2084
Epoch: [12/20], Batch Num: [356/600]
Discriminator Loss: 0.6549, Generator Loss: 2.5724
D(x): 0.7952, D(G(z)): 0.2010
Epoch: [12/20], Batch Num: [357/600]
Discriminator Loss: 0.6845, Generator Loss: 2.1496
D(x): 0.7294, D(G(z)): 0.1326
Epoch: [12/20], Batch Num: [358/600]
Discriminator Loss: 0.5006, Generator Loss: 2.2104
D(x): 0.8396, D(G(z)): 0.1802
Epoch: [12/20], Batch Num: [359/600]
Discriminator Loss: 0.5377, Generator Loss: 2.0013
D(x): 0.8347, D(G(z)): 0.1861
Epoch: [12/20], Batch Num: [360/600]
Discriminator Loss: 0.5958, Generator Loss: 2.1950
D(x): 0.8415, D(G(z)): 0.2375
Epoch: [12/20], Batch Num: [361/600]
Discriminator Loss: 0.5353, Generator Loss: 2.1135
D(x): 0.8615, D(G(z)): 0.2108
Epoch: [12/20], Batch Num: [362/600]
Discriminator Loss: 0.4741, Generator Loss: 2.3512
D(x): 0.8505, D(G(z)): 0.1785
Epoch: [12/20], Batch Num: [363/600]
Discriminator Loss: 0.5529, Generator Loss: 2.4552
D(x): 0.8179, D(G(z)): 0.1690
Epoch: [12/20], Batch Num: [364/600]
Discriminator Loss: 0.6429, Generator Loss: 2.4246
D(x): 0.7761, D(G(z)): 0.1565
Epoch: [12/20], Batch Num: [365/600]
Discriminator Loss: 0.5362, Generator Loss: 2.4063
D(x): 0.8397, D(G(z)): 0.1718
Epoch: [12/20], Batch Num: [366/600]
Discriminator Loss: 0.6637, Generator Loss: 2.2096
D(x): 0.7735, D(G(z)): 0.1623
Epoch: [12/20], Batch Num: [367/600]
Discriminator Loss: 0.6992, Generator Loss: 2.2285
D(x): 0.8312, D(G(z)): 0.2466
Epoch: [12/20], Batch Num: [368/600]
Discriminator Loss: 0.7129, Generator Loss: 2.4780
D(x): 0.8285, D(G(z)): 0.2474
Epoch: [12/20], Batch Num: [369/600]
Discriminator Loss: 0.7363, Generator Loss: 2.4111
D(x): 0.7672, D(G(z)): 0.1821
Epoch: [12/20], Batch Num: [370/600]
Discriminator Loss: 0.5620, Generator Loss: 2.2947
D(x): 0.8190, D(G(z)): 0.1489
Epoch: [12/20], Batch Num: [371/600]
Discriminator Loss: 0.5599, Generator Loss: 2.2252
D(x): 0.7757, D(G(z)): 0.1203
Epoch: [12/20], Batch Num: [372/600]
Discriminator Loss: 0.5060, Generator Loss: 2.1583
D(x): 0.8422, D(G(z)): 0.1799
Epoch: [12/20], Batch Num: [373/600]
Discriminator Loss: 0.4248, Generator Loss: 2.0095
D(x): 0.8966, D(G(z)): 0.2007
Epoch: [12/20], Batch Num: [374/600]
Discriminator Loss: 0.5655, Generator Loss: 2.1661
D(x): 0.8465, D(G(z)): 0.2094
Epoch: [12/20], Batch Num: [375/600]
Discriminator Loss: 0.6222, Generator Loss: 2.3265
D(x): 0.7832, D(G(z)): 0.1878
Epoch: [12/20], Batch Num: [376/600]
Discriminator Loss: 0.6937, Generator Loss: 2.3808
D(x): 0.7744, D(G(z)): 0.1966
Epoch: [12/20], Batch Num: [377/600]
Discriminator Loss: 0.6230, Generator Loss: 2.3272
D(x): 0.8073, D(G(z)): 0.1853
Epoch: [12/20], Batch Num: [378/600]
Discriminator Loss: 0.5807, Generator Loss: 2.2468
D(x): 0.7802, D(G(z)): 0.1422
Epoch: [12/20], Batch Num: [379/600]
Discriminator Loss: 0.5078, Generator Loss: 2.2103
D(x): 0.8584, D(G(z)): 0.1872
Epoch: [12/20], Batch Num: [380/600]
Discriminator Loss: 0.5681, Generator Loss: 2.0792
D(x): 0.8627, D(G(z)): 0.2164
Epoch: [12/20], Batch Num: [381/600]
Discriminator Loss: 0.5229, Generator Loss: 2.3127
D(x): 0.8376, D(G(z)): 0.1851
Epoch: [12/20], Batch Num: [382/600]
Discriminator Loss: 0.6450, Generator Loss: 2.4455
D(x): 0.8334, D(G(z)): 0.2268
Epoch: [12/20], Batch Num: [383/600]
Discriminator Loss: 0.6402, Generator Loss: 2.6806
D(x): 0.7889, D(G(z)): 0.1639
Epoch: [12/20], Batch Num: [384/600]
Discriminator Loss: 0.5435, Generator Loss: 2.3766
D(x): 0.7930, D(G(z)): 0.1306
Epoch: [12/20], Batch Num: [385/600]
Discriminator Loss: 0.5374, Generator Loss: 2.2149
D(x): 0.8098, D(G(z)): 0.1664
Epoch: [12/20], Batch Num: [386/600]
Discriminator Loss: 0.4602, Generator Loss: 2.1225
D(x): 0.8570, D(G(z)): 0.1635
Epoch: [12/20], Batch Num: [387/600]
Discriminator Loss: 0.5000, Generator Loss: 2.2678
D(x): 0.8729, D(G(z)): 0.2008
Epoch: [12/20], Batch Num: [388/600]
Discriminator Loss: 0.6039, Generator Loss: 2.5524
D(x): 0.8381, D(G(z)): 0.2024
Epoch: [12/20], Batch Num: [389/600]
Discriminator Loss: 0.5942, Generator Loss: 2.2961
D(x): 0.7786, D(G(z)): 0.1382
Epoch: [12/20], Batch Num: [390/600]
Discriminator Loss: 0.5810, Generator Loss: 2.2948
D(x): 0.8077, D(G(z)): 0.1481
Epoch: [12/20], Batch Num: [391/600]
Discriminator Loss: 0.6115, Generator Loss: 1.9501
D(x): 0.7777, D(G(z)): 0.1711
Epoch: [12/20], Batch Num: [392/600]
Discriminator Loss: 0.6398, Generator Loss: 2.0681
D(x): 0.8060, D(G(z)): 0.2045
Epoch: [12/20], Batch Num: [393/600]
Discriminator Loss: 0.6373, Generator Loss: 2.1453
D(x): 0.8765, D(G(z)): 0.2888
Epoch: [12/20], Batch Num: [394/600]
Discriminator Loss: 0.4848, Generator Loss: 2.5980
D(x): 0.8873, D(G(z)): 0.2070
Epoch: [12/20], Batch Num: [395/600]
Discriminator Loss: 0.6506, Generator Loss: 2.9168
D(x): 0.7582, D(G(z)): 0.1677
Epoch: [12/20], Batch Num: [396/600]
Discriminator Loss: 0.7874, Generator Loss: 2.4342
D(x): 0.6717, D(G(z)): 0.1182
Epoch: [12/20], Batch Num: [397/600]
Discriminator Loss: 0.6360, Generator Loss: 1.9349
D(x): 0.7530, D(G(z)): 0.1419
Epoch: [12/20], Batch Num: [398/600]
Discriminator Loss: 0.6111, Generator Loss: 1.6595
D(x): 0.8693, D(G(z)): 0.2487
Epoch: [12/20], Batch Num: [399/600]
Discriminator Loss: 0.7235, Generator Loss: 1.9416
D(x): 0.8474, D(G(z)): 0.3194
Epoch: 12, Batch Num: [400/600]

Epoch: [12/20], Batch Num: [400/600]
Discriminator Loss: 0.5514, Generator Loss: 2.4491
D(x): 0.8342, D(G(z)): 0.2297
Epoch: [12/20], Batch Num: [401/600]
Discriminator Loss: 0.6176, Generator Loss: 2.7923
D(x): 0.7943, D(G(z)): 0.1810
Epoch: [12/20], Batch Num: [402/600]
Discriminator Loss: 0.6538, Generator Loss: 3.0704
D(x): 0.7401, D(G(z)): 0.1264
Epoch: [12/20], Batch Num: [403/600]
Discriminator Loss: 0.7512, Generator Loss: 2.3509
D(x): 0.7213, D(G(z)): 0.1350
Epoch: [12/20], Batch Num: [404/600]
Discriminator Loss: 0.5485, Generator Loss: 1.9135
D(x): 0.7924, D(G(z)): 0.1253
Epoch: [12/20], Batch Num: [405/600]
Discriminator Loss: 0.6357, Generator Loss: 1.8669
D(x): 0.8714, D(G(z)): 0.2442
Epoch: [12/20], Batch Num: [406/600]
Discriminator Loss: 0.7991, Generator Loss: 2.3034
D(x): 0.8336, D(G(z)): 0.2877
Epoch: [12/20], Batch Num: [407/600]
Discriminator Loss: 0.6169, Generator Loss: 2.7399
D(x): 0.8608, D(G(z)): 0.2339
Epoch: [12/20], Batch Num: [408/600]
Discriminator Loss: 0.5681, Generator Loss: 3.0286
D(x): 0.7849, D(G(z)): 0.1646
Epoch: [12/20], Batch Num: [409/600]
Discriminator Loss: 0.6444, Generator Loss: 2.9641
D(x): 0.7289, D(G(z)): 0.1109
Epoch: [12/20], Batch Num: [410/600]
Discriminator Loss: 0.5932, Generator Loss: 2.4947
D(x): 0.7587, D(G(z)): 0.1094
Epoch: [12/20], Batch Num: [411/600]
Discriminator Loss: 0.5589, Generator Loss: 2.3636
D(x): 0.7896, D(G(z)): 0.1486
Epoch: [12/20], Batch Num: [412/600]
Discriminator Loss: 0.4283, Generator Loss: 1.9373
D(x): 0.8463, D(G(z)): 0.1483
Epoch: [12/20], Batch Num: [413/600]
Discriminator Loss: 0.6089, Generator Loss: 1.7363
D(x): 0.8728, D(G(z)): 0.2840
Epoch: [12/20], Batch Num: [414/600]
Discriminator Loss: 0.5731, Generator Loss: 2.0016
D(x): 0.8824, D(G(z)): 0.2708
Epoch: [12/20], Batch Num: [415/600]
Discriminator Loss: 0.5060, Generator Loss: 2.7402
D(x): 0.8836, D(G(z)): 0.2126
Epoch: [12/20], Batch Num: [416/600]
Discriminator Loss: 0.6308, Generator Loss: 2.7420
D(x): 0.8027, D(G(z)): 0.1748
Epoch: [12/20], Batch Num: [417/600]
Discriminator Loss: 0.5800, Generator Loss: 3.0167
D(x): 0.7649, D(G(z)): 0.0988
Epoch: [12/20], Batch Num: [418/600]
Discriminator Loss: 0.5965, Generator Loss: 2.5873
D(x): 0.7614, D(G(z)): 0.1233
Epoch: [12/20], Batch Num: [419/600]
Discriminator Loss: 0.5684, Generator Loss: 2.0464
D(x): 0.7874, D(G(z)): 0.1446
Epoch: [12/20], Batch Num: [420/600]
Discriminator Loss: 0.6908, Generator Loss: 1.8999
D(x): 0.8498, D(G(z)): 0.2471
Epoch: [12/20], Batch Num: [421/600]
Discriminator Loss: 0.5667, Generator Loss: 2.0176
D(x): 0.8822, D(G(z)): 0.2475
Epoch: [12/20], Batch Num: [422/600]
Discriminator Loss: 0.7226, Generator Loss: 2.5297
D(x): 0.8682, D(G(z)): 0.2688
Epoch: [12/20], Batch Num: [423/600]
Discriminator Loss: 0.6642, Generator Loss: 2.9102
D(x): 0.7548, D(G(z)): 0.1621
Epoch: [12/20], Batch Num: [424/600]
Discriminator Loss: 0.5745, Generator Loss: 2.9770
D(x): 0.7722, D(G(z)): 0.1318
Epoch: [12/20], Batch Num: [425/600]
Discriminator Loss: 0.5474, Generator Loss: 2.3669
D(x): 0.7772, D(G(z)): 0.1004
Epoch: [12/20], Batch Num: [426/600]
Discriminator Loss: 0.5492, Generator Loss: 2.1943
D(x): 0.7939, D(G(z)): 0.1612
Epoch: [12/20], Batch Num: [427/600]
Discriminator Loss: 0.5502, Generator Loss: 1.9830
D(x): 0.8742, D(G(z)): 0.2576
Epoch: [12/20], Batch Num: [428/600]
Discriminator Loss: 0.6694, Generator Loss: 2.0931
D(x): 0.8351, D(G(z)): 0.2371
Epoch: [12/20], Batch Num: [429/600]
Discriminator Loss: 0.4771, Generator Loss: 2.2618
D(x): 0.8409, D(G(z)): 0.1830
Epoch: [12/20], Batch Num: [430/600]
Discriminator Loss: 0.5588, Generator Loss: 2.5088
D(x): 0.8206, D(G(z)): 0.1686
Epoch: [12/20], Batch Num: [431/600]
Discriminator Loss: 0.5091, Generator Loss: 2.4695
D(x): 0.7887, D(G(z)): 0.1424
Epoch: [12/20], Batch Num: [432/600]
Discriminator Loss: 0.5090, Generator Loss: 2.2398
D(x): 0.8136, D(G(z)): 0.1625
Epoch: [12/20], Batch Num: [433/600]
Discriminator Loss: 0.6200, Generator Loss: 1.7953
D(x): 0.7988, D(G(z)): 0.1649
Epoch: [12/20], Batch Num: [434/600]
Discriminator Loss: 0.6630, Generator Loss: 1.9365
D(x): 0.8727, D(G(z)): 0.3039
Epoch: [12/20], Batch Num: [435/600]
Discriminator Loss: 0.5957, Generator Loss: 2.5221
D(x): 0.8957, D(G(z)): 0.2747
Epoch: [12/20], Batch Num: [436/600]
Discriminator Loss: 0.5748, Generator Loss: 2.8353
D(x): 0.8140, D(G(z)): 0.1644
Epoch: [12/20], Batch Num: [437/600]
Discriminator Loss: 0.6536, Generator Loss: 2.6696
D(x): 0.7579, D(G(z)): 0.1155
Epoch: [12/20], Batch Num: [438/600]
Discriminator Loss: 0.6770, Generator Loss: 2.4499
D(x): 0.7402, D(G(z)): 0.1136
Epoch: [12/20], Batch Num: [439/600]
Discriminator Loss: 0.4133, Generator Loss: 2.0750
D(x): 0.8829, D(G(z)): 0.1739
Epoch: [12/20], Batch Num: [440/600]
Discriminator Loss: 0.6198, Generator Loss: 1.9075
D(x): 0.8880, D(G(z)): 0.2777
Epoch: [12/20], Batch Num: [441/600]
Discriminator Loss: 0.5330, Generator Loss: 2.4173
D(x): 0.8518, D(G(z)): 0.1980
Epoch: [12/20], Batch Num: [442/600]
Discriminator Loss: 0.5033, Generator Loss: 2.5912
D(x): 0.8387, D(G(z)): 0.1746
Epoch: [12/20], Batch Num: [443/600]
Discriminator Loss: 0.4496, Generator Loss: 2.7919
D(x): 0.8377, D(G(z)): 0.1339
Epoch: [12/20], Batch Num: [444/600]
Discriminator Loss: 0.5560, Generator Loss: 3.0116
D(x): 0.7979, D(G(z)): 0.1384
Epoch: [12/20], Batch Num: [445/600]
Discriminator Loss: 0.5097, Generator Loss: 2.9356
D(x): 0.8256, D(G(z)): 0.1604
Epoch: [12/20], Batch Num: [446/600]
Discriminator Loss: 0.5089, Generator Loss: 2.1386
D(x): 0.8159, D(G(z)): 0.1331
Epoch: [12/20], Batch Num: [447/600]
Discriminator Loss: 0.6309, Generator Loss: 2.3428
D(x): 0.8316, D(G(z)): 0.1885
Epoch: [12/20], Batch Num: [448/600]
Discriminator Loss: 0.5763, Generator Loss: 2.1429
D(x): 0.8381, D(G(z)): 0.1920
Epoch: [12/20], Batch Num: [449/600]
Discriminator Loss: 0.4641, Generator Loss: 2.2867
D(x): 0.9008, D(G(z)): 0.2076
Epoch: [12/20], Batch Num: [450/600]
Discriminator Loss: 0.5199, Generator Loss: 2.7641
D(x): 0.8499, D(G(z)): 0.1950
Epoch: [12/20], Batch Num: [451/600]
Discriminator Loss: 0.5139, Generator Loss: 3.1600
D(x): 0.7919, D(G(z)): 0.1063
Epoch: [12/20], Batch Num: [452/600]
Discriminator Loss: 0.5125, Generator Loss: 2.6188
D(x): 0.8250, D(G(z)): 0.1541
Epoch: [12/20], Batch Num: [453/600]
Discriminator Loss: 0.5380, Generator Loss: 2.6925
D(x): 0.8193, D(G(z)): 0.1357
Epoch: [12/20], Batch Num: [454/600]
Discriminator Loss: 0.5355, Generator Loss: 2.5930
D(x): 0.8558, D(G(z)): 0.1797
Epoch: [12/20], Batch Num: [455/600]
Discriminator Loss: 0.5799, Generator Loss: 2.3193
D(x): 0.7958, D(G(z)): 0.1653
Epoch: [12/20], Batch Num: [456/600]
Discriminator Loss: 0.4832, Generator Loss: 1.9713
D(x): 0.8163, D(G(z)): 0.1654
Epoch: [12/20], Batch Num: [457/600]
Discriminator Loss: 0.5831, Generator Loss: 2.1612
D(x): 0.8789, D(G(z)): 0.2286
Epoch: [12/20], Batch Num: [458/600]
Discriminator Loss: 0.4716, Generator Loss: 2.8000
D(x): 0.9000, D(G(z)): 0.2179
Epoch: [12/20], Batch Num: [459/600]
Discriminator Loss: 0.5579, Generator Loss: 2.9212
D(x): 0.8185, D(G(z)): 0.1421
Epoch: [12/20], Batch Num: [460/600]
Discriminator Loss: 0.5760, Generator Loss: 2.9629
D(x): 0.8198, D(G(z)): 0.1411
Epoch: [12/20], Batch Num: [461/600]
Discriminator Loss: 0.6912, Generator Loss: 2.5836
D(x): 0.8037, D(G(z)): 0.1740
Epoch: [12/20], Batch Num: [462/600]
Discriminator Loss: 0.6369, Generator Loss: 2.6184
D(x): 0.8244, D(G(z)): 0.1690
Epoch: [12/20], Batch Num: [463/600]
Discriminator Loss: 0.4993, Generator Loss: 2.2794
D(x): 0.8615, D(G(z)): 0.1838
Epoch: [12/20], Batch Num: [464/600]
Discriminator Loss: 0.6826, Generator Loss: 2.1705
D(x): 0.8264, D(G(z)): 0.1906
Epoch: [12/20], Batch Num: [465/600]
Discriminator Loss: 0.6696, Generator Loss: 2.5498
D(x): 0.7885, D(G(z)): 0.1623
Epoch: [12/20], Batch Num: [466/600]
Discriminator Loss: 0.5960, Generator Loss: 2.1111
D(x): 0.7914, D(G(z)): 0.1353
Epoch: [12/20], Batch Num: [467/600]
Discriminator Loss: 0.6240, Generator Loss: 1.9754
D(x): 0.8075, D(G(z)): 0.1903
Epoch: [12/20], Batch Num: [468/600]
Discriminator Loss: 0.6213, Generator Loss: 2.1734
D(x): 0.8405, D(G(z)): 0.2308
Epoch: [12/20], Batch Num: [469/600]
Discriminator Loss: 0.6279, Generator Loss: 2.3807
D(x): 0.8421, D(G(z)): 0.2331
Epoch: [12/20], Batch Num: [470/600]
Discriminator Loss: 0.4892, Generator Loss: 2.5222
D(x): 0.8250, D(G(z)): 0.1465
Epoch: [12/20], Batch Num: [471/600]
Discriminator Loss: 0.5660, Generator Loss: 2.4818
D(x): 0.7839, D(G(z)): 0.1543
Epoch: [12/20], Batch Num: [472/600]
Discriminator Loss: 0.5299, Generator Loss: 2.3813
D(x): 0.8226, D(G(z)): 0.1774
Epoch: [12/20], Batch Num: [473/600]
Discriminator Loss: 0.5330, Generator Loss: 2.1258
D(x): 0.8203, D(G(z)): 0.1710
Epoch: [12/20], Batch Num: [474/600]
Discriminator Loss: 0.5442, Generator Loss: 2.0068
D(x): 0.8115, D(G(z)): 0.1635
Epoch: [12/20], Batch Num: [475/600]
Discriminator Loss: 0.6284, Generator Loss: 2.0643
D(x): 0.8319, D(G(z)): 0.2268
Epoch: [12/20], Batch Num: [476/600]
Discriminator Loss: 0.5870, Generator Loss: 2.3278
D(x): 0.8795, D(G(z)): 0.2333
Epoch: [12/20], Batch Num: [477/600]
Discriminator Loss: 0.6730, Generator Loss: 2.7909
D(x): 0.7870, D(G(z)): 0.1781
Epoch: [12/20], Batch Num: [478/600]
Discriminator Loss: 0.5030, Generator Loss: 2.7917
D(x): 0.8282, D(G(z)): 0.1536
Epoch: [12/20], Batch Num: [479/600]
Discriminator Loss: 0.4619, Generator Loss: 2.5148
D(x): 0.8315, D(G(z)): 0.1292
Epoch: [12/20], Batch Num: [480/600]
Discriminator Loss: 0.6235, Generator Loss: 1.9603
D(x): 0.7619, D(G(z)): 0.1201
Epoch: [12/20], Batch Num: [481/600]
Discriminator Loss: 0.5920, Generator Loss: 1.8618
D(x): 0.8610, D(G(z)): 0.2321
Epoch: [12/20], Batch Num: [482/600]
Discriminator Loss: 0.4805, Generator Loss: 2.0808
D(x): 0.8733, D(G(z)): 0.2050
Epoch: [12/20], Batch Num: [483/600]
Discriminator Loss: 0.7332, Generator Loss: 2.5654
D(x): 0.8782, D(G(z)): 0.2987
Epoch: [12/20], Batch Num: [484/600]
Discriminator Loss: 0.5836, Generator Loss: 2.9702
D(x): 0.8072, D(G(z)): 0.1393
Epoch: [12/20], Batch Num: [485/600]
Discriminator Loss: 0.7713, Generator Loss: 2.8291
D(x): 0.7337, D(G(z)): 0.1418
Epoch: [12/20], Batch Num: [486/600]
Discriminator Loss: 0.7126, Generator Loss: 2.2522
D(x): 0.7348, D(G(z)): 0.1413
Epoch: [12/20], Batch Num: [487/600]
Discriminator Loss: 0.6213, Generator Loss: 1.8453
D(x): 0.8257, D(G(z)): 0.1814
Epoch: [12/20], Batch Num: [488/600]
Discriminator Loss: 0.5400, Generator Loss: 1.7785
D(x): 0.8950, D(G(z)): 0.2580
Epoch: [12/20], Batch Num: [489/600]
Discriminator Loss: 0.4485, Generator Loss: 2.0775
D(x): 0.8809, D(G(z)): 0.2148
Epoch: [12/20], Batch Num: [490/600]
Discriminator Loss: 0.5186, Generator Loss: 2.1777
D(x): 0.8275, D(G(z)): 0.1562
Epoch: [12/20], Batch Num: [491/600]
Discriminator Loss: 0.6632, Generator Loss: 2.2544
D(x): 0.8245, D(G(z)): 0.2357
Epoch: [12/20], Batch Num: [492/600]
Discriminator Loss: 0.5983, Generator Loss: 2.2029
D(x): 0.7578, D(G(z)): 0.1351
Epoch: [12/20], Batch Num: [493/600]
Discriminator Loss: 0.5043, Generator Loss: 2.2152
D(x): 0.8509, D(G(z)): 0.1877
Epoch: [12/20], Batch Num: [494/600]
Discriminator Loss: 0.6319, Generator Loss: 2.2399
D(x): 0.8036, D(G(z)): 0.1857
Epoch: [12/20], Batch Num: [495/600]
Discriminator Loss: 0.5725, Generator Loss: 2.3374
D(x): 0.8344, D(G(z)): 0.2055
Epoch: [12/20], Batch Num: [496/600]
Discriminator Loss: 0.6857, Generator Loss: 1.9240
D(x): 0.7657, D(G(z)): 0.1732
Epoch: [12/20], Batch Num: [497/600]
Discriminator Loss: 0.6514, Generator Loss: 2.0629
D(x): 0.8439, D(G(z)): 0.2302
Epoch: [12/20], Batch Num: [498/600]
Discriminator Loss: 0.7080, Generator Loss: 2.2028
D(x): 0.7972, D(G(z)): 0.1987
Epoch: [12/20], Batch Num: [499/600]
Discriminator Loss: 0.8352, Generator Loss: 2.2817
D(x): 0.7190, D(G(z)): 0.1859
Epoch: 12, Batch Num: [500/600]

Epoch: [12/20], Batch Num: [500/600]
Discriminator Loss: 0.5712, Generator Loss: 2.2699
D(x): 0.8363, D(G(z)): 0.1836
Epoch: [12/20], Batch Num: [501/600]
Discriminator Loss: 0.5079, Generator Loss: 2.1068
D(x): 0.8264, D(G(z)): 0.1812
Epoch: [12/20], Batch Num: [502/600]
Discriminator Loss: 0.7289, Generator Loss: 2.3056
D(x): 0.8204, D(G(z)): 0.2551
Epoch: [12/20], Batch Num: [503/600]
Discriminator Loss: 0.6767, Generator Loss: 2.4746
D(x): 0.7737, D(G(z)): 0.2307
Epoch: [12/20], Batch Num: [504/600]
Discriminator Loss: 0.6958, Generator Loss: 2.2916
D(x): 0.7755, D(G(z)): 0.1717
Epoch: [12/20], Batch Num: [505/600]
Discriminator Loss: 0.6310, Generator Loss: 2.1828
D(x): 0.7801, D(G(z)): 0.1766
Epoch: [12/20], Batch Num: [506/600]
Discriminator Loss: 0.5980, Generator Loss: 1.9563
D(x): 0.8022, D(G(z)): 0.1781
Epoch: [12/20], Batch Num: [507/600]
Discriminator Loss: 0.6436, Generator Loss: 1.9855
D(x): 0.8480, D(G(z)): 0.2706
Epoch: [12/20], Batch Num: [508/600]
Discriminator Loss: 0.6081, Generator Loss: 2.1661
D(x): 0.8084, D(G(z)): 0.1974
Epoch: [12/20], Batch Num: [509/600]
Discriminator Loss: 0.4700, Generator Loss: 2.3544
D(x): 0.8588, D(G(z)): 0.1884
Epoch: [12/20], Batch Num: [510/600]
Discriminator Loss: 0.5946, Generator Loss: 2.5017
D(x): 0.8400, D(G(z)): 0.1968
Epoch: [12/20], Batch Num: [511/600]
Discriminator Loss: 0.7337, Generator Loss: 2.5903
D(x): 0.7751, D(G(z)): 0.1732
Epoch: [12/20], Batch Num: [512/600]
Discriminator Loss: 0.7337, Generator Loss: 2.3200
D(x): 0.7566, D(G(z)): 0.1494
Epoch: [12/20], Batch Num: [513/600]
Discriminator Loss: 0.6568, Generator Loss: 2.0847
D(x): 0.7664, D(G(z)): 0.1660
Epoch: [12/20], Batch Num: [514/600]
Discriminator Loss: 0.6866, Generator Loss: 2.0620
D(x): 0.8080, D(G(z)): 0.2388
Epoch: [12/20], Batch Num: [515/600]
Discriminator Loss: 0.6142, Generator Loss: 1.9757
D(x): 0.8417, D(G(z)): 0.2378
Epoch: [12/20], Batch Num: [516/600]
Discriminator Loss: 0.5866, Generator Loss: 2.2562
D(x): 0.8685, D(G(z)): 0.2324
Epoch: [12/20], Batch Num: [517/600]
Discriminator Loss: 0.5171, Generator Loss: 2.5322
D(x): 0.8344, D(G(z)): 0.1704
Epoch: [12/20], Batch Num: [518/600]
Discriminator Loss: 0.7522, Generator Loss: 2.3327
D(x): 0.7066, D(G(z)): 0.1616
Epoch: [12/20], Batch Num: [519/600]
Discriminator Loss: 0.7372, Generator Loss: 2.2529
D(x): 0.7303, D(G(z)): 0.1583
Epoch: [12/20], Batch Num: [520/600]
Discriminator Loss: 0.5660, Generator Loss: 2.0894
D(x): 0.8156, D(G(z)): 0.1888
Epoch: [12/20], Batch Num: [521/600]
Discriminator Loss: 0.5594, Generator Loss: 2.0162
D(x): 0.8119, D(G(z)): 0.1820
Epoch: [12/20], Batch Num: [522/600]
Discriminator Loss: 0.6058, Generator Loss: 1.8984
D(x): 0.8224, D(G(z)): 0.2204
Epoch: [12/20], Batch Num: [523/600]
Discriminator Loss: 0.5890, Generator Loss: 2.2618
D(x): 0.8205, D(G(z)): 0.2176
Epoch: [12/20], Batch Num: [524/600]
Discriminator Loss: 0.5662, Generator Loss: 2.3288
D(x): 0.8277, D(G(z)): 0.1899
Epoch: [12/20], Batch Num: [525/600]
Discriminator Loss: 0.5104, Generator Loss: 2.3846
D(x): 0.8120, D(G(z)): 0.1776
Epoch: [12/20], Batch Num: [526/600]
Discriminator Loss: 0.4644, Generator Loss: 2.4507
D(x): 0.8200, D(G(z)): 0.1470
Epoch: [12/20], Batch Num: [527/600]
Discriminator Loss: 0.6156, Generator Loss: 2.0594
D(x): 0.7906, D(G(z)): 0.1720
Epoch: [12/20], Batch Num: [528/600]
Discriminator Loss: 0.4485, Generator Loss: 2.3677
D(x): 0.8832, D(G(z)): 0.1809
Epoch: [12/20], Batch Num: [529/600]
Discriminator Loss: 0.4298, Generator Loss: 2.3555
D(x): 0.8404, D(G(z)): 0.1268
Epoch: [12/20], Batch Num: [530/600]
Discriminator Loss: 0.5139, Generator Loss: 2.4501
D(x): 0.8514, D(G(z)): 0.1729
Epoch: [12/20], Batch Num: [531/600]
Discriminator Loss: 0.4384, Generator Loss: 2.6083
D(x): 0.8505, D(G(z)): 0.1456
Epoch: [12/20], Batch Num: [532/600]
Discriminator Loss: 0.3966, Generator Loss: 2.5649
D(x): 0.8859, D(G(z)): 0.1389
Epoch: [12/20], Batch Num: [533/600]
Discriminator Loss: 0.4722, Generator Loss: 2.8451
D(x): 0.8538, D(G(z)): 0.1641
Epoch: [12/20], Batch Num: [534/600]
Discriminator Loss: 0.6243, Generator Loss: 3.0928
D(x): 0.8540, D(G(z)): 0.1892
Epoch: [12/20], Batch Num: [535/600]
Discriminator Loss: 0.5474, Generator Loss: 3.0995
D(x): 0.7980, D(G(z)): 0.0879
Epoch: [12/20], Batch Num: [536/600]
Discriminator Loss: 0.4952, Generator Loss: 2.9991
D(x): 0.8670, D(G(z)): 0.1360
Epoch: [12/20], Batch Num: [537/600]
Discriminator Loss: 0.5254, Generator Loss: 3.0883
D(x): 0.8670, D(G(z)): 0.1576
Epoch: [12/20], Batch Num: [538/600]
Discriminator Loss: 0.6555, Generator Loss: 2.2961
D(x): 0.7779, D(G(z)): 0.1432
Epoch: [12/20], Batch Num: [539/600]
Discriminator Loss: 0.6026, Generator Loss: 2.1402
D(x): 0.8452, D(G(z)): 0.1716
Epoch: [12/20], Batch Num: [540/600]
Discriminator Loss: 0.5312, Generator Loss: 2.3519
D(x): 0.8868, D(G(z)): 0.1971
Epoch: [12/20], Batch Num: [541/600]
Discriminator Loss: 0.7663, Generator Loss: 2.7980
D(x): 0.7824, D(G(z)): 0.1888
Epoch: [12/20], Batch Num: [542/600]
Discriminator Loss: 0.5509, Generator Loss: 2.7128
D(x): 0.8155, D(G(z)): 0.1530
Epoch: [12/20], Batch Num: [543/600]
Discriminator Loss: 0.5189, Generator Loss: 3.1012
D(x): 0.8436, D(G(z)): 0.1360
Epoch: [12/20], Batch Num: [544/600]
Discriminator Loss: 0.7065, Generator Loss: 2.8328
D(x): 0.7783, D(G(z)): 0.1422
Epoch: [12/20], Batch Num: [545/600]
Discriminator Loss: 0.3903, Generator Loss: 2.4618
D(x): 0.8743, D(G(z)): 0.1533
Epoch: [12/20], Batch Num: [546/600]
Discriminator Loss: 0.7067, Generator Loss: 2.2974
D(x): 0.7993, D(G(z)): 0.2132
Epoch: [12/20], Batch Num: [547/600]
Discriminator Loss: 0.4813, Generator Loss: 2.3941
D(x): 0.8483, D(G(z)): 0.1793
Epoch: [12/20], Batch Num: [548/600]
Discriminator Loss: 0.5879, Generator Loss: 2.4653
D(x): 0.8496, D(G(z)): 0.1935
Epoch: [12/20], Batch Num: [549/600]
Discriminator Loss: 0.6029, Generator Loss: 2.2914
D(x): 0.7913, D(G(z)): 0.1568
Epoch: [12/20], Batch Num: [550/600]
Discriminator Loss: 0.6090, Generator Loss: 2.4421
D(x): 0.8541, D(G(z)): 0.2169
Epoch: [12/20], Batch Num: [551/600]
Discriminator Loss: 0.4834, Generator Loss: 2.3789
D(x): 0.8416, D(G(z)): 0.1584
Epoch: [12/20], Batch Num: [552/600]
Discriminator Loss: 0.5406, Generator Loss: 2.3815
D(x): 0.8179, D(G(z)): 0.1446
Epoch: [12/20], Batch Num: [553/600]
Discriminator Loss: 0.6043, Generator Loss: 2.3205
D(x): 0.8262, D(G(z)): 0.1791
Epoch: [12/20], Batch Num: [554/600]
Discriminator Loss: 0.5567, Generator Loss: 2.2824
D(x): 0.8321, D(G(z)): 0.1575
Epoch: [12/20], Batch Num: [555/600]
Discriminator Loss: 0.4781, Generator Loss: 2.3393
D(x): 0.8351, D(G(z)): 0.1651
Epoch: [12/20], Batch Num: [556/600]
Discriminator Loss: 0.4228, Generator Loss: 2.3647
D(x): 0.8608, D(G(z)): 0.1404
Epoch: [12/20], Batch Num: [557/600]
Discriminator Loss: 0.6433, Generator Loss: 2.5026
D(x): 0.8175, D(G(z)): 0.1826
Epoch: [12/20], Batch Num: [558/600]
Discriminator Loss: 0.5039, Generator Loss: 2.1751
D(x): 0.8317, D(G(z)): 0.1558
Epoch: [12/20], Batch Num: [559/600]
Discriminator Loss: 0.6102, Generator Loss: 2.1713
D(x): 0.8493, D(G(z)): 0.2343
Epoch: [12/20], Batch Num: [560/600]
Discriminator Loss: 0.7563, Generator Loss: 2.4980
D(x): 0.7834, D(G(z)): 0.2027
Epoch: [12/20], Batch Num: [561/600]
Discriminator Loss: 0.5010, Generator Loss: 2.5746
D(x): 0.8748, D(G(z)): 0.1913
Epoch: [12/20], Batch Num: [562/600]
Discriminator Loss: 0.5301, Generator Loss: 2.7076
D(x): 0.8125, D(G(z)): 0.1576
Epoch: [12/20], Batch Num: [563/600]
Discriminator Loss: 0.6202, Generator Loss: 2.5641
D(x): 0.7697, D(G(z)): 0.1311
Epoch: [12/20], Batch Num: [564/600]
Discriminator Loss: 0.7861, Generator Loss: 2.1057
D(x): 0.7933, D(G(z)): 0.2125
Epoch: [12/20], Batch Num: [565/600]
Discriminator Loss: 0.3555, Generator Loss: 1.9971
D(x): 0.9126, D(G(z)): 0.1848
Epoch: [12/20], Batch Num: [566/600]
Discriminator Loss: 0.4948, Generator Loss: 2.1909
D(x): 0.8840, D(G(z)): 0.1929
Epoch: [12/20], Batch Num: [567/600]
Discriminator Loss: 0.6172, Generator Loss: 2.5457
D(x): 0.8543, D(G(z)): 0.2307
Epoch: [12/20], Batch Num: [568/600]
Discriminator Loss: 0.5615, Generator Loss: 2.8016
D(x): 0.8008, D(G(z)): 0.1661
Epoch: [12/20], Batch Num: [569/600]
Discriminator Loss: 0.7370, Generator Loss: 2.7904
D(x): 0.7667, D(G(z)): 0.1600
Epoch: [12/20], Batch Num: [570/600]
Discriminator Loss: 0.6233, Generator Loss: 2.5348
D(x): 0.7531, D(G(z)): 0.1037
Epoch: [12/20], Batch Num: [571/600]
Discriminator Loss: 0.8345, Generator Loss: 2.0581
D(x): 0.7536, D(G(z)): 0.1761
Epoch: [12/20], Batch Num: [572/600]
Discriminator Loss: 0.5895, Generator Loss: 1.7238
D(x): 0.8245, D(G(z)): 0.2220
Epoch: [12/20], Batch Num: [573/600]
Discriminator Loss: 0.8761, Generator Loss: 1.8905
D(x): 0.8285, D(G(z)): 0.3051
Epoch: [12/20], Batch Num: [574/600]
Discriminator Loss: 0.6592, Generator Loss: 2.4082
D(x): 0.8750, D(G(z)): 0.2934
Epoch: [12/20], Batch Num: [575/600]
Discriminator Loss: 0.5148, Generator Loss: 2.8951
D(x): 0.8459, D(G(z)): 0.1918
Epoch: [12/20], Batch Num: [576/600]
Discriminator Loss: 0.8070, Generator Loss: 3.3709
D(x): 0.6919, D(G(z)): 0.1372
Epoch: [12/20], Batch Num: [577/600]
Discriminator Loss: 0.6468, Generator Loss: 2.7658
D(x): 0.7607, D(G(z)): 0.1286
Epoch: [12/20], Batch Num: [578/600]
Discriminator Loss: 0.5670, Generator Loss: 2.1587
D(x): 0.7456, D(G(z)): 0.1189
Epoch: [12/20], Batch Num: [579/600]
Discriminator Loss: 0.6606, Generator Loss: 1.8888
D(x): 0.7877, D(G(z)): 0.2303
Epoch: [12/20], Batch Num: [580/600]
Discriminator Loss: 0.7187, Generator Loss: 1.7797
D(x): 0.8523, D(G(z)): 0.2975
Epoch: [12/20], Batch Num: [581/600]
Discriminator Loss: 0.5537, Generator Loss: 1.9540
D(x): 0.8589, D(G(z)): 0.2215
Epoch: [12/20], Batch Num: [582/600]
Discriminator Loss: 0.5133, Generator Loss: 2.1967
D(x): 0.8518, D(G(z)): 0.2175
Epoch: [12/20], Batch Num: [583/600]
Discriminator Loss: 0.5833, Generator Loss: 2.4560
D(x): 0.7954, D(G(z)): 0.1865
Epoch: [12/20], Batch Num: [584/600]
Discriminator Loss: 0.5446, Generator Loss: 2.3553
D(x): 0.8211, D(G(z)): 0.1677
Epoch: [12/20], Batch Num: [585/600]
Discriminator Loss: 0.6022, Generator Loss: 2.5323
D(x): 0.7621, D(G(z)): 0.1243
Epoch: [12/20], Batch Num: [586/600]
Discriminator Loss: 0.4601, Generator Loss: 2.2004
D(x): 0.8482, D(G(z)): 0.1335
Epoch: [12/20], Batch Num: [587/600]
Discriminator Loss: 0.6607, Generator Loss: 2.1930
D(x): 0.8077, D(G(z)): 0.1987
Epoch: [12/20], Batch Num: [588/600]
Discriminator Loss: 0.6179, Generator Loss: 2.2425
D(x): 0.8411, D(G(z)): 0.1944
Epoch: [12/20], Batch Num: [589/600]
Discriminator Loss: 0.6001, Generator Loss: 2.3045
D(x): 0.8099, D(G(z)): 0.1694
Epoch: [12/20], Batch Num: [590/600]
Discriminator Loss: 0.5393, Generator Loss: 2.3019
D(x): 0.8210, D(G(z)): 0.1631
Epoch: [12/20], Batch Num: [591/600]
Discriminator Loss: 0.6411, Generator Loss: 2.2765
D(x): 0.7882, D(G(z)): 0.1952
Epoch: [12/20], Batch Num: [592/600]
Discriminator Loss: 0.6504, Generator Loss: 2.4862
D(x): 0.8389, D(G(z)): 0.2248
Epoch: [12/20], Batch Num: [593/600]
Discriminator Loss: 0.6157, Generator Loss: 2.1527
D(x): 0.8257, D(G(z)): 0.2274
Epoch: [12/20], Batch Num: [594/600]
Discriminator Loss: 0.5285, Generator Loss: 2.4872
D(x): 0.8198, D(G(z)): 0.1685
Epoch: [12/20], Batch Num: [595/600]
Discriminator Loss: 0.6265, Generator Loss: 2.2063
D(x): 0.7774, D(G(z)): 0.1821
Epoch: [12/20], Batch Num: [596/600]
Discriminator Loss: 0.5637, Generator Loss: 2.3757
D(x): 0.8087, D(G(z)): 0.1680
Epoch: [12/20], Batch Num: [597/600]
Discriminator Loss: 0.5090, Generator Loss: 1.9554
D(x): 0.8227, D(G(z)): 0.1594
Epoch: [12/20], Batch Num: [598/600]
Discriminator Loss: 0.6003, Generator Loss: 2.1097
D(x): 0.8334, D(G(z)): 0.2267
Epoch: [12/20], Batch Num: [599/600]
Discriminator Loss: 0.6101, Generator Loss: 2.2867
D(x): 0.7973, D(G(z)): 0.1864
Epoch: 13, Batch Num: [0/600]

Epoch: [13/20], Batch Num: [0/600]
Discriminator Loss: 0.4899, Generator Loss: 2.4791
D(x): 0.8665, D(G(z)): 0.1806
Epoch: [13/20], Batch Num: [1/600]
Discriminator Loss: 0.4100, Generator Loss: 2.6025
D(x): 0.8774, D(G(z)): 0.1729
Epoch: [13/20], Batch Num: [2/600]
Discriminator Loss: 0.5587, Generator Loss: 2.7043
D(x): 0.8083, D(G(z)): 0.1562
Epoch: [13/20], Batch Num: [3/600]
Discriminator Loss: 0.6290, Generator Loss: 2.7868
D(x): 0.8003, D(G(z)): 0.1474
Epoch: [13/20], Batch Num: [4/600]
Discriminator Loss: 0.5280, Generator Loss: 2.4884
D(x): 0.7950, D(G(z)): 0.1283
Epoch: [13/20], Batch Num: [5/600]
Discriminator Loss: 0.5524, Generator Loss: 2.4835
D(x): 0.8651, D(G(z)): 0.2019
Epoch: [13/20], Batch Num: [6/600]
Discriminator Loss: 0.5697, Generator Loss: 2.4800
D(x): 0.8862, D(G(z)): 0.2223
Epoch: [13/20], Batch Num: [7/600]
Discriminator Loss: 0.5390, Generator Loss: 3.0167
D(x): 0.8299, D(G(z)): 0.1613
Epoch: [13/20], Batch Num: [8/600]
Discriminator Loss: 0.5117, Generator Loss: 2.7758
D(x): 0.7904, D(G(z)): 0.1005
Epoch: [13/20], Batch Num: [9/600]
Discriminator Loss: 0.5633, Generator Loss: 2.6299
D(x): 0.7738, D(G(z)): 0.1211
Epoch: [13/20], Batch Num: [10/600]
Discriminator Loss: 0.5175, Generator Loss: 2.0259
D(x): 0.8423, D(G(z)): 0.1498
Epoch: [13/20], Batch Num: [11/600]
Discriminator Loss: 0.5233, Generator Loss: 1.8380
D(x): 0.8509, D(G(z)): 0.2075
Epoch: [13/20], Batch Num: [12/600]
Discriminator Loss: 0.6440, Generator Loss: 2.1186
D(x): 0.8479, D(G(z)): 0.2536
Epoch: [13/20], Batch Num: [13/600]
Discriminator Loss: 0.6092, Generator Loss: 2.4909
D(x): 0.8161, D(G(z)): 0.2113
Epoch: [13/20], Batch Num: [14/600]
Discriminator Loss: 0.6307, Generator Loss: 2.5870
D(x): 0.7860, D(G(z)): 0.1715
Epoch: [13/20], Batch Num: [15/600]
Discriminator Loss: 0.6499, Generator Loss: 2.6586
D(x): 0.8049, D(G(z)): 0.2103
Epoch: [13/20], Batch Num: [16/600]
Discriminator Loss: 0.5579, Generator Loss: 2.7844
D(x): 0.8244, D(G(z)): 0.1364
Epoch: [13/20], Batch Num: [17/600]
Discriminator Loss: 0.5586, Generator Loss: 2.3441
D(x): 0.8117, D(G(z)): 0.1345
Epoch: [13/20], Batch Num: [18/600]
Discriminator Loss: 0.5603, Generator Loss: 2.0572
D(x): 0.7973, D(G(z)): 0.1611
Epoch: [13/20], Batch Num: [19/600]
Discriminator Loss: 0.6013, Generator Loss: 1.8166
D(x): 0.8310, D(G(z)): 0.1992
Epoch: [13/20], Batch Num: [20/600]
Discriminator Loss: 0.4543, Generator Loss: 2.0335
D(x): 0.8891, D(G(z)): 0.2114
Epoch: [13/20], Batch Num: [21/600]
Discriminator Loss: 0.6713, Generator Loss: 2.4576
D(x): 0.8365, D(G(z)): 0.2492
Epoch: [13/20], Batch Num: [22/600]
Discriminator Loss: 0.6163, Generator Loss: 2.8851
D(x): 0.7821, D(G(z)): 0.1582
Epoch: [13/20], Batch Num: [23/600]
Discriminator Loss: 0.5660, Generator Loss: 2.7670
D(x): 0.7935, D(G(z)): 0.1590
Epoch: [13/20], Batch Num: [24/600]
Discriminator Loss: 0.6573, Generator Loss: 2.4704
D(x): 0.7846, D(G(z)): 0.1567
Epoch: [13/20], Batch Num: [25/600]
Discriminator Loss: 0.7758, Generator Loss: 2.0866
D(x): 0.7357, D(G(z)): 0.1711
Epoch: [13/20], Batch Num: [26/600]
Discriminator Loss: 0.5126, Generator Loss: 1.9343
D(x): 0.8280, D(G(z)): 0.1708
Epoch: [13/20], Batch Num: [27/600]
Discriminator Loss: 0.6196, Generator Loss: 1.5461
D(x): 0.8303, D(G(z)): 0.2519
Epoch: [13/20], Batch Num: [28/600]
Discriminator Loss: 0.6639, Generator Loss: 2.2596
D(x): 0.8618, D(G(z)): 0.2711
Epoch: [13/20], Batch Num: [29/600]
Discriminator Loss: 0.6014, Generator Loss: 2.7179
D(x): 0.8403, D(G(z)): 0.1996
Epoch: [13/20], Batch Num: [30/600]
Discriminator Loss: 0.6208, Generator Loss: 2.8972
D(x): 0.7755, D(G(z)): 0.1322
Epoch: [13/20], Batch Num: [31/600]
Discriminator Loss: 0.5061, Generator Loss: 3.0890
D(x): 0.8012, D(G(z)): 0.1230
Epoch: [13/20], Batch Num: [32/600]
Discriminator Loss: 0.7064, Generator Loss: 2.1357
D(x): 0.7350, D(G(z)): 0.1267
Epoch: [13/20], Batch Num: [33/600]
Discriminator Loss: 0.5873, Generator Loss: 1.7597
D(x): 0.8397, D(G(z)): 0.2001
Epoch: [13/20], Batch Num: [34/600]
Discriminator Loss: 0.7401, Generator Loss: 1.8041
D(x): 0.8732, D(G(z)): 0.3148
Epoch: [13/20], Batch Num: [35/600]
Discriminator Loss: 0.5878, Generator Loss: 2.3938
D(x): 0.8311, D(G(z)): 0.2150
Epoch: [13/20], Batch Num: [36/600]
Discriminator Loss: 0.5305, Generator Loss: 3.1478
D(x): 0.8298, D(G(z)): 0.1656
Epoch: [13/20], Batch Num: [37/600]
Discriminator Loss: 0.6354, Generator Loss: 2.8490
D(x): 0.7352, D(G(z)): 0.1027
Epoch: [13/20], Batch Num: [38/600]
Discriminator Loss: 0.5727, Generator Loss: 2.3428
D(x): 0.8074, D(G(z)): 0.1351
Epoch: [13/20], Batch Num: [39/600]
Discriminator Loss: 0.5544, Generator Loss: 2.1038
D(x): 0.8235, D(G(z)): 0.1787
Epoch: [13/20], Batch Num: [40/600]
Discriminator Loss: 0.5834, Generator Loss: 2.0737
D(x): 0.8456, D(G(z)): 0.2164
Epoch: [13/20], Batch Num: [41/600]
Discriminator Loss: 0.6771, Generator Loss: 2.2534
D(x): 0.8416, D(G(z)): 0.2444
Epoch: [13/20], Batch Num: [42/600]
Discriminator Loss: 0.5152, Generator Loss: 2.3571
D(x): 0.8494, D(G(z)): 0.1961
Epoch: [13/20], Batch Num: [43/600]
Discriminator Loss: 0.6466, Generator Loss: 2.6492
D(x): 0.7704, D(G(z)): 0.1631
Epoch: [13/20], Batch Num: [44/600]
Discriminator Loss: 0.6359, Generator Loss: 2.4419
D(x): 0.7676, D(G(z)): 0.1528
Epoch: [13/20], Batch Num: [45/600]
Discriminator Loss: 0.6331, Generator Loss: 2.2079
D(x): 0.7979, D(G(z)): 0.1754
Epoch: [13/20], Batch Num: [46/600]
Discriminator Loss: 0.5159, Generator Loss: 1.9334
D(x): 0.8192, D(G(z)): 0.1828
Epoch: [13/20], Batch Num: [47/600]
Discriminator Loss: 0.5329, Generator Loss: 1.9678
D(x): 0.8458, D(G(z)): 0.2140
Epoch: [13/20], Batch Num: [48/600]
Discriminator Loss: 0.6339, Generator Loss: 2.1017
D(x): 0.8365, D(G(z)): 0.2452
Epoch: [13/20], Batch Num: [49/600]
Discriminator Loss: 0.5953, Generator Loss: 2.3355
D(x): 0.8369, D(G(z)): 0.2271
Epoch: [13/20], Batch Num: [50/600]
Discriminator Loss: 0.4837, Generator Loss: 2.7721
D(x): 0.8397, D(G(z)): 0.1650
Epoch: [13/20], Batch Num: [51/600]
Discriminator Loss: 0.5926, Generator Loss: 2.8186
D(x): 0.7736, D(G(z)): 0.1306
Epoch: [13/20], Batch Num: [52/600]
Discriminator Loss: 0.5852, Generator Loss: 2.6204
D(x): 0.7883, D(G(z)): 0.1265
Epoch: [13/20], Batch Num: [53/600]
Discriminator Loss: 0.5687, Generator Loss: 2.3931
D(x): 0.8307, D(G(z)): 0.1777
Epoch: [13/20], Batch Num: [54/600]
Discriminator Loss: 0.5393, Generator Loss: 2.2651
D(x): 0.8189, D(G(z)): 0.1421
Epoch: [13/20], Batch Num: [55/600]
Discriminator Loss: 0.4814, Generator Loss: 1.9296
D(x): 0.8368, D(G(z)): 0.1699
Epoch: [13/20], Batch Num: [56/600]
Discriminator Loss: 0.4327, Generator Loss: 1.9291
D(x): 0.8841, D(G(z)): 0.1996
Epoch: [13/20], Batch Num: [57/600]
Discriminator Loss: 0.6255, Generator Loss: 2.1663
D(x): 0.8519, D(G(z)): 0.2270
Epoch: [13/20], Batch Num: [58/600]
Discriminator Loss: 0.6467, Generator Loss: 2.3668
D(x): 0.8045, D(G(z)): 0.2066
Epoch: [13/20], Batch Num: [59/600]
Discriminator Loss: 0.6106, Generator Loss: 2.6026
D(x): 0.8090, D(G(z)): 0.1841
Epoch: [13/20], Batch Num: [60/600]
Discriminator Loss: 0.5473, Generator Loss: 2.5499
D(x): 0.8371, D(G(z)): 0.1467
Epoch: [13/20], Batch Num: [61/600]
Discriminator Loss: 0.4034, Generator Loss: 2.3503
D(x): 0.8480, D(G(z)): 0.1135
Epoch: [13/20], Batch Num: [62/600]
Discriminator Loss: 0.6068, Generator Loss: 2.1806
D(x): 0.7867, D(G(z)): 0.1266
Epoch: [13/20], Batch Num: [63/600]
Discriminator Loss: 0.5639, Generator Loss: 1.9070
D(x): 0.8428, D(G(z)): 0.1967
Epoch: [13/20], Batch Num: [64/600]
Discriminator Loss: 0.6241, Generator Loss: 2.0572
D(x): 0.8707, D(G(z)): 0.2353
Epoch: [13/20], Batch Num: [65/600]
Discriminator Loss: 0.7544, Generator Loss: 3.0172
D(x): 0.8790, D(G(z)): 0.2838
Epoch: [13/20], Batch Num: [66/600]
Discriminator Loss: 0.7737, Generator Loss: 3.4122
D(x): 0.7300, D(G(z)): 0.1490
Epoch: [13/20], Batch Num: [67/600]
Discriminator Loss: 0.5632, Generator Loss: 3.2740
D(x): 0.7977, D(G(z)): 0.0950
Epoch: [13/20], Batch Num: [68/600]
Discriminator Loss: 0.5569, Generator Loss: 2.4808
D(x): 0.7847, D(G(z)): 0.0955
Epoch: [13/20], Batch Num: [69/600]
Discriminator Loss: 0.5459, Generator Loss: 2.1946
D(x): 0.7999, D(G(z)): 0.1432
Epoch: [13/20], Batch Num: [70/600]
Discriminator Loss: 0.7170, Generator Loss: 1.7325
D(x): 0.8421, D(G(z)): 0.2761
Epoch: [13/20], Batch Num: [71/600]
Discriminator Loss: 0.6186, Generator Loss: 1.9715
D(x): 0.8769, D(G(z)): 0.2646
Epoch: [13/20], Batch Num: [72/600]
Discriminator Loss: 0.5648, Generator Loss: 2.3294
D(x): 0.8611, D(G(z)): 0.2406
Epoch: [13/20], Batch Num: [73/600]
Discriminator Loss: 0.5193, Generator Loss: 2.8532
D(x): 0.8227, D(G(z)): 0.1721
Epoch: [13/20], Batch Num: [74/600]
Discriminator Loss: 0.4623, Generator Loss: 2.8619
D(x): 0.8353, D(G(z)): 0.1100
Epoch: [13/20], Batch Num: [75/600]
Discriminator Loss: 0.5311, Generator Loss: 2.7900
D(x): 0.7922, D(G(z)): 0.0979
Epoch: [13/20], Batch Num: [76/600]
Discriminator Loss: 0.6985, Generator Loss: 2.0765
D(x): 0.7343, D(G(z)): 0.1135
Epoch: [13/20], Batch Num: [77/600]
Discriminator Loss: 0.5689, Generator Loss: 1.8943
D(x): 0.8428, D(G(z)): 0.2193
Epoch: [13/20], Batch Num: [78/600]
Discriminator Loss: 0.6270, Generator Loss: 1.6549
D(x): 0.8213, D(G(z)): 0.2390
Epoch: [13/20], Batch Num: [79/600]
Discriminator Loss: 0.6517, Generator Loss: 1.8306
D(x): 0.8718, D(G(z)): 0.2977
Epoch: [13/20], Batch Num: [80/600]
Discriminator Loss: 0.5788, Generator Loss: 2.3766
D(x): 0.8595, D(G(z)): 0.2459
Epoch: [13/20], Batch Num: [81/600]
Discriminator Loss: 0.4877, Generator Loss: 2.8800
D(x): 0.8458, D(G(z)): 0.1654
Epoch: [13/20], Batch Num: [82/600]
Discriminator Loss: 0.5763, Generator Loss: 2.7096
D(x): 0.7929, D(G(z)): 0.1374
Epoch: [13/20], Batch Num: [83/600]
Discriminator Loss: 0.7006, Generator Loss: 2.4293
D(x): 0.7441, D(G(z)): 0.1225
Epoch: [13/20], Batch Num: [84/600]
Discriminator Loss: 0.5489, Generator Loss: 2.1307
D(x): 0.8085, D(G(z)): 0.1621
Epoch: [13/20], Batch Num: [85/600]
Discriminator Loss: 0.4466, Generator Loss: 1.9802
D(x): 0.8523, D(G(z)): 0.1684
Epoch: [13/20], Batch Num: [86/600]
Discriminator Loss: 0.5976, Generator Loss: 1.8000
D(x): 0.8159, D(G(z)): 0.2015
Epoch: [13/20], Batch Num: [87/600]
Discriminator Loss: 0.5658, Generator Loss: 2.0074
D(x): 0.8712, D(G(z)): 0.2468
Epoch: [13/20], Batch Num: [88/600]
Discriminator Loss: 0.4952, Generator Loss: 2.1598
D(x): 0.8881, D(G(z)): 0.2053
Epoch: [13/20], Batch Num: [89/600]
Discriminator Loss: 0.5287, Generator Loss: 2.3251
D(x): 0.8209, D(G(z)): 0.1793
Epoch: [13/20], Batch Num: [90/600]
Discriminator Loss: 0.5550, Generator Loss: 2.4662
D(x): 0.8007, D(G(z)): 0.1554
Epoch: [13/20], Batch Num: [91/600]
Discriminator Loss: 0.4582, Generator Loss: 2.6148
D(x): 0.8260, D(G(z)): 0.1192
Epoch: [13/20], Batch Num: [92/600]
Discriminator Loss: 0.5157, Generator Loss: 2.4235
D(x): 0.8068, D(G(z)): 0.1294
Epoch: [13/20], Batch Num: [93/600]
Discriminator Loss: 0.4281, Generator Loss: 2.4765
D(x): 0.8500, D(G(z)): 0.1423
Epoch: [13/20], Batch Num: [94/600]
Discriminator Loss: 0.5167, Generator Loss: 2.0959
D(x): 0.8140, D(G(z)): 0.1534
Epoch: [13/20], Batch Num: [95/600]
Discriminator Loss: 0.5579, Generator Loss: 2.1941
D(x): 0.8754, D(G(z)): 0.2517
Epoch: [13/20], Batch Num: [96/600]
Discriminator Loss: 0.5471, Generator Loss: 2.3633
D(x): 0.8261, D(G(z)): 0.1984
Epoch: [13/20], Batch Num: [97/600]
Discriminator Loss: 0.5992, Generator Loss: 2.4929
D(x): 0.7892, D(G(z)): 0.1623
Epoch: [13/20], Batch Num: [98/600]
Discriminator Loss: 0.4998, Generator Loss: 2.3012
D(x): 0.8541, D(G(z)): 0.1790
Epoch: [13/20], Batch Num: [99/600]
Discriminator Loss: 0.5644, Generator Loss: 2.4409
D(x): 0.8274, D(G(z)): 0.1750
Epoch: 13, Batch Num: [100/600]

Epoch: [13/20], Batch Num: [100/600]
Discriminator Loss: 0.5703, Generator Loss: 2.4170
D(x): 0.8404, D(G(z)): 0.1879
Epoch: [13/20], Batch Num: [101/600]
Discriminator Loss: 0.4511, Generator Loss: 2.6705
D(x): 0.8906, D(G(z)): 0.1728
Epoch: [13/20], Batch Num: [102/600]
Discriminator Loss: 0.5187, Generator Loss: 2.7713
D(x): 0.8265, D(G(z)): 0.1574
Epoch: [13/20], Batch Num: [103/600]
Discriminator Loss: 0.4353, Generator Loss: 2.6252
D(x): 0.8268, D(G(z)): 0.1236
Epoch: [13/20], Batch Num: [104/600]
Discriminator Loss: 0.7233, Generator Loss: 2.1820
D(x): 0.7570, D(G(z)): 0.1601
Epoch: [13/20], Batch Num: [105/600]
Discriminator Loss: 0.5065, Generator Loss: 2.2235
D(x): 0.8707, D(G(z)): 0.1834
Epoch: [13/20], Batch Num: [106/600]
Discriminator Loss: 0.5377, Generator Loss: 2.2846
D(x): 0.8814, D(G(z)): 0.2203
Epoch: [13/20], Batch Num: [107/600]
Discriminator Loss: 0.5981, Generator Loss: 2.5987
D(x): 0.8111, D(G(z)): 0.1914
Epoch: [13/20], Batch Num: [108/600]
Discriminator Loss: 0.5151, Generator Loss: 2.5674
D(x): 0.8079, D(G(z)): 0.1414
Epoch: [13/20], Batch Num: [109/600]
Discriminator Loss: 0.6012, Generator Loss: 2.5825
D(x): 0.8090, D(G(z)): 0.1637
Epoch: [13/20], Batch Num: [110/600]
Discriminator Loss: 0.6213, Generator Loss: 2.5208
D(x): 0.8345, D(G(z)): 0.1898
Epoch: [13/20], Batch Num: [111/600]
Discriminator Loss: 0.5798, Generator Loss: 2.4274
D(x): 0.8173, D(G(z)): 0.1609
Epoch: [13/20], Batch Num: [112/600]
Discriminator Loss: 0.5613, Generator Loss: 2.4349
D(x): 0.8307, D(G(z)): 0.1658
Epoch: [13/20], Batch Num: [113/600]
Discriminator Loss: 0.6084, Generator Loss: 2.1333
D(x): 0.8155, D(G(z)): 0.1559
Epoch: [13/20], Batch Num: [114/600]
Discriminator Loss: 0.7712, Generator Loss: 2.2194
D(x): 0.7889, D(G(z)): 0.2144
Epoch: [13/20], Batch Num: [115/600]
Discriminator Loss: 0.5165, Generator Loss: 2.3631
D(x): 0.8648, D(G(z)): 0.1925
Epoch: [13/20], Batch Num: [116/600]
Discriminator Loss: 0.6996, Generator Loss: 2.4479
D(x): 0.7982, D(G(z)): 0.2269
Epoch: [13/20], Batch Num: [117/600]
Discriminator Loss: 0.7277, Generator Loss: 2.0690
D(x): 0.7278, D(G(z)): 0.1493
Epoch: [13/20], Batch Num: [118/600]
Discriminator Loss: 0.7331, Generator Loss: 1.7994
D(x): 0.7777, D(G(z)): 0.2032
Epoch: [13/20], Batch Num: [119/600]
Discriminator Loss: 0.6177, Generator Loss: 1.6770
D(x): 0.8047, D(G(z)): 0.2075
Epoch: [13/20], Batch Num: [120/600]
Discriminator Loss: 0.5812, Generator Loss: 1.9363
D(x): 0.9181, D(G(z)): 0.3039
Epoch: [13/20], Batch Num: [121/600]
Discriminator Loss: 0.6801, Generator Loss: 2.5010
D(x): 0.8695, D(G(z)): 0.2911
Epoch: [13/20], Batch Num: [122/600]
Discriminator Loss: 0.4861, Generator Loss: 2.6937
D(x): 0.8254, D(G(z)): 0.1426
Epoch: [13/20], Batch Num: [123/600]
Discriminator Loss: 0.6954, Generator Loss: 2.8764
D(x): 0.7295, D(G(z)): 0.1195
Epoch: [13/20], Batch Num: [124/600]
Discriminator Loss: 0.5388, Generator Loss: 2.7301
D(x): 0.7886, D(G(z)): 0.1252
Epoch: [13/20], Batch Num: [125/600]
Discriminator Loss: 0.8082, Generator Loss: 2.1331
D(x): 0.7125, D(G(z)): 0.1314
Epoch: [13/20], Batch Num: [126/600]
Discriminator Loss: 0.6652, Generator Loss: 1.4717
D(x): 0.8143, D(G(z)): 0.2171
Epoch: [13/20], Batch Num: [127/600]
Discriminator Loss: 0.5854, Generator Loss: 1.7219
D(x): 0.8508, D(G(z)): 0.2454
Epoch: [13/20], Batch Num: [128/600]
Discriminator Loss: 0.6151, Generator Loss: 1.6242
D(x): 0.8640, D(G(z)): 0.2601
Epoch: [13/20], Batch Num: [129/600]
Discriminator Loss: 0.6149, Generator Loss: 1.9698
D(x): 0.8552, D(G(z)): 0.2646
Epoch: [13/20], Batch Num: [130/600]
Discriminator Loss: 0.5386, Generator Loss: 2.2908
D(x): 0.8577, D(G(z)): 0.2240
Epoch: [13/20], Batch Num: [131/600]
Discriminator Loss: 0.5528, Generator Loss: 2.4526
D(x): 0.8192, D(G(z)): 0.1773
Epoch: [13/20], Batch Num: [132/600]
Discriminator Loss: 0.5914, Generator Loss: 2.8531
D(x): 0.8004, D(G(z)): 0.1746
Epoch: [13/20], Batch Num: [133/600]
Discriminator Loss: 0.6254, Generator Loss: 2.6901
D(x): 0.7565, D(G(z)): 0.0894
Epoch: [13/20], Batch Num: [134/600]
Discriminator Loss: 0.5746, Generator Loss: 2.4554
D(x): 0.7663, D(G(z)): 0.1304
Epoch: [13/20], Batch Num: [135/600]
Discriminator Loss: 0.8058, Generator Loss: 2.0544
D(x): 0.7221, D(G(z)): 0.1519
Epoch: [13/20], Batch Num: [136/600]
Discriminator Loss: 0.5593, Generator Loss: 1.7848
D(x): 0.8159, D(G(z)): 0.1834
Epoch: [13/20], Batch Num: [137/600]
Discriminator Loss: 0.4483, Generator Loss: 1.5244
D(x): 0.9039, D(G(z)): 0.2384
Epoch: [13/20], Batch Num: [138/600]
Discriminator Loss: 0.6418, Generator Loss: 2.0519
D(x): 0.8633, D(G(z)): 0.2766
Epoch: [13/20], Batch Num: [139/600]
Discriminator Loss: 0.5188, Generator Loss: 2.3860
D(x): 0.9065, D(G(z)): 0.2443
Epoch: [13/20], Batch Num: [140/600]
Discriminator Loss: 0.4748, Generator Loss: 2.7921
D(x): 0.8497, D(G(z)): 0.1706
Epoch: [13/20], Batch Num: [141/600]
Discriminator Loss: 0.5489, Generator Loss: 3.2263
D(x): 0.7713, D(G(z)): 0.1046
Epoch: [13/20], Batch Num: [142/600]
Discriminator Loss: 0.4742, Generator Loss: 2.9008
D(x): 0.8174, D(G(z)): 0.1212
Epoch: [13/20], Batch Num: [143/600]
Discriminator Loss: 0.6687, Generator Loss: 2.5210
D(x): 0.7109, D(G(z)): 0.1107
Epoch: [13/20], Batch Num: [144/600]
Discriminator Loss: 0.5935, Generator Loss: 2.2177
D(x): 0.8148, D(G(z)): 0.1584
Epoch: [13/20], Batch Num: [145/600]
Discriminator Loss: 0.5029, Generator Loss: 2.1085
D(x): 0.8869, D(G(z)): 0.2107
Epoch: [13/20], Batch Num: [146/600]
Discriminator Loss: 0.6046, Generator Loss: 2.2085
D(x): 0.8631, D(G(z)): 0.2136
Epoch: [13/20], Batch Num: [147/600]
Discriminator Loss: 0.6148, Generator Loss: 2.7303
D(x): 0.8589, D(G(z)): 0.2128
Epoch: [13/20], Batch Num: [148/600]
Discriminator Loss: 0.6202, Generator Loss: 3.1023
D(x): 0.8178, D(G(z)): 0.1546
Epoch: [13/20], Batch Num: [149/600]
Discriminator Loss: 0.6660, Generator Loss: 2.9200
D(x): 0.7529, D(G(z)): 0.0914
Epoch: [13/20], Batch Num: [150/600]
Discriminator Loss: 0.5731, Generator Loss: 2.5698
D(x): 0.7652, D(G(z)): 0.1150
Epoch: [13/20], Batch Num: [151/600]
Discriminator Loss: 0.4793, Generator Loss: 2.0196
D(x): 0.8415, D(G(z)): 0.1474
Epoch: [13/20], Batch Num: [152/600]
Discriminator Loss: 0.6787, Generator Loss: 1.9157
D(x): 0.7765, D(G(z)): 0.2104
Epoch: [13/20], Batch Num: [153/600]
Discriminator Loss: 0.5352, Generator Loss: 2.0164
D(x): 0.8628, D(G(z)): 0.2351
Epoch: [13/20], Batch Num: [154/600]
Discriminator Loss: 0.6776, Generator Loss: 2.2369
D(x): 0.9039, D(G(z)): 0.2994
Epoch: [13/20], Batch Num: [155/600]
Discriminator Loss: 0.8246, Generator Loss: 3.0038
D(x): 0.8008, D(G(z)): 0.2424
Epoch: [13/20], Batch Num: [156/600]
Discriminator Loss: 0.6273, Generator Loss: 2.7154
D(x): 0.7588, D(G(z)): 0.1122
Epoch: [13/20], Batch Num: [157/600]
Discriminator Loss: 0.5395, Generator Loss: 2.8676
D(x): 0.7863, D(G(z)): 0.0989
Epoch: [13/20], Batch Num: [158/600]
Discriminator Loss: 0.4622, Generator Loss: 2.5104
D(x): 0.8397, D(G(z)): 0.1419
Epoch: [13/20], Batch Num: [159/600]
Discriminator Loss: 0.5656, Generator Loss: 2.0919
D(x): 0.8155, D(G(z)): 0.1516
Epoch: [13/20], Batch Num: [160/600]
Discriminator Loss: 0.4238, Generator Loss: 1.8990
D(x): 0.8979, D(G(z)): 0.1757
Epoch: [13/20], Batch Num: [161/600]
Discriminator Loss: 0.7872, Generator Loss: 2.0958
D(x): 0.8404, D(G(z)): 0.2820
Epoch: [13/20], Batch Num: [162/600]
Discriminator Loss: 0.7444, Generator Loss: 2.3835
D(x): 0.8590, D(G(z)): 0.2714
Epoch: [13/20], Batch Num: [163/600]
Discriminator Loss: 0.6620, Generator Loss: 2.6505
D(x): 0.7741, D(G(z)): 0.1533
Epoch: [13/20], Batch Num: [164/600]
Discriminator Loss: 0.5257, Generator Loss: 2.4857
D(x): 0.7910, D(G(z)): 0.1297
Epoch: [13/20], Batch Num: [165/600]
Discriminator Loss: 0.7270, Generator Loss: 2.1013
D(x): 0.7069, D(G(z)): 0.1464
Epoch: [13/20], Batch Num: [166/600]
Discriminator Loss: 0.5110, Generator Loss: 1.7916
D(x): 0.8609, D(G(z)): 0.1969
Epoch: [13/20], Batch Num: [167/600]
Discriminator Loss: 0.6749, Generator Loss: 1.5499
D(x): 0.8217, D(G(z)): 0.2651
Epoch: [13/20], Batch Num: [168/600]
Discriminator Loss: 0.6403, Generator Loss: 2.0600
D(x): 0.8824, D(G(z)): 0.2895
Epoch: [13/20], Batch Num: [169/600]
Discriminator Loss: 0.6509, Generator Loss: 2.6327
D(x): 0.8498, D(G(z)): 0.2459
Epoch: [13/20], Batch Num: [170/600]
Discriminator Loss: 0.7556, Generator Loss: 2.7435
D(x): 0.7265, D(G(z)): 0.1726
Epoch: [13/20], Batch Num: [171/600]
Discriminator Loss: 1.0344, Generator Loss: 2.7502
D(x): 0.6354, D(G(z)): 0.1589
Epoch: [13/20], Batch Num: [172/600]
Discriminator Loss: 0.8529, Generator Loss: 2.1588
D(x): 0.6986, D(G(z)): 0.1799
Epoch: [13/20], Batch Num: [173/600]
Discriminator Loss: 0.6795, Generator Loss: 1.7062
D(x): 0.7629, D(G(z)): 0.1538
Epoch: [13/20], Batch Num: [174/600]
Discriminator Loss: 0.5631, Generator Loss: 1.4760
D(x): 0.8442, D(G(z)): 0.2298
Epoch: [13/20], Batch Num: [175/600]
Discriminator Loss: 0.8921, Generator Loss: 1.7650
D(x): 0.8716, D(G(z)): 0.3750
Epoch: [13/20], Batch Num: [176/600]
Discriminator Loss: 0.7186, Generator Loss: 2.2681
D(x): 0.8487, D(G(z)): 0.3054
Epoch: [13/20], Batch Num: [177/600]
Discriminator Loss: 0.6094, Generator Loss: 2.7120
D(x): 0.7767, D(G(z)): 0.1644
Epoch: [13/20], Batch Num: [178/600]
Discriminator Loss: 0.7843, Generator Loss: 2.8003
D(x): 0.7033, D(G(z)): 0.1521
Epoch: [13/20], Batch Num: [179/600]
Discriminator Loss: 0.6735, Generator Loss: 2.8947
D(x): 0.7026, D(G(z)): 0.1084
Epoch: [13/20], Batch Num: [180/600]
Discriminator Loss: 0.8196, Generator Loss: 1.9157
D(x): 0.6702, D(G(z)): 0.1860
Epoch: [13/20], Batch Num: [181/600]
Discriminator Loss: 0.5923, Generator Loss: 1.6120
D(x): 0.7925, D(G(z)): 0.1987
Epoch: [13/20], Batch Num: [182/600]
Discriminator Loss: 0.7191, Generator Loss: 1.5365
D(x): 0.8174, D(G(z)): 0.3002
Epoch: [13/20], Batch Num: [183/600]
Discriminator Loss: 0.6070, Generator Loss: 1.5475
D(x): 0.8859, D(G(z)): 0.2959
Epoch: [13/20], Batch Num: [184/600]
Discriminator Loss: 0.6815, Generator Loss: 1.6674
D(x): 0.8488, D(G(z)): 0.3178
Epoch: [13/20], Batch Num: [185/600]
Discriminator Loss: 0.5186, Generator Loss: 2.3714
D(x): 0.8625, D(G(z)): 0.2376
Epoch: [13/20], Batch Num: [186/600]
Discriminator Loss: 0.6174, Generator Loss: 2.5645
D(x): 0.7566, D(G(z)): 0.1649
Epoch: [13/20], Batch Num: [187/600]
Discriminator Loss: 0.5765, Generator Loss: 2.8244
D(x): 0.7569, D(G(z)): 0.1331
Epoch: [13/20], Batch Num: [188/600]
Discriminator Loss: 0.7853, Generator Loss: 2.3610
D(x): 0.7361, D(G(z)): 0.1737
Epoch: [13/20], Batch Num: [189/600]
Discriminator Loss: 0.6241, Generator Loss: 2.1625
D(x): 0.7659, D(G(z)): 0.1761
Epoch: [13/20], Batch Num: [190/600]
Discriminator Loss: 0.7802, Generator Loss: 1.7607
D(x): 0.7254, D(G(z)): 0.2135
Epoch: [13/20], Batch Num: [191/600]
Discriminator Loss: 0.6524, Generator Loss: 1.4369
D(x): 0.8419, D(G(z)): 0.2827
Epoch: [13/20], Batch Num: [192/600]
Discriminator Loss: 0.6494, Generator Loss: 1.7047
D(x): 0.8376, D(G(z)): 0.2904
Epoch: [13/20], Batch Num: [193/600]
Discriminator Loss: 0.6817, Generator Loss: 2.1348
D(x): 0.8297, D(G(z)): 0.2702
Epoch: [13/20], Batch Num: [194/600]
Discriminator Loss: 0.8077, Generator Loss: 2.3668
D(x): 0.7634, D(G(z)): 0.2657
Epoch: [13/20], Batch Num: [195/600]
Discriminator Loss: 0.6699, Generator Loss: 2.5316
D(x): 0.7360, D(G(z)): 0.1548
Epoch: [13/20], Batch Num: [196/600]
Discriminator Loss: 0.6255, Generator Loss: 2.2903
D(x): 0.7825, D(G(z)): 0.1646
Epoch: [13/20], Batch Num: [197/600]
Discriminator Loss: 0.7147, Generator Loss: 2.0282
D(x): 0.7023, D(G(z)): 0.1590
Epoch: [13/20], Batch Num: [198/600]
Discriminator Loss: 0.6789, Generator Loss: 1.7546
D(x): 0.7836, D(G(z)): 0.2431
Epoch: [13/20], Batch Num: [199/600]
Discriminator Loss: 0.7260, Generator Loss: 1.5888
D(x): 0.8179, D(G(z)): 0.2753
Epoch: 13, Batch Num: [200/600]

Epoch: [13/20], Batch Num: [200/600]
Discriminator Loss: 0.7456, Generator Loss: 1.9294
D(x): 0.8195, D(G(z)): 0.2781
Epoch: [13/20], Batch Num: [201/600]
Discriminator Loss: 0.7197, Generator Loss: 2.0571
D(x): 0.7940, D(G(z)): 0.2543
Epoch: [13/20], Batch Num: [202/600]
Discriminator Loss: 0.6119, Generator Loss: 2.4734
D(x): 0.7809, D(G(z)): 0.1936
Epoch: [13/20], Batch Num: [203/600]
Discriminator Loss: 0.7258, Generator Loss: 2.3288
D(x): 0.7341, D(G(z)): 0.1825
Epoch: [13/20], Batch Num: [204/600]
Discriminator Loss: 0.7416, Generator Loss: 2.1068
D(x): 0.7534, D(G(z)): 0.2097
Epoch: [13/20], Batch Num: [205/600]
Discriminator Loss: 0.6961, Generator Loss: 1.9263
D(x): 0.7490, D(G(z)): 0.2160
Epoch: [13/20], Batch Num: [206/600]
Discriminator Loss: 0.6339, Generator Loss: 1.8688
D(x): 0.8080, D(G(z)): 0.2469
Epoch: [13/20], Batch Num: [207/600]
Discriminator Loss: 0.6063, Generator Loss: 1.9912
D(x): 0.8175, D(G(z)): 0.2369
Epoch: [13/20], Batch Num: [208/600]
Discriminator Loss: 0.5928, Generator Loss: 2.1320
D(x): 0.8477, D(G(z)): 0.2388
Epoch: [13/20], Batch Num: [209/600]
Discriminator Loss: 0.8308, Generator Loss: 2.2327
D(x): 0.7729, D(G(z)): 0.2292
Epoch: [13/20], Batch Num: [210/600]
Discriminator Loss: 0.6838, Generator Loss: 2.3787
D(x): 0.7567, D(G(z)): 0.1629
Epoch: [13/20], Batch Num: [211/600]
Discriminator Loss: 0.6173, Generator Loss: 2.2601
D(x): 0.7885, D(G(z)): 0.1644
Epoch: [13/20], Batch Num: [212/600]
Discriminator Loss: 0.7554, Generator Loss: 1.8615
D(x): 0.7577, D(G(z)): 0.2098
Epoch: [13/20], Batch Num: [213/600]
Discriminator Loss: 0.6685, Generator Loss: 1.9974
D(x): 0.7882, D(G(z)): 0.2075
Epoch: [13/20], Batch Num: [214/600]
Discriminator Loss: 0.7542, Generator Loss: 1.9057
D(x): 0.7779, D(G(z)): 0.2467
Epoch: [13/20], Batch Num: [215/600]
Discriminator Loss: 0.5690, Generator Loss: 1.8618
D(x): 0.8255, D(G(z)): 0.2027
Epoch: [13/20], Batch Num: [216/600]
Discriminator Loss: 0.5789, Generator Loss: 1.9099
D(x): 0.8254, D(G(z)): 0.2003
Epoch: [13/20], Batch Num: [217/600]
Discriminator Loss: 0.5643, Generator Loss: 2.1998
D(x): 0.8261, D(G(z)): 0.2261
Epoch: [13/20], Batch Num: [218/600]
Discriminator Loss: 0.5021, Generator Loss: 2.3165
D(x): 0.8230, D(G(z)): 0.1711
Epoch: [13/20], Batch Num: [219/600]
Discriminator Loss: 0.5596, Generator Loss: 2.9939
D(x): 0.8079, D(G(z)): 0.1563
Epoch: [13/20], Batch Num: [220/600]
Discriminator Loss: 0.7809, Generator Loss: 2.5723
D(x): 0.7434, D(G(z)): 0.1696
Epoch: [13/20], Batch Num: [221/600]
Discriminator Loss: 0.5322, Generator Loss: 2.1860
D(x): 0.7883, D(G(z)): 0.1284
Epoch: [13/20], Batch Num: [222/600]
Discriminator Loss: 0.5485, Generator Loss: 1.7964
D(x): 0.8369, D(G(z)): 0.1788
Epoch: [13/20], Batch Num: [223/600]
Discriminator Loss: 0.6098, Generator Loss: 2.1829
D(x): 0.8348, D(G(z)): 0.2125
Epoch: [13/20], Batch Num: [224/600]
Discriminator Loss: 0.6954, Generator Loss: 1.8436
D(x): 0.8193, D(G(z)): 0.2542
Epoch: [13/20], Batch Num: [225/600]
Discriminator Loss: 0.5699, Generator Loss: 2.0799
D(x): 0.8417, D(G(z)): 0.2190
Epoch: [13/20], Batch Num: [226/600]
Discriminator Loss: 0.6021, Generator Loss: 2.3357
D(x): 0.7844, D(G(z)): 0.1743
Epoch: [13/20], Batch Num: [227/600]
Discriminator Loss: 0.5801, Generator Loss: 2.4310
D(x): 0.7954, D(G(z)): 0.1487
Epoch: [13/20], Batch Num: [228/600]
Discriminator Loss: 0.5967, Generator Loss: 2.3376
D(x): 0.7852, D(G(z)): 0.1726
Epoch: [13/20], Batch Num: [229/600]
Discriminator Loss: 0.6048, Generator Loss: 1.9673
D(x): 0.8115, D(G(z)): 0.2072
Epoch: [13/20], Batch Num: [230/600]
Discriminator Loss: 0.5916, Generator Loss: 1.9344
D(x): 0.8132, D(G(z)): 0.2056
Epoch: [13/20], Batch Num: [231/600]
Discriminator Loss: 0.5692, Generator Loss: 1.9081
D(x): 0.8353, D(G(z)): 0.2358
Epoch: [13/20], Batch Num: [232/600]
Discriminator Loss: 0.6775, Generator Loss: 2.2378
D(x): 0.8279, D(G(z)): 0.2240
Epoch: [13/20], Batch Num: [233/600]
Discriminator Loss: 0.5703, Generator Loss: 2.7854
D(x): 0.7986, D(G(z)): 0.1975
Epoch: [13/20], Batch Num: [234/600]
Discriminator Loss: 0.7629, Generator Loss: 2.3929
D(x): 0.6931, D(G(z)): 0.1515
Epoch: [13/20], Batch Num: [235/600]
Discriminator Loss: 0.5986, Generator Loss: 1.9558
D(x): 0.7796, D(G(z)): 0.1706
Epoch: [13/20], Batch Num: [236/600]
Discriminator Loss: 0.6424, Generator Loss: 1.7463
D(x): 0.8558, D(G(z)): 0.2572
Epoch: [13/20], Batch Num: [237/600]
Discriminator Loss: 0.5583, Generator Loss: 2.2151
D(x): 0.8675, D(G(z)): 0.2394
Epoch: [13/20], Batch Num: [238/600]
Discriminator Loss: 0.5674, Generator Loss: 2.3476
D(x): 0.8131, D(G(z)): 0.1925
Epoch: [13/20], Batch Num: [239/600]
Discriminator Loss: 0.5718, Generator Loss: 2.3701
D(x): 0.7999, D(G(z)): 0.1622
Epoch: [13/20], Batch Num: [240/600]
Discriminator Loss: 0.5499, Generator Loss: 2.4003
D(x): 0.8185, D(G(z)): 0.1680
Epoch: [13/20], Batch Num: [241/600]
Discriminator Loss: 0.5874, Generator Loss: 2.0775
D(x): 0.7848, D(G(z)): 0.1568
Epoch: [13/20], Batch Num: [242/600]
Discriminator Loss: 0.6238, Generator Loss: 2.3313
D(x): 0.8260, D(G(z)): 0.2033
Epoch: [13/20], Batch Num: [243/600]
Discriminator Loss: 0.6009, Generator Loss: 2.0032
D(x): 0.8388, D(G(z)): 0.2017
Epoch: [13/20], Batch Num: [244/600]
Discriminator Loss: 0.7426, Generator Loss: 2.2273
D(x): 0.8306, D(G(z)): 0.2218
Epoch: [13/20], Batch Num: [245/600]
Discriminator Loss: 0.6120, Generator Loss: 2.3591
D(x): 0.8357, D(G(z)): 0.1895
Epoch: [13/20], Batch Num: [246/600]
Discriminator Loss: 0.5523, Generator Loss: 2.3165
D(x): 0.7817, D(G(z)): 0.1286
Epoch: [13/20], Batch Num: [247/600]
Discriminator Loss: 0.7284, Generator Loss: 2.0851
D(x): 0.7479, D(G(z)): 0.1895
Epoch: [13/20], Batch Num: [248/600]
Discriminator Loss: 0.6024, Generator Loss: 2.0596
D(x): 0.8580, D(G(z)): 0.2374
Epoch: [13/20], Batch Num: [249/600]
Discriminator Loss: 0.6821, Generator Loss: 2.6858
D(x): 0.8291, D(G(z)): 0.2351
Epoch: [13/20], Batch Num: [250/600]
Discriminator Loss: 0.6996, Generator Loss: 2.1678
D(x): 0.7330, D(G(z)): 0.1540
Epoch: [13/20], Batch Num: [251/600]
Discriminator Loss: 0.6859, Generator Loss: 2.3594
D(x): 0.7822, D(G(z)): 0.1980
Epoch: [13/20], Batch Num: [252/600]
Discriminator Loss: 0.7069, Generator Loss: 1.9671
D(x): 0.7897, D(G(z)): 0.2086
Epoch: [13/20], Batch Num: [253/600]
Discriminator Loss: 0.7026, Generator Loss: 1.9819
D(x): 0.7547, D(G(z)): 0.1922
Epoch: [13/20], Batch Num: [254/600]
Discriminator Loss: 0.7192, Generator Loss: 2.3100
D(x): 0.8683, D(G(z)): 0.2946
Epoch: [13/20], Batch Num: [255/600]
Discriminator Loss: 0.8412, Generator Loss: 2.3380
D(x): 0.7217, D(G(z)): 0.2035
Epoch: [13/20], Batch Num: [256/600]
Discriminator Loss: 0.7375, Generator Loss: 2.0758
D(x): 0.7270, D(G(z)): 0.1595
Epoch: [13/20], Batch Num: [257/600]
Discriminator Loss: 0.7210, Generator Loss: 1.8275
D(x): 0.7500, D(G(z)): 0.1791
Epoch: [13/20], Batch Num: [258/600]
Discriminator Loss: 0.7782, Generator Loss: 1.7501
D(x): 0.7606, D(G(z)): 0.2161
Epoch: [13/20], Batch Num: [259/600]
Discriminator Loss: 0.7033, Generator Loss: 1.6034
D(x): 0.7892, D(G(z)): 0.2564
Epoch: [13/20], Batch Num: [260/600]
Discriminator Loss: 0.8632, Generator Loss: 1.5746
D(x): 0.7751, D(G(z)): 0.3141
Epoch: [13/20], Batch Num: [261/600]
Discriminator Loss: 0.6517, Generator Loss: 1.7099
D(x): 0.8347, D(G(z)): 0.2592
Epoch: [13/20], Batch Num: [262/600]
Discriminator Loss: 0.8139, Generator Loss: 1.7261
D(x): 0.7294, D(G(z)): 0.2517
Epoch: [13/20], Batch Num: [263/600]
Discriminator Loss: 0.6903, Generator Loss: 2.2391
D(x): 0.7806, D(G(z)): 0.2296
Epoch: [13/20], Batch Num: [264/600]
Discriminator Loss: 0.7793, Generator Loss: 1.9486
D(x): 0.7209, D(G(z)): 0.2230
Epoch: [13/20], Batch Num: [265/600]
Discriminator Loss: 0.7378, Generator Loss: 2.0756
D(x): 0.7420, D(G(z)): 0.2118
Epoch: [13/20], Batch Num: [266/600]
Discriminator Loss: 0.7114, Generator Loss: 1.7505
D(x): 0.7473, D(G(z)): 0.2032
Epoch: [13/20], Batch Num: [267/600]
Discriminator Loss: 0.7784, Generator Loss: 1.7593
D(x): 0.7667, D(G(z)): 0.2608
Epoch: [13/20], Batch Num: [268/600]
Discriminator Loss: 0.6038, Generator Loss: 1.7606
D(x): 0.8012, D(G(z)): 0.2334
Epoch: [13/20], Batch Num: [269/600]
Discriminator Loss: 0.6623, Generator Loss: 1.5057
D(x): 0.8054, D(G(z)): 0.2435
Epoch: [13/20], Batch Num: [270/600]
Discriminator Loss: 0.6584, Generator Loss: 1.7678
D(x): 0.7936, D(G(z)): 0.2448
Epoch: [13/20], Batch Num: [271/600]
Discriminator Loss: 0.7824, Generator Loss: 1.9758
D(x): 0.7294, D(G(z)): 0.2333
Epoch: [13/20], Batch Num: [272/600]
Discriminator Loss: 0.7448, Generator Loss: 1.9828
D(x): 0.8125, D(G(z)): 0.2772
Epoch: [13/20], Batch Num: [273/600]
Discriminator Loss: 0.6182, Generator Loss: 2.1681
D(x): 0.8029, D(G(z)): 0.1998
Epoch: [13/20], Batch Num: [274/600]
Discriminator Loss: 0.7930, Generator Loss: 2.0408
D(x): 0.6883, D(G(z)): 0.1728
Epoch: [13/20], Batch Num: [275/600]
Discriminator Loss: 0.6119, Generator Loss: 1.9341
D(x): 0.8014, D(G(z)): 0.1821
Epoch: [13/20], Batch Num: [276/600]
Discriminator Loss: 0.6751, Generator Loss: 1.7800
D(x): 0.7780, D(G(z)): 0.2164
Epoch: [13/20], Batch Num: [277/600]
Discriminator Loss: 0.6421, Generator Loss: 2.0231
D(x): 0.8105, D(G(z)): 0.2483
Epoch: [13/20], Batch Num: [278/600]
Discriminator Loss: 0.6692, Generator Loss: 2.0588
D(x): 0.7891, D(G(z)): 0.2324
Epoch: [13/20], Batch Num: [279/600]
Discriminator Loss: 0.5857, Generator Loss: 2.0684
D(x): 0.7699, D(G(z)): 0.1698
Epoch: [13/20], Batch Num: [280/600]
Discriminator Loss: 0.6174, Generator Loss: 1.9103
D(x): 0.7729, D(G(z)): 0.2022
Epoch: [13/20], Batch Num: [281/600]
Discriminator Loss: 0.5074, Generator Loss: 1.9003
D(x): 0.8315, D(G(z)): 0.1910
Epoch: [13/20], Batch Num: [282/600]
Discriminator Loss: 0.5681, Generator Loss: 1.9546
D(x): 0.8183, D(G(z)): 0.2037
Epoch: [13/20], Batch Num: [283/600]
Discriminator Loss: 0.6741, Generator Loss: 1.8656
D(x): 0.7753, D(G(z)): 0.2151
Epoch: [13/20], Batch Num: [284/600]
Discriminator Loss: 0.6813, Generator Loss: 1.8212
D(x): 0.7602, D(G(z)): 0.2168
Epoch: [13/20], Batch Num: [285/600]
Discriminator Loss: 0.6289, Generator Loss: 1.8928
D(x): 0.8066, D(G(z)): 0.2175
Epoch: [13/20], Batch Num: [286/600]
Discriminator Loss: 0.6332, Generator Loss: 2.1819
D(x): 0.8164, D(G(z)): 0.2173
Epoch: [13/20], Batch Num: [287/600]
Discriminator Loss: 0.7200, Generator Loss: 2.3339
D(x): 0.7839, D(G(z)): 0.2322
Epoch: [13/20], Batch Num: [288/600]
Discriminator Loss: 0.6827, Generator Loss: 2.3405
D(x): 0.7848, D(G(z)): 0.2201
Epoch: [13/20], Batch Num: [289/600]
Discriminator Loss: 0.6357, Generator Loss: 2.2417
D(x): 0.7516, D(G(z)): 0.1471
Epoch: [13/20], Batch Num: [290/600]
Discriminator Loss: 0.6367, Generator Loss: 2.0226
D(x): 0.7654, D(G(z)): 0.1787
Epoch: [13/20], Batch Num: [291/600]
Discriminator Loss: 0.6212, Generator Loss: 1.8341
D(x): 0.8346, D(G(z)): 0.2357
Epoch: [13/20], Batch Num: [292/600]
Discriminator Loss: 0.7187, Generator Loss: 1.8416
D(x): 0.8063, D(G(z)): 0.2554
Epoch: [13/20], Batch Num: [293/600]
Discriminator Loss: 0.7727, Generator Loss: 2.1797
D(x): 0.8179, D(G(z)): 0.2921
Epoch: [13/20], Batch Num: [294/600]
Discriminator Loss: 0.7863, Generator Loss: 2.4651
D(x): 0.7320, D(G(z)): 0.2057
Epoch: [13/20], Batch Num: [295/600]
Discriminator Loss: 0.5094, Generator Loss: 2.4313
D(x): 0.8055, D(G(z)): 0.1427
Epoch: [13/20], Batch Num: [296/600]
Discriminator Loss: 0.5719, Generator Loss: 2.1930
D(x): 0.7749, D(G(z)): 0.1420
Epoch: [13/20], Batch Num: [297/600]
Discriminator Loss: 0.7907, Generator Loss: 1.8346
D(x): 0.7685, D(G(z)): 0.2188
Epoch: [13/20], Batch Num: [298/600]
Discriminator Loss: 0.6419, Generator Loss: 1.7761
D(x): 0.8116, D(G(z)): 0.2167
Epoch: [13/20], Batch Num: [299/600]
Discriminator Loss: 0.7079, Generator Loss: 1.7783
D(x): 0.8005, D(G(z)): 0.2612
Epoch: 13, Batch Num: [300/600]

Epoch: [13/20], Batch Num: [300/600]
Discriminator Loss: 0.7098, Generator Loss: 2.0191
D(x): 0.7897, D(G(z)): 0.2324
Epoch: [13/20], Batch Num: [301/600]
Discriminator Loss: 0.6443, Generator Loss: 2.0973
D(x): 0.7860, D(G(z)): 0.2201
Epoch: [13/20], Batch Num: [302/600]
Discriminator Loss: 0.7929, Generator Loss: 2.2322
D(x): 0.7897, D(G(z)): 0.2645
Epoch: [13/20], Batch Num: [303/600]
Discriminator Loss: 0.7626, Generator Loss: 2.4867
D(x): 0.7800, D(G(z)): 0.2165
Epoch: [13/20], Batch Num: [304/600]
Discriminator Loss: 0.7113, Generator Loss: 2.8939
D(x): 0.7463, D(G(z)): 0.1834
Epoch: [13/20], Batch Num: [305/600]
Discriminator Loss: 0.7423, Generator Loss: 1.9699
D(x): 0.6867, D(G(z)): 0.1371
Epoch: [13/20], Batch Num: [306/600]
Discriminator Loss: 0.6770, Generator Loss: 1.8622
D(x): 0.7705, D(G(z)): 0.1860
Epoch: [13/20], Batch Num: [307/600]
Discriminator Loss: 0.6228, Generator Loss: 1.7362
D(x): 0.8246, D(G(z)): 0.2193
Epoch: [13/20], Batch Num: [308/600]
Discriminator Loss: 0.6872, Generator Loss: 1.7598
D(x): 0.8153, D(G(z)): 0.2724
Epoch: [13/20], Batch Num: [309/600]
Discriminator Loss: 0.6694, Generator Loss: 2.2904
D(x): 0.8651, D(G(z)): 0.3034
Epoch: [13/20], Batch Num: [310/600]
Discriminator Loss: 0.6431, Generator Loss: 2.1165
D(x): 0.7817, D(G(z)): 0.2055
Epoch: [13/20], Batch Num: [311/600]
Discriminator Loss: 0.7257, Generator Loss: 2.4569
D(x): 0.7201, D(G(z)): 0.1529
Epoch: [13/20], Batch Num: [312/600]
Discriminator Loss: 0.6035, Generator Loss: 2.2300
D(x): 0.8028, D(G(z)): 0.1925
Epoch: [13/20], Batch Num: [313/600]
Discriminator Loss: 0.6431, Generator Loss: 1.9050
D(x): 0.7936, D(G(z)): 0.2001
Epoch: [13/20], Batch Num: [314/600]
Discriminator Loss: 0.6388, Generator Loss: 1.8074
D(x): 0.8144, D(G(z)): 0.2391
Epoch: [13/20], Batch Num: [315/600]
Discriminator Loss: 0.6175, Generator Loss: 2.1664
D(x): 0.8279, D(G(z)): 0.2224
Epoch: [13/20], Batch Num: [316/600]
Discriminator Loss: 0.6297, Generator Loss: 2.0295
D(x): 0.7822, D(G(z)): 0.1957
Epoch: [13/20], Batch Num: [317/600]
Discriminator Loss: 0.5425, Generator Loss: 1.9387
D(x): 0.8105, D(G(z)): 0.1802
Epoch: [13/20], Batch Num: [318/600]
Discriminator Loss: 0.7212, Generator Loss: 2.3589
D(x): 0.8055, D(G(z)): 0.2375
Epoch: [13/20], Batch Num: [319/600]
Discriminator Loss: 0.6479, Generator Loss: 2.0873
D(x): 0.8012, D(G(z)): 0.2160
Epoch: [13/20], Batch Num: [320/600]
Discriminator Loss: 0.6784, Generator Loss: 2.3091
D(x): 0.7625, D(G(z)): 0.1963
Epoch: [13/20], Batch Num: [321/600]
Discriminator Loss: 0.5399, Generator Loss: 2.3413
D(x): 0.8384, D(G(z)): 0.1865
Epoch: [13/20], Batch Num: [322/600]
Discriminator Loss: 0.5660, Generator Loss: 2.4413
D(x): 0.8216, D(G(z)): 0.1942
Epoch: [13/20], Batch Num: [323/600]
Discriminator Loss: 0.4863, Generator Loss: 2.5623
D(x): 0.8300, D(G(z)): 0.1499
Epoch: [13/20], Batch Num: [324/600]
Discriminator Loss: 0.5431, Generator Loss: 2.4540
D(x): 0.7841, D(G(z)): 0.1314
Epoch: [13/20], Batch Num: [325/600]
Discriminator Loss: 0.5151, Generator Loss: 1.9773
D(x): 0.8171, D(G(z)): 0.1635
Epoch: [13/20], Batch Num: [326/600]
Discriminator Loss: 0.5644, Generator Loss: 2.2404
D(x): 0.8710, D(G(z)): 0.2385
Epoch: [13/20], Batch Num: [327/600]
Discriminator Loss: 0.5579, Generator Loss: 2.2908
D(x): 0.8502, D(G(z)): 0.2159
Epoch: [13/20], Batch Num: [328/600]
Discriminator Loss: 0.5046, Generator Loss: 2.8025
D(x): 0.8543, D(G(z)): 0.1921
Epoch: [13/20], Batch Num: [329/600]
Discriminator Loss: 0.4921, Generator Loss: 2.7125
D(x): 0.8103, D(G(z)): 0.1211
Epoch: [13/20], Batch Num: [330/600]
Discriminator Loss: 0.6151, Generator Loss: 2.6583
D(x): 0.8170, D(G(z)): 0.1630
Epoch: [13/20], Batch Num: [331/600]
Discriminator Loss: 0.3991, Generator Loss: 3.0262
D(x): 0.8571, D(G(z)): 0.1262
Epoch: [13/20], Batch Num: [332/600]
Discriminator Loss: 0.6214, Generator Loss: 2.4649
D(x): 0.8083, D(G(z)): 0.2008
Epoch: [13/20], Batch Num: [333/600]
Discriminator Loss: 0.5751, Generator Loss: 2.5046
D(x): 0.8260, D(G(z)): 0.1936
Epoch: [13/20], Batch Num: [334/600]
Discriminator Loss: 0.5133, Generator Loss: 2.7386
D(x): 0.8306, D(G(z)): 0.1719
Epoch: [13/20], Batch Num: [335/600]
Discriminator Loss: 0.6088, Generator Loss: 2.5529
D(x): 0.8335, D(G(z)): 0.1906
Epoch: [13/20], Batch Num: [336/600]
Discriminator Loss: 0.6809, Generator Loss: 2.6342
D(x): 0.7827, D(G(z)): 0.1631
Epoch: [13/20], Batch Num: [337/600]
Discriminator Loss: 0.6500, Generator Loss: 2.5469
D(x): 0.7858, D(G(z)): 0.1498
Epoch: [13/20], Batch Num: [338/600]
Discriminator Loss: 0.5727, Generator Loss: 2.2990
D(x): 0.8001, D(G(z)): 0.1684
Epoch: [13/20], Batch Num: [339/600]
Discriminator Loss: 0.7693, Generator Loss: 2.0251
D(x): 0.8098, D(G(z)): 0.2330
Epoch: [13/20], Batch Num: [340/600]
Discriminator Loss: 0.5354, Generator Loss: 1.8213
D(x): 0.8643, D(G(z)): 0.2103
Epoch: [13/20], Batch Num: [341/600]
Discriminator Loss: 0.5054, Generator Loss: 2.3066
D(x): 0.8766, D(G(z)): 0.2194
Epoch: [13/20], Batch Num: [342/600]
Discriminator Loss: 0.4897, Generator Loss: 2.5738
D(x): 0.8429, D(G(z)): 0.1502
Epoch: [13/20], Batch Num: [343/600]
Discriminator Loss: 0.7704, Generator Loss: 2.8349
D(x): 0.7974, D(G(z)): 0.2098
Epoch: [13/20], Batch Num: [344/600]
Discriminator Loss: 0.7752, Generator Loss: 2.2596
D(x): 0.7008, D(G(z)): 0.1271
Epoch: [13/20], Batch Num: [345/600]
Discriminator Loss: 0.6576, Generator Loss: 1.8161
D(x): 0.8157, D(G(z)): 0.1888
Epoch: [13/20], Batch Num: [346/600]
Discriminator Loss: 0.6681, Generator Loss: 2.0802
D(x): 0.8495, D(G(z)): 0.2550
Epoch: [13/20], Batch Num: [347/600]
Discriminator Loss: 0.6368, Generator Loss: 2.0289
D(x): 0.8384, D(G(z)): 0.2228
Epoch: [13/20], Batch Num: [348/600]
Discriminator Loss: 0.7072, Generator Loss: 2.2108
D(x): 0.7864, D(G(z)): 0.2015
Epoch: [13/20], Batch Num: [349/600]
Discriminator Loss: 0.7738, Generator Loss: 2.1315
D(x): 0.7370, D(G(z)): 0.1816
Epoch: [13/20], Batch Num: [350/600]
Discriminator Loss: 0.6449, Generator Loss: 1.8963
D(x): 0.7700, D(G(z)): 0.1746
Epoch: [13/20], Batch Num: [351/600]
Discriminator Loss: 0.6689, Generator Loss: 1.5595
D(x): 0.7839, D(G(z)): 0.2299
Epoch: [13/20], Batch Num: [352/600]
Discriminator Loss: 0.5047, Generator Loss: 1.5342
D(x): 0.8674, D(G(z)): 0.2248
Epoch: [13/20], Batch Num: [353/600]
Discriminator Loss: 0.6275, Generator Loss: 1.8721
D(x): 0.8564, D(G(z)): 0.2686
Epoch: [13/20], Batch Num: [354/600]
Discriminator Loss: 0.6805, Generator Loss: 1.8741
D(x): 0.7841, D(G(z)): 0.2012
Epoch: [13/20], Batch Num: [355/600]
Discriminator Loss: 0.6322, Generator Loss: 2.0431
D(x): 0.7855, D(G(z)): 0.1867
Epoch: [13/20], Batch Num: [356/600]
Discriminator Loss: 0.6890, Generator Loss: 2.0246
D(x): 0.7807, D(G(z)): 0.2036
Epoch: [13/20], Batch Num: [357/600]
Discriminator Loss: 0.6881, Generator Loss: 2.0998
D(x): 0.7841, D(G(z)): 0.1810
Epoch: [13/20], Batch Num: [358/600]
Discriminator Loss: 0.4911, Generator Loss: 2.1718
D(x): 0.8460, D(G(z)): 0.1724
Epoch: [13/20], Batch Num: [359/600]
Discriminator Loss: 0.5744, Generator Loss: 1.9796
D(x): 0.8149, D(G(z)): 0.1699
Epoch: [13/20], Batch Num: [360/600]
Discriminator Loss: 0.5920, Generator Loss: 2.1968
D(x): 0.8338, D(G(z)): 0.1932
Epoch: [13/20], Batch Num: [361/600]
Discriminator Loss: 0.5057, Generator Loss: 2.2710
D(x): 0.8177, D(G(z)): 0.1394
Epoch: [13/20], Batch Num: [362/600]
Discriminator Loss: 0.7414, Generator Loss: 2.1912
D(x): 0.7680, D(G(z)): 0.2048
Epoch: [13/20], Batch Num: [363/600]
Discriminator Loss: 0.7154, Generator Loss: 2.1704
D(x): 0.7708, D(G(z)): 0.1907
Epoch: [13/20], Batch Num: [364/600]
Discriminator Loss: 0.5776, Generator Loss: 2.0266
D(x): 0.8354, D(G(z)): 0.1956
Epoch: [13/20], Batch Num: [365/600]
Discriminator Loss: 0.5963, Generator Loss: 1.9396
D(x): 0.8144, D(G(z)): 0.2022
Epoch: [13/20], Batch Num: [366/600]
Discriminator Loss: 0.5336, Generator Loss: 2.1218
D(x): 0.8400, D(G(z)): 0.1889
Epoch: [13/20], Batch Num: [367/600]
Discriminator Loss: 0.5279, Generator Loss: 2.1161
D(x): 0.8401, D(G(z)): 0.2025
Epoch: [13/20], Batch Num: [368/600]
Discriminator Loss: 0.7711, Generator Loss: 2.0448
D(x): 0.7555, D(G(z)): 0.2165
Epoch: [13/20], Batch Num: [369/600]
Discriminator Loss: 0.5091, Generator Loss: 2.1241
D(x): 0.8132, D(G(z)): 0.1567
Epoch: [13/20], Batch Num: [370/600]
Discriminator Loss: 0.4801, Generator Loss: 1.9220
D(x): 0.8187, D(G(z)): 0.1552
Epoch: [13/20], Batch Num: [371/600]
Discriminator Loss: 0.5428, Generator Loss: 1.9833
D(x): 0.8538, D(G(z)): 0.2203
Epoch: [13/20], Batch Num: [372/600]
Discriminator Loss: 0.5360, Generator Loss: 2.0649
D(x): 0.8685, D(G(z)): 0.2080
Epoch: [13/20], Batch Num: [373/600]
Discriminator Loss: 0.5090, Generator Loss: 2.3565
D(x): 0.8538, D(G(z)): 0.1894
Epoch: [13/20], Batch Num: [374/600]
Discriminator Loss: 0.5543, Generator Loss: 2.6746
D(x): 0.8132, D(G(z)): 0.1516
Epoch: [13/20], Batch Num: [375/600]
Discriminator Loss: 0.6821, Generator Loss: 2.3892
D(x): 0.7430, D(G(z)): 0.1256
Epoch: [13/20], Batch Num: [376/600]
Discriminator Loss: 0.6176, Generator Loss: 2.1147
D(x): 0.7816, D(G(z)): 0.1529
Epoch: [13/20], Batch Num: [377/600]
Discriminator Loss: 0.6636, Generator Loss: 1.9673
D(x): 0.8291, D(G(z)): 0.2115
Epoch: [13/20], Batch Num: [378/600]
Discriminator Loss: 0.6489, Generator Loss: 1.9873
D(x): 0.8305, D(G(z)): 0.2436
Epoch: [13/20], Batch Num: [379/600]
Discriminator Loss: 0.5278, Generator Loss: 2.2628
D(x): 0.8345, D(G(z)): 0.1862
Epoch: [13/20], Batch Num: [380/600]
Discriminator Loss: 0.6227, Generator Loss: 2.2536
D(x): 0.7915, D(G(z)): 0.1781
Epoch: [13/20], Batch Num: [381/600]
Discriminator Loss: 0.4422, Generator Loss: 2.2816
D(x): 0.8916, D(G(z)): 0.1939
Epoch: [13/20], Batch Num: [382/600]
Discriminator Loss: 0.5860, Generator Loss: 2.4944
D(x): 0.7971, D(G(z)): 0.1569
Epoch: [13/20], Batch Num: [383/600]
Discriminator Loss: 0.6922, Generator Loss: 2.6198
D(x): 0.7503, D(G(z)): 0.1357
Epoch: [13/20], Batch Num: [384/600]
Discriminator Loss: 0.5447, Generator Loss: 2.1465
D(x): 0.8308, D(G(z)): 0.1546
Epoch: [13/20], Batch Num: [385/600]
Discriminator Loss: 0.5074, Generator Loss: 2.0441
D(x): 0.8169, D(G(z)): 0.1571
Epoch: [13/20], Batch Num: [386/600]
Discriminator Loss: 0.5962, Generator Loss: 2.3197
D(x): 0.8598, D(G(z)): 0.2538
Epoch: [13/20], Batch Num: [387/600]
Discriminator Loss: 0.5032, Generator Loss: 2.4076
D(x): 0.8536, D(G(z)): 0.1973
Epoch: [13/20], Batch Num: [388/600]
Discriminator Loss: 0.6135, Generator Loss: 2.7471
D(x): 0.8018, D(G(z)): 0.1843
Epoch: [13/20], Batch Num: [389/600]
Discriminator Loss: 0.7473, Generator Loss: 2.5616
D(x): 0.7479, D(G(z)): 0.1499
Epoch: [13/20], Batch Num: [390/600]
Discriminator Loss: 0.5625, Generator Loss: 2.6491
D(x): 0.8056, D(G(z)): 0.1305
Epoch: [13/20], Batch Num: [391/600]
Discriminator Loss: 0.5506, Generator Loss: 2.3525
D(x): 0.8101, D(G(z)): 0.1413
Epoch: [13/20], Batch Num: [392/600]
Discriminator Loss: 0.7075, Generator Loss: 2.2281
D(x): 0.8008, D(G(z)): 0.2176
Epoch: [13/20], Batch Num: [393/600]
Discriminator Loss: 0.5643, Generator Loss: 2.0017
D(x): 0.8383, D(G(z)): 0.2009
Epoch: [13/20], Batch Num: [394/600]
Discriminator Loss: 0.5081, Generator Loss: 2.2496
D(x): 0.8585, D(G(z)): 0.1807
Epoch: [13/20], Batch Num: [395/600]
Discriminator Loss: 0.5954, Generator Loss: 2.4910
D(x): 0.8222, D(G(z)): 0.2123
Epoch: [13/20], Batch Num: [396/600]
Discriminator Loss: 0.4841, Generator Loss: 2.5824
D(x): 0.8482, D(G(z)): 0.1616
Epoch: [13/20], Batch Num: [397/600]
Discriminator Loss: 0.7202, Generator Loss: 2.8537
D(x): 0.7564, D(G(z)): 0.1586
Epoch: [13/20], Batch Num: [398/600]
Discriminator Loss: 0.5972, Generator Loss: 2.4154
D(x): 0.7991, D(G(z)): 0.1385
Epoch: [13/20], Batch Num: [399/600]
Discriminator Loss: 0.5890, Generator Loss: 1.9248
D(x): 0.7759, D(G(z)): 0.1372
Epoch: 13, Batch Num: [400/600]

Epoch: [13/20], Batch Num: [400/600]
Discriminator Loss: 0.5978, Generator Loss: 1.4878
D(x): 0.8219, D(G(z)): 0.2061
Epoch: [13/20], Batch Num: [401/600]
Discriminator Loss: 0.6454, Generator Loss: 2.0366
D(x): 0.8741, D(G(z)): 0.2833
Epoch: [13/20], Batch Num: [402/600]
Discriminator Loss: 0.6785, Generator Loss: 2.2263
D(x): 0.8183, D(G(z)): 0.2476
Epoch: [13/20], Batch Num: [403/600]
Discriminator Loss: 0.4860, Generator Loss: 2.6473
D(x): 0.8778, D(G(z)): 0.2021
Epoch: [13/20], Batch Num: [404/600]
Discriminator Loss: 0.5716, Generator Loss: 3.0010
D(x): 0.8081, D(G(z)): 0.1182
Epoch: [13/20], Batch Num: [405/600]
Discriminator Loss: 0.6452, Generator Loss: 2.7796
D(x): 0.7408, D(G(z)): 0.0970
Epoch: [13/20], Batch Num: [406/600]
Discriminator Loss: 0.6930, Generator Loss: 2.3306
D(x): 0.7458, D(G(z)): 0.1060
Epoch: [13/20], Batch Num: [407/600]
Discriminator Loss: 0.5671, Generator Loss: 1.5926
D(x): 0.8085, D(G(z)): 0.1632
Epoch: [13/20], Batch Num: [408/600]
Discriminator Loss: 0.6604, Generator Loss: 1.6421
D(x): 0.8626, D(G(z)): 0.2696
Epoch: [13/20], Batch Num: [409/600]
Discriminator Loss: 0.6963, Generator Loss: 2.0615
D(x): 0.8658, D(G(z)): 0.2895
Epoch: [13/20], Batch Num: [410/600]
Discriminator Loss: 0.5649, Generator Loss: 2.6706
D(x): 0.8931, D(G(z)): 0.2681
Epoch: [13/20], Batch Num: [411/600]
Discriminator Loss: 0.5975, Generator Loss: 2.4722
D(x): 0.7589, D(G(z)): 0.1318
Epoch: [13/20], Batch Num: [412/600]
Discriminator Loss: 0.5788, Generator Loss: 2.3994
D(x): 0.7419, D(G(z)): 0.1107
Epoch: [13/20], Batch Num: [413/600]
Discriminator Loss: 0.7061, Generator Loss: 2.1705
D(x): 0.7137, D(G(z)): 0.1368
Epoch: [13/20], Batch Num: [414/600]
Discriminator Loss: 0.6766, Generator Loss: 1.5957
D(x): 0.7662, D(G(z)): 0.1772
Epoch: [13/20], Batch Num: [415/600]
Discriminator Loss: 0.6231, Generator Loss: 1.6055
D(x): 0.8669, D(G(z)): 0.2710
Epoch: [13/20], Batch Num: [416/600]
Discriminator Loss: 0.5039, Generator Loss: 1.8739
D(x): 0.8586, D(G(z)): 0.2134
Epoch: [13/20], Batch Num: [417/600]
Discriminator Loss: 0.6134, Generator Loss: 1.9941
D(x): 0.8456, D(G(z)): 0.2452
Epoch: [13/20], Batch Num: [418/600]
Discriminator Loss: 0.4760, Generator Loss: 2.1212
D(x): 0.8765, D(G(z)): 0.2010
Epoch: [13/20], Batch Num: [419/600]
Discriminator Loss: 0.6042, Generator Loss: 2.3798
D(x): 0.7644, D(G(z)): 0.1508
Epoch: [13/20], Batch Num: [420/600]
Discriminator Loss: 0.5346, Generator Loss: 2.1959
D(x): 0.7864, D(G(z)): 0.1503
Epoch: [13/20], Batch Num: [421/600]
Discriminator Loss: 0.6392, Generator Loss: 2.1617
D(x): 0.7690, D(G(z)): 0.1531
Epoch: [13/20], Batch Num: [422/600]
Discriminator Loss: 0.6109, Generator Loss: 1.9037
D(x): 0.7695, D(G(z)): 0.1530
Epoch: [13/20], Batch Num: [423/600]
Discriminator Loss: 0.6355, Generator Loss: 1.8062
D(x): 0.8175, D(G(z)): 0.2140
Epoch: [13/20], Batch Num: [424/600]
Discriminator Loss: 0.5091, Generator Loss: 2.0399
D(x): 0.9178, D(G(z)): 0.2398
Epoch: [13/20], Batch Num: [425/600]
Discriminator Loss: 0.6631, Generator Loss: 2.2599
D(x): 0.8326, D(G(z)): 0.2108
Epoch: [13/20], Batch Num: [426/600]
Discriminator Loss: 0.5307, Generator Loss: 2.5066
D(x): 0.8192, D(G(z)): 0.1402
Epoch: [13/20], Batch Num: [427/600]
Discriminator Loss: 0.7908, Generator Loss: 2.2904
D(x): 0.7201, D(G(z)): 0.1560
Epoch: [13/20], Batch Num: [428/600]
Discriminator Loss: 0.8026, Generator Loss: 1.8808
D(x): 0.7301, D(G(z)): 0.1748
Epoch: [13/20], Batch Num: [429/600]
Discriminator Loss: 0.5836, Generator Loss: 1.6970
D(x): 0.8371, D(G(z)): 0.2145
Epoch: [13/20], Batch Num: [430/600]
Discriminator Loss: 0.5587, Generator Loss: 1.9384
D(x): 0.8854, D(G(z)): 0.2576
Epoch: [13/20], Batch Num: [431/600]
Discriminator Loss: 0.7221, Generator Loss: 2.1579
D(x): 0.8354, D(G(z)): 0.2601
Epoch: [13/20], Batch Num: [432/600]
Discriminator Loss: 0.7086, Generator Loss: 2.5116
D(x): 0.8080, D(G(z)): 0.2223
Epoch: [13/20], Batch Num: [433/600]
Discriminator Loss: 0.7067, Generator Loss: 2.4404
D(x): 0.7004, D(G(z)): 0.1434
Epoch: [13/20], Batch Num: [434/600]
Discriminator Loss: 0.5629, Generator Loss: 2.4444
D(x): 0.7911, D(G(z)): 0.1206
Epoch: [13/20], Batch Num: [435/600]
Discriminator Loss: 0.7050, Generator Loss: 2.2378
D(x): 0.7494, D(G(z)): 0.1583
Epoch: [13/20], Batch Num: [436/600]
Discriminator Loss: 0.5524, Generator Loss: 1.7053
D(x): 0.8355, D(G(z)): 0.1969
Epoch: [13/20], Batch Num: [437/600]
Discriminator Loss: 0.7023, Generator Loss: 1.7653
D(x): 0.8015, D(G(z)): 0.2297
Epoch: [13/20], Batch Num: [438/600]
Discriminator Loss: 0.7466, Generator Loss: 1.8384
D(x): 0.8503, D(G(z)): 0.2699
Epoch: [13/20], Batch Num: [439/600]
Discriminator Loss: 0.6984, Generator Loss: 2.0506
D(x): 0.8134, D(G(z)): 0.2307
Epoch: [13/20], Batch Num: [440/600]
Discriminator Loss: 0.6085, Generator Loss: 2.3574
D(x): 0.7994, D(G(z)): 0.1888
Epoch: [13/20], Batch Num: [441/600]
Discriminator Loss: 0.4898, Generator Loss: 2.4231
D(x): 0.8466, D(G(z)): 0.1722
Epoch: [13/20], Batch Num: [442/600]
Discriminator Loss: 0.7751, Generator Loss: 2.3171
D(x): 0.7091, D(G(z)): 0.1555
Epoch: [13/20], Batch Num: [443/600]
Discriminator Loss: 0.5268, Generator Loss: 2.1027
D(x): 0.8026, D(G(z)): 0.1486
Epoch: [13/20], Batch Num: [444/600]
Discriminator Loss: 0.6771, Generator Loss: 2.0083
D(x): 0.7774, D(G(z)): 0.2026
Epoch: [13/20], Batch Num: [445/600]
Discriminator Loss: 0.8373, Generator Loss: 1.8837
D(x): 0.8114, D(G(z)): 0.2845
Epoch: [13/20], Batch Num: [446/600]
Discriminator Loss: 0.6407, Generator Loss: 2.1814
D(x): 0.8054, D(G(z)): 0.2195
Epoch: [13/20], Batch Num: [447/600]
Discriminator Loss: 0.5060, Generator Loss: 2.1496
D(x): 0.8434, D(G(z)): 0.1922
Epoch: [13/20], Batch Num: [448/600]
Discriminator Loss: 0.5420, Generator Loss: 2.2369
D(x): 0.8128, D(G(z)): 0.1718
Epoch: [13/20], Batch Num: [449/600]
Discriminator Loss: 0.5941, Generator Loss: 2.1541
D(x): 0.7763, D(G(z)): 0.1614
Epoch: [13/20], Batch Num: [450/600]
Discriminator Loss: 0.5228, Generator Loss: 2.0906
D(x): 0.8165, D(G(z)): 0.1610
Epoch: [13/20], Batch Num: [451/600]
Discriminator Loss: 0.5848, Generator Loss: 1.9615
D(x): 0.8203, D(G(z)): 0.1891
Epoch: [13/20], Batch Num: [452/600]
Discriminator Loss: 0.6062, Generator Loss: 2.0178
D(x): 0.8455, D(G(z)): 0.2406
Epoch: [13/20], Batch Num: [453/600]
Discriminator Loss: 0.5804, Generator Loss: 2.2761
D(x): 0.7961, D(G(z)): 0.1667
Epoch: [13/20], Batch Num: [454/600]
Discriminator Loss: 0.4933, Generator Loss: 2.4135
D(x): 0.8489, D(G(z)): 0.1775
Epoch: [13/20], Batch Num: [455/600]
Discriminator Loss: 0.5625, Generator Loss: 2.3944
D(x): 0.8372, D(G(z)): 0.1872
Epoch: [13/20], Batch Num: [456/600]
Discriminator Loss: 0.6398, Generator Loss: 2.5604
D(x): 0.8036, D(G(z)): 0.1860
Epoch: [13/20], Batch Num: [457/600]
Discriminator Loss: 0.5427, Generator Loss: 2.4956
D(x): 0.8032, D(G(z)): 0.1431
Epoch: [13/20], Batch Num: [458/600]
Discriminator Loss: 0.4899, Generator Loss: 2.2975
D(x): 0.8450, D(G(z)): 0.1674
Epoch: [13/20], Batch Num: [459/600]
Discriminator Loss: 0.5746, Generator Loss: 2.1969
D(x): 0.7942, D(G(z)): 0.1547
Epoch: [13/20], Batch Num: [460/600]
Discriminator Loss: 0.6125, Generator Loss: 2.1681
D(x): 0.8063, D(G(z)): 0.1894
Epoch: [13/20], Batch Num: [461/600]
Discriminator Loss: 0.6120, Generator Loss: 1.9831
D(x): 0.7972, D(G(z)): 0.1735
Epoch: [13/20], Batch Num: [462/600]
Discriminator Loss: 0.5699, Generator Loss: 2.1429
D(x): 0.8456, D(G(z)): 0.1972
Epoch: [13/20], Batch Num: [463/600]
Discriminator Loss: 0.7477, Generator Loss: 2.3883
D(x): 0.7926, D(G(z)): 0.2390
Epoch: [13/20], Batch Num: [464/600]
Discriminator Loss: 0.6391, Generator Loss: 2.3260
D(x): 0.7980, D(G(z)): 0.1556
Epoch: [13/20], Batch Num: [465/600]
Discriminator Loss: 0.6549, Generator Loss: 2.6470
D(x): 0.8563, D(G(z)): 0.2366
Epoch: [13/20], Batch Num: [466/600]
Discriminator Loss: 0.7581, Generator Loss: 2.6903
D(x): 0.7416, D(G(z)): 0.1797
Epoch: [13/20], Batch Num: [467/600]
Discriminator Loss: 0.6226, Generator Loss: 2.4517
D(x): 0.7889, D(G(z)): 0.1573
Epoch: [13/20], Batch Num: [468/600]
Discriminator Loss: 0.5046, Generator Loss: 2.0726
D(x): 0.8153, D(G(z)): 0.1601
Epoch: [13/20], Batch Num: [469/600]
Discriminator Loss: 0.6122, Generator Loss: 1.9644
D(x): 0.8173, D(G(z)): 0.2057
Epoch: [13/20], Batch Num: [470/600]
Discriminator Loss: 0.6305, Generator Loss: 2.0427
D(x): 0.8303, D(G(z)): 0.2228
Epoch: [13/20], Batch Num: [471/600]
Discriminator Loss: 0.7928, Generator Loss: 2.3305
D(x): 0.8028, D(G(z)): 0.2349
Epoch: [13/20], Batch Num: [472/600]
Discriminator Loss: 0.4663, Generator Loss: 2.4131
D(x): 0.8255, D(G(z)): 0.1377
Epoch: [13/20], Batch Num: [473/600]
Discriminator Loss: 0.4894, Generator Loss: 2.1852
D(x): 0.8282, D(G(z)): 0.1483
Epoch: [13/20], Batch Num: [474/600]
Discriminator Loss: 0.4482, Generator Loss: 2.2589
D(x): 0.8312, D(G(z)): 0.1394
Epoch: [13/20], Batch Num: [475/600]
Discriminator Loss: 0.5481, Generator Loss: 2.3933
D(x): 0.8530, D(G(z)): 0.1926
Epoch: [13/20], Batch Num: [476/600]
Discriminator Loss: 0.5590, Generator Loss: 2.5272
D(x): 0.8284, D(G(z)): 0.1768
Epoch: [13/20], Batch Num: [477/600]
Discriminator Loss: 0.6095, Generator Loss: 2.5147
D(x): 0.8117, D(G(z)): 0.1580
Epoch: [13/20], Batch Num: [478/600]
Discriminator Loss: 0.5840, Generator Loss: 2.5359
D(x): 0.8325, D(G(z)): 0.1907
Epoch: [13/20], Batch Num: [479/600]
Discriminator Loss: 0.5846, Generator Loss: 2.4253
D(x): 0.8110, D(G(z)): 0.1708
Epoch: [13/20], Batch Num: [480/600]
Discriminator Loss: 0.8005, Generator Loss: 2.3773
D(x): 0.7362, D(G(z)): 0.1942
Epoch: [13/20], Batch Num: [481/600]
Discriminator Loss: 0.5736, Generator Loss: 1.8396
D(x): 0.7911, D(G(z)): 0.1617
Epoch: [13/20], Batch Num: [482/600]
Discriminator Loss: 0.7024, Generator Loss: 1.9802
D(x): 0.8019, D(G(z)): 0.2259
Epoch: [13/20], Batch Num: [483/600]
Discriminator Loss: 0.6117, Generator Loss: 1.9017
D(x): 0.8100, D(G(z)): 0.2225
Epoch: [13/20], Batch Num: [484/600]
Discriminator Loss: 0.7172, Generator Loss: 2.1537
D(x): 0.8175, D(G(z)): 0.2663
Epoch: [13/20], Batch Num: [485/600]
Discriminator Loss: 0.7108, Generator Loss: 2.4438
D(x): 0.7764, D(G(z)): 0.2373
Epoch: [13/20], Batch Num: [486/600]
Discriminator Loss: 0.7553, Generator Loss: 2.2524
D(x): 0.7297, D(G(z)): 0.2018
Epoch: [13/20], Batch Num: [487/600]
Discriminator Loss: 0.6446, Generator Loss: 1.9954
D(x): 0.7679, D(G(z)): 0.1603
Epoch: [13/20], Batch Num: [488/600]
Discriminator Loss: 0.7167, Generator Loss: 1.9865
D(x): 0.7658, D(G(z)): 0.2009
Epoch: [13/20], Batch Num: [489/600]
Discriminator Loss: 0.5833, Generator Loss: 1.9448
D(x): 0.8689, D(G(z)): 0.2453
Epoch: [13/20], Batch Num: [490/600]
Discriminator Loss: 0.5801, Generator Loss: 2.1833
D(x): 0.8472, D(G(z)): 0.2366
Epoch: [13/20], Batch Num: [491/600]
Discriminator Loss: 0.6410, Generator Loss: 2.4057
D(x): 0.8045, D(G(z)): 0.1970
Epoch: [13/20], Batch Num: [492/600]
Discriminator Loss: 0.5493, Generator Loss: 2.4805
D(x): 0.8048, D(G(z)): 0.1487
Epoch: [13/20], Batch Num: [493/600]
Discriminator Loss: 0.8986, Generator Loss: 2.3414
D(x): 0.6872, D(G(z)): 0.1722
Epoch: [13/20], Batch Num: [494/600]
Discriminator Loss: 0.5644, Generator Loss: 2.2903
D(x): 0.8489, D(G(z)): 0.2047
Epoch: [13/20], Batch Num: [495/600]
Discriminator Loss: 0.6296, Generator Loss: 2.3533
D(x): 0.8409, D(G(z)): 0.2148
Epoch: [13/20], Batch Num: [496/600]
Discriminator Loss: 0.6908, Generator Loss: 2.6357
D(x): 0.8017, D(G(z)): 0.2189
Epoch: [13/20], Batch Num: [497/600]
Discriminator Loss: 0.6630, Generator Loss: 2.3145
D(x): 0.8005, D(G(z)): 0.2140
Epoch: [13/20], Batch Num: [498/600]
Discriminator Loss: 0.7402, Generator Loss: 2.5777
D(x): 0.7565, D(G(z)): 0.1822
Epoch: [13/20], Batch Num: [499/600]
Discriminator Loss: 0.5907, Generator Loss: 2.2053
D(x): 0.7723, D(G(z)): 0.1690
Epoch: 13, Batch Num: [500/600]

Epoch: [13/20], Batch Num: [500/600]
Discriminator Loss: 0.5850, Generator Loss: 1.8775
D(x): 0.8118, D(G(z)): 0.1905
Epoch: [13/20], Batch Num: [501/600]
Discriminator Loss: 0.7077, Generator Loss: 1.9589
D(x): 0.7989, D(G(z)): 0.2518
Epoch: [13/20], Batch Num: [502/600]
Discriminator Loss: 0.6120, Generator Loss: 2.0870
D(x): 0.8073, D(G(z)): 0.2269
Epoch: [13/20], Batch Num: [503/600]
Discriminator Loss: 0.5954, Generator Loss: 2.1564
D(x): 0.8572, D(G(z)): 0.2475
Epoch: [13/20], Batch Num: [504/600]
Discriminator Loss: 0.6532, Generator Loss: 2.2654
D(x): 0.7750, D(G(z)): 0.1702
Epoch: [13/20], Batch Num: [505/600]
Discriminator Loss: 0.8742, Generator Loss: 2.3437
D(x): 0.7513, D(G(z)): 0.2392
Epoch: [13/20], Batch Num: [506/600]
Discriminator Loss: 0.7698, Generator Loss: 2.2162
D(x): 0.6940, D(G(z)): 0.1578
Epoch: [13/20], Batch Num: [507/600]
Discriminator Loss: 0.6335, Generator Loss: 1.8311
D(x): 0.7833, D(G(z)): 0.1969
Epoch: [13/20], Batch Num: [508/600]
Discriminator Loss: 0.8085, Generator Loss: 1.6952
D(x): 0.7898, D(G(z)): 0.2570
Epoch: [13/20], Batch Num: [509/600]
Discriminator Loss: 0.8841, Generator Loss: 1.8263
D(x): 0.8314, D(G(z)): 0.3449
Epoch: [13/20], Batch Num: [510/600]
Discriminator Loss: 0.6751, Generator Loss: 2.3197
D(x): 0.8138, D(G(z)): 0.2519
Epoch: [13/20], Batch Num: [511/600]
Discriminator Loss: 0.5131, Generator Loss: 2.3919
D(x): 0.7990, D(G(z)): 0.1497
Epoch: [13/20], Batch Num: [512/600]
Discriminator Loss: 0.8316, Generator Loss: 2.3040
D(x): 0.6886, D(G(z)): 0.1485
Epoch: [13/20], Batch Num: [513/600]
Discriminator Loss: 0.9303, Generator Loss: 1.6881
D(x): 0.6377, D(G(z)): 0.1705
Epoch: [13/20], Batch Num: [514/600]
Discriminator Loss: 0.7753, Generator Loss: 1.3539
D(x): 0.7826, D(G(z)): 0.2628
Epoch: [13/20], Batch Num: [515/600]
Discriminator Loss: 0.7589, Generator Loss: 1.3571
D(x): 0.8509, D(G(z)): 0.3223
Epoch: [13/20], Batch Num: [516/600]
Discriminator Loss: 0.7739, Generator Loss: 1.7487
D(x): 0.8428, D(G(z)): 0.3387
Epoch: [13/20], Batch Num: [517/600]
Discriminator Loss: 0.7558, Generator Loss: 1.8614
D(x): 0.7623, D(G(z)): 0.2572
Epoch: [13/20], Batch Num: [518/600]
Discriminator Loss: 0.6870, Generator Loss: 2.1007
D(x): 0.7676, D(G(z)): 0.2222
Epoch: [13/20], Batch Num: [519/600]
Discriminator Loss: 0.6617, Generator Loss: 1.9785
D(x): 0.7315, D(G(z)): 0.1643
Epoch: [13/20], Batch Num: [520/600]
Discriminator Loss: 0.6430, Generator Loss: 2.0968
D(x): 0.7437, D(G(z)): 0.1660
Epoch: [13/20], Batch Num: [521/600]
Discriminator Loss: 0.6710, Generator Loss: 1.8264
D(x): 0.7929, D(G(z)): 0.2289
Epoch: [13/20], Batch Num: [522/600]
Discriminator Loss: 0.8417, Generator Loss: 2.0038
D(x): 0.7630, D(G(z)): 0.2771
Epoch: [13/20], Batch Num: [523/600]
Discriminator Loss: 0.5475, Generator Loss: 1.9821
D(x): 0.8538, D(G(z)): 0.2352
Epoch: [13/20], Batch Num: [524/600]
Discriminator Loss: 0.6901, Generator Loss: 1.9642
D(x): 0.7576, D(G(z)): 0.2268
Epoch: [13/20], Batch Num: [525/600]
Discriminator Loss: 0.6957, Generator Loss: 1.9420
D(x): 0.7504, D(G(z)): 0.2169
Epoch: [13/20], Batch Num: [526/600]
Discriminator Loss: 0.8315, Generator Loss: 1.9653
D(x): 0.7624, D(G(z)): 0.2644
Epoch: [13/20], Batch Num: [527/600]
Discriminator Loss: 0.6116, Generator Loss: 1.9000
D(x): 0.7930, D(G(z)): 0.2011
Epoch: [13/20], Batch Num: [528/600]
Discriminator Loss: 0.5170, Generator Loss: 2.0199
D(x): 0.8578, D(G(z)): 0.2095
Epoch: [13/20], Batch Num: [529/600]
Discriminator Loss: 0.6106, Generator Loss: 2.5843
D(x): 0.8159, D(G(z)): 0.2173
Epoch: [13/20], Batch Num: [530/600]
Discriminator Loss: 0.5654, Generator Loss: 2.2342
D(x): 0.7906, D(G(z)): 0.1605
Epoch: [13/20], Batch Num: [531/600]
Discriminator Loss: 0.6235, Generator Loss: 2.2870
D(x): 0.7386, D(G(z)): 0.1267
Epoch: [13/20], Batch Num: [532/600]
Discriminator Loss: 0.5651, Generator Loss: 1.9159
D(x): 0.8289, D(G(z)): 0.2001
Epoch: [13/20], Batch Num: [533/600]
Discriminator Loss: 0.7094, Generator Loss: 1.9487
D(x): 0.7982, D(G(z)): 0.2332
Epoch: [13/20], Batch Num: [534/600]
Discriminator Loss: 0.5960, Generator Loss: 2.1602
D(x): 0.8387, D(G(z)): 0.2340
Epoch: [13/20], Batch Num: [535/600]
Discriminator Loss: 0.5852, Generator Loss: 2.3418
D(x): 0.7988, D(G(z)): 0.1908
Epoch: [13/20], Batch Num: [536/600]
Discriminator Loss: 0.7285, Generator Loss: 2.2828
D(x): 0.7397, D(G(z)): 0.1920
Epoch: [13/20], Batch Num: [537/600]
Discriminator Loss: 0.5546, Generator Loss: 2.2392
D(x): 0.8504, D(G(z)): 0.1972
Epoch: [13/20], Batch Num: [538/600]
Discriminator Loss: 0.6062, Generator Loss: 2.0546
D(x): 0.7742, D(G(z)): 0.1623
Epoch: [13/20], Batch Num: [539/600]
Discriminator Loss: 0.4589, Generator Loss: 2.2464
D(x): 0.8775, D(G(z)): 0.2033
Epoch: [13/20], Batch Num: [540/600]
Discriminator Loss: 0.7288, Generator Loss: 2.1039
D(x): 0.7511, D(G(z)): 0.2201
Epoch: [13/20], Batch Num: [541/600]
Discriminator Loss: 0.6672, Generator Loss: 2.0796
D(x): 0.7866, D(G(z)): 0.2050
Epoch: [13/20], Batch Num: [542/600]
Discriminator Loss: 0.6854, Generator Loss: 2.1450
D(x): 0.7969, D(G(z)): 0.2089
Epoch: [13/20], Batch Num: [543/600]
Discriminator Loss: 0.7371, Generator Loss: 1.9110
D(x): 0.7749, D(G(z)): 0.2196
Epoch: [13/20], Batch Num: [544/600]
Discriminator Loss: 0.6703, Generator Loss: 2.0517
D(x): 0.8282, D(G(z)): 0.2467
Epoch: [13/20], Batch Num: [545/600]
Discriminator Loss: 0.8549, Generator Loss: 2.1195
D(x): 0.7692, D(G(z)): 0.2591
Epoch: [13/20], Batch Num: [546/600]
Discriminator Loss: 0.7321, Generator Loss: 2.2203
D(x): 0.8331, D(G(z)): 0.2211
Epoch: [13/20], Batch Num: [547/600]
Discriminator Loss: 0.8420, Generator Loss: 2.4488
D(x): 0.6852, D(G(z)): 0.1868
Epoch: [13/20], Batch Num: [548/600]
Discriminator Loss: 0.7539, Generator Loss: 2.1425
D(x): 0.7461, D(G(z)): 0.1957
Epoch: [13/20], Batch Num: [549/600]
Discriminator Loss: 0.6591, Generator Loss: 1.8350
D(x): 0.7715, D(G(z)): 0.2078
Epoch: [13/20], Batch Num: [550/600]
Discriminator Loss: 0.7198, Generator Loss: 1.7395
D(x): 0.7679, D(G(z)): 0.2500
Epoch: [13/20], Batch Num: [551/600]
Discriminator Loss: 0.8360, Generator Loss: 1.9542
D(x): 0.7545, D(G(z)): 0.2902
Epoch: [13/20], Batch Num: [552/600]
Discriminator Loss: 0.6254, Generator Loss: 2.0092
D(x): 0.7979, D(G(z)): 0.2337
Epoch: [13/20], Batch Num: [553/600]
Discriminator Loss: 0.7333, Generator Loss: 2.2151
D(x): 0.7743, D(G(z)): 0.2377
Epoch: [13/20], Batch Num: [554/600]
Discriminator Loss: 0.5667, Generator Loss: 2.1999
D(x): 0.8046, D(G(z)): 0.1924
Epoch: [13/20], Batch Num: [555/600]
Discriminator Loss: 0.7142, Generator Loss: 2.4098
D(x): 0.7821, D(G(z)): 0.2319
Epoch: [13/20], Batch Num: [556/600]
Discriminator Loss: 0.6726, Generator Loss: 2.5011
D(x): 0.7777, D(G(z)): 0.1870
Epoch: [13/20], Batch Num: [557/600]
Discriminator Loss: 0.7555, Generator Loss: 2.3902
D(x): 0.7640, D(G(z)): 0.1878
Epoch: [13/20], Batch Num: [558/600]
Discriminator Loss: 0.5206, Generator Loss: 2.1701
D(x): 0.8339, D(G(z)): 0.1946
Epoch: [13/20], Batch Num: [559/600]
Discriminator Loss: 0.6644, Generator Loss: 2.0547
D(x): 0.8457, D(G(z)): 0.2224
Epoch: [13/20], Batch Num: [560/600]
Discriminator Loss: 0.5570, Generator Loss: 2.1642
D(x): 0.8302, D(G(z)): 0.1951
Epoch: [13/20], Batch Num: [561/600]
Discriminator Loss: 0.4956, Generator Loss: 2.1683
D(x): 0.8094, D(G(z)): 0.1557
Epoch: [13/20], Batch Num: [562/600]
Discriminator Loss: 0.5385, Generator Loss: 2.2702
D(x): 0.8299, D(G(z)): 0.1828
Epoch: [13/20], Batch Num: [563/600]
Discriminator Loss: 0.7115, Generator Loss: 2.3381
D(x): 0.7633, D(G(z)): 0.2180
Epoch: [13/20], Batch Num: [564/600]
Discriminator Loss: 0.5481, Generator Loss: 2.0811
D(x): 0.8171, D(G(z)): 0.1794
Epoch: [13/20], Batch Num: [565/600]
Discriminator Loss: 0.6646, Generator Loss: 2.0217
D(x): 0.7999, D(G(z)): 0.2323
Epoch: [13/20], Batch Num: [566/600]
Discriminator Loss: 0.6126, Generator Loss: 2.3267
D(x): 0.8049, D(G(z)): 0.2117
Epoch: [13/20], Batch Num: [567/600]
Discriminator Loss: 0.5297, Generator Loss: 2.1583
D(x): 0.8312, D(G(z)): 0.2036
Epoch: [13/20], Batch Num: [568/600]
Discriminator Loss: 0.4896, Generator Loss: 2.1854
D(x): 0.8040, D(G(z)): 0.1501
Epoch: [13/20], Batch Num: [569/600]
Discriminator Loss: 0.6984, Generator Loss: 2.4810
D(x): 0.8001, D(G(z)): 0.2050
Epoch: [13/20], Batch Num: [570/600]
Discriminator Loss: 0.6470, Generator Loss: 2.4938
D(x): 0.7756, D(G(z)): 0.1668
Epoch: [13/20], Batch Num: [571/600]
Discriminator Loss: 0.5253, Generator Loss: 2.4330
D(x): 0.8582, D(G(z)): 0.1971
Epoch: [13/20], Batch Num: [572/600]
Discriminator Loss: 0.5719, Generator Loss: 2.5962
D(x): 0.8243, D(G(z)): 0.1829
Epoch: [13/20], Batch Num: [573/600]
Discriminator Loss: 0.6786, Generator Loss: 2.5399
D(x): 0.7874, D(G(z)): 0.1885
Epoch: [13/20], Batch Num: [574/600]
Discriminator Loss: 0.7000, Generator Loss: 2.1903
D(x): 0.7852, D(G(z)): 0.2021
Epoch: [13/20], Batch Num: [575/600]
Discriminator Loss: 0.5891, Generator Loss: 2.2960
D(x): 0.8736, D(G(z)): 0.2503
Epoch: [13/20], Batch Num: [576/600]
Discriminator Loss: 0.5031, Generator Loss: 2.6218
D(x): 0.8502, D(G(z)): 0.1661
Epoch: [13/20], Batch Num: [577/600]
Discriminator Loss: 0.5350, Generator Loss: 2.8221
D(x): 0.7995, D(G(z)): 0.1422
Epoch: [13/20], Batch Num: [578/600]
Discriminator Loss: 0.4481, Generator Loss: 2.7186
D(x): 0.8393, D(G(z)): 0.1362
Epoch: [13/20], Batch Num: [579/600]
Discriminator Loss: 0.6457, Generator Loss: 2.2478
D(x): 0.7848, D(G(z)): 0.1440
Epoch: [13/20], Batch Num: [580/600]
Discriminator Loss: 0.6052, Generator Loss: 2.2668
D(x): 0.8312, D(G(z)): 0.1950
Epoch: [13/20], Batch Num: [581/600]
Discriminator Loss: 0.5508, Generator Loss: 2.0362
D(x): 0.8340, D(G(z)): 0.1871
Epoch: [13/20], Batch Num: [582/600]
Discriminator Loss: 0.6220, Generator Loss: 2.1354
D(x): 0.8497, D(G(z)): 0.2112
Epoch: [13/20], Batch Num: [583/600]
Discriminator Loss: 0.7102, Generator Loss: 2.6048
D(x): 0.8365, D(G(z)): 0.2350
Epoch: [13/20], Batch Num: [584/600]
Discriminator Loss: 0.5398, Generator Loss: 2.4095
D(x): 0.7962, D(G(z)): 0.1317
Epoch: [13/20], Batch Num: [585/600]
Discriminator Loss: 0.5981, Generator Loss: 2.3548
D(x): 0.7921, D(G(z)): 0.1568
Epoch: [13/20], Batch Num: [586/600]
Discriminator Loss: 0.7568, Generator Loss: 1.8839
D(x): 0.7555, D(G(z)): 0.1864
Epoch: [13/20], Batch Num: [587/600]
Discriminator Loss: 0.7668, Generator Loss: 1.5765
D(x): 0.7640, D(G(z)): 0.2116
Epoch: [13/20], Batch Num: [588/600]
Discriminator Loss: 0.7449, Generator Loss: 1.5227
D(x): 0.7997, D(G(z)): 0.2556
Epoch: [13/20], Batch Num: [589/600]
Discriminator Loss: 0.7755, Generator Loss: 1.6204
D(x): 0.8424, D(G(z)): 0.3232
Epoch: [13/20], Batch Num: [590/600]
Discriminator Loss: 0.7686, Generator Loss: 1.8365
D(x): 0.8158, D(G(z)): 0.2805
Epoch: [13/20], Batch Num: [591/600]
Discriminator Loss: 0.6652, Generator Loss: 2.2060
D(x): 0.7827, D(G(z)): 0.1927
Epoch: [13/20], Batch Num: [592/600]
Discriminator Loss: 0.7908, Generator Loss: 2.5128
D(x): 0.7273, D(G(z)): 0.1576
Epoch: [13/20], Batch Num: [593/600]
Discriminator Loss: 0.6203, Generator Loss: 2.2780
D(x): 0.7473, D(G(z)): 0.1332
Epoch: [13/20], Batch Num: [594/600]
Discriminator Loss: 0.7098, Generator Loss: 2.1169
D(x): 0.7559, D(G(z)): 0.1866
Epoch: [13/20], Batch Num: [595/600]
Discriminator Loss: 0.8111, Generator Loss: 1.6708
D(x): 0.7504, D(G(z)): 0.2350
Epoch: [13/20], Batch Num: [596/600]
Discriminator Loss: 0.7081, Generator Loss: 1.6010
D(x): 0.7960, D(G(z)): 0.2571
Epoch: [13/20], Batch Num: [597/600]
Discriminator Loss: 0.8443, Generator Loss: 1.6564
D(x): 0.8180, D(G(z)): 0.3261
Epoch: [13/20], Batch Num: [598/600]
Discriminator Loss: 0.7709, Generator Loss: 1.8066
D(x): 0.7997, D(G(z)): 0.2628
Epoch: [13/20], Batch Num: [599/600]
Discriminator Loss: 0.6976, Generator Loss: 2.1885
D(x): 0.7960, D(G(z)): 0.2566
Epoch: 14, Batch Num: [0/600]

Epoch: [14/20], Batch Num: [0/600]
Discriminator Loss: 0.6927, Generator Loss: 2.0624
D(x): 0.7551, D(G(z)): 0.2137
Epoch: [14/20], Batch Num: [1/600]
Discriminator Loss: 0.5777, Generator Loss: 2.2423
D(x): 0.7837, D(G(z)): 0.1585
Epoch: [14/20], Batch Num: [2/600]
Discriminator Loss: 0.6839, Generator Loss: 2.0237
D(x): 0.7634, D(G(z)): 0.1732
Epoch: [14/20], Batch Num: [3/600]
Discriminator Loss: 0.6819, Generator Loss: 2.0189
D(x): 0.7697, D(G(z)): 0.1825
Epoch: [14/20], Batch Num: [4/600]
Discriminator Loss: 0.6362, Generator Loss: 1.9634
D(x): 0.8060, D(G(z)): 0.2199
Epoch: [14/20], Batch Num: [5/600]
Discriminator Loss: 0.7274, Generator Loss: 1.8910
D(x): 0.7868, D(G(z)): 0.2298
Epoch: [14/20], Batch Num: [6/600]
Discriminator Loss: 0.5991, Generator Loss: 1.9171
D(x): 0.8150, D(G(z)): 0.2224
Epoch: [14/20], Batch Num: [7/600]
Discriminator Loss: 0.5778, Generator Loss: 1.7937
D(x): 0.8025, D(G(z)): 0.2117
Epoch: [14/20], Batch Num: [8/600]
Discriminator Loss: 0.6345, Generator Loss: 2.3262
D(x): 0.8256, D(G(z)): 0.2561
Epoch: [14/20], Batch Num: [9/600]
Discriminator Loss: 0.7565, Generator Loss: 1.9829
D(x): 0.7425, D(G(z)): 0.2203
Epoch: [14/20], Batch Num: [10/600]
Discriminator Loss: 0.7170, Generator Loss: 2.4577
D(x): 0.8252, D(G(z)): 0.2446
Epoch: [14/20], Batch Num: [11/600]
Discriminator Loss: 0.6931, Generator Loss: 2.4301
D(x): 0.7732, D(G(z)): 0.1758
Epoch: [14/20], Batch Num: [12/600]
Discriminator Loss: 0.6937, Generator Loss: 2.1942
D(x): 0.7663, D(G(z)): 0.2230
Epoch: [14/20], Batch Num: [13/600]
Discriminator Loss: 0.6346, Generator Loss: 2.2134
D(x): 0.8048, D(G(z)): 0.2075
Epoch: [14/20], Batch Num: [14/600]
Discriminator Loss: 0.7507, Generator Loss: 2.0354
D(x): 0.7570, D(G(z)): 0.2222
Epoch: [14/20], Batch Num: [15/600]
Discriminator Loss: 0.6638, Generator Loss: 2.1328
D(x): 0.7718, D(G(z)): 0.1940
Epoch: [14/20], Batch Num: [16/600]
Discriminator Loss: 0.6893, Generator Loss: 1.8546
D(x): 0.7693, D(G(z)): 0.2280
Epoch: [14/20], Batch Num: [17/600]
Discriminator Loss: 0.5872, Generator Loss: 1.7119
D(x): 0.8023, D(G(z)): 0.2086
Epoch: [14/20], Batch Num: [18/600]
Discriminator Loss: 0.6322, Generator Loss: 1.8923
D(x): 0.8221, D(G(z)): 0.2388
Epoch: [14/20], Batch Num: [19/600]
Discriminator Loss: 0.6694, Generator Loss: 2.0278
D(x): 0.7934, D(G(z)): 0.2481
Epoch: [14/20], Batch Num: [20/600]
Discriminator Loss: 0.6712, Generator Loss: 2.2054
D(x): 0.7673, D(G(z)): 0.2068
Epoch: [14/20], Batch Num: [21/600]
Discriminator Loss: 0.6122, Generator Loss: 2.0193
D(x): 0.7413, D(G(z)): 0.1555
Epoch: [14/20], Batch Num: [22/600]
Discriminator Loss: 0.6743, Generator Loss: 1.9685
D(x): 0.7863, D(G(z)): 0.2093
Epoch: [14/20], Batch Num: [23/600]
Discriminator Loss: 0.5496, Generator Loss: 1.8064
D(x): 0.8111, D(G(z)): 0.1718
Epoch: [14/20], Batch Num: [24/600]
Discriminator Loss: 0.7325, Generator Loss: 1.8696
D(x): 0.8069, D(G(z)): 0.2716
Epoch: [14/20], Batch Num: [25/600]
Discriminator Loss: 0.6034, Generator Loss: 2.2654
D(x): 0.8569, D(G(z)): 0.2527
Epoch: [14/20], Batch Num: [26/600]
Discriminator Loss: 0.5742, Generator Loss: 2.4341
D(x): 0.8024, D(G(z)): 0.1853
Epoch: [14/20], Batch Num: [27/600]
Discriminator Loss: 0.5773, Generator Loss: 2.1995
D(x): 0.7694, D(G(z)): 0.1620
Epoch: [14/20], Batch Num: [28/600]
Discriminator Loss: 0.6750, Generator Loss: 2.0717
D(x): 0.7512, D(G(z)): 0.1646
Epoch: [14/20], Batch Num: [29/600]
Discriminator Loss: 0.7476, Generator Loss: 1.8973
D(x): 0.7863, D(G(z)): 0.2445
Epoch: [14/20], Batch Num: [30/600]
Discriminator Loss: 0.6639, Generator Loss: 1.9942
D(x): 0.8590, D(G(z)): 0.2776
Epoch: [14/20], Batch Num: [31/600]
Discriminator Loss: 0.7419, Generator Loss: 2.4574
D(x): 0.8228, D(G(z)): 0.2595
Epoch: [14/20], Batch Num: [32/600]
Discriminator Loss: 0.7117, Generator Loss: 2.7290
D(x): 0.7424, D(G(z)): 0.1768
Epoch: [14/20], Batch Num: [33/600]
Discriminator Loss: 0.6993, Generator Loss: 2.5919
D(x): 0.7551, D(G(z)): 0.1503
Epoch: [14/20], Batch Num: [34/600]
Discriminator Loss: 0.6631, Generator Loss: 2.4229
D(x): 0.7575, D(G(z)): 0.1481
Epoch: [14/20], Batch Num: [35/600]
Discriminator Loss: 0.7046, Generator Loss: 1.7323
D(x): 0.7643, D(G(z)): 0.2075
Epoch: [14/20], Batch Num: [36/600]
Discriminator Loss: 0.7748, Generator Loss: 1.9905
D(x): 0.8460, D(G(z)): 0.3007
Epoch: [14/20], Batch Num: [37/600]
Discriminator Loss: 0.5598, Generator Loss: 2.2782
D(x): 0.8073, D(G(z)): 0.2065
Epoch: [14/20], Batch Num: [38/600]
Discriminator Loss: 0.5250, Generator Loss: 2.3438
D(x): 0.8191, D(G(z)): 0.1927
Epoch: [14/20], Batch Num: [39/600]
Discriminator Loss: 0.6000, Generator Loss: 2.6458
D(x): 0.8059, D(G(z)): 0.1787
Epoch: [14/20], Batch Num: [40/600]
Discriminator Loss: 0.6010, Generator Loss: 2.6535
D(x): 0.7637, D(G(z)): 0.1522
Epoch: [14/20], Batch Num: [41/600]
Discriminator Loss: 0.7482, Generator Loss: 2.0835
D(x): 0.7509, D(G(z)): 0.1722
Epoch: [14/20], Batch Num: [42/600]
Discriminator Loss: 0.6595, Generator Loss: 1.8819
D(x): 0.8466, D(G(z)): 0.2632
Epoch: [14/20], Batch Num: [43/600]
Discriminator Loss: 0.6487, Generator Loss: 1.9682
D(x): 0.8253, D(G(z)): 0.2600
Epoch: [14/20], Batch Num: [44/600]
Discriminator Loss: 0.7238, Generator Loss: 2.3711
D(x): 0.7870, D(G(z)): 0.2389
Epoch: [14/20], Batch Num: [45/600]
Discriminator Loss: 0.6878, Generator Loss: 2.6475
D(x): 0.7906, D(G(z)): 0.2334
Epoch: [14/20], Batch Num: [46/600]
Discriminator Loss: 0.6079, Generator Loss: 2.6980
D(x): 0.7788, D(G(z)): 0.1556
Epoch: [14/20], Batch Num: [47/600]
Discriminator Loss: 0.7286, Generator Loss: 2.4254
D(x): 0.7648, D(G(z)): 0.1786
Epoch: [14/20], Batch Num: [48/600]
Discriminator Loss: 0.6692, Generator Loss: 2.0719
D(x): 0.7385, D(G(z)): 0.1661
Epoch: [14/20], Batch Num: [49/600]
Discriminator Loss: 0.6218, Generator Loss: 1.5189
D(x): 0.7906, D(G(z)): 0.2214
Epoch: [14/20], Batch Num: [50/600]
Discriminator Loss: 0.5756, Generator Loss: 1.8097
D(x): 0.8852, D(G(z)): 0.3075
Epoch: [14/20], Batch Num: [51/600]
Discriminator Loss: 0.6983, Generator Loss: 2.0215
D(x): 0.8098, D(G(z)): 0.2738
Epoch: [14/20], Batch Num: [52/600]
Discriminator Loss: 0.6298, Generator Loss: 2.4238
D(x): 0.8180, D(G(z)): 0.2207
Epoch: [14/20], Batch Num: [53/600]
Discriminator Loss: 0.5814, Generator Loss: 2.7950
D(x): 0.7801, D(G(z)): 0.1555
Epoch: [14/20], Batch Num: [54/600]
Discriminator Loss: 0.6567, Generator Loss: 2.3750
D(x): 0.7360, D(G(z)): 0.1428
Epoch: [14/20], Batch Num: [55/600]
Discriminator Loss: 0.5264, Generator Loss: 2.4388
D(x): 0.8395, D(G(z)): 0.1621
Epoch: [14/20], Batch Num: [56/600]
Discriminator Loss: 0.6555, Generator Loss: 2.2266
D(x): 0.7842, D(G(z)): 0.1706
Epoch: [14/20], Batch Num: [57/600]
Discriminator Loss: 0.7792, Generator Loss: 1.9825
D(x): 0.8051, D(G(z)): 0.2721
Epoch: [14/20], Batch Num: [58/600]
Discriminator Loss: 0.6281, Generator Loss: 1.9562
D(x): 0.8272, D(G(z)): 0.2014
Epoch: [14/20], Batch Num: [59/600]
Discriminator Loss: 0.7050, Generator Loss: 2.5625
D(x): 0.8505, D(G(z)): 0.2697
Epoch: [14/20], Batch Num: [60/600]
Discriminator Loss: 0.4988, Generator Loss: 2.5578
D(x): 0.8193, D(G(z)): 0.1438
Epoch: [14/20], Batch Num: [61/600]
Discriminator Loss: 0.6359, Generator Loss: 2.4581
D(x): 0.7639, D(G(z)): 0.1395
Epoch: [14/20], Batch Num: [62/600]
Discriminator Loss: 0.7077, Generator Loss: 1.9944
D(x): 0.7290, D(G(z)): 0.1810
Epoch: [14/20], Batch Num: [63/600]
Discriminator Loss: 0.6298, Generator Loss: 1.8858
D(x): 0.8272, D(G(z)): 0.2414
Epoch: [14/20], Batch Num: [64/600]
Discriminator Loss: 0.6644, Generator Loss: 1.8056
D(x): 0.8159, D(G(z)): 0.2420
Epoch: [14/20], Batch Num: [65/600]
Discriminator Loss: 0.6258, Generator Loss: 2.0606
D(x): 0.8144, D(G(z)): 0.2392
Epoch: [14/20], Batch Num: [66/600]
Discriminator Loss: 0.6161, Generator Loss: 2.1619
D(x): 0.8216, D(G(z)): 0.2250
Epoch: [14/20], Batch Num: [67/600]
Discriminator Loss: 0.6806, Generator Loss: 2.2965
D(x): 0.7616, D(G(z)): 0.2119
Epoch: [14/20], Batch Num: [68/600]
Discriminator Loss: 0.6286, Generator Loss: 2.3799
D(x): 0.7490, D(G(z)): 0.1377
Epoch: [14/20], Batch Num: [69/600]
Discriminator Loss: 0.6186, Generator Loss: 2.0493
D(x): 0.8087, D(G(z)): 0.1920
Epoch: [14/20], Batch Num: [70/600]
Discriminator Loss: 0.7844, Generator Loss: 1.7621
D(x): 0.7624, D(G(z)): 0.2291
Epoch: [14/20], Batch Num: [71/600]
Discriminator Loss: 0.6668, Generator Loss: 1.9592
D(x): 0.8534, D(G(z)): 0.2824
Epoch: [14/20], Batch Num: [72/600]
Discriminator Loss: 0.5920, Generator Loss: 2.2322
D(x): 0.8202, D(G(z)): 0.2106
Epoch: [14/20], Batch Num: [73/600]
Discriminator Loss: 0.8096, Generator Loss: 2.3573
D(x): 0.7356, D(G(z)): 0.2107
Epoch: [14/20], Batch Num: [74/600]
Discriminator Loss: 0.6041, Generator Loss: 2.1507
D(x): 0.7621, D(G(z)): 0.1341
Epoch: [14/20], Batch Num: [75/600]
Discriminator Loss: 0.6644, Generator Loss: 2.1113
D(x): 0.7948, D(G(z)): 0.1781
Epoch: [14/20], Batch Num: [76/600]
Discriminator Loss: 0.6735, Generator Loss: 2.0529
D(x): 0.7904, D(G(z)): 0.2128
Epoch: [14/20], Batch Num: [77/600]
Discriminator Loss: 0.7162, Generator Loss: 1.9206
D(x): 0.7930, D(G(z)): 0.2369
Epoch: [14/20], Batch Num: [78/600]
Discriminator Loss: 0.7567, Generator Loss: 2.0774
D(x): 0.8456, D(G(z)): 0.2805
Epoch: [14/20], Batch Num: [79/600]
Discriminator Loss: 0.5875, Generator Loss: 2.3222
D(x): 0.8031, D(G(z)): 0.1979
Epoch: [14/20], Batch Num: [80/600]
Discriminator Loss: 0.5337, Generator Loss: 2.5222
D(x): 0.8132, D(G(z)): 0.1822
Epoch: [14/20], Batch Num: [81/600]
Discriminator Loss: 0.5568, Generator Loss: 2.5188
D(x): 0.7686, D(G(z)): 0.1372
Epoch: [14/20], Batch Num: [82/600]
Discriminator Loss: 0.5378, Generator Loss: 2.3534
D(x): 0.7875, D(G(z)): 0.1382
Epoch: [14/20], Batch Num: [83/600]
Discriminator Loss: 0.6736, Generator Loss: 1.8079
D(x): 0.7631, D(G(z)): 0.1827
Epoch: [14/20], Batch Num: [84/600]
Discriminator Loss: 0.6772, Generator Loss: 1.6389
D(x): 0.8692, D(G(z)): 0.2635
Epoch: [14/20], Batch Num: [85/600]
Discriminator Loss: 0.7950, Generator Loss: 2.4046
D(x): 0.8601, D(G(z)): 0.3195
Epoch: [14/20], Batch Num: [86/600]
Discriminator Loss: 0.6653, Generator Loss: 2.5397
D(x): 0.7666, D(G(z)): 0.1890
Epoch: [14/20], Batch Num: [87/600]
Discriminator Loss: 0.6462, Generator Loss: 2.5144
D(x): 0.7610, D(G(z)): 0.1401
Epoch: [14/20], Batch Num: [88/600]
Discriminator Loss: 0.7169, Generator Loss: 2.1982
D(x): 0.7512, D(G(z)): 0.1430
Epoch: [14/20], Batch Num: [89/600]
Discriminator Loss: 0.5557, Generator Loss: 2.1553
D(x): 0.7787, D(G(z)): 0.1393
Epoch: [14/20], Batch Num: [90/600]
Discriminator Loss: 0.6544, Generator Loss: 1.9167
D(x): 0.8132, D(G(z)): 0.2395
Epoch: [14/20], Batch Num: [91/600]
Discriminator Loss: 0.6507, Generator Loss: 1.7100
D(x): 0.8252, D(G(z)): 0.2492
Epoch: [14/20], Batch Num: [92/600]
Discriminator Loss: 0.6773, Generator Loss: 1.8852
D(x): 0.8356, D(G(z)): 0.2578
Epoch: [14/20], Batch Num: [93/600]
Discriminator Loss: 0.5516, Generator Loss: 2.1182
D(x): 0.8288, D(G(z)): 0.1940
Epoch: [14/20], Batch Num: [94/600]
Discriminator Loss: 0.7119, Generator Loss: 2.2709
D(x): 0.7440, D(G(z)): 0.1616
Epoch: [14/20], Batch Num: [95/600]
Discriminator Loss: 0.6583, Generator Loss: 2.0007
D(x): 0.7717, D(G(z)): 0.1744
Epoch: [14/20], Batch Num: [96/600]
Discriminator Loss: 0.7316, Generator Loss: 1.7994
D(x): 0.7452, D(G(z)): 0.1976
Epoch: [14/20], Batch Num: [97/600]
Discriminator Loss: 0.6670, Generator Loss: 1.6955
D(x): 0.8195, D(G(z)): 0.2504
Epoch: [14/20], Batch Num: [98/600]
Discriminator Loss: 0.8556, Generator Loss: 2.1366
D(x): 0.7503, D(G(z)): 0.2771
Epoch: [14/20], Batch Num: [99/600]
Discriminator Loss: 0.5829, Generator Loss: 2.2855
D(x): 0.8415, D(G(z)): 0.2389
Epoch: 14, Batch Num: [100/600]

Epoch: [14/20], Batch Num: [100/600]
Discriminator Loss: 0.6410, Generator Loss: 2.2160
D(x): 0.7483, D(G(z)): 0.1626
Epoch: [14/20], Batch Num: [101/600]
Discriminator Loss: 0.7231, Generator Loss: 2.1686
D(x): 0.7213, D(G(z)): 0.1900
Epoch: [14/20], Batch Num: [102/600]
Discriminator Loss: 0.7455, Generator Loss: 1.9637
D(x): 0.7697, D(G(z)): 0.2131
Epoch: [14/20], Batch Num: [103/600]
Discriminator Loss: 0.6879, Generator Loss: 2.0389
D(x): 0.8101, D(G(z)): 0.2475
Epoch: [14/20], Batch Num: [104/600]
Discriminator Loss: 0.8250, Generator Loss: 2.0223
D(x): 0.8076, D(G(z)): 0.3034
Epoch: [14/20], Batch Num: [105/600]
Discriminator Loss: 0.6328, Generator Loss: 2.5264
D(x): 0.7873, D(G(z)): 0.1843
Epoch: [14/20], Batch Num: [106/600]
Discriminator Loss: 0.7060, Generator Loss: 2.0499
D(x): 0.7330, D(G(z)): 0.1613
Epoch: [14/20], Batch Num: [107/600]
Discriminator Loss: 0.7733, Generator Loss: 2.0889
D(x): 0.7765, D(G(z)): 0.2578
Epoch: [14/20], Batch Num: [108/600]
Discriminator Loss: 0.6958, Generator Loss: 1.9876
D(x): 0.7826, D(G(z)): 0.2148
Epoch: [14/20], Batch Num: [109/600]
Discriminator Loss: 0.4912, Generator Loss: 2.1945
D(x): 0.8607, D(G(z)): 0.1943
Epoch: [14/20], Batch Num: [110/600]
Discriminator Loss: 0.6541, Generator Loss: 2.1763
D(x): 0.8144, D(G(z)): 0.2142
Epoch: [14/20], Batch Num: [111/600]
Discriminator Loss: 0.6092, Generator Loss: 2.2505
D(x): 0.8134, D(G(z)): 0.1964
Epoch: [14/20], Batch Num: [112/600]
Discriminator Loss: 0.5952, Generator Loss: 2.4158
D(x): 0.8321, D(G(z)): 0.1992
Epoch: [14/20], Batch Num: [113/600]
Discriminator Loss: 0.6281, Generator Loss: 2.6196
D(x): 0.7747, D(G(z)): 0.1564
Epoch: [14/20], Batch Num: [114/600]
Discriminator Loss: 0.8642, Generator Loss: 2.4820
D(x): 0.7003, D(G(z)): 0.1740
Epoch: [14/20], Batch Num: [115/600]
Discriminator Loss: 0.6968, Generator Loss: 1.9291
D(x): 0.7660, D(G(z)): 0.1974
Epoch: [14/20], Batch Num: [116/600]
Discriminator Loss: 0.5723, Generator Loss: 1.8609
D(x): 0.8485, D(G(z)): 0.2268
Epoch: [14/20], Batch Num: [117/600]
Discriminator Loss: 0.5620, Generator Loss: 1.9276
D(x): 0.8487, D(G(z)): 0.2304
Epoch: [14/20], Batch Num: [118/600]
Discriminator Loss: 0.7050, Generator Loss: 1.9713
D(x): 0.7907, D(G(z)): 0.2431
Epoch: [14/20], Batch Num: [119/600]
Discriminator Loss: 0.6677, Generator Loss: 1.9997
D(x): 0.7621, D(G(z)): 0.1997
Epoch: [14/20], Batch Num: [120/600]
Discriminator Loss: 0.7272, Generator Loss: 2.0826
D(x): 0.7604, D(G(z)): 0.2138
Epoch: [14/20], Batch Num: [121/600]
Discriminator Loss: 0.5945, Generator Loss: 1.9555
D(x): 0.7953, D(G(z)): 0.1711
Epoch: [14/20], Batch Num: [122/600]
Discriminator Loss: 0.6767, Generator Loss: 1.6399
D(x): 0.7778, D(G(z)): 0.2229
Epoch: [14/20], Batch Num: [123/600]
Discriminator Loss: 0.7116, Generator Loss: 1.8450
D(x): 0.8406, D(G(z)): 0.2655
Epoch: [14/20], Batch Num: [124/600]
Discriminator Loss: 0.7152, Generator Loss: 1.9391
D(x): 0.7987, D(G(z)): 0.2470
Epoch: [14/20], Batch Num: [125/600]
Discriminator Loss: 0.6321, Generator Loss: 2.4428
D(x): 0.7755, D(G(z)): 0.1970
Epoch: [14/20], Batch Num: [126/600]
Discriminator Loss: 0.7030, Generator Loss: 2.2193
D(x): 0.7502, D(G(z)): 0.1887
Epoch: [14/20], Batch Num: [127/600]
Discriminator Loss: 0.6342, Generator Loss: 2.0186
D(x): 0.7534, D(G(z)): 0.1638
Epoch: [14/20], Batch Num: [128/600]
Discriminator Loss: 0.7526, Generator Loss: 1.6791
D(x): 0.7330, D(G(z)): 0.2207
Epoch: [14/20], Batch Num: [129/600]
Discriminator Loss: 0.8282, Generator Loss: 1.5898
D(x): 0.7777, D(G(z)): 0.2630
Epoch: [14/20], Batch Num: [130/600]
Discriminator Loss: 0.6281, Generator Loss: 1.9870
D(x): 0.8441, D(G(z)): 0.2678
Epoch: [14/20], Batch Num: [131/600]
Discriminator Loss: 0.7765, Generator Loss: 2.2806
D(x): 0.8020, D(G(z)): 0.2713
Epoch: [14/20], Batch Num: [132/600]
Discriminator Loss: 0.7241, Generator Loss: 2.3877
D(x): 0.7584, D(G(z)): 0.1818
Epoch: [14/20], Batch Num: [133/600]
Discriminator Loss: 0.5963, Generator Loss: 2.6228
D(x): 0.7695, D(G(z)): 0.1471
Epoch: [14/20], Batch Num: [134/600]
Discriminator Loss: 0.6249, Generator Loss: 2.4084
D(x): 0.7801, D(G(z)): 0.1662
Epoch: [14/20], Batch Num: [135/600]
Discriminator Loss: 0.7061, Generator Loss: 1.8129
D(x): 0.7424, D(G(z)): 0.1585
Epoch: [14/20], Batch Num: [136/600]
Discriminator Loss: 0.6095, Generator Loss: 2.0817
D(x): 0.8699, D(G(z)): 0.2522
Epoch: [14/20], Batch Num: [137/600]
Discriminator Loss: 0.7564, Generator Loss: 2.1148
D(x): 0.8338, D(G(z)): 0.2603
Epoch: [14/20], Batch Num: [138/600]
Discriminator Loss: 0.6234, Generator Loss: 2.0855
D(x): 0.7962, D(G(z)): 0.2023
Epoch: [14/20], Batch Num: [139/600]
Discriminator Loss: 0.6306, Generator Loss: 2.3040
D(x): 0.7968, D(G(z)): 0.1782
Epoch: [14/20], Batch Num: [140/600]
Discriminator Loss: 0.7383, Generator Loss: 2.1935
D(x): 0.7958, D(G(z)): 0.2219
Epoch: [14/20], Batch Num: [141/600]
Discriminator Loss: 0.7948, Generator Loss: 2.1696
D(x): 0.7305, D(G(z)): 0.1883
Epoch: [14/20], Batch Num: [142/600]
Discriminator Loss: 0.5993, Generator Loss: 2.0470
D(x): 0.7896, D(G(z)): 0.1680
Epoch: [14/20], Batch Num: [143/600]
Discriminator Loss: 0.6130, Generator Loss: 1.9278
D(x): 0.8016, D(G(z)): 0.2035
Epoch: [14/20], Batch Num: [144/600]
Discriminator Loss: 0.6237, Generator Loss: 1.7399
D(x): 0.7900, D(G(z)): 0.1772
Epoch: [14/20], Batch Num: [145/600]
Discriminator Loss: 0.7302, Generator Loss: 1.7417
D(x): 0.8233, D(G(z)): 0.2658
Epoch: [14/20], Batch Num: [146/600]
Discriminator Loss: 0.7896, Generator Loss: 1.8181
D(x): 0.7902, D(G(z)): 0.2504
Epoch: [14/20], Batch Num: [147/600]
Discriminator Loss: 0.8342, Generator Loss: 2.0728
D(x): 0.7314, D(G(z)): 0.2267
Epoch: [14/20], Batch Num: [148/600]
Discriminator Loss: 0.7560, Generator Loss: 1.9344
D(x): 0.7402, D(G(z)): 0.1816
Epoch: [14/20], Batch Num: [149/600]
Discriminator Loss: 0.8187, Generator Loss: 1.8354
D(x): 0.7378, D(G(z)): 0.2219
Epoch: [14/20], Batch Num: [150/600]
Discriminator Loss: 0.7592, Generator Loss: 2.0254
D(x): 0.7750, D(G(z)): 0.2253
Epoch: [14/20], Batch Num: [151/600]
Discriminator Loss: 0.6452, Generator Loss: 1.9534
D(x): 0.7914, D(G(z)): 0.1854
Epoch: [14/20], Batch Num: [152/600]
Discriminator Loss: 0.7544, Generator Loss: 1.9781
D(x): 0.7747, D(G(z)): 0.2126
Epoch: [14/20], Batch Num: [153/600]
Discriminator Loss: 0.6203, Generator Loss: 1.9001
D(x): 0.7781, D(G(z)): 0.1852
Epoch: [14/20], Batch Num: [154/600]
Discriminator Loss: 0.6851, Generator Loss: 1.7942
D(x): 0.7348, D(G(z)): 0.1882
Epoch: [14/20], Batch Num: [155/600]
Discriminator Loss: 0.6949, Generator Loss: 1.8851
D(x): 0.8353, D(G(z)): 0.2676
Epoch: [14/20], Batch Num: [156/600]
Discriminator Loss: 0.5417, Generator Loss: 1.7568
D(x): 0.8414, D(G(z)): 0.2374
Epoch: [14/20], Batch Num: [157/600]
Discriminator Loss: 0.6453, Generator Loss: 1.9835
D(x): 0.7779, D(G(z)): 0.2293
Epoch: [14/20], Batch Num: [158/600]
Discriminator Loss: 0.6006, Generator Loss: 1.9560
D(x): 0.7920, D(G(z)): 0.2039
Epoch: [14/20], Batch Num: [159/600]
Discriminator Loss: 0.5969, Generator Loss: 1.9610
D(x): 0.8033, D(G(z)): 0.2005
Epoch: [14/20], Batch Num: [160/600]
Discriminator Loss: 0.7457, Generator Loss: 1.7185
D(x): 0.7390, D(G(z)): 0.2158
Epoch: [14/20], Batch Num: [161/600]
Discriminator Loss: 0.6135, Generator Loss: 1.9528
D(x): 0.7974, D(G(z)): 0.2054
Epoch: [14/20], Batch Num: [162/600]
Discriminator Loss: 0.6588, Generator Loss: 1.9770
D(x): 0.8026, D(G(z)): 0.2153
Epoch: [14/20], Batch Num: [163/600]
Discriminator Loss: 0.6735, Generator Loss: 1.9333
D(x): 0.7982, D(G(z)): 0.1916
Epoch: [14/20], Batch Num: [164/600]
Discriminator Loss: 0.6930, Generator Loss: 2.0256
D(x): 0.8425, D(G(z)): 0.2517
Epoch: [14/20], Batch Num: [165/600]
Discriminator Loss: 0.7480, Generator Loss: 2.2395
D(x): 0.7597, D(G(z)): 0.1764
Epoch: [14/20], Batch Num: [166/600]
Discriminator Loss: 0.7222, Generator Loss: 2.2507
D(x): 0.8005, D(G(z)): 0.2114
Epoch: [14/20], Batch Num: [167/600]
Discriminator Loss: 0.7466, Generator Loss: 2.0620
D(x): 0.7590, D(G(z)): 0.1964
Epoch: [14/20], Batch Num: [168/600]
Discriminator Loss: 0.7264, Generator Loss: 2.0557
D(x): 0.7414, D(G(z)): 0.1901
Epoch: [14/20], Batch Num: [169/600]
Discriminator Loss: 0.6983, Generator Loss: 1.7574
D(x): 0.8021, D(G(z)): 0.2239
Epoch: [14/20], Batch Num: [170/600]
Discriminator Loss: 0.6480, Generator Loss: 1.7145
D(x): 0.8054, D(G(z)): 0.2372
Epoch: [14/20], Batch Num: [171/600]
Discriminator Loss: 0.6617, Generator Loss: 1.6560
D(x): 0.7965, D(G(z)): 0.2452
Epoch: [14/20], Batch Num: [172/600]
Discriminator Loss: 0.6538, Generator Loss: 1.8863
D(x): 0.8293, D(G(z)): 0.2685
Epoch: [14/20], Batch Num: [173/600]
Discriminator Loss: 0.7014, Generator Loss: 2.0688
D(x): 0.7733, D(G(z)): 0.2248
Epoch: [14/20], Batch Num: [174/600]
Discriminator Loss: 0.7195, Generator Loss: 2.0578
D(x): 0.7449, D(G(z)): 0.1990
Epoch: [14/20], Batch Num: [175/600]
Discriminator Loss: 0.6851, Generator Loss: 2.0263
D(x): 0.7534, D(G(z)): 0.1991
Epoch: [14/20], Batch Num: [176/600]
Discriminator Loss: 0.6603, Generator Loss: 1.9208
D(x): 0.7996, D(G(z)): 0.2318
Epoch: [14/20], Batch Num: [177/600]
Discriminator Loss: 0.6509, Generator Loss: 1.7167
D(x): 0.7910, D(G(z)): 0.2109
Epoch: [14/20], Batch Num: [178/600]
Discriminator Loss: 0.6847, Generator Loss: 1.8504
D(x): 0.7692, D(G(z)): 0.2251
Epoch: [14/20], Batch Num: [179/600]
Discriminator Loss: 0.4835, Generator Loss: 1.6544
D(x): 0.8472, D(G(z)): 0.1893
Epoch: [14/20], Batch Num: [180/600]
Discriminator Loss: 0.7369, Generator Loss: 1.9563
D(x): 0.8206, D(G(z)): 0.2642
Epoch: [14/20], Batch Num: [181/600]
Discriminator Loss: 0.4663, Generator Loss: 2.0078
D(x): 0.8495, D(G(z)): 0.2004
Epoch: [14/20], Batch Num: [182/600]
Discriminator Loss: 0.6351, Generator Loss: 2.2150
D(x): 0.7915, D(G(z)): 0.2092
Epoch: [14/20], Batch Num: [183/600]
Discriminator Loss: 0.7740, Generator Loss: 2.1120
D(x): 0.7403, D(G(z)): 0.1846
Epoch: [14/20], Batch Num: [184/600]
Discriminator Loss: 0.7339, Generator Loss: 2.1630
D(x): 0.7715, D(G(z)): 0.2125
Epoch: [14/20], Batch Num: [185/600]
Discriminator Loss: 0.6669, Generator Loss: 2.1302
D(x): 0.7881, D(G(z)): 0.1987
Epoch: [14/20], Batch Num: [186/600]
Discriminator Loss: 0.6914, Generator Loss: 1.9940
D(x): 0.7827, D(G(z)): 0.2103
Epoch: [14/20], Batch Num: [187/600]
Discriminator Loss: 0.5999, Generator Loss: 1.9390
D(x): 0.8006, D(G(z)): 0.2054
Epoch: [14/20], Batch Num: [188/600]
Discriminator Loss: 0.6658, Generator Loss: 1.7830
D(x): 0.8153, D(G(z)): 0.2245
Epoch: [14/20], Batch Num: [189/600]
Discriminator Loss: 0.6853, Generator Loss: 2.1927
D(x): 0.8146, D(G(z)): 0.2487
Epoch: [14/20], Batch Num: [190/600]
Discriminator Loss: 0.5465, Generator Loss: 2.4370
D(x): 0.8285, D(G(z)): 0.1910
Epoch: [14/20], Batch Num: [191/600]
Discriminator Loss: 0.5953, Generator Loss: 2.3325
D(x): 0.8076, D(G(z)): 0.1939
Epoch: [14/20], Batch Num: [192/600]
Discriminator Loss: 0.6368, Generator Loss: 2.3550
D(x): 0.7844, D(G(z)): 0.1844
Epoch: [14/20], Batch Num: [193/600]
Discriminator Loss: 0.6745, Generator Loss: 1.9644
D(x): 0.7662, D(G(z)): 0.1808
Epoch: [14/20], Batch Num: [194/600]
Discriminator Loss: 0.7387, Generator Loss: 2.2076
D(x): 0.7755, D(G(z)): 0.2171
Epoch: [14/20], Batch Num: [195/600]
Discriminator Loss: 0.6748, Generator Loss: 2.0852
D(x): 0.7788, D(G(z)): 0.2218
Epoch: [14/20], Batch Num: [196/600]
Discriminator Loss: 0.7407, Generator Loss: 1.8748
D(x): 0.7857, D(G(z)): 0.2257
Epoch: [14/20], Batch Num: [197/600]
Discriminator Loss: 0.6784, Generator Loss: 1.7773
D(x): 0.7830, D(G(z)): 0.2161
Epoch: [14/20], Batch Num: [198/600]
Discriminator Loss: 0.6620, Generator Loss: 1.8321
D(x): 0.8167, D(G(z)): 0.2426
Epoch: [14/20], Batch Num: [199/600]
Discriminator Loss: 0.5760, Generator Loss: 1.8745
D(x): 0.8645, D(G(z)): 0.2460
Epoch: 14, Batch Num: [200/600]

Epoch: [14/20], Batch Num: [200/600]
Discriminator Loss: 0.7758, Generator Loss: 2.1792
D(x): 0.7645, D(G(z)): 0.2251
Epoch: [14/20], Batch Num: [201/600]
Discriminator Loss: 0.6428, Generator Loss: 2.0254
D(x): 0.7450, D(G(z)): 0.1604
Epoch: [14/20], Batch Num: [202/600]
Discriminator Loss: 0.7129, Generator Loss: 1.9597
D(x): 0.7715, D(G(z)): 0.2025
Epoch: [14/20], Batch Num: [203/600]
Discriminator Loss: 0.7556, Generator Loss: 2.0801
D(x): 0.7448, D(G(z)): 0.2204
Epoch: [14/20], Batch Num: [204/600]
Discriminator Loss: 0.6769, Generator Loss: 1.7907
D(x): 0.7702, D(G(z)): 0.2006
Epoch: [14/20], Batch Num: [205/600]
Discriminator Loss: 0.8112, Generator Loss: 1.6654
D(x): 0.7559, D(G(z)): 0.2616
Epoch: [14/20], Batch Num: [206/600]
Discriminator Loss: 0.9573, Generator Loss: 1.6229
D(x): 0.7551, D(G(z)): 0.3147
Epoch: [14/20], Batch Num: [207/600]
Discriminator Loss: 0.6563, Generator Loss: 2.0763
D(x): 0.8441, D(G(z)): 0.2729
Epoch: [14/20], Batch Num: [208/600]
Discriminator Loss: 0.6745, Generator Loss: 2.5358
D(x): 0.8057, D(G(z)): 0.2379
Epoch: [14/20], Batch Num: [209/600]
Discriminator Loss: 0.7105, Generator Loss: 2.2216
D(x): 0.7080, D(G(z)): 0.1485
Epoch: [14/20], Batch Num: [210/600]
Discriminator Loss: 0.7622, Generator Loss: 2.0086
D(x): 0.6990, D(G(z)): 0.1478
Epoch: [14/20], Batch Num: [211/600]
Discriminator Loss: 0.6099, Generator Loss: 1.9100
D(x): 0.8041, D(G(z)): 0.1950
Epoch: [14/20], Batch Num: [212/600]
Discriminator Loss: 0.6214, Generator Loss: 1.7136
D(x): 0.8215, D(G(z)): 0.2169
Epoch: [14/20], Batch Num: [213/600]
Discriminator Loss: 0.7718, Generator Loss: 1.7822
D(x): 0.7894, D(G(z)): 0.2622
Epoch: [14/20], Batch Num: [214/600]
Discriminator Loss: 0.7260, Generator Loss: 1.9296
D(x): 0.8103, D(G(z)): 0.2640
Epoch: [14/20], Batch Num: [215/600]
Discriminator Loss: 0.8009, Generator Loss: 2.1129
D(x): 0.7845, D(G(z)): 0.2590
Epoch: [14/20], Batch Num: [216/600]
Discriminator Loss: 0.7803, Generator Loss: 2.3167
D(x): 0.7335, D(G(z)): 0.2078
Epoch: [14/20], Batch Num: [217/600]
Discriminator Loss: 0.7380, Generator Loss: 2.1124
D(x): 0.7174, D(G(z)): 0.1795
Epoch: [14/20], Batch Num: [218/600]
Discriminator Loss: 0.6433, Generator Loss: 1.8799
D(x): 0.7537, D(G(z)): 0.1751
Epoch: [14/20], Batch Num: [219/600]
Discriminator Loss: 0.7397, Generator Loss: 1.7630
D(x): 0.7515, D(G(z)): 0.2371
Epoch: [14/20], Batch Num: [220/600]
Discriminator Loss: 0.6779, Generator Loss: 1.8308
D(x): 0.7988, D(G(z)): 0.2553
Epoch: [14/20], Batch Num: [221/600]
Discriminator Loss: 0.6173, Generator Loss: 1.6890
D(x): 0.8398, D(G(z)): 0.2696
Epoch: [14/20], Batch Num: [222/600]
Discriminator Loss: 0.6299, Generator Loss: 1.7945
D(x): 0.7995, D(G(z)): 0.2340
Epoch: [14/20], Batch Num: [223/600]
Discriminator Loss: 0.7095, Generator Loss: 1.9208
D(x): 0.7594, D(G(z)): 0.2286
Epoch: [14/20], Batch Num: [224/600]
Discriminator Loss: 0.8668, Generator Loss: 2.0162
D(x): 0.6949, D(G(z)): 0.2218
Epoch: [14/20], Batch Num: [225/600]
Discriminator Loss: 0.5250, Generator Loss: 2.0027
D(x): 0.8277, D(G(z)): 0.1906
Epoch: [14/20], Batch Num: [226/600]
Discriminator Loss: 0.7668, Generator Loss: 1.9900
D(x): 0.7394, D(G(z)): 0.2253
Epoch: [14/20], Batch Num: [227/600]
Discriminator Loss: 0.6539, Generator Loss: 1.8492
D(x): 0.8075, D(G(z)): 0.2372
Epoch: [14/20], Batch Num: [228/600]
Discriminator Loss: 0.6702, Generator Loss: 1.8901
D(x): 0.7794, D(G(z)): 0.2050
Epoch: [14/20], Batch Num: [229/600]
Discriminator Loss: 0.6725, Generator Loss: 1.9380
D(x): 0.7834, D(G(z)): 0.2094
Epoch: [14/20], Batch Num: [230/600]
Discriminator Loss: 0.6457, Generator Loss: 2.0166
D(x): 0.8095, D(G(z)): 0.2089
Epoch: [14/20], Batch Num: [231/600]
Discriminator Loss: 0.6542, Generator Loss: 2.1754
D(x): 0.7868, D(G(z)): 0.2036
Epoch: [14/20], Batch Num: [232/600]
Discriminator Loss: 0.6454, Generator Loss: 2.0836
D(x): 0.7777, D(G(z)): 0.1927
Epoch: [14/20], Batch Num: [233/600]
Discriminator Loss: 0.6645, Generator Loss: 2.1505
D(x): 0.8112, D(G(z)): 0.1970
Epoch: [14/20], Batch Num: [234/600]
Discriminator Loss: 0.7195, Generator Loss: 2.0811
D(x): 0.7599, D(G(z)): 0.1907
Epoch: [14/20], Batch Num: [235/600]
Discriminator Loss: 0.7548, Generator Loss: 2.1978
D(x): 0.7938, D(G(z)): 0.2393
Epoch: [14/20], Batch Num: [236/600]
Discriminator Loss: 0.5695, Generator Loss: 2.1633
D(x): 0.8155, D(G(z)): 0.1910
Epoch: [14/20], Batch Num: [237/600]
Discriminator Loss: 0.5410, Generator Loss: 2.2190
D(x): 0.8075, D(G(z)): 0.1643
Epoch: [14/20], Batch Num: [238/600]
Discriminator Loss: 0.6603, Generator Loss: 2.1377
D(x): 0.7785, D(G(z)): 0.1657
Epoch: [14/20], Batch Num: [239/600]
Discriminator Loss: 0.5406, Generator Loss: 2.0894
D(x): 0.8452, D(G(z)): 0.2033
Epoch: [14/20], Batch Num: [240/600]
Discriminator Loss: 0.6799, Generator Loss: 2.1317
D(x): 0.7827, D(G(z)): 0.1851
Epoch: [14/20], Batch Num: [241/600]
Discriminator Loss: 0.6152, Generator Loss: 2.1224
D(x): 0.8101, D(G(z)): 0.1903
Epoch: [14/20], Batch Num: [242/600]
Discriminator Loss: 0.6914, Generator Loss: 2.2074
D(x): 0.8193, D(G(z)): 0.2313
Epoch: [14/20], Batch Num: [243/600]
Discriminator Loss: 0.7692, Generator Loss: 2.3240
D(x): 0.7861, D(G(z)): 0.2251
Epoch: [14/20], Batch Num: [244/600]
Discriminator Loss: 0.7939, Generator Loss: 2.3677
D(x): 0.7373, D(G(z)): 0.2028
Epoch: [14/20], Batch Num: [245/600]
Discriminator Loss: 0.6170, Generator Loss: 1.9119
D(x): 0.7642, D(G(z)): 0.1779
Epoch: [14/20], Batch Num: [246/600]
Discriminator Loss: 0.6434, Generator Loss: 1.8898
D(x): 0.8024, D(G(z)): 0.2149
Epoch: [14/20], Batch Num: [247/600]
Discriminator Loss: 0.8200, Generator Loss: 2.0067
D(x): 0.7586, D(G(z)): 0.2398
Epoch: [14/20], Batch Num: [248/600]
Discriminator Loss: 0.6435, Generator Loss: 2.0004
D(x): 0.8213, D(G(z)): 0.2397
Epoch: [14/20], Batch Num: [249/600]
Discriminator Loss: 0.5969, Generator Loss: 2.1303
D(x): 0.8306, D(G(z)): 0.2327
Epoch: [14/20], Batch Num: [250/600]
Discriminator Loss: 0.6922, Generator Loss: 2.6983
D(x): 0.7560, D(G(z)): 0.1920
Epoch: [14/20], Batch Num: [251/600]
Discriminator Loss: 0.7362, Generator Loss: 2.1502
D(x): 0.7307, D(G(z)): 0.1531
Epoch: [14/20], Batch Num: [252/600]
Discriminator Loss: 0.7819, Generator Loss: 1.7521
D(x): 0.7211, D(G(z)): 0.1948
Epoch: [14/20], Batch Num: [253/600]
Discriminator Loss: 0.8540, Generator Loss: 1.5912
D(x): 0.7604, D(G(z)): 0.2806
Epoch: [14/20], Batch Num: [254/600]
Discriminator Loss: 0.6448, Generator Loss: 1.9388
D(x): 0.8634, D(G(z)): 0.2945
Epoch: [14/20], Batch Num: [255/600]
Discriminator Loss: 0.6700, Generator Loss: 2.3115
D(x): 0.8215, D(G(z)): 0.2634
Epoch: [14/20], Batch Num: [256/600]
Discriminator Loss: 0.5395, Generator Loss: 2.4545
D(x): 0.8212, D(G(z)): 0.1863
Epoch: [14/20], Batch Num: [257/600]
Discriminator Loss: 0.6593, Generator Loss: 2.8070
D(x): 0.7332, D(G(z)): 0.1499
Epoch: [14/20], Batch Num: [258/600]
Discriminator Loss: 0.7669, Generator Loss: 2.3155
D(x): 0.6816, D(G(z)): 0.1335
Epoch: [14/20], Batch Num: [259/600]
Discriminator Loss: 0.6363, Generator Loss: 1.6204
D(x): 0.7526, D(G(z)): 0.1717
Epoch: [14/20], Batch Num: [260/600]
Discriminator Loss: 0.7379, Generator Loss: 1.7230
D(x): 0.8789, D(G(z)): 0.3272
Epoch: [14/20], Batch Num: [261/600]
Discriminator Loss: 0.7015, Generator Loss: 1.7707
D(x): 0.8075, D(G(z)): 0.2643
Epoch: [14/20], Batch Num: [262/600]
Discriminator Loss: 0.6770, Generator Loss: 1.9644
D(x): 0.8181, D(G(z)): 0.2539
Epoch: [14/20], Batch Num: [263/600]
Discriminator Loss: 0.6883, Generator Loss: 2.1705
D(x): 0.7293, D(G(z)): 0.1984
Epoch: [14/20], Batch Num: [264/600]
Discriminator Loss: 0.5957, Generator Loss: 2.2521
D(x): 0.7828, D(G(z)): 0.1979
Epoch: [14/20], Batch Num: [265/600]
Discriminator Loss: 0.5980, Generator Loss: 2.2248
D(x): 0.7943, D(G(z)): 0.1844
Epoch: [14/20], Batch Num: [266/600]
Discriminator Loss: 0.6914, Generator Loss: 2.4326
D(x): 0.8034, D(G(z)): 0.2415
Epoch: [14/20], Batch Num: [267/600]
Discriminator Loss: 0.6898, Generator Loss: 1.9442
D(x): 0.7217, D(G(z)): 0.1462
Epoch: [14/20], Batch Num: [268/600]
Discriminator Loss: 0.6944, Generator Loss: 1.6829
D(x): 0.7519, D(G(z)): 0.2081
Epoch: [14/20], Batch Num: [269/600]
Discriminator Loss: 0.7561, Generator Loss: 1.8479
D(x): 0.8412, D(G(z)): 0.3269
Epoch: [14/20], Batch Num: [270/600]
Discriminator Loss: 0.6935, Generator Loss: 2.0210
D(x): 0.8089, D(G(z)): 0.2692
Epoch: [14/20], Batch Num: [271/600]
Discriminator Loss: 0.6497, Generator Loss: 2.3309
D(x): 0.7756, D(G(z)): 0.1936
Epoch: [14/20], Batch Num: [272/600]
Discriminator Loss: 0.6398, Generator Loss: 2.2193
D(x): 0.7925, D(G(z)): 0.1830
Epoch: [14/20], Batch Num: [273/600]
Discriminator Loss: 0.8231, Generator Loss: 2.0950
D(x): 0.7180, D(G(z)): 0.1725
Epoch: [14/20], Batch Num: [274/600]
Discriminator Loss: 0.7118, Generator Loss: 1.9007
D(x): 0.7684, D(G(z)): 0.2139
Epoch: [14/20], Batch Num: [275/600]
Discriminator Loss: 0.7688, Generator Loss: 1.8207
D(x): 0.7520, D(G(z)): 0.2307
Epoch: [14/20], Batch Num: [276/600]
Discriminator Loss: 0.7054, Generator Loss: 1.8527
D(x): 0.8291, D(G(z)): 0.2685
Epoch: [14/20], Batch Num: [277/600]
Discriminator Loss: 0.8363, Generator Loss: 1.8947
D(x): 0.7660, D(G(z)): 0.2830
Epoch: [14/20], Batch Num: [278/600]
Discriminator Loss: 0.6782, Generator Loss: 1.9251
D(x): 0.8277, D(G(z)): 0.2636
Epoch: [14/20], Batch Num: [279/600]
Discriminator Loss: 0.5952, Generator Loss: 2.2906
D(x): 0.8022, D(G(z)): 0.1882
Epoch: [14/20], Batch Num: [280/600]
Discriminator Loss: 0.6885, Generator Loss: 2.0073
D(x): 0.7113, D(G(z)): 0.1545
Epoch: [14/20], Batch Num: [281/600]
Discriminator Loss: 0.7089, Generator Loss: 1.7471
D(x): 0.7401, D(G(z)): 0.2024
Epoch: [14/20], Batch Num: [282/600]
Discriminator Loss: 0.7936, Generator Loss: 1.7103
D(x): 0.8073, D(G(z)): 0.2858
Epoch: [14/20], Batch Num: [283/600]
Discriminator Loss: 0.7765, Generator Loss: 1.8796
D(x): 0.7761, D(G(z)): 0.2845
Epoch: [14/20], Batch Num: [284/600]
Discriminator Loss: 0.7570, Generator Loss: 1.7783
D(x): 0.7533, D(G(z)): 0.2328
Epoch: [14/20], Batch Num: [285/600]
Discriminator Loss: 0.7041, Generator Loss: 1.9861
D(x): 0.7459, D(G(z)): 0.2076
Epoch: [14/20], Batch Num: [286/600]
Discriminator Loss: 0.7747, Generator Loss: 1.7604
D(x): 0.7267, D(G(z)): 0.2093
Epoch: [14/20], Batch Num: [287/600]
Discriminator Loss: 0.7479, Generator Loss: 1.7174
D(x): 0.7635, D(G(z)): 0.2431
Epoch: [14/20], Batch Num: [288/600]
Discriminator Loss: 0.6984, Generator Loss: 1.8913
D(x): 0.7779, D(G(z)): 0.2217
Epoch: [14/20], Batch Num: [289/600]
Discriminator Loss: 0.8524, Generator Loss: 1.7063
D(x): 0.7333, D(G(z)): 0.2741
Epoch: [14/20], Batch Num: [290/600]
Discriminator Loss: 0.8264, Generator Loss: 1.6666
D(x): 0.7669, D(G(z)): 0.2808
Epoch: [14/20], Batch Num: [291/600]
Discriminator Loss: 0.7307, Generator Loss: 1.9545
D(x): 0.8029, D(G(z)): 0.2604
Epoch: [14/20], Batch Num: [292/600]
Discriminator Loss: 0.6929, Generator Loss: 2.3909
D(x): 0.7913, D(G(z)): 0.2328
Epoch: [14/20], Batch Num: [293/600]
Discriminator Loss: 0.6438, Generator Loss: 2.1675
D(x): 0.7520, D(G(z)): 0.1911
Epoch: [14/20], Batch Num: [294/600]
Discriminator Loss: 0.8629, Generator Loss: 2.0833
D(x): 0.6949, D(G(z)): 0.1980
Epoch: [14/20], Batch Num: [295/600]
Discriminator Loss: 0.6276, Generator Loss: 1.7309
D(x): 0.7659, D(G(z)): 0.1961
Epoch: [14/20], Batch Num: [296/600]
Discriminator Loss: 0.6693, Generator Loss: 1.7235
D(x): 0.7699, D(G(z)): 0.2291
Epoch: [14/20], Batch Num: [297/600]
Discriminator Loss: 0.6561, Generator Loss: 1.6674
D(x): 0.8242, D(G(z)): 0.2688
Epoch: [14/20], Batch Num: [298/600]
Discriminator Loss: 0.6937, Generator Loss: 1.8118
D(x): 0.8100, D(G(z)): 0.2780
Epoch: [14/20], Batch Num: [299/600]
Discriminator Loss: 0.7880, Generator Loss: 2.1782
D(x): 0.7885, D(G(z)): 0.2822
Epoch: 14, Batch Num: [300/600]

Epoch: [14/20], Batch Num: [300/600]
Discriminator Loss: 0.8275, Generator Loss: 1.9760
D(x): 0.7225, D(G(z)): 0.2197
Epoch: [14/20], Batch Num: [301/600]
Discriminator Loss: 0.8107, Generator Loss: 2.1150
D(x): 0.7075, D(G(z)): 0.2073
Epoch: [14/20], Batch Num: [302/600]
Discriminator Loss: 0.8051, Generator Loss: 1.8613
D(x): 0.7032, D(G(z)): 0.1711
Epoch: [14/20], Batch Num: [303/600]
Discriminator Loss: 0.6742, Generator Loss: 1.8135
D(x): 0.7582, D(G(z)): 0.2192
Epoch: [14/20], Batch Num: [304/600]
Discriminator Loss: 0.8448, Generator Loss: 1.6608
D(x): 0.7577, D(G(z)): 0.2943
Epoch: [14/20], Batch Num: [305/600]
Discriminator Loss: 0.7471, Generator Loss: 1.6253
D(x): 0.8071, D(G(z)): 0.3005
Epoch: [14/20], Batch Num: [306/600]
Discriminator Loss: 0.6308, Generator Loss: 1.7553
D(x): 0.8360, D(G(z)): 0.2660
Epoch: [14/20], Batch Num: [307/600]
Discriminator Loss: 0.6444, Generator Loss: 2.0445
D(x): 0.7958, D(G(z)): 0.2444
Epoch: [14/20], Batch Num: [308/600]
Discriminator Loss: 0.7411, Generator Loss: 2.1695
D(x): 0.7187, D(G(z)): 0.1902
Epoch: [14/20], Batch Num: [309/600]
Discriminator Loss: 0.7083, Generator Loss: 2.0255
D(x): 0.7532, D(G(z)): 0.2009
Epoch: [14/20], Batch Num: [310/600]
Discriminator Loss: 0.6984, Generator Loss: 2.0677
D(x): 0.7473, D(G(z)): 0.1898
Epoch: [14/20], Batch Num: [311/600]
Discriminator Loss: 0.6372, Generator Loss: 1.9349
D(x): 0.7515, D(G(z)): 0.1867
Epoch: [14/20], Batch Num: [312/600]
Discriminator Loss: 0.5431, Generator Loss: 1.9775
D(x): 0.7993, D(G(z)): 0.1949
Epoch: [14/20], Batch Num: [313/600]
Discriminator Loss: 0.6678, Generator Loss: 1.8123
D(x): 0.7980, D(G(z)): 0.2458
Epoch: [14/20], Batch Num: [314/600]
Discriminator Loss: 0.6103, Generator Loss: 1.9107
D(x): 0.8268, D(G(z)): 0.2327
Epoch: [14/20], Batch Num: [315/600]
Discriminator Loss: 0.6427, Generator Loss: 2.1526
D(x): 0.8036, D(G(z)): 0.2371
Epoch: [14/20], Batch Num: [316/600]
Discriminator Loss: 0.8035, Generator Loss: 2.2097
D(x): 0.7521, D(G(z)): 0.2176
Epoch: [14/20], Batch Num: [317/600]
Discriminator Loss: 0.6427, Generator Loss: 2.4242
D(x): 0.7989, D(G(z)): 0.1918
Epoch: [14/20], Batch Num: [318/600]
Discriminator Loss: 0.6588, Generator Loss: 2.5485
D(x): 0.7745, D(G(z)): 0.1822
Epoch: [14/20], Batch Num: [319/600]
Discriminator Loss: 0.6827, Generator Loss: 2.1295
D(x): 0.7278, D(G(z)): 0.1402
Epoch: [14/20], Batch Num: [320/600]
Discriminator Loss: 0.7261, Generator Loss: 1.8457
D(x): 0.7604, D(G(z)): 0.1943
Epoch: [14/20], Batch Num: [321/600]
Discriminator Loss: 0.6513, Generator Loss: 1.6283
D(x): 0.7987, D(G(z)): 0.2366
Epoch: [14/20], Batch Num: [322/600]
Discriminator Loss: 0.5821, Generator Loss: 1.8071
D(x): 0.8429, D(G(z)): 0.2583
Epoch: [14/20], Batch Num: [323/600]
Discriminator Loss: 0.6993, Generator Loss: 1.8506
D(x): 0.8121, D(G(z)): 0.2773
Epoch: [14/20], Batch Num: [324/600]
Discriminator Loss: 0.8308, Generator Loss: 2.2819
D(x): 0.7956, D(G(z)): 0.2981
Epoch: [14/20], Batch Num: [325/600]
Discriminator Loss: 0.8178, Generator Loss: 2.3444
D(x): 0.7175, D(G(z)): 0.1940
Epoch: [14/20], Batch Num: [326/600]
Discriminator Loss: 0.9085, Generator Loss: 2.1235
D(x): 0.6786, D(G(z)): 0.1781
Epoch: [14/20], Batch Num: [327/600]
Discriminator Loss: 0.9308, Generator Loss: 1.8882
D(x): 0.6758, D(G(z)): 0.1867
Epoch: [14/20], Batch Num: [328/600]
Discriminator Loss: 0.6510, Generator Loss: 1.5161
D(x): 0.7545, D(G(z)): 0.1893
Epoch: [14/20], Batch Num: [329/600]
Discriminator Loss: 0.7538, Generator Loss: 1.5358
D(x): 0.8113, D(G(z)): 0.3117
Epoch: [14/20], Batch Num: [330/600]
Discriminator Loss: 0.7213, Generator Loss: 1.5279
D(x): 0.8091, D(G(z)): 0.3143
Epoch: [14/20], Batch Num: [331/600]
Discriminator Loss: 0.7455, Generator Loss: 1.7966
D(x): 0.8010, D(G(z)): 0.3144
Epoch: [14/20], Batch Num: [332/600]
Discriminator Loss: 0.7166, Generator Loss: 1.8883
D(x): 0.7727, D(G(z)): 0.2583
Epoch: [14/20], Batch Num: [333/600]
Discriminator Loss: 0.7272, Generator Loss: 2.2599
D(x): 0.7376, D(G(z)): 0.2025
Epoch: [14/20], Batch Num: [334/600]
Discriminator Loss: 0.7907, Generator Loss: 1.9408
D(x): 0.6754, D(G(z)): 0.1574
Epoch: [14/20], Batch Num: [335/600]
Discriminator Loss: 0.6557, Generator Loss: 1.8696
D(x): 0.7496, D(G(z)): 0.1803
Epoch: [14/20], Batch Num: [336/600]
Discriminator Loss: 0.7915, Generator Loss: 1.4794
D(x): 0.7595, D(G(z)): 0.2624
Epoch: [14/20], Batch Num: [337/600]
Discriminator Loss: 0.6759, Generator Loss: 1.6844
D(x): 0.8404, D(G(z)): 0.2883
Epoch: [14/20], Batch Num: [338/600]
Discriminator Loss: 0.7633, Generator Loss: 1.9887
D(x): 0.8447, D(G(z)): 0.2841
Epoch: [14/20], Batch Num: [339/600]
Discriminator Loss: 0.6583, Generator Loss: 2.0378
D(x): 0.7861, D(G(z)): 0.2117
Epoch: [14/20], Batch Num: [340/600]
Discriminator Loss: 0.5919, Generator Loss: 2.4895
D(x): 0.7899, D(G(z)): 0.1794
Epoch: [14/20], Batch Num: [341/600]
Discriminator Loss: 0.6466, Generator Loss: 2.6945
D(x): 0.7513, D(G(z)): 0.1457
Epoch: [14/20], Batch Num: [342/600]
Discriminator Loss: 0.5446, Generator Loss: 1.8735
D(x): 0.7700, D(G(z)): 0.1194
Epoch: [14/20], Batch Num: [343/600]
Discriminator Loss: 0.6983, Generator Loss: 1.8106
D(x): 0.8055, D(G(z)): 0.2474
Epoch: [14/20], Batch Num: [344/600]
Discriminator Loss: 0.7741, Generator Loss: 1.6591
D(x): 0.7772, D(G(z)): 0.2594
Epoch: [14/20], Batch Num: [345/600]
Discriminator Loss: 0.6183, Generator Loss: 1.8677
D(x): 0.8732, D(G(z)): 0.2914
Epoch: [14/20], Batch Num: [346/600]
Discriminator Loss: 0.6347, Generator Loss: 1.9614
D(x): 0.8012, D(G(z)): 0.2343
Epoch: [14/20], Batch Num: [347/600]
Discriminator Loss: 0.5957, Generator Loss: 2.6503
D(x): 0.8331, D(G(z)): 0.2254
Epoch: [14/20], Batch Num: [348/600]
Discriminator Loss: 0.8570, Generator Loss: 2.3433
D(x): 0.6718, D(G(z)): 0.1714
Epoch: [14/20], Batch Num: [349/600]
Discriminator Loss: 0.7849, Generator Loss: 2.1205
D(x): 0.6663, D(G(z)): 0.1400
Epoch: [14/20], Batch Num: [350/600]
Discriminator Loss: 0.7432, Generator Loss: 1.6843
D(x): 0.7340, D(G(z)): 0.2146
Epoch: [14/20], Batch Num: [351/600]
Discriminator Loss: 0.6317, Generator Loss: 1.5449
D(x): 0.8057, D(G(z)): 0.2397
Epoch: [14/20], Batch Num: [352/600]
Discriminator Loss: 0.8190, Generator Loss: 1.6463
D(x): 0.8467, D(G(z)): 0.3446
Epoch: [14/20], Batch Num: [353/600]
Discriminator Loss: 0.8469, Generator Loss: 1.8594
D(x): 0.8344, D(G(z)): 0.3401
Epoch: [14/20], Batch Num: [354/600]
Discriminator Loss: 0.6702, Generator Loss: 2.1461
D(x): 0.7712, D(G(z)): 0.2262
Epoch: [14/20], Batch Num: [355/600]
Discriminator Loss: 0.7432, Generator Loss: 2.3239
D(x): 0.7445, D(G(z)): 0.1973
Epoch: [14/20], Batch Num: [356/600]
Discriminator Loss: 0.8171, Generator Loss: 2.2745
D(x): 0.6380, D(G(z)): 0.1641
Epoch: [14/20], Batch Num: [357/600]
Discriminator Loss: 0.6786, Generator Loss: 2.1073
D(x): 0.7489, D(G(z)): 0.1908
Epoch: [14/20], Batch Num: [358/600]
Discriminator Loss: 0.5268, Generator Loss: 1.8027
D(x): 0.8285, D(G(z)): 0.2115
Epoch: [14/20], Batch Num: [359/600]
Discriminator Loss: 0.8171, Generator Loss: 1.7151
D(x): 0.7809, D(G(z)): 0.2508
Epoch: [14/20], Batch Num: [360/600]
Discriminator Loss: 0.5746, Generator Loss: 1.6398
D(x): 0.8503, D(G(z)): 0.2309
Epoch: [14/20], Batch Num: [361/600]
Discriminator Loss: 0.6496, Generator Loss: 2.1088
D(x): 0.8024, D(G(z)): 0.2398
Epoch: [14/20], Batch Num: [362/600]
Discriminator Loss: 0.5440, Generator Loss: 1.9755
D(x): 0.8243, D(G(z)): 0.2131
Epoch: [14/20], Batch Num: [363/600]
Discriminator Loss: 0.6862, Generator Loss: 2.3811
D(x): 0.7898, D(G(z)): 0.2128
Epoch: [14/20], Batch Num: [364/600]
Discriminator Loss: 0.4984, Generator Loss: 2.3866
D(x): 0.8188, D(G(z)): 0.1508
Epoch: [14/20], Batch Num: [365/600]
Discriminator Loss: 0.5597, Generator Loss: 2.0030
D(x): 0.8081, D(G(z)): 0.1840
Epoch: [14/20], Batch Num: [366/600]
Discriminator Loss: 0.6453, Generator Loss: 2.1722
D(x): 0.7842, D(G(z)): 0.2002
Epoch: [14/20], Batch Num: [367/600]
Discriminator Loss: 0.7054, Generator Loss: 2.3360
D(x): 0.8218, D(G(z)): 0.2443
Epoch: [14/20], Batch Num: [368/600]
Discriminator Loss: 0.7313, Generator Loss: 2.1587
D(x): 0.7356, D(G(z)): 0.1882
Epoch: [14/20], Batch Num: [369/600]
Discriminator Loss: 0.6730, Generator Loss: 2.0685
D(x): 0.7462, D(G(z)): 0.1651
Epoch: [14/20], Batch Num: [370/600]
Discriminator Loss: 0.7185, Generator Loss: 1.9894
D(x): 0.7574, D(G(z)): 0.1998
Epoch: [14/20], Batch Num: [371/600]
Discriminator Loss: 0.6821, Generator Loss: 1.9033
D(x): 0.8310, D(G(z)): 0.2625
Epoch: [14/20], Batch Num: [372/600]
Discriminator Loss: 0.6055, Generator Loss: 2.1913
D(x): 0.8595, D(G(z)): 0.2554
Epoch: [14/20], Batch Num: [373/600]
Discriminator Loss: 0.5842, Generator Loss: 2.3439
D(x): 0.8116, D(G(z)): 0.2065
Epoch: [14/20], Batch Num: [374/600]
Discriminator Loss: 0.6261, Generator Loss: 2.3083
D(x): 0.7585, D(G(z)): 0.1727
Epoch: [14/20], Batch Num: [375/600]
Discriminator Loss: 0.6550, Generator Loss: 2.2393
D(x): 0.7609, D(G(z)): 0.1792
Epoch: [14/20], Batch Num: [376/600]
Discriminator Loss: 0.8900, Generator Loss: 1.8492
D(x): 0.7340, D(G(z)): 0.2487
Epoch: [14/20], Batch Num: [377/600]
Discriminator Loss: 0.6448, Generator Loss: 1.9417
D(x): 0.8191, D(G(z)): 0.2416
Epoch: [14/20], Batch Num: [378/600]
Discriminator Loss: 0.8564, Generator Loss: 1.9224
D(x): 0.7624, D(G(z)): 0.2837
Epoch: [14/20], Batch Num: [379/600]
Discriminator Loss: 0.8188, Generator Loss: 2.0416
D(x): 0.7764, D(G(z)): 0.2655
Epoch: [14/20], Batch Num: [380/600]
Discriminator Loss: 0.6576, Generator Loss: 2.1392
D(x): 0.7734, D(G(z)): 0.1892
Epoch: [14/20], Batch Num: [381/600]
Discriminator Loss: 0.6946, Generator Loss: 1.9759
D(x): 0.7446, D(G(z)): 0.1890
Epoch: [14/20], Batch Num: [382/600]
Discriminator Loss: 0.7703, Generator Loss: 1.9610
D(x): 0.7862, D(G(z)): 0.2195
Epoch: [14/20], Batch Num: [383/600]
Discriminator Loss: 0.6941, Generator Loss: 1.8927
D(x): 0.7622, D(G(z)): 0.2002
Epoch: [14/20], Batch Num: [384/600]
Discriminator Loss: 0.6666, Generator Loss: 1.7354
D(x): 0.7851, D(G(z)): 0.2050
Epoch: [14/20], Batch Num: [385/600]
Discriminator Loss: 0.7200, Generator Loss: 1.9243
D(x): 0.7895, D(G(z)): 0.2412
Epoch: [14/20], Batch Num: [386/600]
Discriminator Loss: 0.7571, Generator Loss: 1.9004
D(x): 0.7982, D(G(z)): 0.2867
Epoch: [14/20], Batch Num: [387/600]
Discriminator Loss: 0.7693, Generator Loss: 1.8184
D(x): 0.7450, D(G(z)): 0.2252
Epoch: [14/20], Batch Num: [388/600]
Discriminator Loss: 0.6609, Generator Loss: 1.8285
D(x): 0.7796, D(G(z)): 0.2008
Epoch: [14/20], Batch Num: [389/600]
Discriminator Loss: 0.7486, Generator Loss: 1.8171
D(x): 0.7406, D(G(z)): 0.2093
Epoch: [14/20], Batch Num: [390/600]
Discriminator Loss: 0.7377, Generator Loss: 1.7537
D(x): 0.7772, D(G(z)): 0.2280
Epoch: [14/20], Batch Num: [391/600]
Discriminator Loss: 0.6828, Generator Loss: 1.7147
D(x): 0.8105, D(G(z)): 0.2737
Epoch: [14/20], Batch Num: [392/600]
Discriminator Loss: 0.5538, Generator Loss: 1.8974
D(x): 0.8167, D(G(z)): 0.1933
Epoch: [14/20], Batch Num: [393/600]
Discriminator Loss: 0.6696, Generator Loss: 1.7963
D(x): 0.7732, D(G(z)): 0.1932
Epoch: [14/20], Batch Num: [394/600]
Discriminator Loss: 0.7782, Generator Loss: 1.9840
D(x): 0.7456, D(G(z)): 0.2323
Epoch: [14/20], Batch Num: [395/600]
Discriminator Loss: 0.7300, Generator Loss: 1.9458
D(x): 0.8167, D(G(z)): 0.2540
Epoch: [14/20], Batch Num: [396/600]
Discriminator Loss: 0.5989, Generator Loss: 2.5373
D(x): 0.8511, D(G(z)): 0.2401
Epoch: [14/20], Batch Num: [397/600]
Discriminator Loss: 0.7429, Generator Loss: 2.4353
D(x): 0.7310, D(G(z)): 0.1747
Epoch: [14/20], Batch Num: [398/600]
Discriminator Loss: 0.5634, Generator Loss: 2.4263
D(x): 0.7905, D(G(z)): 0.1609
Epoch: [14/20], Batch Num: [399/600]
Discriminator Loss: 0.5848, Generator Loss: 2.3321
D(x): 0.7913, D(G(z)): 0.1549
Epoch: 14, Batch Num: [400/600]

Epoch: [14/20], Batch Num: [400/600]
Discriminator Loss: 0.6083, Generator Loss: 1.9906
D(x): 0.8080, D(G(z)): 0.2128
Epoch: [14/20], Batch Num: [401/600]
Discriminator Loss: 0.5746, Generator Loss: 1.8264
D(x): 0.7881, D(G(z)): 0.1733
Epoch: [14/20], Batch Num: [402/600]
Discriminator Loss: 0.6458, Generator Loss: 1.8748
D(x): 0.8209, D(G(z)): 0.2476
Epoch: [14/20], Batch Num: [403/600]
Discriminator Loss: 0.6371, Generator Loss: 2.0872
D(x): 0.8611, D(G(z)): 0.2752
Epoch: [14/20], Batch Num: [404/600]
Discriminator Loss: 0.5672, Generator Loss: 2.7094
D(x): 0.8265, D(G(z)): 0.1954
Epoch: [14/20], Batch Num: [405/600]
Discriminator Loss: 0.5523, Generator Loss: 2.9571
D(x): 0.8242, D(G(z)): 0.1768
Epoch: [14/20], Batch Num: [406/600]
Discriminator Loss: 0.6662, Generator Loss: 2.8765
D(x): 0.7703, D(G(z)): 0.1577
Epoch: [14/20], Batch Num: [407/600]
Discriminator Loss: 0.6913, Generator Loss: 2.6827
D(x): 0.7450, D(G(z)): 0.1291
Epoch: [14/20], Batch Num: [408/600]
Discriminator Loss: 0.6504, Generator Loss: 2.1365
D(x): 0.7733, D(G(z)): 0.1524
Epoch: [14/20], Batch Num: [409/600]
Discriminator Loss: 0.6652, Generator Loss: 1.8194
D(x): 0.8218, D(G(z)): 0.2332
Epoch: [14/20], Batch Num: [410/600]
Discriminator Loss: 0.5240, Generator Loss: 2.1628
D(x): 0.8645, D(G(z)): 0.2182
Epoch: [14/20], Batch Num: [411/600]
Discriminator Loss: 0.7365, Generator Loss: 2.5980
D(x): 0.8227, D(G(z)): 0.2580
Epoch: [14/20], Batch Num: [412/600]
Discriminator Loss: 0.6928, Generator Loss: 2.4799
D(x): 0.7401, D(G(z)): 0.1549
Epoch: [14/20], Batch Num: [413/600]
Discriminator Loss: 0.6474, Generator Loss: 2.2130
D(x): 0.7782, D(G(z)): 0.1636
Epoch: [14/20], Batch Num: [414/600]
Discriminator Loss: 0.6234, Generator Loss: 2.0470
D(x): 0.7997, D(G(z)): 0.1851
Epoch: [14/20], Batch Num: [415/600]
Discriminator Loss: 0.7997, Generator Loss: 1.8345
D(x): 0.8093, D(G(z)): 0.2572
Epoch: [14/20], Batch Num: [416/600]
Discriminator Loss: 0.8027, Generator Loss: 2.1490
D(x): 0.7996, D(G(z)): 0.2632
Epoch: [14/20], Batch Num: [417/600]
Discriminator Loss: 0.8098, Generator Loss: 2.3750
D(x): 0.8117, D(G(z)): 0.2458
Epoch: [14/20], Batch Num: [418/600]
Discriminator Loss: 0.7293, Generator Loss: 2.5570
D(x): 0.7516, D(G(z)): 0.1839
Epoch: [14/20], Batch Num: [419/600]
Discriminator Loss: 0.6687, Generator Loss: 2.3061
D(x): 0.7714, D(G(z)): 0.1819
Epoch: [14/20], Batch Num: [420/600]
Discriminator Loss: 0.6706, Generator Loss: 1.8365
D(x): 0.7192, D(G(z)): 0.1601
Epoch: [14/20], Batch Num: [421/600]
Discriminator Loss: 0.6841, Generator Loss: 1.4831
D(x): 0.7875, D(G(z)): 0.2359
Epoch: [14/20], Batch Num: [422/600]
Discriminator Loss: 0.7273, Generator Loss: 1.4759
D(x): 0.8187, D(G(z)): 0.2962
Epoch: [14/20], Batch Num: [423/600]
Discriminator Loss: 0.6689, Generator Loss: 1.9004
D(x): 0.8596, D(G(z)): 0.3228
Epoch: [14/20], Batch Num: [424/600]
Discriminator Loss: 0.6762, Generator Loss: 2.2583
D(x): 0.8029, D(G(z)): 0.2509
Epoch: [14/20], Batch Num: [425/600]
Discriminator Loss: 0.6636, Generator Loss: 2.5093
D(x): 0.7454, D(G(z)): 0.1602
Epoch: [14/20], Batch Num: [426/600]
Discriminator Loss: 0.9076, Generator Loss: 2.2877
D(x): 0.6370, D(G(z)): 0.1632
Epoch: [14/20], Batch Num: [427/600]
Discriminator Loss: 0.9472, Generator Loss: 1.6363
D(x): 0.6952, D(G(z)): 0.2006
Epoch: [14/20], Batch Num: [428/600]
Discriminator Loss: 0.6949, Generator Loss: 1.6044
D(x): 0.7997, D(G(z)): 0.2493
Epoch: [14/20], Batch Num: [429/600]
Discriminator Loss: 0.8376, Generator Loss: 1.7903
D(x): 0.8684, D(G(z)): 0.3634
Epoch: [14/20], Batch Num: [430/600]
Discriminator Loss: 0.6368, Generator Loss: 2.1048
D(x): 0.8415, D(G(z)): 0.2695
Epoch: [14/20], Batch Num: [431/600]
Discriminator Loss: 0.7073, Generator Loss: 2.2252
D(x): 0.7239, D(G(z)): 0.1562
Epoch: [14/20], Batch Num: [432/600]
Discriminator Loss: 0.5639, Generator Loss: 2.2864
D(x): 0.8043, D(G(z)): 0.1740
Epoch: [14/20], Batch Num: [433/600]
Discriminator Loss: 0.6159, Generator Loss: 2.3970
D(x): 0.7508, D(G(z)): 0.1303
Epoch: [14/20], Batch Num: [434/600]
Discriminator Loss: 0.7703, Generator Loss: 1.6530
D(x): 0.6723, D(G(z)): 0.1460
Epoch: [14/20], Batch Num: [435/600]
Discriminator Loss: 0.6419, Generator Loss: 1.4018
D(x): 0.7965, D(G(z)): 0.2211
Epoch: [14/20], Batch Num: [436/600]
Discriminator Loss: 0.6970, Generator Loss: 1.4358
D(x): 0.8512, D(G(z)): 0.2926
Epoch: [14/20], Batch Num: [437/600]
Discriminator Loss: 0.6569, Generator Loss: 1.7375
D(x): 0.8544, D(G(z)): 0.2934
Epoch: [14/20], Batch Num: [438/600]
Discriminator Loss: 0.6290, Generator Loss: 2.0326
D(x): 0.8086, D(G(z)): 0.2379
Epoch: [14/20], Batch Num: [439/600]
Discriminator Loss: 0.5116, Generator Loss: 2.1303
D(x): 0.8296, D(G(z)): 0.1703
Epoch: [14/20], Batch Num: [440/600]
Discriminator Loss: 0.6337, Generator Loss: 2.1851
D(x): 0.7524, D(G(z)): 0.1570
Epoch: [14/20], Batch Num: [441/600]
Discriminator Loss: 0.7363, Generator Loss: 2.2514
D(x): 0.7280, D(G(z)): 0.1732
Epoch: [14/20], Batch Num: [442/600]
Discriminator Loss: 0.8274, Generator Loss: 2.1196
D(x): 0.7402, D(G(z)): 0.2253
Epoch: [14/20], Batch Num: [443/600]
Discriminator Loss: 0.6276, Generator Loss: 1.9653
D(x): 0.7870, D(G(z)): 0.2020
Epoch: [14/20], Batch Num: [444/600]
Discriminator Loss: 0.5511, Generator Loss: 1.6793
D(x): 0.8447, D(G(z)): 0.2341
Epoch: [14/20], Batch Num: [445/600]
Discriminator Loss: 0.8096, Generator Loss: 1.9132
D(x): 0.7668, D(G(z)): 0.2824
Epoch: [14/20], Batch Num: [446/600]
Discriminator Loss: 0.8474, Generator Loss: 2.3241
D(x): 0.8074, D(G(z)): 0.2787
Epoch: [14/20], Batch Num: [447/600]
Discriminator Loss: 0.6522, Generator Loss: 2.1348
D(x): 0.7970, D(G(z)): 0.1943
Epoch: [14/20], Batch Num: [448/600]
Discriminator Loss: 0.6532, Generator Loss: 2.5008
D(x): 0.7870, D(G(z)): 0.1955
Epoch: [14/20], Batch Num: [449/600]
Discriminator Loss: 0.7325, Generator Loss: 2.3143
D(x): 0.7250, D(G(z)): 0.1719
Epoch: [14/20], Batch Num: [450/600]
Discriminator Loss: 0.5355, Generator Loss: 2.1432
D(x): 0.7963, D(G(z)): 0.1615
Epoch: [14/20], Batch Num: [451/600]
Discriminator Loss: 0.6138, Generator Loss: 1.9352
D(x): 0.8209, D(G(z)): 0.2019
Epoch: [14/20], Batch Num: [452/600]
Discriminator Loss: 0.8285, Generator Loss: 2.0279
D(x): 0.7897, D(G(z)): 0.2599
Epoch: [14/20], Batch Num: [453/600]
Discriminator Loss: 0.6128, Generator Loss: 2.0920
D(x): 0.7697, D(G(z)): 0.1626
Epoch: [14/20], Batch Num: [454/600]
Discriminator Loss: 0.6642, Generator Loss: 2.0413
D(x): 0.8351, D(G(z)): 0.2352
Epoch: [14/20], Batch Num: [455/600]
Discriminator Loss: 0.6618, Generator Loss: 2.1550
D(x): 0.8215, D(G(z)): 0.1870
Epoch: [14/20], Batch Num: [456/600]
Discriminator Loss: 0.4846, Generator Loss: 2.3669
D(x): 0.8564, D(G(z)): 0.1723
Epoch: [14/20], Batch Num: [457/600]
Discriminator Loss: 0.6414, Generator Loss: 2.5447
D(x): 0.7712, D(G(z)): 0.1701
Epoch: [14/20], Batch Num: [458/600]
Discriminator Loss: 0.7869, Generator Loss: 2.0785
D(x): 0.7213, D(G(z)): 0.1586
Epoch: [14/20], Batch Num: [459/600]
Discriminator Loss: 0.6722, Generator Loss: 1.9993
D(x): 0.7938, D(G(z)): 0.2043
Epoch: [14/20], Batch Num: [460/600]
Discriminator Loss: 0.8207, Generator Loss: 1.9344
D(x): 0.7780, D(G(z)): 0.2513
Epoch: [14/20], Batch Num: [461/600]
Discriminator Loss: 0.7556, Generator Loss: 2.1180
D(x): 0.8120, D(G(z)): 0.2795
Epoch: [14/20], Batch Num: [462/600]
Discriminator Loss: 0.6915, Generator Loss: 2.3682
D(x): 0.8171, D(G(z)): 0.2622
Epoch: [14/20], Batch Num: [463/600]
Discriminator Loss: 0.7404, Generator Loss: 2.4229
D(x): 0.7236, D(G(z)): 0.1778
Epoch: [14/20], Batch Num: [464/600]
Discriminator Loss: 0.8312, Generator Loss: 2.0801
D(x): 0.6874, D(G(z)): 0.1737
Epoch: [14/20], Batch Num: [465/600]
Discriminator Loss: 0.6574, Generator Loss: 1.8280
D(x): 0.8152, D(G(z)): 0.2356
Epoch: [14/20], Batch Num: [466/600]
Discriminator Loss: 0.7267, Generator Loss: 1.6159
D(x): 0.7546, D(G(z)): 0.2305
Epoch: [14/20], Batch Num: [467/600]
Discriminator Loss: 0.7526, Generator Loss: 1.7419
D(x): 0.8210, D(G(z)): 0.2690
Epoch: [14/20], Batch Num: [468/600]
Discriminator Loss: 0.6542, Generator Loss: 2.0588
D(x): 0.7922, D(G(z)): 0.2386
Epoch: [14/20], Batch Num: [469/600]
Discriminator Loss: 0.7442, Generator Loss: 2.0898
D(x): 0.7392, D(G(z)): 0.2226
Epoch: [14/20], Batch Num: [470/600]
Discriminator Loss: 0.7298, Generator Loss: 2.0425
D(x): 0.7393, D(G(z)): 0.1982
Epoch: [14/20], Batch Num: [471/600]
Discriminator Loss: 0.6800, Generator Loss: 1.9557
D(x): 0.8157, D(G(z)): 0.2350
Epoch: [14/20], Batch Num: [472/600]
Discriminator Loss: 0.7630, Generator Loss: 2.1208
D(x): 0.7837, D(G(z)): 0.2319
Epoch: [14/20], Batch Num: [473/600]
Discriminator Loss: 0.5844, Generator Loss: 2.4109
D(x): 0.8129, D(G(z)): 0.1982
Epoch: [14/20], Batch Num: [474/600]
Discriminator Loss: 0.5126, Generator Loss: 2.8366
D(x): 0.8375, D(G(z)): 0.1840
Epoch: [14/20], Batch Num: [475/600]
Discriminator Loss: 0.6913, Generator Loss: 2.5319
D(x): 0.7362, D(G(z)): 0.1316
Epoch: [14/20], Batch Num: [476/600]
Discriminator Loss: 0.7053, Generator Loss: 2.1142
D(x): 0.7132, D(G(z)): 0.1422
Epoch: [14/20], Batch Num: [477/600]
Discriminator Loss: 0.6410, Generator Loss: 1.6982
D(x): 0.7947, D(G(z)): 0.2150
Epoch: [14/20], Batch Num: [478/600]
Discriminator Loss: 0.7391, Generator Loss: 1.6711
D(x): 0.8814, D(G(z)): 0.3252
Epoch: [14/20], Batch Num: [479/600]
Discriminator Loss: 0.5944, Generator Loss: 2.1130
D(x): 0.8691, D(G(z)): 0.2657
Epoch: [14/20], Batch Num: [480/600]
Discriminator Loss: 0.6972, Generator Loss: 2.5886
D(x): 0.7810, D(G(z)): 0.2226
Epoch: [14/20], Batch Num: [481/600]
Discriminator Loss: 0.6707, Generator Loss: 2.4445
D(x): 0.7292, D(G(z)): 0.1575
Epoch: [14/20], Batch Num: [482/600]
Discriminator Loss: 0.7062, Generator Loss: 2.0352
D(x): 0.7186, D(G(z)): 0.1453
Epoch: [14/20], Batch Num: [483/600]
Discriminator Loss: 0.6038, Generator Loss: 2.1734
D(x): 0.7857, D(G(z)): 0.1719
Epoch: [14/20], Batch Num: [484/600]
Discriminator Loss: 0.7539, Generator Loss: 1.8685
D(x): 0.7852, D(G(z)): 0.2461
Epoch: [14/20], Batch Num: [485/600]
Discriminator Loss: 0.6766, Generator Loss: 1.6755
D(x): 0.7775, D(G(z)): 0.2117
Epoch: [14/20], Batch Num: [486/600]
Discriminator Loss: 0.5533, Generator Loss: 1.7591
D(x): 0.8587, D(G(z)): 0.2422
Epoch: [14/20], Batch Num: [487/600]
Discriminator Loss: 0.5664, Generator Loss: 2.1348
D(x): 0.8588, D(G(z)): 0.2458
Epoch: [14/20], Batch Num: [488/600]
Discriminator Loss: 0.5963, Generator Loss: 2.3531
D(x): 0.8318, D(G(z)): 0.2293
Epoch: [14/20], Batch Num: [489/600]
Discriminator Loss: 0.7237, Generator Loss: 2.5807
D(x): 0.7568, D(G(z)): 0.1796
Epoch: [14/20], Batch Num: [490/600]
Discriminator Loss: 0.6884, Generator Loss: 2.4562
D(x): 0.7517, D(G(z)): 0.1548
Epoch: [14/20], Batch Num: [491/600]
Discriminator Loss: 0.6720, Generator Loss: 2.3004
D(x): 0.7407, D(G(z)): 0.1516
Epoch: [14/20], Batch Num: [492/600]
Discriminator Loss: 0.7479, Generator Loss: 1.8622
D(x): 0.7485, D(G(z)): 0.1909
Epoch: [14/20], Batch Num: [493/600]
Discriminator Loss: 0.5876, Generator Loss: 1.6376
D(x): 0.8653, D(G(z)): 0.2621
Epoch: [14/20], Batch Num: [494/600]
Discriminator Loss: 0.6748, Generator Loss: 1.8397
D(x): 0.8751, D(G(z)): 0.2741
Epoch: [14/20], Batch Num: [495/600]
Discriminator Loss: 0.7397, Generator Loss: 2.2492
D(x): 0.7787, D(G(z)): 0.2373
Epoch: [14/20], Batch Num: [496/600]
Discriminator Loss: 0.6474, Generator Loss: 2.1496
D(x): 0.7517, D(G(z)): 0.1667
Epoch: [14/20], Batch Num: [497/600]
Discriminator Loss: 0.6726, Generator Loss: 2.0443
D(x): 0.7776, D(G(z)): 0.1905
Epoch: [14/20], Batch Num: [498/600]
Discriminator Loss: 0.8453, Generator Loss: 2.0101
D(x): 0.6885, D(G(z)): 0.1726
Epoch: [14/20], Batch Num: [499/600]
Discriminator Loss: 0.6678, Generator Loss: 1.6589
D(x): 0.7922, D(G(z)): 0.2271
Epoch: 14, Batch Num: [500/600]

Epoch: [14/20], Batch Num: [500/600]
Discriminator Loss: 0.7349, Generator Loss: 1.7015
D(x): 0.8327, D(G(z)): 0.3154
Epoch: [14/20], Batch Num: [501/600]
Discriminator Loss: 0.6690, Generator Loss: 2.0084
D(x): 0.8356, D(G(z)): 0.2610
Epoch: [14/20], Batch Num: [502/600]
Discriminator Loss: 0.6283, Generator Loss: 2.2212
D(x): 0.8254, D(G(z)): 0.2369
Epoch: [14/20], Batch Num: [503/600]
Discriminator Loss: 0.5790, Generator Loss: 2.2830
D(x): 0.7803, D(G(z)): 0.1565
Epoch: [14/20], Batch Num: [504/600]
Discriminator Loss: 0.6572, Generator Loss: 2.2246
D(x): 0.7427, D(G(z)): 0.1340
Epoch: [14/20], Batch Num: [505/600]
Discriminator Loss: 0.6838, Generator Loss: 2.0858
D(x): 0.7633, D(G(z)): 0.1774
Epoch: [14/20], Batch Num: [506/600]
Discriminator Loss: 0.6926, Generator Loss: 1.8542
D(x): 0.7415, D(G(z)): 0.2004
Epoch: [14/20], Batch Num: [507/600]
Discriminator Loss: 0.6388, Generator Loss: 1.6060
D(x): 0.8371, D(G(z)): 0.2612
Epoch: [14/20], Batch Num: [508/600]
Discriminator Loss: 0.6912, Generator Loss: 1.8291
D(x): 0.8122, D(G(z)): 0.2602
Epoch: [14/20], Batch Num: [509/600]
Discriminator Loss: 0.7020, Generator Loss: 1.9942
D(x): 0.8278, D(G(z)): 0.2827
Epoch: [14/20], Batch Num: [510/600]
Discriminator Loss: 0.7233, Generator Loss: 2.2812
D(x): 0.7442, D(G(z)): 0.2062
Epoch: [14/20], Batch Num: [511/600]
Discriminator Loss: 0.8041, Generator Loss: 2.1313
D(x): 0.7123, D(G(z)): 0.2002
Epoch: [14/20], Batch Num: [512/600]
Discriminator Loss: 0.9857, Generator Loss: 1.7990
D(x): 0.6635, D(G(z)): 0.2226
Epoch: [14/20], Batch Num: [513/600]
Discriminator Loss: 0.7132, Generator Loss: 1.6512
D(x): 0.7713, D(G(z)): 0.2438
Epoch: [14/20], Batch Num: [514/600]
Discriminator Loss: 0.8252, Generator Loss: 1.7445
D(x): 0.7651, D(G(z)): 0.3039
Epoch: [14/20], Batch Num: [515/600]
Discriminator Loss: 0.6377, Generator Loss: 1.5816
D(x): 0.8194, D(G(z)): 0.2652
Epoch: [14/20], Batch Num: [516/600]
Discriminator Loss: 0.8128, Generator Loss: 1.9322
D(x): 0.8015, D(G(z)): 0.3017
Epoch: [14/20], Batch Num: [517/600]
Discriminator Loss: 0.7411, Generator Loss: 2.1003
D(x): 0.7958, D(G(z)): 0.2691
Epoch: [14/20], Batch Num: [518/600]
Discriminator Loss: 0.6581, Generator Loss: 2.3071
D(x): 0.7431, D(G(z)): 0.1810
Epoch: [14/20], Batch Num: [519/600]
Discriminator Loss: 0.7835, Generator Loss: 2.1552
D(x): 0.7012, D(G(z)): 0.1752
Epoch: [14/20], Batch Num: [520/600]
Discriminator Loss: 0.8357, Generator Loss: 1.8933
D(x): 0.7166, D(G(z)): 0.2142
Epoch: [14/20], Batch Num: [521/600]
Discriminator Loss: 0.5951, Generator Loss: 1.7341
D(x): 0.8349, D(G(z)): 0.2389
Epoch: [14/20], Batch Num: [522/600]
Discriminator Loss: 0.6632, Generator Loss: 1.8213
D(x): 0.8112, D(G(z)): 0.2471
Epoch: [14/20], Batch Num: [523/600]
Discriminator Loss: 0.7259, Generator Loss: 2.0361
D(x): 0.8100, D(G(z)): 0.2658
Epoch: [14/20], Batch Num: [524/600]
Discriminator Loss: 0.8188, Generator Loss: 2.2189
D(x): 0.7414, D(G(z)): 0.2495
Epoch: [14/20], Batch Num: [525/600]
Discriminator Loss: 0.7587, Generator Loss: 2.1107
D(x): 0.7377, D(G(z)): 0.2168
Epoch: [14/20], Batch Num: [526/600]
Discriminator Loss: 0.6314, Generator Loss: 2.2121
D(x): 0.8077, D(G(z)): 0.2099
Epoch: [14/20], Batch Num: [527/600]
Discriminator Loss: 0.9221, Generator Loss: 2.0615
D(x): 0.6556, D(G(z)): 0.2182
Epoch: [14/20], Batch Num: [528/600]
Discriminator Loss: 0.7056, Generator Loss: 1.6758
D(x): 0.7383, D(G(z)): 0.2218
Epoch: [14/20], Batch Num: [529/600]
Discriminator Loss: 0.7066, Generator Loss: 1.5786
D(x): 0.8120, D(G(z)): 0.2678
Epoch: [14/20], Batch Num: [530/600]
Discriminator Loss: 0.6237, Generator Loss: 1.6119
D(x): 0.8268, D(G(z)): 0.2484
Epoch: [14/20], Batch Num: [531/600]
Discriminator Loss: 0.6928, Generator Loss: 1.9349
D(x): 0.7740, D(G(z)): 0.2416
Epoch: [14/20], Batch Num: [532/600]
Discriminator Loss: 0.5775, Generator Loss: 1.9757
D(x): 0.8176, D(G(z)): 0.2078
Epoch: [14/20], Batch Num: [533/600]
Discriminator Loss: 0.6287, Generator Loss: 2.0020
D(x): 0.7861, D(G(z)): 0.2111
Epoch: [14/20], Batch Num: [534/600]
Discriminator Loss: 0.7858, Generator Loss: 2.2065
D(x): 0.7378, D(G(z)): 0.2238
Epoch: [14/20], Batch Num: [535/600]
Discriminator Loss: 0.6913, Generator Loss: 2.2391
D(x): 0.7463, D(G(z)): 0.1849
Epoch: [14/20], Batch Num: [536/600]
Discriminator Loss: 0.6756, Generator Loss: 1.8663
D(x): 0.7616, D(G(z)): 0.1971
Epoch: [14/20], Batch Num: [537/600]
Discriminator Loss: 0.6513, Generator Loss: 1.8445
D(x): 0.8101, D(G(z)): 0.2342
Epoch: [14/20], Batch Num: [538/600]
Discriminator Loss: 0.7481, Generator Loss: 2.0498
D(x): 0.8319, D(G(z)): 0.3024
Epoch: [14/20], Batch Num: [539/600]
Discriminator Loss: 0.7233, Generator Loss: 2.1151
D(x): 0.7572, D(G(z)): 0.2085
Epoch: [14/20], Batch Num: [540/600]
Discriminator Loss: 0.6904, Generator Loss: 2.3961
D(x): 0.7712, D(G(z)): 0.1797
Epoch: [14/20], Batch Num: [541/600]
Discriminator Loss: 0.6085, Generator Loss: 2.2599
D(x): 0.7629, D(G(z)): 0.1602
Epoch: [14/20], Batch Num: [542/600]
Discriminator Loss: 0.8127, Generator Loss: 1.9821
D(x): 0.7397, D(G(z)): 0.2082
Epoch: [14/20], Batch Num: [543/600]
Discriminator Loss: 0.6783, Generator Loss: 1.8051
D(x): 0.7813, D(G(z)): 0.2196
Epoch: [14/20], Batch Num: [544/600]
Discriminator Loss: 0.7134, Generator Loss: 1.6298
D(x): 0.8005, D(G(z)): 0.2485
Epoch: [14/20], Batch Num: [545/600]
Discriminator Loss: 0.6792, Generator Loss: 1.7660
D(x): 0.8251, D(G(z)): 0.2577
Epoch: [14/20], Batch Num: [546/600]
Discriminator Loss: 0.5574, Generator Loss: 2.1825
D(x): 0.8478, D(G(z)): 0.2340
Epoch: [14/20], Batch Num: [547/600]
Discriminator Loss: 0.6090, Generator Loss: 2.5066
D(x): 0.7984, D(G(z)): 0.1997
Epoch: [14/20], Batch Num: [548/600]
Discriminator Loss: 0.5813, Generator Loss: 2.5159
D(x): 0.7840, D(G(z)): 0.1545
Epoch: [14/20], Batch Num: [549/600]
Discriminator Loss: 0.7690, Generator Loss: 2.2504
D(x): 0.7373, D(G(z)): 0.1894
Epoch: [14/20], Batch Num: [550/600]
Discriminator Loss: 0.7226, Generator Loss: 2.1483
D(x): 0.7599, D(G(z)): 0.2188
Epoch: [14/20], Batch Num: [551/600]
Discriminator Loss: 0.6997, Generator Loss: 1.8092
D(x): 0.7396, D(G(z)): 0.1981
Epoch: [14/20], Batch Num: [552/600]
Discriminator Loss: 0.7980, Generator Loss: 1.8328
D(x): 0.8012, D(G(z)): 0.2693
Epoch: [14/20], Batch Num: [553/600]
Discriminator Loss: 0.6511, Generator Loss: 1.8962
D(x): 0.8210, D(G(z)): 0.2441
Epoch: [14/20], Batch Num: [554/600]
Discriminator Loss: 0.7109, Generator Loss: 2.2257
D(x): 0.7542, D(G(z)): 0.2129
Epoch: [14/20], Batch Num: [555/600]
Discriminator Loss: 0.6856, Generator Loss: 2.2709
D(x): 0.7778, D(G(z)): 0.2266
Epoch: [14/20], Batch Num: [556/600]
Discriminator Loss: 0.6969, Generator Loss: 2.1124
D(x): 0.7708, D(G(z)): 0.1953
Epoch: [14/20], Batch Num: [557/600]
Discriminator Loss: 0.6353, Generator Loss: 2.0743
D(x): 0.7807, D(G(z)): 0.1912
Epoch: [14/20], Batch Num: [558/600]
Discriminator Loss: 0.7348, Generator Loss: 1.8993
D(x): 0.7398, D(G(z)): 0.1855
Epoch: [14/20], Batch Num: [559/600]
Discriminator Loss: 0.8522, Generator Loss: 1.7221
D(x): 0.7890, D(G(z)): 0.3041
Epoch: [14/20], Batch Num: [560/600]
Discriminator Loss: 0.7571, Generator Loss: 1.9291
D(x): 0.7812, D(G(z)): 0.2790
Epoch: [14/20], Batch Num: [561/600]
Discriminator Loss: 0.6247, Generator Loss: 2.3839
D(x): 0.7951, D(G(z)): 0.1795
Epoch: [14/20], Batch Num: [562/600]
Discriminator Loss: 0.7273, Generator Loss: 2.0302
D(x): 0.7503, D(G(z)): 0.2040
Epoch: [14/20], Batch Num: [563/600]
Discriminator Loss: 0.8192, Generator Loss: 2.3444
D(x): 0.7550, D(G(z)): 0.2506
Epoch: [14/20], Batch Num: [564/600]
Discriminator Loss: 0.5294, Generator Loss: 2.1376
D(x): 0.8195, D(G(z)): 0.1712
Epoch: [14/20], Batch Num: [565/600]
Discriminator Loss: 0.7275, Generator Loss: 2.2887
D(x): 0.7587, D(G(z)): 0.2067
Epoch: [14/20], Batch Num: [566/600]
Discriminator Loss: 0.7201, Generator Loss: 2.0054
D(x): 0.7923, D(G(z)): 0.2329
Epoch: [14/20], Batch Num: [567/600]
Discriminator Loss: 0.7431, Generator Loss: 1.6086
D(x): 0.7421, D(G(z)): 0.2006
Epoch: [14/20], Batch Num: [568/600]
Discriminator Loss: 0.7132, Generator Loss: 1.6418
D(x): 0.7523, D(G(z)): 0.2186
Epoch: [14/20], Batch Num: [569/600]
Discriminator Loss: 0.7364, Generator Loss: 1.8015
D(x): 0.8331, D(G(z)): 0.3121
Epoch: [14/20], Batch Num: [570/600]
Discriminator Loss: 0.7341, Generator Loss: 2.0495
D(x): 0.7665, D(G(z)): 0.2570
Epoch: [14/20], Batch Num: [571/600]
Discriminator Loss: 0.7316, Generator Loss: 2.2684
D(x): 0.7547, D(G(z)): 0.2282
Epoch: [14/20], Batch Num: [572/600]
Discriminator Loss: 0.7953, Generator Loss: 2.0979
D(x): 0.7177, D(G(z)): 0.2175
Epoch: [14/20], Batch Num: [573/600]
Discriminator Loss: 0.6362, Generator Loss: 1.9245
D(x): 0.7693, D(G(z)): 0.1746
Epoch: [14/20], Batch Num: [574/600]
Discriminator Loss: 0.6630, Generator Loss: 1.8374
D(x): 0.8221, D(G(z)): 0.2594
Epoch: [14/20], Batch Num: [575/600]
Discriminator Loss: 0.7416, Generator Loss: 1.8670
D(x): 0.8353, D(G(z)): 0.2933
Epoch: [14/20], Batch Num: [576/600]
Discriminator Loss: 0.6226, Generator Loss: 1.8786
D(x): 0.7967, D(G(z)): 0.2094
Epoch: [14/20], Batch Num: [577/600]
Discriminator Loss: 0.7025, Generator Loss: 2.0733
D(x): 0.7835, D(G(z)): 0.2225
Epoch: [14/20], Batch Num: [578/600]
Discriminator Loss: 0.7797, Generator Loss: 2.2296
D(x): 0.7324, D(G(z)): 0.2096
Epoch: [14/20], Batch Num: [579/600]
Discriminator Loss: 0.7571, Generator Loss: 1.8322
D(x): 0.7449, D(G(z)): 0.2289
Epoch: [14/20], Batch Num: [580/600]
Discriminator Loss: 0.6961, Generator Loss: 1.6114
D(x): 0.7830, D(G(z)): 0.2415
Epoch: [14/20], Batch Num: [581/600]
Discriminator Loss: 0.7118, Generator Loss: 2.1048
D(x): 0.8187, D(G(z)): 0.2651
Epoch: [14/20], Batch Num: [582/600]
Discriminator Loss: 0.6639, Generator Loss: 1.8736
D(x): 0.8048, D(G(z)): 0.2421
Epoch: [14/20], Batch Num: [583/600]
Discriminator Loss: 0.8524, Generator Loss: 2.1287
D(x): 0.7541, D(G(z)): 0.2234
Epoch: [14/20], Batch Num: [584/600]
Discriminator Loss: 0.6575, Generator Loss: 2.3059
D(x): 0.7915, D(G(z)): 0.1957
Epoch: [14/20], Batch Num: [585/600]
Discriminator Loss: 0.5542, Generator Loss: 2.2050
D(x): 0.7762, D(G(z)): 0.1674
Epoch: [14/20], Batch Num: [586/600]
Discriminator Loss: 0.7884, Generator Loss: 1.8697
D(x): 0.7061, D(G(z)): 0.1926
Epoch: [14/20], Batch Num: [587/600]
Discriminator Loss: 0.5822, Generator Loss: 1.7333
D(x): 0.8525, D(G(z)): 0.2389
Epoch: [14/20], Batch Num: [588/600]
Discriminator Loss: 0.6678, Generator Loss: 1.9028
D(x): 0.8063, D(G(z)): 0.2344
Epoch: [14/20], Batch Num: [589/600]
Discriminator Loss: 0.6634, Generator Loss: 1.9917
D(x): 0.8193, D(G(z)): 0.2532
Epoch: [14/20], Batch Num: [590/600]
Discriminator Loss: 0.6515, Generator Loss: 2.2861
D(x): 0.8106, D(G(z)): 0.2287
Epoch: [14/20], Batch Num: [591/600]
Discriminator Loss: 0.7612, Generator Loss: 2.4969
D(x): 0.7699, D(G(z)): 0.1988
Epoch: [14/20], Batch Num: [592/600]
Discriminator Loss: 0.5379, Generator Loss: 2.4364
D(x): 0.7994, D(G(z)): 0.1422
Epoch: [14/20], Batch Num: [593/600]
Discriminator Loss: 0.6636, Generator Loss: 2.3533
D(x): 0.7797, D(G(z)): 0.1809
Epoch: [14/20], Batch Num: [594/600]
Discriminator Loss: 0.6204, Generator Loss: 2.0441
D(x): 0.7659, D(G(z)): 0.1785
Epoch: [14/20], Batch Num: [595/600]
Discriminator Loss: 0.7346, Generator Loss: 1.7561
D(x): 0.7860, D(G(z)): 0.2324
Epoch: [14/20], Batch Num: [596/600]
Discriminator Loss: 0.6578, Generator Loss: 1.6623
D(x): 0.8194, D(G(z)): 0.2414
Epoch: [14/20], Batch Num: [597/600]
Discriminator Loss: 0.8600, Generator Loss: 2.1278
D(x): 0.8101, D(G(z)): 0.3232
Epoch: [14/20], Batch Num: [598/600]
Discriminator Loss: 0.7555, Generator Loss: 2.5840
D(x): 0.7948, D(G(z)): 0.2601
Epoch: [14/20], Batch Num: [599/600]
Discriminator Loss: 0.7916, Generator Loss: 2.4516
D(x): 0.7208, D(G(z)): 0.1590
Epoch: 15, Batch Num: [0/600]

Epoch: [15/20], Batch Num: [0/600]
Discriminator Loss: 0.7187, Generator Loss: 2.3357
D(x): 0.6980, D(G(z)): 0.1226
Epoch: [15/20], Batch Num: [1/600]
Discriminator Loss: 0.7042, Generator Loss: 1.8585
D(x): 0.7479, D(G(z)): 0.1698
Epoch: [15/20], Batch Num: [2/600]
Discriminator Loss: 0.7387, Generator Loss: 1.6548
D(x): 0.7811, D(G(z)): 0.2523
Epoch: [15/20], Batch Num: [3/600]
Discriminator Loss: 0.7197, Generator Loss: 1.7683
D(x): 0.8330, D(G(z)): 0.2889
Epoch: [15/20], Batch Num: [4/600]
Discriminator Loss: 0.6761, Generator Loss: 1.9427
D(x): 0.8372, D(G(z)): 0.2895
Epoch: [15/20], Batch Num: [5/600]
Discriminator Loss: 0.6162, Generator Loss: 2.2099
D(x): 0.7991, D(G(z)): 0.2258
Epoch: [15/20], Batch Num: [6/600]
Discriminator Loss: 0.6699, Generator Loss: 2.1754
D(x): 0.7443, D(G(z)): 0.1752
Epoch: [15/20], Batch Num: [7/600]
Discriminator Loss: 0.8164, Generator Loss: 2.0924
D(x): 0.7135, D(G(z)): 0.1886
Epoch: [15/20], Batch Num: [8/600]
Discriminator Loss: 0.6282, Generator Loss: 1.7409
D(x): 0.7627, D(G(z)): 0.1624
Epoch: [15/20], Batch Num: [9/600]
Discriminator Loss: 0.6694, Generator Loss: 2.1054
D(x): 0.8038, D(G(z)): 0.2205
Epoch: [15/20], Batch Num: [10/600]
Discriminator Loss: 0.7215, Generator Loss: 1.7871
D(x): 0.7925, D(G(z)): 0.2517
Epoch: [15/20], Batch Num: [11/600]
Discriminator Loss: 0.5576, Generator Loss: 1.8017
D(x): 0.8366, D(G(z)): 0.2247
Epoch: [15/20], Batch Num: [12/600]
Discriminator Loss: 0.6807, Generator Loss: 1.8257
D(x): 0.7974, D(G(z)): 0.2370
Epoch: [15/20], Batch Num: [13/600]
Discriminator Loss: 0.7084, Generator Loss: 2.2089
D(x): 0.7850, D(G(z)): 0.2203
Epoch: [15/20], Batch Num: [14/600]
Discriminator Loss: 0.6370, Generator Loss: 2.0799
D(x): 0.7778, D(G(z)): 0.1926
Epoch: [15/20], Batch Num: [15/600]
Discriminator Loss: 0.8102, Generator Loss: 2.1173
D(x): 0.7421, D(G(z)): 0.2439
Epoch: [15/20], Batch Num: [16/600]
Discriminator Loss: 0.6992, Generator Loss: 1.7337
D(x): 0.7575, D(G(z)): 0.1985
Epoch: [15/20], Batch Num: [17/600]
Discriminator Loss: 0.6357, Generator Loss: 1.9249
D(x): 0.8468, D(G(z)): 0.2687
Epoch: [15/20], Batch Num: [18/600]
Discriminator Loss: 0.6546, Generator Loss: 1.9367
D(x): 0.7828, D(G(z)): 0.2263
Epoch: [15/20], Batch Num: [19/600]
Discriminator Loss: 0.6289, Generator Loss: 2.0927
D(x): 0.8194, D(G(z)): 0.2556
Epoch: [15/20], Batch Num: [20/600]
Discriminator Loss: 0.7774, Generator Loss: 2.1536
D(x): 0.7356, D(G(z)): 0.2131
Epoch: [15/20], Batch Num: [21/600]
Discriminator Loss: 0.8531, Generator Loss: 1.8725
D(x): 0.7089, D(G(z)): 0.2253
Epoch: [15/20], Batch Num: [22/600]
Discriminator Loss: 0.6963, Generator Loss: 1.6987
D(x): 0.7488, D(G(z)): 0.2140
Epoch: [15/20], Batch Num: [23/600]
Discriminator Loss: 0.6062, Generator Loss: 1.5668
D(x): 0.8084, D(G(z)): 0.2385
Epoch: [15/20], Batch Num: [24/600]
Discriminator Loss: 0.7251, Generator Loss: 1.7526
D(x): 0.8651, D(G(z)): 0.3195
Epoch: [15/20], Batch Num: [25/600]
Discriminator Loss: 0.7019, Generator Loss: 2.2052
D(x): 0.8309, D(G(z)): 0.2839
Epoch: [15/20], Batch Num: [26/600]
Discriminator Loss: 0.7288, Generator Loss: 2.7788
D(x): 0.7585, D(G(z)): 0.2062
Epoch: [15/20], Batch Num: [27/600]
Discriminator Loss: 0.7263, Generator Loss: 2.6234
D(x): 0.6998, D(G(z)): 0.1228
Epoch: [15/20], Batch Num: [28/600]
Discriminator Loss: 0.6110, Generator Loss: 2.2073
D(x): 0.7637, D(G(z)): 0.1476
Epoch: [15/20], Batch Num: [29/600]
Discriminator Loss: 0.5241, Generator Loss: 1.9439
D(x): 0.8404, D(G(z)): 0.2017
Epoch: [15/20], Batch Num: [30/600]
Discriminator Loss: 0.5837, Generator Loss: 1.7752
D(x): 0.8148, D(G(z)): 0.1929
Epoch: [15/20], Batch Num: [31/600]
Discriminator Loss: 0.8019, Generator Loss: 1.9365
D(x): 0.8009, D(G(z)): 0.2455
Epoch: [15/20], Batch Num: [32/600]
Discriminator Loss: 0.5383, Generator Loss: 2.3568
D(x): 0.8345, D(G(z)): 0.1898
Epoch: [15/20], Batch Num: [33/600]
Discriminator Loss: 0.7834, Generator Loss: 2.4009
D(x): 0.7972, D(G(z)): 0.2642
Epoch: [15/20], Batch Num: [34/600]
Discriminator Loss: 0.7267, Generator Loss: 2.5110
D(x): 0.7418, D(G(z)): 0.1651
Epoch: [15/20], Batch Num: [35/600]
Discriminator Loss: 0.7070, Generator Loss: 1.9914
D(x): 0.7242, D(G(z)): 0.1368
Epoch: [15/20], Batch Num: [36/600]
Discriminator Loss: 0.7025, Generator Loss: 1.8884
D(x): 0.7819, D(G(z)): 0.2291
Epoch: [15/20], Batch Num: [37/600]
Discriminator Loss: 0.6695, Generator Loss: 1.7699
D(x): 0.8409, D(G(z)): 0.2676
Epoch: [15/20], Batch Num: [38/600]
Discriminator Loss: 0.6329, Generator Loss: 2.0572
D(x): 0.8272, D(G(z)): 0.2546
Epoch: [15/20], Batch Num: [39/600]
Discriminator Loss: 0.7380, Generator Loss: 2.0241
D(x): 0.7772, D(G(z)): 0.2273
Epoch: [15/20], Batch Num: [40/600]
Discriminator Loss: 0.6277, Generator Loss: 2.4201
D(x): 0.7787, D(G(z)): 0.1772
Epoch: [15/20], Batch Num: [41/600]
Discriminator Loss: 0.7278, Generator Loss: 2.3041
D(x): 0.7202, D(G(z)): 0.1698
Epoch: [15/20], Batch Num: [42/600]
Discriminator Loss: 0.6562, Generator Loss: 2.1601
D(x): 0.7744, D(G(z)): 0.2035
Epoch: [15/20], Batch Num: [43/600]
Discriminator Loss: 0.6524, Generator Loss: 1.7223
D(x): 0.7742, D(G(z)): 0.1992
Epoch: [15/20], Batch Num: [44/600]
Discriminator Loss: 0.6272, Generator Loss: 1.9141
D(x): 0.8202, D(G(z)): 0.2371
Epoch: [15/20], Batch Num: [45/600]
Discriminator Loss: 0.6611, Generator Loss: 1.8833
D(x): 0.8116, D(G(z)): 0.2646
Epoch: [15/20], Batch Num: [46/600]
Discriminator Loss: 0.7347, Generator Loss: 2.0637
D(x): 0.7680, D(G(z)): 0.2677
Epoch: [15/20], Batch Num: [47/600]
Discriminator Loss: 0.7522, Generator Loss: 1.9807
D(x): 0.7514, D(G(z)): 0.2205
Epoch: [15/20], Batch Num: [48/600]
Discriminator Loss: 0.5429, Generator Loss: 2.1748
D(x): 0.8710, D(G(z)): 0.2470
Epoch: [15/20], Batch Num: [49/600]
Discriminator Loss: 0.7726, Generator Loss: 2.5173
D(x): 0.7714, D(G(z)): 0.2118
Epoch: [15/20], Batch Num: [50/600]
Discriminator Loss: 0.7788, Generator Loss: 2.4183
D(x): 0.7149, D(G(z)): 0.1416
Epoch: [15/20], Batch Num: [51/600]
Discriminator Loss: 0.7278, Generator Loss: 2.0624
D(x): 0.7332, D(G(z)): 0.1651
Epoch: [15/20], Batch Num: [52/600]
Discriminator Loss: 0.6392, Generator Loss: 1.8698
D(x): 0.7944, D(G(z)): 0.1972
Epoch: [15/20], Batch Num: [53/600]
Discriminator Loss: 0.6343, Generator Loss: 1.6808
D(x): 0.8382, D(G(z)): 0.2592
Epoch: [15/20], Batch Num: [54/600]
Discriminator Loss: 0.5646, Generator Loss: 1.9287
D(x): 0.8889, D(G(z)): 0.2674
Epoch: [15/20], Batch Num: [55/600]
Discriminator Loss: 0.6966, Generator Loss: 2.3654
D(x): 0.8311, D(G(z)): 0.2661
Epoch: [15/20], Batch Num: [56/600]
Discriminator Loss: 0.7634, Generator Loss: 2.4477
D(x): 0.7452, D(G(z)): 0.1741
Epoch: [15/20], Batch Num: [57/600]
Discriminator Loss: 0.6363, Generator Loss: 2.6448
D(x): 0.7853, D(G(z)): 0.1226
Epoch: [15/20], Batch Num: [58/600]
Discriminator Loss: 0.5365, Generator Loss: 2.2918
D(x): 0.7935, D(G(z)): 0.1372
Epoch: [15/20], Batch Num: [59/600]
Discriminator Loss: 0.7180, Generator Loss: 2.0304
D(x): 0.7865, D(G(z)): 0.1963
Epoch: [15/20], Batch Num: [60/600]
Discriminator Loss: 0.7931, Generator Loss: 2.0552
D(x): 0.8014, D(G(z)): 0.2713
Epoch: [15/20], Batch Num: [61/600]
Discriminator Loss: 0.8097, Generator Loss: 1.9502
D(x): 0.7710, D(G(z)): 0.2416
Epoch: [15/20], Batch Num: [62/600]
Discriminator Loss: 0.6980, Generator Loss: 1.9871
D(x): 0.7588, D(G(z)): 0.2103
Epoch: [15/20], Batch Num: [63/600]
Discriminator Loss: 0.6023, Generator Loss: 2.0355
D(x): 0.8331, D(G(z)): 0.2006
Epoch: [15/20], Batch Num: [64/600]
Discriminator Loss: 0.6720, Generator Loss: 2.0491
D(x): 0.7661, D(G(z)): 0.1799
Epoch: [15/20], Batch Num: [65/600]
Discriminator Loss: 0.7692, Generator Loss: 1.9198
D(x): 0.7617, D(G(z)): 0.2294
Epoch: [15/20], Batch Num: [66/600]
Discriminator Loss: 0.9055, Generator Loss: 1.8844
D(x): 0.6960, D(G(z)): 0.2012
Epoch: [15/20], Batch Num: [67/600]
Discriminator Loss: 0.6532, Generator Loss: 1.8598
D(x): 0.8045, D(G(z)): 0.2278
Epoch: [15/20], Batch Num: [68/600]
Discriminator Loss: 0.8167, Generator Loss: 1.7290
D(x): 0.7644, D(G(z)): 0.2692
Epoch: [15/20], Batch Num: [69/600]
Discriminator Loss: 0.8222, Generator Loss: 1.9659
D(x): 0.7568, D(G(z)): 0.2927
Epoch: [15/20], Batch Num: [70/600]
Discriminator Loss: 0.6039, Generator Loss: 1.9449
D(x): 0.8415, D(G(z)): 0.2600
Epoch: [15/20], Batch Num: [71/600]
Discriminator Loss: 0.7504, Generator Loss: 2.1556
D(x): 0.7503, D(G(z)): 0.2204
Epoch: [15/20], Batch Num: [72/600]
Discriminator Loss: 0.7606, Generator Loss: 2.0683
D(x): 0.7320, D(G(z)): 0.1926
Epoch: [15/20], Batch Num: [73/600]
Discriminator Loss: 0.7001, Generator Loss: 2.0819
D(x): 0.7571, D(G(z)): 0.1993
Epoch: [15/20], Batch Num: [74/600]
Discriminator Loss: 0.6522, Generator Loss: 1.8960
D(x): 0.7904, D(G(z)): 0.2043
Epoch: [15/20], Batch Num: [75/600]
Discriminator Loss: 0.7859, Generator Loss: 1.6963
D(x): 0.7291, D(G(z)): 0.2194
Epoch: [15/20], Batch Num: [76/600]
Discriminator Loss: 0.8447, Generator Loss: 1.7015
D(x): 0.7287, D(G(z)): 0.2686
Epoch: [15/20], Batch Num: [77/600]
Discriminator Loss: 0.7966, Generator Loss: 1.6817
D(x): 0.8093, D(G(z)): 0.2897
Epoch: [15/20], Batch Num: [78/600]
Discriminator Loss: 0.7247, Generator Loss: 1.9138
D(x): 0.7887, D(G(z)): 0.2803
Epoch: [15/20], Batch Num: [79/600]
Discriminator Loss: 0.6827, Generator Loss: 2.1496
D(x): 0.7791, D(G(z)): 0.2182
Epoch: [15/20], Batch Num: [80/600]
Discriminator Loss: 0.7515, Generator Loss: 2.3834
D(x): 0.7612, D(G(z)): 0.2247
Epoch: [15/20], Batch Num: [81/600]
Discriminator Loss: 0.6052, Generator Loss: 2.3075
D(x): 0.7589, D(G(z)): 0.1600
Epoch: [15/20], Batch Num: [82/600]
Discriminator Loss: 0.6987, Generator Loss: 2.0764
D(x): 0.7558, D(G(z)): 0.2023
Epoch: [15/20], Batch Num: [83/600]
Discriminator Loss: 0.7340, Generator Loss: 1.7468
D(x): 0.7213, D(G(z)): 0.1932
Epoch: [15/20], Batch Num: [84/600]
Discriminator Loss: 0.7053, Generator Loss: 1.5244
D(x): 0.7795, D(G(z)): 0.2220
Epoch: [15/20], Batch Num: [85/600]
Discriminator Loss: 0.7369, Generator Loss: 1.4445
D(x): 0.8335, D(G(z)): 0.3248
Epoch: [15/20], Batch Num: [86/600]
Discriminator Loss: 0.9050, Generator Loss: 1.6003
D(x): 0.8053, D(G(z)): 0.3419
Epoch: [15/20], Batch Num: [87/600]
Discriminator Loss: 0.7621, Generator Loss: 2.0647
D(x): 0.7946, D(G(z)): 0.2735
Epoch: [15/20], Batch Num: [88/600]
Discriminator Loss: 0.7548, Generator Loss: 2.6841
D(x): 0.7774, D(G(z)): 0.2502
Epoch: [15/20], Batch Num: [89/600]
Discriminator Loss: 0.7868, Generator Loss: 2.3199
D(x): 0.6846, D(G(z)): 0.1622
Epoch: [15/20], Batch Num: [90/600]
Discriminator Loss: 0.7141, Generator Loss: 1.9905
D(x): 0.7021, D(G(z)): 0.1547
Epoch: [15/20], Batch Num: [91/600]
Discriminator Loss: 0.8037, Generator Loss: 1.6573
D(x): 0.7093, D(G(z)): 0.1988
Epoch: [15/20], Batch Num: [92/600]
Discriminator Loss: 0.6014, Generator Loss: 1.4304
D(x): 0.8091, D(G(z)): 0.2338
Epoch: [15/20], Batch Num: [93/600]
Discriminator Loss: 0.9606, Generator Loss: 1.4288
D(x): 0.7674, D(G(z)): 0.3705
Epoch: [15/20], Batch Num: [94/600]
Discriminator Loss: 0.7857, Generator Loss: 1.5666
D(x): 0.7926, D(G(z)): 0.3050
Epoch: [15/20], Batch Num: [95/600]
Discriminator Loss: 0.7642, Generator Loss: 1.7277
D(x): 0.7843, D(G(z)): 0.2908
Epoch: [15/20], Batch Num: [96/600]
Discriminator Loss: 0.8239, Generator Loss: 1.7521
D(x): 0.7141, D(G(z)): 0.2546
Epoch: [15/20], Batch Num: [97/600]
Discriminator Loss: 0.7619, Generator Loss: 1.9574
D(x): 0.7476, D(G(z)): 0.2440
Epoch: [15/20], Batch Num: [98/600]
Discriminator Loss: 0.6851, Generator Loss: 1.9648
D(x): 0.7289, D(G(z)): 0.1894
Epoch: [15/20], Batch Num: [99/600]
Discriminator Loss: 0.8319, Generator Loss: 1.8996
D(x): 0.7126, D(G(z)): 0.2235
Epoch: 15, Batch Num: [100/600]

Epoch: [15/20], Batch Num: [100/600]
Discriminator Loss: 0.7410, Generator Loss: 1.8491
D(x): 0.7351, D(G(z)): 0.2158
Epoch: [15/20], Batch Num: [101/600]
Discriminator Loss: 0.6688, Generator Loss: 1.6616
D(x): 0.8065, D(G(z)): 0.2516
Epoch: [15/20], Batch Num: [102/600]
Discriminator Loss: 0.7340, Generator Loss: 1.7735
D(x): 0.7800, D(G(z)): 0.2571
Epoch: [15/20], Batch Num: [103/600]
Discriminator Loss: 0.8665, Generator Loss: 1.7147
D(x): 0.6852, D(G(z)): 0.2401
Epoch: [15/20], Batch Num: [104/600]
Discriminator Loss: 0.5943, Generator Loss: 1.6893
D(x): 0.8065, D(G(z)): 0.2430
Epoch: [15/20], Batch Num: [105/600]
Discriminator Loss: 0.7001, Generator Loss: 1.6775
D(x): 0.7980, D(G(z)): 0.2482
Epoch: [15/20], Batch Num: [106/600]
Discriminator Loss: 0.6163, Generator Loss: 1.8575
D(x): 0.8143, D(G(z)): 0.2369
Epoch: [15/20], Batch Num: [107/600]
Discriminator Loss: 0.7875, Generator Loss: 1.9015
D(x): 0.7469, D(G(z)): 0.2641
Epoch: [15/20], Batch Num: [108/600]
Discriminator Loss: 0.6853, Generator Loss: 1.9737
D(x): 0.7736, D(G(z)): 0.2337
Epoch: [15/20], Batch Num: [109/600]
Discriminator Loss: 0.6489, Generator Loss: 1.8267
D(x): 0.7793, D(G(z)): 0.2321
Epoch: [15/20], Batch Num: [110/600]
Discriminator Loss: 0.7989, Generator Loss: 1.9689
D(x): 0.7721, D(G(z)): 0.2592
Epoch: [15/20], Batch Num: [111/600]
Discriminator Loss: 0.7353, Generator Loss: 2.0280
D(x): 0.7555, D(G(z)): 0.2195
Epoch: [15/20], Batch Num: [112/600]
Discriminator Loss: 0.6336, Generator Loss: 1.9559
D(x): 0.7851, D(G(z)): 0.1881
Epoch: [15/20], Batch Num: [113/600]
Discriminator Loss: 0.8512, Generator Loss: 1.9401
D(x): 0.7100, D(G(z)): 0.2284
Epoch: [15/20], Batch Num: [114/600]
Discriminator Loss: 0.6851, Generator Loss: 1.6705
D(x): 0.7819, D(G(z)): 0.2164
Epoch: [15/20], Batch Num: [115/600]
Discriminator Loss: 0.6854, Generator Loss: 1.6760
D(x): 0.7860, D(G(z)): 0.2449
Epoch: [15/20], Batch Num: [116/600]
Discriminator Loss: 0.7748, Generator Loss: 1.8302
D(x): 0.7988, D(G(z)): 0.2962
Epoch: [15/20], Batch Num: [117/600]
Discriminator Loss: 0.6338, Generator Loss: 1.9268
D(x): 0.7939, D(G(z)): 0.2312
Epoch: [15/20], Batch Num: [118/600]
Discriminator Loss: 0.6124, Generator Loss: 2.0022
D(x): 0.7860, D(G(z)): 0.2019
Epoch: [15/20], Batch Num: [119/600]
Discriminator Loss: 0.6415, Generator Loss: 2.1763
D(x): 0.7841, D(G(z)): 0.1944
Epoch: [15/20], Batch Num: [120/600]
Discriminator Loss: 0.7424, Generator Loss: 2.1587
D(x): 0.7359, D(G(z)): 0.1900
Epoch: [15/20], Batch Num: [121/600]
Discriminator Loss: 0.6668, Generator Loss: 1.9237
D(x): 0.7803, D(G(z)): 0.2098
Epoch: [15/20], Batch Num: [122/600]
Discriminator Loss: 0.7912, Generator Loss: 1.8604
D(x): 0.8071, D(G(z)): 0.2719
Epoch: [15/20], Batch Num: [123/600]
Discriminator Loss: 0.6344, Generator Loss: 2.0347
D(x): 0.7838, D(G(z)): 0.2163
Epoch: [15/20], Batch Num: [124/600]
Discriminator Loss: 0.7235, Generator Loss: 1.9125
D(x): 0.7610, D(G(z)): 0.2409
Epoch: [15/20], Batch Num: [125/600]
Discriminator Loss: 0.6305, Generator Loss: 1.9608
D(x): 0.7658, D(G(z)): 0.2123
Epoch: [15/20], Batch Num: [126/600]
Discriminator Loss: 0.7659, Generator Loss: 1.8450
D(x): 0.7977, D(G(z)): 0.2469
Epoch: [15/20], Batch Num: [127/600]
Discriminator Loss: 0.6084, Generator Loss: 1.8424
D(x): 0.7940, D(G(z)): 0.2056
Epoch: [15/20], Batch Num: [128/600]
Discriminator Loss: 0.7745, Generator Loss: 1.6795
D(x): 0.7378, D(G(z)): 0.2347
Epoch: [15/20], Batch Num: [129/600]
Discriminator Loss: 0.7507, Generator Loss: 1.9548
D(x): 0.8006, D(G(z)): 0.2664
Epoch: [15/20], Batch Num: [130/600]
Discriminator Loss: 0.4859, Generator Loss: 1.8607
D(x): 0.8337, D(G(z)): 0.1827
Epoch: [15/20], Batch Num: [131/600]
Discriminator Loss: 0.7363, Generator Loss: 2.2927
D(x): 0.7597, D(G(z)): 0.2198
Epoch: [15/20], Batch Num: [132/600]
Discriminator Loss: 0.7288, Generator Loss: 2.1761
D(x): 0.7555, D(G(z)): 0.2002
Epoch: [15/20], Batch Num: [133/600]
Discriminator Loss: 0.5722, Generator Loss: 2.2635
D(x): 0.8071, D(G(z)): 0.2053
Epoch: [15/20], Batch Num: [134/600]
Discriminator Loss: 0.6716, Generator Loss: 2.2517
D(x): 0.7785, D(G(z)): 0.2088
Epoch: [15/20], Batch Num: [135/600]
Discriminator Loss: 0.5974, Generator Loss: 2.1756
D(x): 0.7638, D(G(z)): 0.1612
Epoch: [15/20], Batch Num: [136/600]
Discriminator Loss: 0.8251, Generator Loss: 1.9981
D(x): 0.7533, D(G(z)): 0.2668
Epoch: [15/20], Batch Num: [137/600]
Discriminator Loss: 0.6844, Generator Loss: 1.7074
D(x): 0.8045, D(G(z)): 0.2542
Epoch: [15/20], Batch Num: [138/600]
Discriminator Loss: 0.7065, Generator Loss: 2.3877
D(x): 0.8356, D(G(z)): 0.2696
Epoch: [15/20], Batch Num: [139/600]
Discriminator Loss: 0.6298, Generator Loss: 2.2526
D(x): 0.7954, D(G(z)): 0.1951
Epoch: [15/20], Batch Num: [140/600]
Discriminator Loss: 0.7072, Generator Loss: 2.5161
D(x): 0.7504, D(G(z)): 0.1846
Epoch: [15/20], Batch Num: [141/600]
Discriminator Loss: 0.6994, Generator Loss: 2.3285
D(x): 0.7265, D(G(z)): 0.1507
Epoch: [15/20], Batch Num: [142/600]
Discriminator Loss: 0.6325, Generator Loss: 1.8997
D(x): 0.7642, D(G(z)): 0.1832
Epoch: [15/20], Batch Num: [143/600]
Discriminator Loss: 0.5999, Generator Loss: 1.8467
D(x): 0.8847, D(G(z)): 0.2778
Epoch: [15/20], Batch Num: [144/600]
Discriminator Loss: 0.6627, Generator Loss: 2.2657
D(x): 0.8487, D(G(z)): 0.2650
Epoch: [15/20], Batch Num: [145/600]
Discriminator Loss: 0.6343, Generator Loss: 2.4442
D(x): 0.8009, D(G(z)): 0.2224
Epoch: [15/20], Batch Num: [146/600]
Discriminator Loss: 0.5656, Generator Loss: 2.5722
D(x): 0.8137, D(G(z)): 0.1630
Epoch: [15/20], Batch Num: [147/600]
Discriminator Loss: 0.7423, Generator Loss: 2.4532
D(x): 0.7186, D(G(z)): 0.1454
Epoch: [15/20], Batch Num: [148/600]
Discriminator Loss: 0.6591, Generator Loss: 1.9925
D(x): 0.7823, D(G(z)): 0.1833
Epoch: [15/20], Batch Num: [149/600]
Discriminator Loss: 0.6376, Generator Loss: 2.1248
D(x): 0.7893, D(G(z)): 0.1758
Epoch: [15/20], Batch Num: [150/600]
Discriminator Loss: 0.6529, Generator Loss: 1.6148
D(x): 0.8440, D(G(z)): 0.2364
Epoch: [15/20], Batch Num: [151/600]
Discriminator Loss: 0.8620, Generator Loss: 1.9108
D(x): 0.8034, D(G(z)): 0.2805
Epoch: [15/20], Batch Num: [152/600]
Discriminator Loss: 0.5686, Generator Loss: 2.2666
D(x): 0.8149, D(G(z)): 0.1879
Epoch: [15/20], Batch Num: [153/600]
Discriminator Loss: 0.7414, Generator Loss: 2.0457
D(x): 0.7226, D(G(z)): 0.1656
Epoch: [15/20], Batch Num: [154/600]
Discriminator Loss: 0.6426, Generator Loss: 2.0092
D(x): 0.7604, D(G(z)): 0.1746
Epoch: [15/20], Batch Num: [155/600]
Discriminator Loss: 0.8862, Generator Loss: 2.0116
D(x): 0.7457, D(G(z)): 0.2720
Epoch: [15/20], Batch Num: [156/600]
Discriminator Loss: 0.8514, Generator Loss: 1.7377
D(x): 0.7409, D(G(z)): 0.2550
Epoch: [15/20], Batch Num: [157/600]
Discriminator Loss: 0.7332, Generator Loss: 1.4111
D(x): 0.7530, D(G(z)): 0.2285
Epoch: [15/20], Batch Num: [158/600]
Discriminator Loss: 0.8694, Generator Loss: 1.8367
D(x): 0.7982, D(G(z)): 0.3295
Epoch: [15/20], Batch Num: [159/600]
Discriminator Loss: 0.7563, Generator Loss: 2.0868
D(x): 0.8178, D(G(z)): 0.3123
Epoch: [15/20], Batch Num: [160/600]
Discriminator Loss: 0.7695, Generator Loss: 2.3861
D(x): 0.7467, D(G(z)): 0.2147
Epoch: [15/20], Batch Num: [161/600]
Discriminator Loss: 0.8549, Generator Loss: 2.2100
D(x): 0.7068, D(G(z)): 0.1933
Epoch: [15/20], Batch Num: [162/600]
Discriminator Loss: 0.8182, Generator Loss: 2.0470
D(x): 0.7093, D(G(z)): 0.1842
Epoch: [15/20], Batch Num: [163/600]
Discriminator Loss: 0.7404, Generator Loss: 1.7690
D(x): 0.7614, D(G(z)): 0.2544
Epoch: [15/20], Batch Num: [164/600]
Discriminator Loss: 0.7772, Generator Loss: 1.8392
D(x): 0.7734, D(G(z)): 0.2714
Epoch: [15/20], Batch Num: [165/600]
Discriminator Loss: 0.6844, Generator Loss: 1.8855
D(x): 0.7765, D(G(z)): 0.2418
Epoch: [15/20], Batch Num: [166/600]
Discriminator Loss: 0.7044, Generator Loss: 1.8036
D(x): 0.7604, D(G(z)): 0.2370
Epoch: [15/20], Batch Num: [167/600]
Discriminator Loss: 0.7426, Generator Loss: 1.9876
D(x): 0.7576, D(G(z)): 0.2305
Epoch: [15/20], Batch Num: [168/600]
Discriminator Loss: 0.7408, Generator Loss: 1.7417
D(x): 0.7658, D(G(z)): 0.2417
Epoch: [15/20], Batch Num: [169/600]
Discriminator Loss: 0.8004, Generator Loss: 1.8775
D(x): 0.7378, D(G(z)): 0.2418
Epoch: [15/20], Batch Num: [170/600]
Discriminator Loss: 0.6579, Generator Loss: 1.8796
D(x): 0.7901, D(G(z)): 0.2370
Epoch: [15/20], Batch Num: [171/600]
Discriminator Loss: 0.5834, Generator Loss: 1.7651
D(x): 0.7904, D(G(z)): 0.1948
Epoch: [15/20], Batch Num: [172/600]
Discriminator Loss: 0.6464, Generator Loss: 1.6036
D(x): 0.7865, D(G(z)): 0.2135
Epoch: [15/20], Batch Num: [173/600]
Discriminator Loss: 0.7755, Generator Loss: 1.8131
D(x): 0.7704, D(G(z)): 0.2674
Epoch: [15/20], Batch Num: [174/600]
Discriminator Loss: 0.6664, Generator Loss: 1.8722
D(x): 0.8266, D(G(z)): 0.2622
Epoch: [15/20], Batch Num: [175/600]
Discriminator Loss: 0.6924, Generator Loss: 1.7382
D(x): 0.7479, D(G(z)): 0.1984
Epoch: [15/20], Batch Num: [176/600]
Discriminator Loss: 0.6305, Generator Loss: 1.9422
D(x): 0.8171, D(G(z)): 0.2414
Epoch: [15/20], Batch Num: [177/600]
Discriminator Loss: 0.5724, Generator Loss: 2.1808
D(x): 0.8231, D(G(z)): 0.2094
Epoch: [15/20], Batch Num: [178/600]
Discriminator Loss: 0.6136, Generator Loss: 2.1256
D(x): 0.7900, D(G(z)): 0.1897
Epoch: [15/20], Batch Num: [179/600]
Discriminator Loss: 0.6993, Generator Loss: 2.3050
D(x): 0.7692, D(G(z)): 0.2121
Epoch: [15/20], Batch Num: [180/600]
Discriminator Loss: 0.7273, Generator Loss: 2.3308
D(x): 0.7508, D(G(z)): 0.1732
Epoch: [15/20], Batch Num: [181/600]
Discriminator Loss: 0.5695, Generator Loss: 2.0428
D(x): 0.7784, D(G(z)): 0.1477
Epoch: [15/20], Batch Num: [182/600]
Discriminator Loss: 0.6256, Generator Loss: 1.9525
D(x): 0.8365, D(G(z)): 0.2349
Epoch: [15/20], Batch Num: [183/600]
Discriminator Loss: 0.7369, Generator Loss: 2.0045
D(x): 0.7518, D(G(z)): 0.2156
Epoch: [15/20], Batch Num: [184/600]
Discriminator Loss: 0.5771, Generator Loss: 2.1249
D(x): 0.8596, D(G(z)): 0.2414
Epoch: [15/20], Batch Num: [185/600]
Discriminator Loss: 0.6047, Generator Loss: 1.9570
D(x): 0.8067, D(G(z)): 0.1817
Epoch: [15/20], Batch Num: [186/600]
Discriminator Loss: 0.6513, Generator Loss: 2.0197
D(x): 0.8026, D(G(z)): 0.1978
Epoch: [15/20], Batch Num: [187/600]
Discriminator Loss: 0.6038, Generator Loss: 2.2069
D(x): 0.8049, D(G(z)): 0.2079
Epoch: [15/20], Batch Num: [188/600]
Discriminator Loss: 0.6266, Generator Loss: 1.8588
D(x): 0.7871, D(G(z)): 0.1949
Epoch: [15/20], Batch Num: [189/600]
Discriminator Loss: 0.6935, Generator Loss: 2.0712
D(x): 0.8032, D(G(z)): 0.2250
Epoch: [15/20], Batch Num: [190/600]
Discriminator Loss: 0.7442, Generator Loss: 2.0726
D(x): 0.7805, D(G(z)): 0.2325
Epoch: [15/20], Batch Num: [191/600]
Discriminator Loss: 0.6299, Generator Loss: 2.3080
D(x): 0.7964, D(G(z)): 0.2105
Epoch: [15/20], Batch Num: [192/600]
Discriminator Loss: 0.7560, Generator Loss: 2.3941
D(x): 0.7592, D(G(z)): 0.2278
Epoch: [15/20], Batch Num: [193/600]
Discriminator Loss: 0.6820, Generator Loss: 2.2508
D(x): 0.7975, D(G(z)): 0.2203
Epoch: [15/20], Batch Num: [194/600]
Discriminator Loss: 0.8413, Generator Loss: 2.4714
D(x): 0.7282, D(G(z)): 0.2170
Epoch: [15/20], Batch Num: [195/600]
Discriminator Loss: 0.6907, Generator Loss: 2.0304
D(x): 0.7507, D(G(z)): 0.1700
Epoch: [15/20], Batch Num: [196/600]
Discriminator Loss: 0.7788, Generator Loss: 1.9570
D(x): 0.7343, D(G(z)): 0.2175
Epoch: [15/20], Batch Num: [197/600]
Discriminator Loss: 0.8335, Generator Loss: 1.4694
D(x): 0.7359, D(G(z)): 0.2488
Epoch: [15/20], Batch Num: [198/600]
Discriminator Loss: 0.7672, Generator Loss: 1.6539
D(x): 0.8092, D(G(z)): 0.2821
Epoch: [15/20], Batch Num: [199/600]
Discriminator Loss: 0.6891, Generator Loss: 1.7972
D(x): 0.7770, D(G(z)): 0.2426
Epoch: 15, Batch Num: [200/600]

Epoch: [15/20], Batch Num: [200/600]
Discriminator Loss: 0.6744, Generator Loss: 1.8880
D(x): 0.8042, D(G(z)): 0.2400
Epoch: [15/20], Batch Num: [201/600]
Discriminator Loss: 0.8593, Generator Loss: 2.0259
D(x): 0.7158, D(G(z)): 0.2586
Epoch: [15/20], Batch Num: [202/600]
Discriminator Loss: 0.8794, Generator Loss: 1.9455
D(x): 0.6592, D(G(z)): 0.2163
Epoch: [15/20], Batch Num: [203/600]
Discriminator Loss: 0.6204, Generator Loss: 1.7693
D(x): 0.8001, D(G(z)): 0.2200
Epoch: [15/20], Batch Num: [204/600]
Discriminator Loss: 0.6477, Generator Loss: 1.8389
D(x): 0.7931, D(G(z)): 0.2254
Epoch: [15/20], Batch Num: [205/600]
Discriminator Loss: 0.5505, Generator Loss: 1.5096
D(x): 0.8236, D(G(z)): 0.1968
Epoch: [15/20], Batch Num: [206/600]
Discriminator Loss: 0.7744, Generator Loss: 1.9172
D(x): 0.8097, D(G(z)): 0.2951
Epoch: [15/20], Batch Num: [207/600]
Discriminator Loss: 0.7032, Generator Loss: 1.6869
D(x): 0.7737, D(G(z)): 0.2232
Epoch: [15/20], Batch Num: [208/600]
Discriminator Loss: 0.6591, Generator Loss: 1.9614
D(x): 0.7945, D(G(z)): 0.2299
Epoch: [15/20], Batch Num: [209/600]
Discriminator Loss: 0.6546, Generator Loss: 2.1856
D(x): 0.8131, D(G(z)): 0.2335
Epoch: [15/20], Batch Num: [210/600]
Discriminator Loss: 0.7978, Generator Loss: 2.0468
D(x): 0.7077, D(G(z)): 0.1962
Epoch: [15/20], Batch Num: [211/600]
Discriminator Loss: 0.6299, Generator Loss: 1.8324
D(x): 0.7784, D(G(z)): 0.1789
Epoch: [15/20], Batch Num: [212/600]
Discriminator Loss: 0.7558, Generator Loss: 1.9172
D(x): 0.7415, D(G(z)): 0.2018
Epoch: [15/20], Batch Num: [213/600]
Discriminator Loss: 0.7692, Generator Loss: 1.7751
D(x): 0.7852, D(G(z)): 0.2698
Epoch: [15/20], Batch Num: [214/600]
Discriminator Loss: 0.7194, Generator Loss: 1.7189
D(x): 0.7833, D(G(z)): 0.2563
Epoch: [15/20], Batch Num: [215/600]
Discriminator Loss: 0.5517, Generator Loss: 2.2457
D(x): 0.8847, D(G(z)): 0.2648
Epoch: [15/20], Batch Num: [216/600]
Discriminator Loss: 0.6514, Generator Loss: 2.4877
D(x): 0.7991, D(G(z)): 0.2055
Epoch: [15/20], Batch Num: [217/600]
Discriminator Loss: 0.5878, Generator Loss: 2.6191
D(x): 0.8125, D(G(z)): 0.1697
Epoch: [15/20], Batch Num: [218/600]
Discriminator Loss: 0.6994, Generator Loss: 2.4857
D(x): 0.6943, D(G(z)): 0.0998
Epoch: [15/20], Batch Num: [219/600]
Discriminator Loss: 0.6576, Generator Loss: 2.1451
D(x): 0.7163, D(G(z)): 0.1346
Epoch: [15/20], Batch Num: [220/600]
Discriminator Loss: 0.7770, Generator Loss: 1.9236
D(x): 0.7280, D(G(z)): 0.1946
Epoch: [15/20], Batch Num: [221/600]
Discriminator Loss: 0.6942, Generator Loss: 1.6181
D(x): 0.8356, D(G(z)): 0.2748
Epoch: [15/20], Batch Num: [222/600]
Discriminator Loss: 0.6786, Generator Loss: 1.8351
D(x): 0.8538, D(G(z)): 0.2928
Epoch: [15/20], Batch Num: [223/600]
Discriminator Loss: 0.6606, Generator Loss: 2.3108
D(x): 0.8552, D(G(z)): 0.2803
Epoch: [15/20], Batch Num: [224/600]
Discriminator Loss: 0.5397, Generator Loss: 3.1170
D(x): 0.8314, D(G(z)): 0.1908
Epoch: [15/20], Batch Num: [225/600]
Discriminator Loss: 0.5593, Generator Loss: 3.1987
D(x): 0.7733, D(G(z)): 0.1229
Epoch: [15/20], Batch Num: [226/600]
Discriminator Loss: 0.6776, Generator Loss: 2.8824
D(x): 0.7127, D(G(z)): 0.1005
Epoch: [15/20], Batch Num: [227/600]
Discriminator Loss: 0.6936, Generator Loss: 2.3650
D(x): 0.7398, D(G(z)): 0.1361
Epoch: [15/20], Batch Num: [228/600]
Discriminator Loss: 0.7190, Generator Loss: 1.7477
D(x): 0.7269, D(G(z)): 0.1851
Epoch: [15/20], Batch Num: [229/600]
Discriminator Loss: 0.5777, Generator Loss: 1.5671
D(x): 0.8968, D(G(z)): 0.3037
Epoch: [15/20], Batch Num: [230/600]
Discriminator Loss: 0.7606, Generator Loss: 2.0919
D(x): 0.9106, D(G(z)): 0.3414
Epoch: [15/20], Batch Num: [231/600]
Discriminator Loss: 0.7126, Generator Loss: 2.6858
D(x): 0.8136, D(G(z)): 0.2444
Epoch: [15/20], Batch Num: [232/600]
Discriminator Loss: 0.6878, Generator Loss: 2.7338
D(x): 0.7387, D(G(z)): 0.1276
Epoch: [15/20], Batch Num: [233/600]
Discriminator Loss: 0.8613, Generator Loss: 2.3692
D(x): 0.6895, D(G(z)): 0.1217
Epoch: [15/20], Batch Num: [234/600]
Discriminator Loss: 0.7773, Generator Loss: 2.0541
D(x): 0.6940, D(G(z)): 0.1523
Epoch: [15/20], Batch Num: [235/600]
Discriminator Loss: 0.8024, Generator Loss: 1.4357
D(x): 0.7796, D(G(z)): 0.2753
Epoch: [15/20], Batch Num: [236/600]
Discriminator Loss: 0.8460, Generator Loss: 1.4676
D(x): 0.8324, D(G(z)): 0.3693
Epoch: [15/20], Batch Num: [237/600]
Discriminator Loss: 0.7100, Generator Loss: 1.8730
D(x): 0.8456, D(G(z)): 0.3173
Epoch: [15/20], Batch Num: [238/600]
Discriminator Loss: 0.7458, Generator Loss: 1.9358
D(x): 0.7533, D(G(z)): 0.2467
Epoch: [15/20], Batch Num: [239/600]
Discriminator Loss: 0.7796, Generator Loss: 2.2008
D(x): 0.7304, D(G(z)): 0.2126
Epoch: [15/20], Batch Num: [240/600]
Discriminator Loss: 0.9169, Generator Loss: 2.0931
D(x): 0.6842, D(G(z)): 0.2050
Epoch: [15/20], Batch Num: [241/600]
Discriminator Loss: 0.7953, Generator Loss: 2.0039
D(x): 0.7016, D(G(z)): 0.1896
Epoch: [15/20], Batch Num: [242/600]
Discriminator Loss: 0.7314, Generator Loss: 1.6291
D(x): 0.7127, D(G(z)): 0.1962
Epoch: [15/20], Batch Num: [243/600]
Discriminator Loss: 0.7602, Generator Loss: 1.5988
D(x): 0.7837, D(G(z)): 0.2789
Epoch: [15/20], Batch Num: [244/600]
Discriminator Loss: 0.7832, Generator Loss: 1.4469
D(x): 0.8222, D(G(z)): 0.3125
Epoch: [15/20], Batch Num: [245/600]
Discriminator Loss: 0.7133, Generator Loss: 1.5176
D(x): 0.7760, D(G(z)): 0.2772
Epoch: [15/20], Batch Num: [246/600]
Discriminator Loss: 0.8095, Generator Loss: 1.8522
D(x): 0.7486, D(G(z)): 0.2591
Epoch: [15/20], Batch Num: [247/600]
Discriminator Loss: 0.6364, Generator Loss: 1.8185
D(x): 0.7867, D(G(z)): 0.2397
Epoch: [15/20], Batch Num: [248/600]
Discriminator Loss: 0.5902, Generator Loss: 1.9244
D(x): 0.8061, D(G(z)): 0.1985
Epoch: [15/20], Batch Num: [249/600]
Discriminator Loss: 0.7375, Generator Loss: 1.8779
D(x): 0.7007, D(G(z)): 0.1836
Epoch: [15/20], Batch Num: [250/600]
Discriminator Loss: 0.6433, Generator Loss: 1.8150
D(x): 0.7570, D(G(z)): 0.1892
Epoch: [15/20], Batch Num: [251/600]
Discriminator Loss: 0.6994, Generator Loss: 1.5742
D(x): 0.7574, D(G(z)): 0.2104
Epoch: [15/20], Batch Num: [252/600]
Discriminator Loss: 0.8888, Generator Loss: 1.6030
D(x): 0.7659, D(G(z)): 0.3058
Epoch: [15/20], Batch Num: [253/600]
Discriminator Loss: 0.6704, Generator Loss: 1.8218
D(x): 0.8291, D(G(z)): 0.2823
Epoch: [15/20], Batch Num: [254/600]
Discriminator Loss: 0.6785, Generator Loss: 1.8686
D(x): 0.8030, D(G(z)): 0.2569
Epoch: [15/20], Batch Num: [255/600]
Discriminator Loss: 0.8140, Generator Loss: 2.0536
D(x): 0.7479, D(G(z)): 0.2372
Epoch: [15/20], Batch Num: [256/600]
Discriminator Loss: 0.6881, Generator Loss: 2.1437
D(x): 0.7294, D(G(z)): 0.1730
Epoch: [15/20], Batch Num: [257/600]
Discriminator Loss: 0.6570, Generator Loss: 2.2783
D(x): 0.7600, D(G(z)): 0.1850
Epoch: [15/20], Batch Num: [258/600]
Discriminator Loss: 0.5863, Generator Loss: 1.9559
D(x): 0.7745, D(G(z)): 0.1628
Epoch: [15/20], Batch Num: [259/600]
Discriminator Loss: 0.5849, Generator Loss: 2.0480
D(x): 0.8286, D(G(z)): 0.2152
Epoch: [15/20], Batch Num: [260/600]
Discriminator Loss: 0.8095, Generator Loss: 1.8015
D(x): 0.7885, D(G(z)): 0.2804
Epoch: [15/20], Batch Num: [261/600]
Discriminator Loss: 0.5704, Generator Loss: 2.0666
D(x): 0.8022, D(G(z)): 0.1858
Epoch: [15/20], Batch Num: [262/600]
Discriminator Loss: 0.7270, Generator Loss: 1.8567
D(x): 0.7344, D(G(z)): 0.1843
Epoch: [15/20], Batch Num: [263/600]
Discriminator Loss: 0.6860, Generator Loss: 1.5867
D(x): 0.7825, D(G(z)): 0.2133
Epoch: [15/20], Batch Num: [264/600]
Discriminator Loss: 0.7053, Generator Loss: 1.8682
D(x): 0.8114, D(G(z)): 0.2453
Epoch: [15/20], Batch Num: [265/600]
Discriminator Loss: 0.5289, Generator Loss: 2.1348
D(x): 0.8452, D(G(z)): 0.2262
Epoch: [15/20], Batch Num: [266/600]
Discriminator Loss: 0.7834, Generator Loss: 1.7986
D(x): 0.7419, D(G(z)): 0.2297
Epoch: [15/20], Batch Num: [267/600]
Discriminator Loss: 0.6621, Generator Loss: 2.0533
D(x): 0.7671, D(G(z)): 0.1966
Epoch: [15/20], Batch Num: [268/600]
Discriminator Loss: 0.7654, Generator Loss: 1.9109
D(x): 0.7441, D(G(z)): 0.2242
Epoch: [15/20], Batch Num: [269/600]
Discriminator Loss: 0.6945, Generator Loss: 2.0390
D(x): 0.8176, D(G(z)): 0.2577
Epoch: [15/20], Batch Num: [270/600]
Discriminator Loss: 0.7117, Generator Loss: 1.9186
D(x): 0.7744, D(G(z)): 0.2317
Epoch: [15/20], Batch Num: [271/600]
Discriminator Loss: 0.7619, Generator Loss: 1.9615
D(x): 0.7705, D(G(z)): 0.2551
Epoch: [15/20], Batch Num: [272/600]
Discriminator Loss: 0.6551, Generator Loss: 2.0650
D(x): 0.7407, D(G(z)): 0.1834
Epoch: [15/20], Batch Num: [273/600]
Discriminator Loss: 0.5711, Generator Loss: 2.0112
D(x): 0.7998, D(G(z)): 0.1945
Epoch: [15/20], Batch Num: [274/600]
Discriminator Loss: 0.7275, Generator Loss: 2.1538
D(x): 0.7575, D(G(z)): 0.2123
Epoch: [15/20], Batch Num: [275/600]
Discriminator Loss: 0.7685, Generator Loss: 1.9937
D(x): 0.7541, D(G(z)): 0.2400
Epoch: [15/20], Batch Num: [276/600]
Discriminator Loss: 0.7743, Generator Loss: 2.2309
D(x): 0.7764, D(G(z)): 0.2556
Epoch: [15/20], Batch Num: [277/600]
Discriminator Loss: 0.6585, Generator Loss: 2.3537
D(x): 0.8309, D(G(z)): 0.2351
Epoch: [15/20], Batch Num: [278/600]
Discriminator Loss: 0.6657, Generator Loss: 2.3850
D(x): 0.7607, D(G(z)): 0.1951
Epoch: [15/20], Batch Num: [279/600]
Discriminator Loss: 0.6276, Generator Loss: 2.2461
D(x): 0.7749, D(G(z)): 0.1898
Epoch: [15/20], Batch Num: [280/600]
Discriminator Loss: 0.7846, Generator Loss: 2.0480
D(x): 0.7241, D(G(z)): 0.1779
Epoch: [15/20], Batch Num: [281/600]
Discriminator Loss: 0.7017, Generator Loss: 1.8333
D(x): 0.7475, D(G(z)): 0.1698
Epoch: [15/20], Batch Num: [282/600]
Discriminator Loss: 0.7069, Generator Loss: 1.5980
D(x): 0.8003, D(G(z)): 0.2518
Epoch: [15/20], Batch Num: [283/600]
Discriminator Loss: 0.8722, Generator Loss: 1.6880
D(x): 0.8153, D(G(z)): 0.3140
Epoch: [15/20], Batch Num: [284/600]
Discriminator Loss: 0.6636, Generator Loss: 2.0657
D(x): 0.8429, D(G(z)): 0.2889
Epoch: [15/20], Batch Num: [285/600]
Discriminator Loss: 0.7005, Generator Loss: 2.3294
D(x): 0.7930, D(G(z)): 0.2201
Epoch: [15/20], Batch Num: [286/600]
Discriminator Loss: 0.7088, Generator Loss: 2.5146
D(x): 0.7520, D(G(z)): 0.1865
Epoch: [15/20], Batch Num: [287/600]
Discriminator Loss: 0.7010, Generator Loss: 2.1729
D(x): 0.6824, D(G(z)): 0.1247
Epoch: [15/20], Batch Num: [288/600]
Discriminator Loss: 0.6786, Generator Loss: 2.1113
D(x): 0.7312, D(G(z)): 0.1727
Epoch: [15/20], Batch Num: [289/600]
Discriminator Loss: 0.7219, Generator Loss: 1.6338
D(x): 0.7588, D(G(z)): 0.2556
Epoch: [15/20], Batch Num: [290/600]
Discriminator Loss: 0.6605, Generator Loss: 1.6507
D(x): 0.8325, D(G(z)): 0.2878
Epoch: [15/20], Batch Num: [291/600]
Discriminator Loss: 0.5745, Generator Loss: 1.7470
D(x): 0.8901, D(G(z)): 0.2752
Epoch: [15/20], Batch Num: [292/600]
Discriminator Loss: 0.7146, Generator Loss: 2.1482
D(x): 0.8042, D(G(z)): 0.2549
Epoch: [15/20], Batch Num: [293/600]
Discriminator Loss: 0.6824, Generator Loss: 2.4894
D(x): 0.7687, D(G(z)): 0.1814
Epoch: [15/20], Batch Num: [294/600]
Discriminator Loss: 0.9387, Generator Loss: 2.1259
D(x): 0.6788, D(G(z)): 0.1693
Epoch: [15/20], Batch Num: [295/600]
Discriminator Loss: 0.7929, Generator Loss: 2.1722
D(x): 0.7031, D(G(z)): 0.1600
Epoch: [15/20], Batch Num: [296/600]
Discriminator Loss: 0.7707, Generator Loss: 1.5976
D(x): 0.7274, D(G(z)): 0.2105
Epoch: [15/20], Batch Num: [297/600]
Discriminator Loss: 0.6587, Generator Loss: 1.3885
D(x): 0.7950, D(G(z)): 0.2354
Epoch: [15/20], Batch Num: [298/600]
Discriminator Loss: 0.8118, Generator Loss: 1.4990
D(x): 0.8642, D(G(z)): 0.3435
Epoch: [15/20], Batch Num: [299/600]
Discriminator Loss: 0.7132, Generator Loss: 1.9490
D(x): 0.8488, D(G(z)): 0.3136
Epoch: 15, Batch Num: [300/600]

Epoch: [15/20], Batch Num: [300/600]
Discriminator Loss: 0.7004, Generator Loss: 2.4902
D(x): 0.7777, D(G(z)): 0.2360
Epoch: [15/20], Batch Num: [301/600]
Discriminator Loss: 0.7799, Generator Loss: 2.5374
D(x): 0.6888, D(G(z)): 0.1680
Epoch: [15/20], Batch Num: [302/600]
Discriminator Loss: 0.8363, Generator Loss: 2.0954
D(x): 0.6349, D(G(z)): 0.1128
Epoch: [15/20], Batch Num: [303/600]
Discriminator Loss: 0.7399, Generator Loss: 1.6259
D(x): 0.6786, D(G(z)): 0.1441
Epoch: [15/20], Batch Num: [304/600]
Discriminator Loss: 0.6885, Generator Loss: 1.3204
D(x): 0.8087, D(G(z)): 0.2720
Epoch: [15/20], Batch Num: [305/600]
Discriminator Loss: 0.8423, Generator Loss: 1.3119
D(x): 0.8295, D(G(z)): 0.3702
Epoch: [15/20], Batch Num: [306/600]
Discriminator Loss: 0.7448, Generator Loss: 1.4077
D(x): 0.8413, D(G(z)): 0.3303
Epoch: [15/20], Batch Num: [307/600]
Discriminator Loss: 0.7417, Generator Loss: 1.9613
D(x): 0.8473, D(G(z)): 0.3253
Epoch: [15/20], Batch Num: [308/600]
Discriminator Loss: 0.7697, Generator Loss: 2.0204
D(x): 0.7197, D(G(z)): 0.2137
Epoch: [15/20], Batch Num: [309/600]
Discriminator Loss: 0.6658, Generator Loss: 2.1908
D(x): 0.7480, D(G(z)): 0.1778
Epoch: [15/20], Batch Num: [310/600]
Discriminator Loss: 0.7315, Generator Loss: 2.1482
D(x): 0.7224, D(G(z)): 0.1700
Epoch: [15/20], Batch Num: [311/600]
Discriminator Loss: 0.6955, Generator Loss: 1.8639
D(x): 0.7526, D(G(z)): 0.1795
Epoch: [15/20], Batch Num: [312/600]
Discriminator Loss: 0.7423, Generator Loss: 1.9311
D(x): 0.7135, D(G(z)): 0.1864
Epoch: [15/20], Batch Num: [313/600]
Discriminator Loss: 0.8040, Generator Loss: 1.6833
D(x): 0.7620, D(G(z)): 0.2713
Epoch: [15/20], Batch Num: [314/600]
Discriminator Loss: 0.6135, Generator Loss: 1.8091
D(x): 0.8218, D(G(z)): 0.2422
Epoch: [15/20], Batch Num: [315/600]
Discriminator Loss: 0.6756, Generator Loss: 1.5993
D(x): 0.8388, D(G(z)): 0.3031
Epoch: [15/20], Batch Num: [316/600]
Discriminator Loss: 0.6631, Generator Loss: 2.0200
D(x): 0.8231, D(G(z)): 0.2773
Epoch: [15/20], Batch Num: [317/600]
Discriminator Loss: 0.7008, Generator Loss: 2.0126
D(x): 0.7138, D(G(z)): 0.1971
Epoch: [15/20], Batch Num: [318/600]
Discriminator Loss: 0.6103, Generator Loss: 2.0104
D(x): 0.7924, D(G(z)): 0.2115
Epoch: [15/20], Batch Num: [319/600]
Discriminator Loss: 0.6098, Generator Loss: 2.1300
D(x): 0.7701, D(G(z)): 0.1902
Epoch: [15/20], Batch Num: [320/600]
Discriminator Loss: 0.6592, Generator Loss: 1.8068
D(x): 0.7510, D(G(z)): 0.1940
Epoch: [15/20], Batch Num: [321/600]
Discriminator Loss: 0.6865, Generator Loss: 1.8350
D(x): 0.7632, D(G(z)): 0.2139
Epoch: [15/20], Batch Num: [322/600]
Discriminator Loss: 0.6358, Generator Loss: 1.8750
D(x): 0.8351, D(G(z)): 0.2382
Epoch: [15/20], Batch Num: [323/600]
Discriminator Loss: 0.8928, Generator Loss: 1.9886
D(x): 0.7664, D(G(z)): 0.2855
Epoch: [15/20], Batch Num: [324/600]
Discriminator Loss: 0.7403, Generator Loss: 1.6664
D(x): 0.7369, D(G(z)): 0.2256
Epoch: [15/20], Batch Num: [325/600]
Discriminator Loss: 0.7091, Generator Loss: 1.9007
D(x): 0.7629, D(G(z)): 0.2272
Epoch: [15/20], Batch Num: [326/600]
Discriminator Loss: 0.6354, Generator Loss: 1.9325
D(x): 0.7951, D(G(z)): 0.2159
Epoch: [15/20], Batch Num: [327/600]
Discriminator Loss: 0.7079, Generator Loss: 1.8860
D(x): 0.8141, D(G(z)): 0.2609
Epoch: [15/20], Batch Num: [328/600]
Discriminator Loss: 0.6365, Generator Loss: 2.3592
D(x): 0.8326, D(G(z)): 0.2481
Epoch: [15/20], Batch Num: [329/600]
Discriminator Loss: 0.7236, Generator Loss: 2.3342
D(x): 0.7260, D(G(z)): 0.1607
Epoch: [15/20], Batch Num: [330/600]
Discriminator Loss: 0.6931, Generator Loss: 2.2909
D(x): 0.7793, D(G(z)): 0.2018
Epoch: [15/20], Batch Num: [331/600]
Discriminator Loss: 0.6840, Generator Loss: 1.9960
D(x): 0.7173, D(G(z)): 0.1467
Epoch: [15/20], Batch Num: [332/600]
Discriminator Loss: 0.7079, Generator Loss: 1.8069
D(x): 0.7690, D(G(z)): 0.2027
Epoch: [15/20], Batch Num: [333/600]
Discriminator Loss: 0.7629, Generator Loss: 1.6086
D(x): 0.7872, D(G(z)): 0.2667
Epoch: [15/20], Batch Num: [334/600]
Discriminator Loss: 0.7533, Generator Loss: 1.7667
D(x): 0.8309, D(G(z)): 0.3140
Epoch: [15/20], Batch Num: [335/600]
Discriminator Loss: 0.8242, Generator Loss: 1.8113
D(x): 0.7875, D(G(z)): 0.2630
Epoch: [15/20], Batch Num: [336/600]
Discriminator Loss: 0.7838, Generator Loss: 2.1997
D(x): 0.7522, D(G(z)): 0.2339
Epoch: [15/20], Batch Num: [337/600]
Discriminator Loss: 0.7320, Generator Loss: 2.0713
D(x): 0.7203, D(G(z)): 0.1675
Epoch: [15/20], Batch Num: [338/600]
Discriminator Loss: 0.6731, Generator Loss: 1.9400
D(x): 0.7365, D(G(z)): 0.1749
Epoch: [15/20], Batch Num: [339/600]
Discriminator Loss: 0.6880, Generator Loss: 1.7531
D(x): 0.7860, D(G(z)): 0.2306
Epoch: [15/20], Batch Num: [340/600]
Discriminator Loss: 0.6576, Generator Loss: 1.7246
D(x): 0.7810, D(G(z)): 0.2189
Epoch: [15/20], Batch Num: [341/600]
Discriminator Loss: 0.7307, Generator Loss: 1.6745
D(x): 0.7716, D(G(z)): 0.2538
Epoch: [15/20], Batch Num: [342/600]
Discriminator Loss: 0.7476, Generator Loss: 1.6404
D(x): 0.7387, D(G(z)): 0.2257
Epoch: [15/20], Batch Num: [343/600]
Discriminator Loss: 0.8443, Generator Loss: 1.8446
D(x): 0.7570, D(G(z)): 0.2620
Epoch: [15/20], Batch Num: [344/600]
Discriminator Loss: 0.7537, Generator Loss: 1.7768
D(x): 0.7563, D(G(z)): 0.2387
Epoch: [15/20], Batch Num: [345/600]
Discriminator Loss: 0.8334, Generator Loss: 1.8413
D(x): 0.7421, D(G(z)): 0.2485
Epoch: [15/20], Batch Num: [346/600]
Discriminator Loss: 0.5989, Generator Loss: 1.8936
D(x): 0.8011, D(G(z)): 0.2094
Epoch: [15/20], Batch Num: [347/600]
Discriminator Loss: 0.6958, Generator Loss: 1.9578
D(x): 0.7823, D(G(z)): 0.2447
Epoch: [15/20], Batch Num: [348/600]
Discriminator Loss: 0.6798, Generator Loss: 1.9057
D(x): 0.7843, D(G(z)): 0.2340
Epoch: [15/20], Batch Num: [349/600]
Discriminator Loss: 0.6978, Generator Loss: 2.0235
D(x): 0.7741, D(G(z)): 0.2222
Epoch: [15/20], Batch Num: [350/600]
Discriminator Loss: 0.7495, Generator Loss: 2.0057
D(x): 0.7399, D(G(z)): 0.2269
Epoch: [15/20], Batch Num: [351/600]
Discriminator Loss: 0.7251, Generator Loss: 2.0294
D(x): 0.7460, D(G(z)): 0.1903
Epoch: [15/20], Batch Num: [352/600]
Discriminator Loss: 0.6114, Generator Loss: 1.8733
D(x): 0.8129, D(G(z)): 0.2327
Epoch: [15/20], Batch Num: [353/600]
Discriminator Loss: 0.7717, Generator Loss: 2.1404
D(x): 0.7515, D(G(z)): 0.2434
Epoch: [15/20], Batch Num: [354/600]
Discriminator Loss: 0.5893, Generator Loss: 2.0338
D(x): 0.8180, D(G(z)): 0.2161
Epoch: [15/20], Batch Num: [355/600]
Discriminator Loss: 0.7049, Generator Loss: 2.0249
D(x): 0.7853, D(G(z)): 0.2139
Epoch: [15/20], Batch Num: [356/600]
Discriminator Loss: 0.6491, Generator Loss: 2.1516
D(x): 0.8023, D(G(z)): 0.2245
Epoch: [15/20], Batch Num: [357/600]
Discriminator Loss: 0.6889, Generator Loss: 2.0442
D(x): 0.7683, D(G(z)): 0.2098
Epoch: [15/20], Batch Num: [358/600]
Discriminator Loss: 0.5962, Generator Loss: 2.1492
D(x): 0.8133, D(G(z)): 0.2035
Epoch: [15/20], Batch Num: [359/600]
Discriminator Loss: 0.6105, Generator Loss: 2.0676
D(x): 0.8056, D(G(z)): 0.2080
Epoch: [15/20], Batch Num: [360/600]
Discriminator Loss: 0.6200, Generator Loss: 2.2343
D(x): 0.8011, D(G(z)): 0.1819
Epoch: [15/20], Batch Num: [361/600]
Discriminator Loss: 0.6163, Generator Loss: 1.9323
D(x): 0.7984, D(G(z)): 0.1901
Epoch: [15/20], Batch Num: [362/600]
Discriminator Loss: 0.7089, Generator Loss: 1.9025
D(x): 0.7668, D(G(z)): 0.2070
Epoch: [15/20], Batch Num: [363/600]
Discriminator Loss: 0.7076, Generator Loss: 1.7798
D(x): 0.7806, D(G(z)): 0.2408
Epoch: [15/20], Batch Num: [364/600]
Discriminator Loss: 0.5589, Generator Loss: 2.0156
D(x): 0.8650, D(G(z)): 0.2270
Epoch: [15/20], Batch Num: [365/600]
Discriminator Loss: 0.7595, Generator Loss: 2.1028
D(x): 0.7611, D(G(z)): 0.2385
Epoch: [15/20], Batch Num: [366/600]
Discriminator Loss: 0.7376, Generator Loss: 2.1488
D(x): 0.7674, D(G(z)): 0.2260
Epoch: [15/20], Batch Num: [367/600]
Discriminator Loss: 0.6863, Generator Loss: 2.1357
D(x): 0.7797, D(G(z)): 0.2378
Epoch: [15/20], Batch Num: [368/600]
Discriminator Loss: 0.6608, Generator Loss: 2.0985
D(x): 0.7846, D(G(z)): 0.1916
Epoch: [15/20], Batch Num: [369/600]
Discriminator Loss: 0.7810, Generator Loss: 1.8703
D(x): 0.7559, D(G(z)): 0.2282
Epoch: [15/20], Batch Num: [370/600]
Discriminator Loss: 0.7028, Generator Loss: 1.8429
D(x): 0.7519, D(G(z)): 0.2036
Epoch: [15/20], Batch Num: [371/600]
Discriminator Loss: 0.6037, Generator Loss: 1.8628
D(x): 0.8218, D(G(z)): 0.2393
Epoch: [15/20], Batch Num: [372/600]
Discriminator Loss: 0.7158, Generator Loss: 1.9972
D(x): 0.7844, D(G(z)): 0.2548
Epoch: [15/20], Batch Num: [373/600]
Discriminator Loss: 0.7128, Generator Loss: 1.9168
D(x): 0.8214, D(G(z)): 0.2453
Epoch: [15/20], Batch Num: [374/600]
Discriminator Loss: 0.8049, Generator Loss: 2.3139
D(x): 0.7335, D(G(z)): 0.2358
Epoch: [15/20], Batch Num: [375/600]
Discriminator Loss: 0.6647, Generator Loss: 2.1729
D(x): 0.7662, D(G(z)): 0.1812
Epoch: [15/20], Batch Num: [376/600]
Discriminator Loss: 0.7674, Generator Loss: 2.1445
D(x): 0.7203, D(G(z)): 0.1715
Epoch: [15/20], Batch Num: [377/600]
Discriminator Loss: 0.6824, Generator Loss: 1.8323
D(x): 0.8012, D(G(z)): 0.2281
Epoch: [15/20], Batch Num: [378/600]
Discriminator Loss: 0.7646, Generator Loss: 2.0212
D(x): 0.7835, D(G(z)): 0.2499
Epoch: [15/20], Batch Num: [379/600]
Discriminator Loss: 0.6975, Generator Loss: 1.9194
D(x): 0.8023, D(G(z)): 0.2500
Epoch: [15/20], Batch Num: [380/600]
Discriminator Loss: 0.5872, Generator Loss: 1.8584
D(x): 0.8057, D(G(z)): 0.1880
Epoch: [15/20], Batch Num: [381/600]
Discriminator Loss: 0.7497, Generator Loss: 1.9884
D(x): 0.7422, D(G(z)): 0.2171
Epoch: [15/20], Batch Num: [382/600]
Discriminator Loss: 0.5904, Generator Loss: 1.9389
D(x): 0.8053, D(G(z)): 0.2072
Epoch: [15/20], Batch Num: [383/600]
Discriminator Loss: 0.6903, Generator Loss: 2.2549
D(x): 0.7681, D(G(z)): 0.2160
Epoch: [15/20], Batch Num: [384/600]
Discriminator Loss: 0.6345, Generator Loss: 2.0090
D(x): 0.7684, D(G(z)): 0.1815
Epoch: [15/20], Batch Num: [385/600]
Discriminator Loss: 0.7927, Generator Loss: 1.7919
D(x): 0.7000, D(G(z)): 0.1910
Epoch: [15/20], Batch Num: [386/600]
Discriminator Loss: 0.6651, Generator Loss: 1.6771
D(x): 0.7991, D(G(z)): 0.2327
Epoch: [15/20], Batch Num: [387/600]
Discriminator Loss: 0.6369, Generator Loss: 1.7263
D(x): 0.8361, D(G(z)): 0.2436
Epoch: [15/20], Batch Num: [388/600]
Discriminator Loss: 0.6482, Generator Loss: 1.8554
D(x): 0.8493, D(G(z)): 0.2756
Epoch: [15/20], Batch Num: [389/600]
Discriminator Loss: 0.6408, Generator Loss: 2.5482
D(x): 0.8188, D(G(z)): 0.2418
Epoch: [15/20], Batch Num: [390/600]
Discriminator Loss: 0.6236, Generator Loss: 2.7030
D(x): 0.7397, D(G(z)): 0.1452
Epoch: [15/20], Batch Num: [391/600]
Discriminator Loss: 0.4915, Generator Loss: 2.3370
D(x): 0.8073, D(G(z)): 0.1383
Epoch: [15/20], Batch Num: [392/600]
Discriminator Loss: 0.6682, Generator Loss: 2.3092
D(x): 0.7648, D(G(z)): 0.1646
Epoch: [15/20], Batch Num: [393/600]
Discriminator Loss: 0.6902, Generator Loss: 1.8879
D(x): 0.7517, D(G(z)): 0.1729
Epoch: [15/20], Batch Num: [394/600]
Discriminator Loss: 0.7716, Generator Loss: 1.5250
D(x): 0.7821, D(G(z)): 0.2210
Epoch: [15/20], Batch Num: [395/600]
Discriminator Loss: 0.8140, Generator Loss: 1.6949
D(x): 0.8600, D(G(z)): 0.3088
Epoch: [15/20], Batch Num: [396/600]
Discriminator Loss: 0.6231, Generator Loss: 2.0256
D(x): 0.8750, D(G(z)): 0.2866
Epoch: [15/20], Batch Num: [397/600]
Discriminator Loss: 0.7587, Generator Loss: 2.5046
D(x): 0.7484, D(G(z)): 0.1997
Epoch: [15/20], Batch Num: [398/600]
Discriminator Loss: 0.6753, Generator Loss: 2.5054
D(x): 0.7397, D(G(z)): 0.1419
Epoch: [15/20], Batch Num: [399/600]
Discriminator Loss: 0.8364, Generator Loss: 2.2566
D(x): 0.7236, D(G(z)): 0.1614
Epoch: 15, Batch Num: [400/600]

Epoch: [15/20], Batch Num: [400/600]
Discriminator Loss: 0.7011, Generator Loss: 2.0899
D(x): 0.7616, D(G(z)): 0.1833
Epoch: [15/20], Batch Num: [401/600]
Discriminator Loss: 0.6263, Generator Loss: 1.5757
D(x): 0.7642, D(G(z)): 0.1815
Epoch: [15/20], Batch Num: [402/600]
Discriminator Loss: 0.8570, Generator Loss: 1.6049
D(x): 0.7962, D(G(z)): 0.3297
Epoch: [15/20], Batch Num: [403/600]
Discriminator Loss: 0.8390, Generator Loss: 1.6758
D(x): 0.8075, D(G(z)): 0.3463
Epoch: [15/20], Batch Num: [404/600]
Discriminator Loss: 0.7731, Generator Loss: 2.2851
D(x): 0.8211, D(G(z)): 0.3117
Epoch: [15/20], Batch Num: [405/600]
Discriminator Loss: 0.7400, Generator Loss: 2.4121
D(x): 0.7363, D(G(z)): 0.1671
Epoch: [15/20], Batch Num: [406/600]
Discriminator Loss: 0.8518, Generator Loss: 2.1518
D(x): 0.6403, D(G(z)): 0.1267
Epoch: [15/20], Batch Num: [407/600]
Discriminator Loss: 0.7728, Generator Loss: 1.7892
D(x): 0.6847, D(G(z)): 0.1395
Epoch: [15/20], Batch Num: [408/600]
Discriminator Loss: 0.8912, Generator Loss: 1.6382
D(x): 0.7467, D(G(z)): 0.2993
Epoch: [15/20], Batch Num: [409/600]
Discriminator Loss: 0.6752, Generator Loss: 1.3259
D(x): 0.8423, D(G(z)): 0.2846
Epoch: [15/20], Batch Num: [410/600]
Discriminator Loss: 0.7076, Generator Loss: 1.5757
D(x): 0.8280, D(G(z)): 0.2926
Epoch: [15/20], Batch Num: [411/600]
Discriminator Loss: 0.8680, Generator Loss: 1.7145
D(x): 0.7883, D(G(z)): 0.3301
Epoch: [15/20], Batch Num: [412/600]
Discriminator Loss: 0.7302, Generator Loss: 1.9935
D(x): 0.8046, D(G(z)): 0.2671
Epoch: [15/20], Batch Num: [413/600]
Discriminator Loss: 0.7561, Generator Loss: 2.1767
D(x): 0.7247, D(G(z)): 0.1895
Epoch: [15/20], Batch Num: [414/600]
Discriminator Loss: 0.8158, Generator Loss: 2.2935
D(x): 0.7128, D(G(z)): 0.1834
Epoch: [15/20], Batch Num: [415/600]
Discriminator Loss: 0.7177, Generator Loss: 2.0417
D(x): 0.7262, D(G(z)): 0.1648
Epoch: [15/20], Batch Num: [416/600]
Discriminator Loss: 0.6569, Generator Loss: 1.8561
D(x): 0.7921, D(G(z)): 0.2025
Epoch: [15/20], Batch Num: [417/600]
Discriminator Loss: 0.6651, Generator Loss: 1.6612
D(x): 0.7520, D(G(z)): 0.1956
Epoch: [15/20], Batch Num: [418/600]
Discriminator Loss: 0.7252, Generator Loss: 1.4806
D(x): 0.7666, D(G(z)): 0.2515
Epoch: [15/20], Batch Num: [419/600]
Discriminator Loss: 0.6474, Generator Loss: 1.6347
D(x): 0.8230, D(G(z)): 0.2839
Epoch: [15/20], Batch Num: [420/600]
Discriminator Loss: 0.5891, Generator Loss: 1.7447
D(x): 0.8628, D(G(z)): 0.2796
Epoch: [15/20], Batch Num: [421/600]
Discriminator Loss: 0.7365, Generator Loss: 1.9447
D(x): 0.7799, D(G(z)): 0.2655
Epoch: [15/20], Batch Num: [422/600]
Discriminator Loss: 0.6179, Generator Loss: 2.0725
D(x): 0.7706, D(G(z)): 0.2038
Epoch: [15/20], Batch Num: [423/600]
Discriminator Loss: 0.8381, Generator Loss: 2.1214
D(x): 0.6930, D(G(z)): 0.2035
Epoch: [15/20], Batch Num: [424/600]
Discriminator Loss: 0.6998, Generator Loss: 2.2064
D(x): 0.7739, D(G(z)): 0.1878
Epoch: [15/20], Batch Num: [425/600]
Discriminator Loss: 0.6942, Generator Loss: 2.1762
D(x): 0.7113, D(G(z)): 0.1539
Epoch: [15/20], Batch Num: [426/600]
Discriminator Loss: 0.8315, Generator Loss: 1.7219
D(x): 0.6711, D(G(z)): 0.1763
Epoch: [15/20], Batch Num: [427/600]
Discriminator Loss: 0.8046, Generator Loss: 1.4297
D(x): 0.7735, D(G(z)): 0.2826
Epoch: [15/20], Batch Num: [428/600]
Discriminator Loss: 0.7191, Generator Loss: 1.4847
D(x): 0.8344, D(G(z)): 0.3157
Epoch: [15/20], Batch Num: [429/600]
Discriminator Loss: 0.7682, Generator Loss: 1.4256
D(x): 0.7965, D(G(z)): 0.2781
Epoch: [15/20], Batch Num: [430/600]
Discriminator Loss: 0.7329, Generator Loss: 1.9602
D(x): 0.8121, D(G(z)): 0.2739
Epoch: [15/20], Batch Num: [431/600]
Discriminator Loss: 0.6740, Generator Loss: 2.3128
D(x): 0.7846, D(G(z)): 0.2272
Epoch: [15/20], Batch Num: [432/600]
Discriminator Loss: 0.7736, Generator Loss: 2.1915
D(x): 0.7251, D(G(z)): 0.2023
Epoch: [15/20], Batch Num: [433/600]
Discriminator Loss: 0.7537, Generator Loss: 2.2650
D(x): 0.7364, D(G(z)): 0.1871
Epoch: [15/20], Batch Num: [434/600]
Discriminator Loss: 0.6192, Generator Loss: 2.1250
D(x): 0.7626, D(G(z)): 0.1519
Epoch: [15/20], Batch Num: [435/600]
Discriminator Loss: 0.8121, Generator Loss: 1.6878
D(x): 0.7109, D(G(z)): 0.2044
Epoch: [15/20], Batch Num: [436/600]
Discriminator Loss: 0.7542, Generator Loss: 1.5402
D(x): 0.7954, D(G(z)): 0.2562
Epoch: [15/20], Batch Num: [437/600]
Discriminator Loss: 0.9180, Generator Loss: 1.5408
D(x): 0.7725, D(G(z)): 0.3451
Epoch: [15/20], Batch Num: [438/600]
Discriminator Loss: 0.8105, Generator Loss: 1.7588
D(x): 0.7727, D(G(z)): 0.3004
Epoch: [15/20], Batch Num: [439/600]
Discriminator Loss: 0.8615, Generator Loss: 2.0823
D(x): 0.7716, D(G(z)): 0.3110
Epoch: [15/20], Batch Num: [440/600]
Discriminator Loss: 0.7191, Generator Loss: 1.9994
D(x): 0.7376, D(G(z)): 0.1944
Epoch: [15/20], Batch Num: [441/600]
Discriminator Loss: 0.7368, Generator Loss: 2.0983
D(x): 0.7105, D(G(z)): 0.1786
Epoch: [15/20], Batch Num: [442/600]
Discriminator Loss: 0.7945, Generator Loss: 1.8496
D(x): 0.7146, D(G(z)): 0.2151
Epoch: [15/20], Batch Num: [443/600]
Discriminator Loss: 0.8038, Generator Loss: 1.8121
D(x): 0.7207, D(G(z)): 0.2179
Epoch: [15/20], Batch Num: [444/600]
Discriminator Loss: 0.7871, Generator Loss: 1.4323
D(x): 0.7641, D(G(z)): 0.2633
Epoch: [15/20], Batch Num: [445/600]
Discriminator Loss: 0.8723, Generator Loss: 1.5293
D(x): 0.7642, D(G(z)): 0.3242
Epoch: [15/20], Batch Num: [446/600]
Discriminator Loss: 0.7775, Generator Loss: 1.5175
D(x): 0.8013, D(G(z)): 0.3070
Epoch: [15/20], Batch Num: [447/600]
Discriminator Loss: 0.7621, Generator Loss: 1.8197
D(x): 0.7813, D(G(z)): 0.2866
Epoch: [15/20], Batch Num: [448/600]
Discriminator Loss: 0.7071, Generator Loss: 1.8334
D(x): 0.7313, D(G(z)): 0.2306
Epoch: [15/20], Batch Num: [449/600]
Discriminator Loss: 0.7199, Generator Loss: 1.8873
D(x): 0.7413, D(G(z)): 0.2034
Epoch: [15/20], Batch Num: [450/600]
Discriminator Loss: 0.6834, Generator Loss: 1.8040
D(x): 0.7327, D(G(z)): 0.2081
Epoch: [15/20], Batch Num: [451/600]
Discriminator Loss: 0.7597, Generator Loss: 1.8456
D(x): 0.7407, D(G(z)): 0.2284
Epoch: [15/20], Batch Num: [452/600]
Discriminator Loss: 0.7939, Generator Loss: 1.7274
D(x): 0.7096, D(G(z)): 0.2358
Epoch: [15/20], Batch Num: [453/600]
Discriminator Loss: 0.8551, Generator Loss: 1.6171
D(x): 0.6920, D(G(z)): 0.2524
Epoch: [15/20], Batch Num: [454/600]
Discriminator Loss: 0.7832, Generator Loss: 1.4884
D(x): 0.7763, D(G(z)): 0.3122
Epoch: [15/20], Batch Num: [455/600]
Discriminator Loss: 0.7858, Generator Loss: 1.6242
D(x): 0.7963, D(G(z)): 0.3156
Epoch: [15/20], Batch Num: [456/600]
Discriminator Loss: 0.7376, Generator Loss: 1.7829
D(x): 0.7813, D(G(z)): 0.2885
Epoch: [15/20], Batch Num: [457/600]
Discriminator Loss: 0.7343, Generator Loss: 1.8089
D(x): 0.7373, D(G(z)): 0.2275
Epoch: [15/20], Batch Num: [458/600]
Discriminator Loss: 0.8210, Generator Loss: 1.7991
D(x): 0.7346, D(G(z)): 0.2469
Epoch: [15/20], Batch Num: [459/600]
Discriminator Loss: 0.6342, Generator Loss: 2.0337
D(x): 0.7833, D(G(z)): 0.2160
Epoch: [15/20], Batch Num: [460/600]
Discriminator Loss: 0.8020, Generator Loss: 2.0416
D(x): 0.6958, D(G(z)): 0.1943
Epoch: [15/20], Batch Num: [461/600]
Discriminator Loss: 0.8201, Generator Loss: 1.8651
D(x): 0.7139, D(G(z)): 0.2339
Epoch: [15/20], Batch Num: [462/600]
Discriminator Loss: 0.7931, Generator Loss: 1.6800
D(x): 0.7399, D(G(z)): 0.2435
Epoch: [15/20], Batch Num: [463/600]
Discriminator Loss: 0.7503, Generator Loss: 1.6444
D(x): 0.7925, D(G(z)): 0.2874
Epoch: [15/20], Batch Num: [464/600]
Discriminator Loss: 0.7338, Generator Loss: 1.7492
D(x): 0.8089, D(G(z)): 0.2958
Epoch: [15/20], Batch Num: [465/600]
Discriminator Loss: 0.7693, Generator Loss: 1.8134
D(x): 0.7701, D(G(z)): 0.2707
Epoch: [15/20], Batch Num: [466/600]
Discriminator Loss: 0.6690, Generator Loss: 1.8891
D(x): 0.7714, D(G(z)): 0.2228
Epoch: [15/20], Batch Num: [467/600]
Discriminator Loss: 0.6412, Generator Loss: 2.0710
D(x): 0.7418, D(G(z)): 0.1790
Epoch: [15/20], Batch Num: [468/600]
Discriminator Loss: 0.6812, Generator Loss: 1.8639
D(x): 0.7334, D(G(z)): 0.1902
Epoch: [15/20], Batch Num: [469/600]
Discriminator Loss: 0.8360, Generator Loss: 1.6996
D(x): 0.7516, D(G(z)): 0.2780
Epoch: [15/20], Batch Num: [470/600]
Discriminator Loss: 0.6921, Generator Loss: 1.9329
D(x): 0.7859, D(G(z)): 0.2358
Epoch: [15/20], Batch Num: [471/600]
Discriminator Loss: 0.5658, Generator Loss: 1.7393
D(x): 0.8541, D(G(z)): 0.2524
Epoch: [15/20], Batch Num: [472/600]
Discriminator Loss: 0.6765, Generator Loss: 1.7833
D(x): 0.7898, D(G(z)): 0.2240
Epoch: [15/20], Batch Num: [473/600]
Discriminator Loss: 0.8048, Generator Loss: 2.0133
D(x): 0.7310, D(G(z)): 0.2441
Epoch: [15/20], Batch Num: [474/600]
Discriminator Loss: 0.7670, Generator Loss: 1.9291
D(x): 0.7225, D(G(z)): 0.2176
Epoch: [15/20], Batch Num: [475/600]
Discriminator Loss: 0.7623, Generator Loss: 1.8394
D(x): 0.7508, D(G(z)): 0.2462
Epoch: [15/20], Batch Num: [476/600]
Discriminator Loss: 0.6737, Generator Loss: 1.8238
D(x): 0.7917, D(G(z)): 0.2386
Epoch: [15/20], Batch Num: [477/600]
Discriminator Loss: 0.7698, Generator Loss: 1.8906
D(x): 0.7649, D(G(z)): 0.2585
Epoch: [15/20], Batch Num: [478/600]
Discriminator Loss: 0.6559, Generator Loss: 1.8507
D(x): 0.8050, D(G(z)): 0.2578
Epoch: [15/20], Batch Num: [479/600]
Discriminator Loss: 0.6768, Generator Loss: 1.9173
D(x): 0.7757, D(G(z)): 0.2122
Epoch: [15/20], Batch Num: [480/600]
Discriminator Loss: 0.8161, Generator Loss: 1.9435
D(x): 0.7364, D(G(z)): 0.2315
Epoch: [15/20], Batch Num: [481/600]
Discriminator Loss: 0.8102, Generator Loss: 1.8355
D(x): 0.7502, D(G(z)): 0.2410
Epoch: [15/20], Batch Num: [482/600]
Discriminator Loss: 0.7269, Generator Loss: 1.9389
D(x): 0.7895, D(G(z)): 0.2406
Epoch: [15/20], Batch Num: [483/600]
Discriminator Loss: 0.7538, Generator Loss: 1.7686
D(x): 0.7322, D(G(z)): 0.1990
Epoch: [15/20], Batch Num: [484/600]
Discriminator Loss: 0.8129, Generator Loss: 1.8283
D(x): 0.7518, D(G(z)): 0.2559
Epoch: [15/20], Batch Num: [485/600]
Discriminator Loss: 0.5973, Generator Loss: 1.7497
D(x): 0.7896, D(G(z)): 0.1958
Epoch: [15/20], Batch Num: [486/600]
Discriminator Loss: 0.7080, Generator Loss: 1.6879
D(x): 0.8022, D(G(z)): 0.2427
Epoch: [15/20], Batch Num: [487/600]
Discriminator Loss: 0.7172, Generator Loss: 2.0292
D(x): 0.8285, D(G(z)): 0.2763
Epoch: [15/20], Batch Num: [488/600]
Discriminator Loss: 0.6733, Generator Loss: 1.9510
D(x): 0.7867, D(G(z)): 0.2222
Epoch: [15/20], Batch Num: [489/600]
Discriminator Loss: 0.7646, Generator Loss: 2.1366
D(x): 0.7469, D(G(z)): 0.2186
Epoch: [15/20], Batch Num: [490/600]
Discriminator Loss: 0.8692, Generator Loss: 2.0105
D(x): 0.6761, D(G(z)): 0.2035
Epoch: [15/20], Batch Num: [491/600]
Discriminator Loss: 0.6763, Generator Loss: 1.8194
D(x): 0.7956, D(G(z)): 0.2200
Epoch: [15/20], Batch Num: [492/600]
Discriminator Loss: 0.6977, Generator Loss: 1.6949
D(x): 0.7677, D(G(z)): 0.2113
Epoch: [15/20], Batch Num: [493/600]
Discriminator Loss: 0.8677, Generator Loss: 1.6199
D(x): 0.7987, D(G(z)): 0.3327
Epoch: [15/20], Batch Num: [494/600]
Discriminator Loss: 0.8743, Generator Loss: 1.9579
D(x): 0.8005, D(G(z)): 0.3059
Epoch: [15/20], Batch Num: [495/600]
Discriminator Loss: 0.6364, Generator Loss: 2.2720
D(x): 0.8295, D(G(z)): 0.2493
Epoch: [15/20], Batch Num: [496/600]
Discriminator Loss: 0.9429, Generator Loss: 2.2289
D(x): 0.6604, D(G(z)): 0.1772
Epoch: [15/20], Batch Num: [497/600]
Discriminator Loss: 0.6466, Generator Loss: 2.2828
D(x): 0.7210, D(G(z)): 0.1169
Epoch: [15/20], Batch Num: [498/600]
Discriminator Loss: 0.7792, Generator Loss: 2.0012
D(x): 0.7469, D(G(z)): 0.2318
Epoch: [15/20], Batch Num: [499/600]
Discriminator Loss: 0.7642, Generator Loss: 1.6062
D(x): 0.7644, D(G(z)): 0.2255
Epoch: 15, Batch Num: [500/600]

Epoch: [15/20], Batch Num: [500/600]
Discriminator Loss: 0.5763, Generator Loss: 1.6446
D(x): 0.8456, D(G(z)): 0.2441
Epoch: [15/20], Batch Num: [501/600]
Discriminator Loss: 0.6298, Generator Loss: 1.7868
D(x): 0.8361, D(G(z)): 0.2671
Epoch: [15/20], Batch Num: [502/600]
Discriminator Loss: 0.6349, Generator Loss: 1.8337
D(x): 0.7924, D(G(z)): 0.2180
Epoch: [15/20], Batch Num: [503/600]
Discriminator Loss: 0.7189, Generator Loss: 1.8841
D(x): 0.7760, D(G(z)): 0.2382
Epoch: [15/20], Batch Num: [504/600]
Discriminator Loss: 0.6873, Generator Loss: 1.7133
D(x): 0.7519, D(G(z)): 0.2054
Epoch: [15/20], Batch Num: [505/600]
Discriminator Loss: 0.5528, Generator Loss: 1.8994
D(x): 0.7904, D(G(z)): 0.1741
Epoch: [15/20], Batch Num: [506/600]
Discriminator Loss: 0.6015, Generator Loss: 1.9608
D(x): 0.7925, D(G(z)): 0.2072
Epoch: [15/20], Batch Num: [507/600]
Discriminator Loss: 0.7676, Generator Loss: 1.7913
D(x): 0.7828, D(G(z)): 0.2609
Epoch: [15/20], Batch Num: [508/600]
Discriminator Loss: 0.5888, Generator Loss: 2.0321
D(x): 0.8128, D(G(z)): 0.2342
Epoch: [15/20], Batch Num: [509/600]
Discriminator Loss: 0.6732, Generator Loss: 2.3159
D(x): 0.8191, D(G(z)): 0.2425
Epoch: [15/20], Batch Num: [510/600]
Discriminator Loss: 0.7089, Generator Loss: 2.1899
D(x): 0.7343, D(G(z)): 0.1723
Epoch: [15/20], Batch Num: [511/600]
Discriminator Loss: 0.6725, Generator Loss: 2.2278
D(x): 0.7868, D(G(z)): 0.1783
Epoch: [15/20], Batch Num: [512/600]
Discriminator Loss: 0.7075, Generator Loss: 1.9112
D(x): 0.7496, D(G(z)): 0.1829
Epoch: [15/20], Batch Num: [513/600]
Discriminator Loss: 0.6262, Generator Loss: 1.8791
D(x): 0.8028, D(G(z)): 0.1907
Epoch: [15/20], Batch Num: [514/600]
Discriminator Loss: 0.7484, Generator Loss: 1.7764
D(x): 0.8074, D(G(z)): 0.2455
Epoch: [15/20], Batch Num: [515/600]
Discriminator Loss: 0.6153, Generator Loss: 1.9894
D(x): 0.8411, D(G(z)): 0.2572
Epoch: [15/20], Batch Num: [516/600]
Discriminator Loss: 0.7605, Generator Loss: 2.3351
D(x): 0.7848, D(G(z)): 0.2307
Epoch: [15/20], Batch Num: [517/600]
Discriminator Loss: 0.8706, Generator Loss: 2.2315
D(x): 0.6910, D(G(z)): 0.1911
Epoch: [15/20], Batch Num: [518/600]
Discriminator Loss: 0.8037, Generator Loss: 1.8973
D(x): 0.6740, D(G(z)): 0.1400
Epoch: [15/20], Batch Num: [519/600]
Discriminator Loss: 0.7520, Generator Loss: 1.4323
D(x): 0.7453, D(G(z)): 0.2051
Epoch: [15/20], Batch Num: [520/600]
Discriminator Loss: 0.8691, Generator Loss: 1.3806
D(x): 0.7900, D(G(z)): 0.3181
Epoch: [15/20], Batch Num: [521/600]
Discriminator Loss: 0.8102, Generator Loss: 1.5420
D(x): 0.8298, D(G(z)): 0.3460
Epoch: [15/20], Batch Num: [522/600]
Discriminator Loss: 0.7246, Generator Loss: 1.8818
D(x): 0.8105, D(G(z)): 0.2878
Epoch: [15/20], Batch Num: [523/600]
Discriminator Loss: 0.6702, Generator Loss: 2.0928
D(x): 0.7808, D(G(z)): 0.2346
Epoch: [15/20], Batch Num: [524/600]
Discriminator Loss: 0.8217, Generator Loss: 2.3246
D(x): 0.6910, D(G(z)): 0.2017
Epoch: [15/20], Batch Num: [525/600]
Discriminator Loss: 0.8016, Generator Loss: 2.0876
D(x): 0.6693, D(G(z)): 0.1643
Epoch: [15/20], Batch Num: [526/600]
Discriminator Loss: 0.7576, Generator Loss: 1.8454
D(x): 0.7677, D(G(z)): 0.2206
Epoch: [15/20], Batch Num: [527/600]
Discriminator Loss: 0.9369, Generator Loss: 1.6702
D(x): 0.6889, D(G(z)): 0.2528
Epoch: [15/20], Batch Num: [528/600]
Discriminator Loss: 0.6364, Generator Loss: 1.7807
D(x): 0.8191, D(G(z)): 0.2577
Epoch: [15/20], Batch Num: [529/600]
Discriminator Loss: 0.7925, Generator Loss: 1.4311
D(x): 0.7967, D(G(z)): 0.3047
Epoch: [15/20], Batch Num: [530/600]
Discriminator Loss: 0.7821, Generator Loss: 1.8321
D(x): 0.8016, D(G(z)): 0.3094
Epoch: [15/20], Batch Num: [531/600]
Discriminator Loss: 0.8978, Generator Loss: 1.8591
D(x): 0.7279, D(G(z)): 0.2810
Epoch: [15/20], Batch Num: [532/600]
Discriminator Loss: 0.6670, Generator Loss: 1.9434
D(x): 0.7568, D(G(z)): 0.2078
Epoch: [15/20], Batch Num: [533/600]
Discriminator Loss: 0.7212, Generator Loss: 2.0118
D(x): 0.7360, D(G(z)): 0.2043
Epoch: [15/20], Batch Num: [534/600]
Discriminator Loss: 0.6813, Generator Loss: 1.7389
D(x): 0.7357, D(G(z)): 0.1907
Epoch: [15/20], Batch Num: [535/600]
Discriminator Loss: 0.8100, Generator Loss: 1.5848
D(x): 0.7361, D(G(z)): 0.2695
Epoch: [15/20], Batch Num: [536/600]
Discriminator Loss: 0.6431, Generator Loss: 1.6023
D(x): 0.7883, D(G(z)): 0.2351
Epoch: [15/20], Batch Num: [537/600]
Discriminator Loss: 0.7274, Generator Loss: 1.5951
D(x): 0.7970, D(G(z)): 0.2816
Epoch: [15/20], Batch Num: [538/600]
Discriminator Loss: 0.7083, Generator Loss: 1.7683
D(x): 0.7835, D(G(z)): 0.2780
Epoch: [15/20], Batch Num: [539/600]
Discriminator Loss: 0.8125, Generator Loss: 1.7230
D(x): 0.7692, D(G(z)): 0.2925
Epoch: [15/20], Batch Num: [540/600]
Discriminator Loss: 0.6886, Generator Loss: 2.0528
D(x): 0.7572, D(G(z)): 0.2072
Epoch: [15/20], Batch Num: [541/600]
Discriminator Loss: 0.7455, Generator Loss: 2.0491
D(x): 0.7094, D(G(z)): 0.2000
Epoch: [15/20], Batch Num: [542/600]
Discriminator Loss: 0.7779, Generator Loss: 1.9196
D(x): 0.7060, D(G(z)): 0.2205
Epoch: [15/20], Batch Num: [543/600]
Discriminator Loss: 0.6684, Generator Loss: 1.8024
D(x): 0.7396, D(G(z)): 0.1877
Epoch: [15/20], Batch Num: [544/600]
Discriminator Loss: 0.7144, Generator Loss: 1.8006
D(x): 0.8348, D(G(z)): 0.2949
Epoch: [15/20], Batch Num: [545/600]
Discriminator Loss: 0.9162, Generator Loss: 1.9571
D(x): 0.7895, D(G(z)): 0.3024
Epoch: [15/20], Batch Num: [546/600]
Discriminator Loss: 0.8015, Generator Loss: 2.2099
D(x): 0.7236, D(G(z)): 0.2062
Epoch: [15/20], Batch Num: [547/600]
Discriminator Loss: 0.7104, Generator Loss: 2.0739
D(x): 0.7312, D(G(z)): 0.1843
Epoch: [15/20], Batch Num: [548/600]
Discriminator Loss: 0.6868, Generator Loss: 1.8087
D(x): 0.7616, D(G(z)): 0.1949
Epoch: [15/20], Batch Num: [549/600]
Discriminator Loss: 0.6947, Generator Loss: 1.7579
D(x): 0.7924, D(G(z)): 0.2660
Epoch: [15/20], Batch Num: [550/600]
Discriminator Loss: 0.7627, Generator Loss: 1.8479
D(x): 0.7597, D(G(z)): 0.2171
Epoch: [15/20], Batch Num: [551/600]
Discriminator Loss: 0.5532, Generator Loss: 1.7255
D(x): 0.8468, D(G(z)): 0.2144
Epoch: [15/20], Batch Num: [552/600]
Discriminator Loss: 0.5618, Generator Loss: 2.0917
D(x): 0.8546, D(G(z)): 0.2299
Epoch: [15/20], Batch Num: [553/600]
Discriminator Loss: 0.5018, Generator Loss: 2.4217
D(x): 0.8442, D(G(z)): 0.1848
Epoch: [15/20], Batch Num: [554/600]
Discriminator Loss: 0.6964, Generator Loss: 2.4110
D(x): 0.7437, D(G(z)): 0.1652
Epoch: [15/20], Batch Num: [555/600]
Discriminator Loss: 0.7000, Generator Loss: 2.0249
D(x): 0.7254, D(G(z)): 0.1839
Epoch: [15/20], Batch Num: [556/600]
Discriminator Loss: 0.6259, Generator Loss: 1.7671
D(x): 0.7818, D(G(z)): 0.1981
Epoch: [15/20], Batch Num: [557/600]
Discriminator Loss: 0.6977, Generator Loss: 1.9339
D(x): 0.8423, D(G(z)): 0.2640
Epoch: [15/20], Batch Num: [558/600]
Discriminator Loss: 0.6285, Generator Loss: 2.1459
D(x): 0.8208, D(G(z)): 0.2437
Epoch: [15/20], Batch Num: [559/600]
Discriminator Loss: 0.6414, Generator Loss: 2.4594
D(x): 0.8299, D(G(z)): 0.2309
Epoch: [15/20], Batch Num: [560/600]
Discriminator Loss: 0.7962, Generator Loss: 2.4834
D(x): 0.7080, D(G(z)): 0.1403
Epoch: [15/20], Batch Num: [561/600]
Discriminator Loss: 0.7174, Generator Loss: 2.1096
D(x): 0.7519, D(G(z)): 0.1750
Epoch: [15/20], Batch Num: [562/600]
Discriminator Loss: 0.7687, Generator Loss: 1.9700
D(x): 0.7323, D(G(z)): 0.2246
Epoch: [15/20], Batch Num: [563/600]
Discriminator Loss: 0.5437, Generator Loss: 1.8382
D(x): 0.8472, D(G(z)): 0.2239
Epoch: [15/20], Batch Num: [564/600]
Discriminator Loss: 0.7095, Generator Loss: 1.9914
D(x): 0.8299, D(G(z)): 0.2657
Epoch: [15/20], Batch Num: [565/600]
Discriminator Loss: 0.6772, Generator Loss: 1.9718
D(x): 0.7716, D(G(z)): 0.2176
Epoch: [15/20], Batch Num: [566/600]
Discriminator Loss: 0.6648, Generator Loss: 1.9656
D(x): 0.7869, D(G(z)): 0.2023
Epoch: [15/20], Batch Num: [567/600]
Discriminator Loss: 0.7165, Generator Loss: 2.1283
D(x): 0.7329, D(G(z)): 0.1930
Epoch: [15/20], Batch Num: [568/600]
Discriminator Loss: 0.6440, Generator Loss: 1.9560
D(x): 0.7876, D(G(z)): 0.2011
Epoch: [15/20], Batch Num: [569/600]
Discriminator Loss: 0.4887, Generator Loss: 2.2818
D(x): 0.8799, D(G(z)): 0.2152
Epoch: [15/20], Batch Num: [570/600]
Discriminator Loss: 0.6797, Generator Loss: 2.1884
D(x): 0.7642, D(G(z)): 0.1879
Epoch: [15/20], Batch Num: [571/600]
Discriminator Loss: 0.9224, Generator Loss: 2.0402
D(x): 0.7417, D(G(z)): 0.2530
Epoch: [15/20], Batch Num: [572/600]
Discriminator Loss: 0.7485, Generator Loss: 2.0467
D(x): 0.7661, D(G(z)): 0.2425
Epoch: [15/20], Batch Num: [573/600]
Discriminator Loss: 0.7130, Generator Loss: 2.0596
D(x): 0.8086, D(G(z)): 0.2685
Epoch: [15/20], Batch Num: [574/600]
Discriminator Loss: 0.7953, Generator Loss: 2.0181
D(x): 0.7358, D(G(z)): 0.2233
Epoch: [15/20], Batch Num: [575/600]
Discriminator Loss: 0.7356, Generator Loss: 2.1490
D(x): 0.7410, D(G(z)): 0.1837
Epoch: [15/20], Batch Num: [576/600]
Discriminator Loss: 0.7689, Generator Loss: 1.9491
D(x): 0.7398, D(G(z)): 0.2262
Epoch: [15/20], Batch Num: [577/600]
Discriminator Loss: 0.7407, Generator Loss: 1.7315
D(x): 0.8039, D(G(z)): 0.2838
Epoch: [15/20], Batch Num: [578/600]
Discriminator Loss: 0.7420, Generator Loss: 2.1052
D(x): 0.8135, D(G(z)): 0.2791
Epoch: [15/20], Batch Num: [579/600]
Discriminator Loss: 0.6928, Generator Loss: 2.1589
D(x): 0.7599, D(G(z)): 0.2189
Epoch: [15/20], Batch Num: [580/600]
Discriminator Loss: 0.7080, Generator Loss: 2.1086
D(x): 0.7435, D(G(z)): 0.2024
Epoch: [15/20], Batch Num: [581/600]
Discriminator Loss: 0.8495, Generator Loss: 2.1911
D(x): 0.7108, D(G(z)): 0.2074
Epoch: [15/20], Batch Num: [582/600]
Discriminator Loss: 0.7282, Generator Loss: 1.9126
D(x): 0.7828, D(G(z)): 0.2188
Epoch: [15/20], Batch Num: [583/600]
Discriminator Loss: 0.7719, Generator Loss: 2.0462
D(x): 0.7732, D(G(z)): 0.2453
Epoch: [15/20], Batch Num: [584/600]
Discriminator Loss: 0.7583, Generator Loss: 1.7426
D(x): 0.7793, D(G(z)): 0.2695
Epoch: [15/20], Batch Num: [585/600]
Discriminator Loss: 0.9460, Generator Loss: 2.0588
D(x): 0.7079, D(G(z)): 0.2582
Epoch: [15/20], Batch Num: [586/600]
Discriminator Loss: 0.8805, Generator Loss: 1.5969
D(x): 0.7209, D(G(z)): 0.2616
Epoch: [15/20], Batch Num: [587/600]
Discriminator Loss: 0.7651, Generator Loss: 1.7380
D(x): 0.7562, D(G(z)): 0.2618
Epoch: [15/20], Batch Num: [588/600]
Discriminator Loss: 0.9093, Generator Loss: 1.5573
D(x): 0.7149, D(G(z)): 0.2834
Epoch: [15/20], Batch Num: [589/600]
Discriminator Loss: 0.8674, Generator Loss: 1.5824
D(x): 0.7621, D(G(z)): 0.2916
Epoch: [15/20], Batch Num: [590/600]
Discriminator Loss: 0.7774, Generator Loss: 1.5271
D(x): 0.7440, D(G(z)): 0.2599
Epoch: [15/20], Batch Num: [591/600]
Discriminator Loss: 0.9893, Generator Loss: 1.5338
D(x): 0.7013, D(G(z)): 0.2956
Epoch: [15/20], Batch Num: [592/600]
Discriminator Loss: 0.7380, Generator Loss: 1.7076
D(x): 0.7680, D(G(z)): 0.2790
Epoch: [15/20], Batch Num: [593/600]
Discriminator Loss: 0.7570, Generator Loss: 1.8985
D(x): 0.7654, D(G(z)): 0.2666
Epoch: [15/20], Batch Num: [594/600]
Discriminator Loss: 0.7819, Generator Loss: 1.8188
D(x): 0.7323, D(G(z)): 0.2512
Epoch: [15/20], Batch Num: [595/600]
Discriminator Loss: 0.7423, Generator Loss: 2.0225
D(x): 0.7518, D(G(z)): 0.2498
Epoch: [15/20], Batch Num: [596/600]
Discriminator Loss: 0.9191, Generator Loss: 1.6109
D(x): 0.6456, D(G(z)): 0.2345
Epoch: [15/20], Batch Num: [597/600]
Discriminator Loss: 0.7564, Generator Loss: 1.5641
D(x): 0.7234, D(G(z)): 0.2199
Epoch: [15/20], Batch Num: [598/600]
Discriminator Loss: 0.6588, Generator Loss: 1.4830
D(x): 0.8029, D(G(z)): 0.2583
Epoch: [15/20], Batch Num: [599/600]
Discriminator Loss: 0.7808, Generator Loss: 1.5655
D(x): 0.7856, D(G(z)): 0.3016
Epoch: 16, Batch Num: [0/600]

Epoch: [16/20], Batch Num: [0/600]
Discriminator Loss: 0.6774, Generator Loss: 1.7286
D(x): 0.7871, D(G(z)): 0.2548
Epoch: [16/20], Batch Num: [1/600]
Discriminator Loss: 0.7168, Generator Loss: 1.9106
D(x): 0.7492, D(G(z)): 0.2300
Epoch: [16/20], Batch Num: [2/600]
Discriminator Loss: 0.8187, Generator Loss: 2.0155
D(x): 0.7521, D(G(z)): 0.2438
Epoch: [16/20], Batch Num: [3/600]
Discriminator Loss: 0.7429, Generator Loss: 1.8167
D(x): 0.7239, D(G(z)): 0.2295
Epoch: [16/20], Batch Num: [4/600]
Discriminator Loss: 0.7307, Generator Loss: 1.7714
D(x): 0.7495, D(G(z)): 0.2288
Epoch: [16/20], Batch Num: [5/600]
Discriminator Loss: 0.7570, Generator Loss: 1.8821
D(x): 0.7690, D(G(z)): 0.2585
Epoch: [16/20], Batch Num: [6/600]
Discriminator Loss: 0.7036, Generator Loss: 1.9149
D(x): 0.7885, D(G(z)): 0.2548
Epoch: [16/20], Batch Num: [7/600]
Discriminator Loss: 0.7809, Generator Loss: 2.0611
D(x): 0.7745, D(G(z)): 0.2377
Epoch: [16/20], Batch Num: [8/600]
Discriminator Loss: 0.7011, Generator Loss: 2.0095
D(x): 0.7762, D(G(z)): 0.2240
Epoch: [16/20], Batch Num: [9/600]
Discriminator Loss: 0.7486, Generator Loss: 2.0153
D(x): 0.7116, D(G(z)): 0.2057
Epoch: [16/20], Batch Num: [10/600]
Discriminator Loss: 0.5771, Generator Loss: 2.1839
D(x): 0.8219, D(G(z)): 0.2174
Epoch: [16/20], Batch Num: [11/600]
Discriminator Loss: 0.7489, Generator Loss: 2.2064
D(x): 0.7217, D(G(z)): 0.1800
Epoch: [16/20], Batch Num: [12/600]
Discriminator Loss: 0.6819, Generator Loss: 1.8054
D(x): 0.7531, D(G(z)): 0.1873
Epoch: [16/20], Batch Num: [13/600]
Discriminator Loss: 0.6461, Generator Loss: 1.6678
D(x): 0.8328, D(G(z)): 0.2494
Epoch: [16/20], Batch Num: [14/600]
Discriminator Loss: 0.6571, Generator Loss: 1.9282
D(x): 0.8280, D(G(z)): 0.2525
Epoch: [16/20], Batch Num: [15/600]
Discriminator Loss: 0.7352, Generator Loss: 1.9916
D(x): 0.7801, D(G(z)): 0.2425
Epoch: [16/20], Batch Num: [16/600]
Discriminator Loss: 0.7225, Generator Loss: 2.0857
D(x): 0.7901, D(G(z)): 0.2305
Epoch: [16/20], Batch Num: [17/600]
Discriminator Loss: 0.6272, Generator Loss: 2.1373
D(x): 0.7937, D(G(z)): 0.1970
Epoch: [16/20], Batch Num: [18/600]
Discriminator Loss: 0.7864, Generator Loss: 2.3050
D(x): 0.7387, D(G(z)): 0.1938
Epoch: [16/20], Batch Num: [19/600]
Discriminator Loss: 0.5638, Generator Loss: 2.3415
D(x): 0.8149, D(G(z)): 0.1809
Epoch: [16/20], Batch Num: [20/600]
Discriminator Loss: 0.6890, Generator Loss: 2.2452
D(x): 0.7651, D(G(z)): 0.1918
Epoch: [16/20], Batch Num: [21/600]
Discriminator Loss: 0.6970, Generator Loss: 1.9805
D(x): 0.7747, D(G(z)): 0.2096
Epoch: [16/20], Batch Num: [22/600]
Discriminator Loss: 0.6438, Generator Loss: 1.9908
D(x): 0.7771, D(G(z)): 0.1955
Epoch: [16/20], Batch Num: [23/600]
Discriminator Loss: 0.6225, Generator Loss: 1.7348
D(x): 0.7992, D(G(z)): 0.2235
Epoch: [16/20], Batch Num: [24/600]
Discriminator Loss: 0.7913, Generator Loss: 1.9838
D(x): 0.7597, D(G(z)): 0.2583
Epoch: [16/20], Batch Num: [25/600]
Discriminator Loss: 0.7618, Generator Loss: 1.9989
D(x): 0.7893, D(G(z)): 0.2589
Epoch: [16/20], Batch Num: [26/600]
Discriminator Loss: 0.6712, Generator Loss: 2.2502
D(x): 0.8319, D(G(z)): 0.2535
Epoch: [16/20], Batch Num: [27/600]
Discriminator Loss: 0.5503, Generator Loss: 2.5963
D(x): 0.8011, D(G(z)): 0.1880
Epoch: [16/20], Batch Num: [28/600]
Discriminator Loss: 0.6659, Generator Loss: 2.3854
D(x): 0.7434, D(G(z)): 0.1867
Epoch: [16/20], Batch Num: [29/600]
Discriminator Loss: 0.7519, Generator Loss: 2.3598
D(x): 0.7506, D(G(z)): 0.1844
Epoch: [16/20], Batch Num: [30/600]
Discriminator Loss: 0.6304, Generator Loss: 1.9618
D(x): 0.7788, D(G(z)): 0.1872
Epoch: [16/20], Batch Num: [31/600]
Discriminator Loss: 0.7338, Generator Loss: 1.9160
D(x): 0.8273, D(G(z)): 0.2720
Epoch: [16/20], Batch Num: [32/600]
Discriminator Loss: 0.8027, Generator Loss: 1.6088
D(x): 0.7646, D(G(z)): 0.2551
Epoch: [16/20], Batch Num: [33/600]
Discriminator Loss: 0.6530, Generator Loss: 1.9483
D(x): 0.8285, D(G(z)): 0.2658
Epoch: [16/20], Batch Num: [34/600]
Discriminator Loss: 0.8533, Generator Loss: 2.3961
D(x): 0.7769, D(G(z)): 0.2939
Epoch: [16/20], Batch Num: [35/600]
Discriminator Loss: 0.7568, Generator Loss: 2.4773
D(x): 0.7388, D(G(z)): 0.2033
Epoch: [16/20], Batch Num: [36/600]
Discriminator Loss: 0.7101, Generator Loss: 2.2726
D(x): 0.7691, D(G(z)): 0.1845
Epoch: [16/20], Batch Num: [37/600]
Discriminator Loss: 0.8710, Generator Loss: 2.2354
D(x): 0.7294, D(G(z)): 0.2009
Epoch: [16/20], Batch Num: [38/600]
Discriminator Loss: 0.7330, Generator Loss: 2.0859
D(x): 0.7484, D(G(z)): 0.1727
Epoch: [16/20], Batch Num: [39/600]
Discriminator Loss: 0.8327, Generator Loss: 1.6245
D(x): 0.7789, D(G(z)): 0.2596
Epoch: [16/20], Batch Num: [40/600]
Discriminator Loss: 0.7096, Generator Loss: 1.5650
D(x): 0.7785, D(G(z)): 0.2470
Epoch: [16/20], Batch Num: [41/600]
Discriminator Loss: 0.7203, Generator Loss: 1.5868
D(x): 0.8243, D(G(z)): 0.2965
Epoch: [16/20], Batch Num: [42/600]
Discriminator Loss: 0.8114, Generator Loss: 1.7602
D(x): 0.8044, D(G(z)): 0.3142
Epoch: [16/20], Batch Num: [43/600]
Discriminator Loss: 0.8241, Generator Loss: 1.9395
D(x): 0.7127, D(G(z)): 0.2417
Epoch: [16/20], Batch Num: [44/600]
Discriminator Loss: 0.6886, Generator Loss: 2.0991
D(x): 0.7859, D(G(z)): 0.2503
Epoch: [16/20], Batch Num: [45/600]
Discriminator Loss: 0.9299, Generator Loss: 1.7608
D(x): 0.6281, D(G(z)): 0.1821
Epoch: [16/20], Batch Num: [46/600]
Discriminator Loss: 0.9243, Generator Loss: 1.7669
D(x): 0.7081, D(G(z)): 0.2736
Epoch: [16/20], Batch Num: [47/600]
Discriminator Loss: 0.7357, Generator Loss: 1.5236
D(x): 0.7549, D(G(z)): 0.2548
Epoch: [16/20], Batch Num: [48/600]
Discriminator Loss: 0.7409, Generator Loss: 1.4899
D(x): 0.7887, D(G(z)): 0.2855
Epoch: [16/20], Batch Num: [49/600]
Discriminator Loss: 0.7401, Generator Loss: 1.7695
D(x): 0.8042, D(G(z)): 0.2896
Epoch: [16/20], Batch Num: [50/600]
Discriminator Loss: 0.6577, Generator Loss: 1.8783
D(x): 0.8113, D(G(z)): 0.2374
Epoch: [16/20], Batch Num: [51/600]
Discriminator Loss: 0.8480, Generator Loss: 1.9580
D(x): 0.6778, D(G(z)): 0.2087
Epoch: [16/20], Batch Num: [52/600]
Discriminator Loss: 0.6449, Generator Loss: 1.8293
D(x): 0.7284, D(G(z)): 0.1733
Epoch: [16/20], Batch Num: [53/600]
Discriminator Loss: 0.8029, Generator Loss: 1.6644
D(x): 0.7427, D(G(z)): 0.2519
Epoch: [16/20], Batch Num: [54/600]
Discriminator Loss: 0.8093, Generator Loss: 1.8288
D(x): 0.7850, D(G(z)): 0.2933
Epoch: [16/20], Batch Num: [55/600]
Discriminator Loss: 0.8214, Generator Loss: 1.8502
D(x): 0.7350, D(G(z)): 0.2602
Epoch: [16/20], Batch Num: [56/600]
Discriminator Loss: 0.6232, Generator Loss: 1.7344
D(x): 0.7818, D(G(z)): 0.2152
Epoch: [16/20], Batch Num: [57/600]
Discriminator Loss: 0.6269, Generator Loss: 1.7011
D(x): 0.8008, D(G(z)): 0.2367
Epoch: [16/20], Batch Num: [58/600]
Discriminator Loss: 0.7136, Generator Loss: 1.9711
D(x): 0.7699, D(G(z)): 0.2463
Epoch: [16/20], Batch Num: [59/600]
Discriminator Loss: 0.8518, Generator Loss: 1.9035
D(x): 0.7187, D(G(z)): 0.2466
Epoch: [16/20], Batch Num: [60/600]
Discriminator Loss: 0.6748, Generator Loss: 1.9072
D(x): 0.7690, D(G(z)): 0.2096
Epoch: [16/20], Batch Num: [61/600]
Discriminator Loss: 0.6316, Generator Loss: 1.7166
D(x): 0.7953, D(G(z)): 0.2355
Epoch: [16/20], Batch Num: [62/600]
Discriminator Loss: 0.7346, Generator Loss: 1.7360
D(x): 0.7998, D(G(z)): 0.2529
Epoch: [16/20], Batch Num: [63/600]
Discriminator Loss: 0.8454, Generator Loss: 1.6259
D(x): 0.6973, D(G(z)): 0.2265
Epoch: [16/20], Batch Num: [64/600]
Discriminator Loss: 0.8084, Generator Loss: 1.7706
D(x): 0.7372, D(G(z)): 0.2788
Epoch: [16/20], Batch Num: [65/600]
Discriminator Loss: 0.8293, Generator Loss: 1.8247
D(x): 0.7858, D(G(z)): 0.2997
Epoch: [16/20], Batch Num: [66/600]
Discriminator Loss: 0.7056, Generator Loss: 1.7162
D(x): 0.7546, D(G(z)): 0.2188
Epoch: [16/20], Batch Num: [67/600]
Discriminator Loss: 0.6314, Generator Loss: 1.7253
D(x): 0.8065, D(G(z)): 0.2366
Epoch: [16/20], Batch Num: [68/600]
Discriminator Loss: 0.6442, Generator Loss: 1.8672
D(x): 0.8186, D(G(z)): 0.2257
Epoch: [16/20], Batch Num: [69/600]
Discriminator Loss: 0.7624, Generator Loss: 1.8868
D(x): 0.7284, D(G(z)): 0.2076
Epoch: [16/20], Batch Num: [70/600]
Discriminator Loss: 0.7094, Generator Loss: 2.0727
D(x): 0.7667, D(G(z)): 0.2171
Epoch: [16/20], Batch Num: [71/600]
Discriminator Loss: 0.7053, Generator Loss: 1.8311
D(x): 0.7439, D(G(z)): 0.1951
Epoch: [16/20], Batch Num: [72/600]
Discriminator Loss: 0.8802, Generator Loss: 1.8650
D(x): 0.7355, D(G(z)): 0.2656
Epoch: [16/20], Batch Num: [73/600]
Discriminator Loss: 0.8141, Generator Loss: 1.7452
D(x): 0.7958, D(G(z)): 0.3033
Epoch: [16/20], Batch Num: [74/600]
Discriminator Loss: 0.7022, Generator Loss: 2.0907
D(x): 0.8325, D(G(z)): 0.2853
Epoch: [16/20], Batch Num: [75/600]
Discriminator Loss: 0.7399, Generator Loss: 2.4271
D(x): 0.7373, D(G(z)): 0.1954
Epoch: [16/20], Batch Num: [76/600]
Discriminator Loss: 0.8222, Generator Loss: 2.1898
D(x): 0.7034, D(G(z)): 0.1831
Epoch: [16/20], Batch Num: [77/600]
Discriminator Loss: 0.7622, Generator Loss: 2.0607
D(x): 0.6903, D(G(z)): 0.1725
Epoch: [16/20], Batch Num: [78/600]
Discriminator Loss: 0.9110, Generator Loss: 1.5108
D(x): 0.7063, D(G(z)): 0.2497
Epoch: [16/20], Batch Num: [79/600]
Discriminator Loss: 0.6738, Generator Loss: 1.4162
D(x): 0.8121, D(G(z)): 0.2707
Epoch: [16/20], Batch Num: [80/600]
Discriminator Loss: 0.6973, Generator Loss: 1.4967
D(x): 0.8514, D(G(z)): 0.3198
Epoch: [16/20], Batch Num: [81/600]
Discriminator Loss: 0.8685, Generator Loss: 1.7630
D(x): 0.7774, D(G(z)): 0.3285
Epoch: [16/20], Batch Num: [82/600]
Discriminator Loss: 0.6995, Generator Loss: 2.1509
D(x): 0.7962, D(G(z)): 0.2683
Epoch: [16/20], Batch Num: [83/600]
Discriminator Loss: 0.7263, Generator Loss: 2.3084
D(x): 0.7447, D(G(z)): 0.1985
Epoch: [16/20], Batch Num: [84/600]
Discriminator Loss: 0.8675, Generator Loss: 1.9660
D(x): 0.6507, D(G(z)): 0.1739
Epoch: [16/20], Batch Num: [85/600]
Discriminator Loss: 0.8795, Generator Loss: 1.7160
D(x): 0.6951, D(G(z)): 0.2394
Epoch: [16/20], Batch Num: [86/600]
Discriminator Loss: 0.9065, Generator Loss: 1.7796
D(x): 0.7643, D(G(z)): 0.3097
Epoch: [16/20], Batch Num: [87/600]
Discriminator Loss: 0.9388, Generator Loss: 1.8109
D(x): 0.7823, D(G(z)): 0.3367
Epoch: [16/20], Batch Num: [88/600]
Discriminator Loss: 0.9495, Generator Loss: 1.8484
D(x): 0.7504, D(G(z)): 0.2951
Epoch: [16/20], Batch Num: [89/600]
Discriminator Loss: 0.8097, Generator Loss: 2.0900
D(x): 0.7468, D(G(z)): 0.2685
Epoch: [16/20], Batch Num: [90/600]
Discriminator Loss: 0.7589, Generator Loss: 1.9969
D(x): 0.7246, D(G(z)): 0.2208
Epoch: [16/20], Batch Num: [91/600]
Discriminator Loss: 0.7711, Generator Loss: 1.8617
D(x): 0.7447, D(G(z)): 0.2045
Epoch: [16/20], Batch Num: [92/600]
Discriminator Loss: 0.8425, Generator Loss: 1.7917
D(x): 0.6878, D(G(z)): 0.2194
Epoch: [16/20], Batch Num: [93/600]
Discriminator Loss: 0.8251, Generator Loss: 1.4682
D(x): 0.6997, D(G(z)): 0.2408
Epoch: [16/20], Batch Num: [94/600]
Discriminator Loss: 0.7991, Generator Loss: 1.4212
D(x): 0.7713, D(G(z)): 0.3153
Epoch: [16/20], Batch Num: [95/600]
Discriminator Loss: 0.7752, Generator Loss: 1.3187
D(x): 0.7979, D(G(z)): 0.3270
Epoch: [16/20], Batch Num: [96/600]
Discriminator Loss: 0.7630, Generator Loss: 1.5851
D(x): 0.7965, D(G(z)): 0.3198
Epoch: [16/20], Batch Num: [97/600]
Discriminator Loss: 0.7475, Generator Loss: 1.9817
D(x): 0.7710, D(G(z)): 0.2913
Epoch: [16/20], Batch Num: [98/600]
Discriminator Loss: 0.8783, Generator Loss: 1.7853
D(x): 0.7177, D(G(z)): 0.2536
Epoch: [16/20], Batch Num: [99/600]
Discriminator Loss: 0.8455, Generator Loss: 2.2572
D(x): 0.6870, D(G(z)): 0.1980
Epoch: 16, Batch Num: [100/600]

Epoch: [16/20], Batch Num: [100/600]
Discriminator Loss: 0.8262, Generator Loss: 1.8288
D(x): 0.7098, D(G(z)): 0.2154
Epoch: [16/20], Batch Num: [101/600]
Discriminator Loss: 0.7904, Generator Loss: 1.7699
D(x): 0.7646, D(G(z)): 0.2495
Epoch: [16/20], Batch Num: [102/600]
Discriminator Loss: 0.8167, Generator Loss: 1.5600
D(x): 0.7452, D(G(z)): 0.2563
Epoch: [16/20], Batch Num: [103/600]
Discriminator Loss: 0.7941, Generator Loss: 1.5192
D(x): 0.7551, D(G(z)): 0.2931
Epoch: [16/20], Batch Num: [104/600]
Discriminator Loss: 0.7426, Generator Loss: 1.5934
D(x): 0.7747, D(G(z)): 0.2875
Epoch: [16/20], Batch Num: [105/600]
Discriminator Loss: 0.7816, Generator Loss: 1.6145
D(x): 0.7505, D(G(z)): 0.2889
Epoch: [16/20], Batch Num: [106/600]
Discriminator Loss: 0.8469, Generator Loss: 1.9157
D(x): 0.7803, D(G(z)): 0.3131
Epoch: [16/20], Batch Num: [107/600]
Discriminator Loss: 0.8428, Generator Loss: 2.1578
D(x): 0.7504, D(G(z)): 0.2620
Epoch: [16/20], Batch Num: [108/600]
Discriminator Loss: 0.9375, Generator Loss: 1.7880
D(x): 0.6290, D(G(z)): 0.1962
Epoch: [16/20], Batch Num: [109/600]
Discriminator Loss: 0.7683, Generator Loss: 1.6409
D(x): 0.6799, D(G(z)): 0.1887
Epoch: [16/20], Batch Num: [110/600]
Discriminator Loss: 0.7301, Generator Loss: 1.5487
D(x): 0.7787, D(G(z)): 0.2883
Epoch: [16/20], Batch Num: [111/600]
Discriminator Loss: 0.7643, Generator Loss: 1.7735
D(x): 0.8014, D(G(z)): 0.3181
Epoch: [16/20], Batch Num: [112/600]
Discriminator Loss: 0.8045, Generator Loss: 1.7320
D(x): 0.7618, D(G(z)): 0.2682
Epoch: [16/20], Batch Num: [113/600]
Discriminator Loss: 0.6871, Generator Loss: 1.7677
D(x): 0.7773, D(G(z)): 0.2397
Epoch: [16/20], Batch Num: [114/600]
Discriminator Loss: 0.7560, Generator Loss: 1.9065
D(x): 0.7635, D(G(z)): 0.2473
Epoch: [16/20], Batch Num: [115/600]
Discriminator Loss: 0.7595, Generator Loss: 1.7864
D(x): 0.7100, D(G(z)): 0.2129
Epoch: [16/20], Batch Num: [116/600]
Discriminator Loss: 0.6921, Generator Loss: 1.7243
D(x): 0.7443, D(G(z)): 0.1958
Epoch: [16/20], Batch Num: [117/600]
Discriminator Loss: 0.7183, Generator Loss: 1.5353
D(x): 0.7670, D(G(z)): 0.2485
Epoch: [16/20], Batch Num: [118/600]
Discriminator Loss: 0.8382, Generator Loss: 1.5123
D(x): 0.7618, D(G(z)): 0.2865
Epoch: [16/20], Batch Num: [119/600]
Discriminator Loss: 0.8295, Generator Loss: 1.7669
D(x): 0.7989, D(G(z)): 0.2893
Epoch: [16/20], Batch Num: [120/600]
Discriminator Loss: 0.6760, Generator Loss: 1.8883
D(x): 0.7771, D(G(z)): 0.2201
Epoch: [16/20], Batch Num: [121/600]
Discriminator Loss: 0.7419, Generator Loss: 1.8817
D(x): 0.7251, D(G(z)): 0.1921
Epoch: [16/20], Batch Num: [122/600]
Discriminator Loss: 0.7713, Generator Loss: 1.6283
D(x): 0.7095, D(G(z)): 0.1981
Epoch: [16/20], Batch Num: [123/600]
Discriminator Loss: 0.6958, Generator Loss: 1.6961
D(x): 0.7944, D(G(z)): 0.2644
Epoch: [16/20], Batch Num: [124/600]
Discriminator Loss: 0.8372, Generator Loss: 1.8985
D(x): 0.7896, D(G(z)): 0.3138
Epoch: [16/20], Batch Num: [125/600]
Discriminator Loss: 0.8281, Generator Loss: 2.0577
D(x): 0.7379, D(G(z)): 0.2679
Epoch: [16/20], Batch Num: [126/600]
Discriminator Loss: 0.8952, Generator Loss: 1.9666
D(x): 0.6658, D(G(z)): 0.2326
Epoch: [16/20], Batch Num: [127/600]
Discriminator Loss: 0.7115, Generator Loss: 1.9905
D(x): 0.7769, D(G(z)): 0.2339
Epoch: [16/20], Batch Num: [128/600]
Discriminator Loss: 0.7442, Generator Loss: 1.8302
D(x): 0.7132, D(G(z)): 0.2024
Epoch: [16/20], Batch Num: [129/600]
Discriminator Loss: 0.6755, Generator Loss: 1.7897
D(x): 0.7859, D(G(z)): 0.2264
Epoch: [16/20], Batch Num: [130/600]
Discriminator Loss: 0.6687, Generator Loss: 1.7438
D(x): 0.8083, D(G(z)): 0.2749
Epoch: [16/20], Batch Num: [131/600]
Discriminator Loss: 0.6538, Generator Loss: 1.7679
D(x): 0.7953, D(G(z)): 0.2458
Epoch: [16/20], Batch Num: [132/600]
Discriminator Loss: 0.7708, Generator Loss: 1.7887
D(x): 0.7172, D(G(z)): 0.2281
Epoch: [16/20], Batch Num: [133/600]
Discriminator Loss: 0.7756, Generator Loss: 1.6715
D(x): 0.7715, D(G(z)): 0.2815
Epoch: [16/20], Batch Num: [134/600]
Discriminator Loss: 0.6828, Generator Loss: 1.6946
D(x): 0.7785, D(G(z)): 0.2253
Epoch: [16/20], Batch Num: [135/600]
Discriminator Loss: 0.7893, Generator Loss: 1.8533
D(x): 0.7563, D(G(z)): 0.2706
Epoch: [16/20], Batch Num: [136/600]
Discriminator Loss: 0.8311, Generator Loss: 1.9076
D(x): 0.7570, D(G(z)): 0.2920
Epoch: [16/20], Batch Num: [137/600]
Discriminator Loss: 0.7236, Generator Loss: 2.1318
D(x): 0.7887, D(G(z)): 0.2574
Epoch: [16/20], Batch Num: [138/600]
Discriminator Loss: 0.7879, Generator Loss: 2.0545
D(x): 0.7239, D(G(z)): 0.1933
Epoch: [16/20], Batch Num: [139/600]
Discriminator Loss: 0.9468, Generator Loss: 1.8981
D(x): 0.6386, D(G(z)): 0.2041
Epoch: [16/20], Batch Num: [140/600]
Discriminator Loss: 0.7005, Generator Loss: 1.6092
D(x): 0.7561, D(G(z)): 0.2095
Epoch: [16/20], Batch Num: [141/600]
Discriminator Loss: 0.8792, Generator Loss: 1.5500
D(x): 0.7595, D(G(z)): 0.2618
Epoch: [16/20], Batch Num: [142/600]
Discriminator Loss: 0.6927, Generator Loss: 1.5030
D(x): 0.8636, D(G(z)): 0.3278
Epoch: [16/20], Batch Num: [143/600]
Discriminator Loss: 0.6744, Generator Loss: 1.8718
D(x): 0.8480, D(G(z)): 0.2932
Epoch: [16/20], Batch Num: [144/600]
Discriminator Loss: 0.6631, Generator Loss: 2.2064
D(x): 0.7777, D(G(z)): 0.2413
Epoch: [16/20], Batch Num: [145/600]
Discriminator Loss: 0.7782, Generator Loss: 2.3056
D(x): 0.6889, D(G(z)): 0.1743
Epoch: [16/20], Batch Num: [146/600]
Discriminator Loss: 0.7387, Generator Loss: 1.9927
D(x): 0.7010, D(G(z)): 0.1723
Epoch: [16/20], Batch Num: [147/600]
Discriminator Loss: 0.6485, Generator Loss: 1.8911
D(x): 0.7651, D(G(z)): 0.2105
Epoch: [16/20], Batch Num: [148/600]
Discriminator Loss: 0.7095, Generator Loss: 1.6839
D(x): 0.7997, D(G(z)): 0.2641
Epoch: [16/20], Batch Num: [149/600]
Discriminator Loss: 0.7546, Generator Loss: 1.5427
D(x): 0.7573, D(G(z)): 0.2752
Epoch: [16/20], Batch Num: [150/600]
Discriminator Loss: 0.6682, Generator Loss: 1.6146
D(x): 0.8216, D(G(z)): 0.2868
Epoch: [16/20], Batch Num: [151/600]
Discriminator Loss: 0.6990, Generator Loss: 1.7250
D(x): 0.7811, D(G(z)): 0.2602
Epoch: [16/20], Batch Num: [152/600]
Discriminator Loss: 0.7534, Generator Loss: 1.8509
D(x): 0.7648, D(G(z)): 0.2784
Epoch: [16/20], Batch Num: [153/600]
Discriminator Loss: 0.6604, Generator Loss: 1.9452
D(x): 0.7733, D(G(z)): 0.2133
Epoch: [16/20], Batch Num: [154/600]
Discriminator Loss: 0.7707, Generator Loss: 2.0604
D(x): 0.7299, D(G(z)): 0.2323
Epoch: [16/20], Batch Num: [155/600]
Discriminator Loss: 0.8048, Generator Loss: 1.7923
D(x): 0.7082, D(G(z)): 0.1929
Epoch: [16/20], Batch Num: [156/600]
Discriminator Loss: 0.7880, Generator Loss: 1.8644
D(x): 0.7609, D(G(z)): 0.2713
Epoch: [16/20], Batch Num: [157/600]
Discriminator Loss: 0.8575, Generator Loss: 1.8259
D(x): 0.7557, D(G(z)): 0.2926
Epoch: [16/20], Batch Num: [158/600]
Discriminator Loss: 0.8119, Generator Loss: 1.8827
D(x): 0.7556, D(G(z)): 0.2492
Epoch: [16/20], Batch Num: [159/600]
Discriminator Loss: 0.7099, Generator Loss: 2.0169
D(x): 0.7680, D(G(z)): 0.2390
Epoch: [16/20], Batch Num: [160/600]
Discriminator Loss: 0.7260, Generator Loss: 1.9494
D(x): 0.7480, D(G(z)): 0.2037
Epoch: [16/20], Batch Num: [161/600]
Discriminator Loss: 0.6407, Generator Loss: 1.8982
D(x): 0.7787, D(G(z)): 0.2093
Epoch: [16/20], Batch Num: [162/600]
Discriminator Loss: 0.6585, Generator Loss: 1.8401
D(x): 0.7617, D(G(z)): 0.2036
Epoch: [16/20], Batch Num: [163/600]
Discriminator Loss: 0.6378, Generator Loss: 1.6364
D(x): 0.7852, D(G(z)): 0.2369
Epoch: [16/20], Batch Num: [164/600]
Discriminator Loss: 0.6925, Generator Loss: 1.6812
D(x): 0.7931, D(G(z)): 0.2495
Epoch: [16/20], Batch Num: [165/600]
Discriminator Loss: 0.6215, Generator Loss: 1.8751
D(x): 0.8172, D(G(z)): 0.2544
Epoch: [16/20], Batch Num: [166/600]
Discriminator Loss: 0.7362, Generator Loss: 1.8203
D(x): 0.7960, D(G(z)): 0.2738
Epoch: [16/20], Batch Num: [167/600]
Discriminator Loss: 0.6689, Generator Loss: 2.1923
D(x): 0.7992, D(G(z)): 0.2406
Epoch: [16/20], Batch Num: [168/600]
Discriminator Loss: 0.8190, Generator Loss: 2.1254
D(x): 0.7237, D(G(z)): 0.1986
Epoch: [16/20], Batch Num: [169/600]
Discriminator Loss: 0.8752, Generator Loss: 2.1813
D(x): 0.6938, D(G(z)): 0.1952
Epoch: [16/20], Batch Num: [170/600]
Discriminator Loss: 0.7975, Generator Loss: 2.1157
D(x): 0.7538, D(G(z)): 0.2243
Epoch: [16/20], Batch Num: [171/600]
Discriminator Loss: 0.7703, Generator Loss: 1.8288
D(x): 0.7863, D(G(z)): 0.2534
Epoch: [16/20], Batch Num: [172/600]
Discriminator Loss: 0.6898, Generator Loss: 2.0219
D(x): 0.8308, D(G(z)): 0.2661
Epoch: [16/20], Batch Num: [173/600]
Discriminator Loss: 0.7791, Generator Loss: 1.8632
D(x): 0.7679, D(G(z)): 0.2669
Epoch: [16/20], Batch Num: [174/600]
Discriminator Loss: 0.7627, Generator Loss: 1.9309
D(x): 0.7378, D(G(z)): 0.2318
Epoch: [16/20], Batch Num: [175/600]
Discriminator Loss: 0.7657, Generator Loss: 2.1449
D(x): 0.7637, D(G(z)): 0.2304
Epoch: [16/20], Batch Num: [176/600]
Discriminator Loss: 0.8156, Generator Loss: 2.0490
D(x): 0.6988, D(G(z)): 0.1912
Epoch: [16/20], Batch Num: [177/600]
Discriminator Loss: 0.8303, Generator Loss: 1.7408
D(x): 0.7126, D(G(z)): 0.2370
Epoch: [16/20], Batch Num: [178/600]
Discriminator Loss: 0.7692, Generator Loss: 1.7859
D(x): 0.7713, D(G(z)): 0.2672
Epoch: [16/20], Batch Num: [179/600]
Discriminator Loss: 0.9809, Generator Loss: 1.5457
D(x): 0.7219, D(G(z)): 0.2853
Epoch: [16/20], Batch Num: [180/600]
Discriminator Loss: 0.7395, Generator Loss: 1.5104
D(x): 0.7654, D(G(z)): 0.2817
Epoch: [16/20], Batch Num: [181/600]
Discriminator Loss: 0.7908, Generator Loss: 1.7895
D(x): 0.7203, D(G(z)): 0.2629
Epoch: [16/20], Batch Num: [182/600]
Discriminator Loss: 0.7568, Generator Loss: 1.5198
D(x): 0.7155, D(G(z)): 0.2405
Epoch: [16/20], Batch Num: [183/600]
Discriminator Loss: 0.7221, Generator Loss: 1.4974
D(x): 0.7377, D(G(z)): 0.2559
Epoch: [16/20], Batch Num: [184/600]
Discriminator Loss: 0.7768, Generator Loss: 1.8763
D(x): 0.7948, D(G(z)): 0.2967
Epoch: [16/20], Batch Num: [185/600]
Discriminator Loss: 0.7781, Generator Loss: 1.8579
D(x): 0.7561, D(G(z)): 0.2936
Epoch: [16/20], Batch Num: [186/600]
Discriminator Loss: 0.6653, Generator Loss: 1.8937
D(x): 0.7656, D(G(z)): 0.2203
Epoch: [16/20], Batch Num: [187/600]
Discriminator Loss: 0.7192, Generator Loss: 2.1768
D(x): 0.7170, D(G(z)): 0.2143
Epoch: [16/20], Batch Num: [188/600]
Discriminator Loss: 0.8464, Generator Loss: 1.8088
D(x): 0.7145, D(G(z)): 0.1962
Epoch: [16/20], Batch Num: [189/600]
Discriminator Loss: 0.7087, Generator Loss: 1.9428
D(x): 0.7829, D(G(z)): 0.2501
Epoch: [16/20], Batch Num: [190/600]
Discriminator Loss: 0.6207, Generator Loss: 1.5600
D(x): 0.8139, D(G(z)): 0.2340
Epoch: [16/20], Batch Num: [191/600]
Discriminator Loss: 0.6339, Generator Loss: 1.8573
D(x): 0.8191, D(G(z)): 0.2505
Epoch: [16/20], Batch Num: [192/600]
Discriminator Loss: 0.8366, Generator Loss: 2.0386
D(x): 0.7697, D(G(z)): 0.2774
Epoch: [16/20], Batch Num: [193/600]
Discriminator Loss: 0.7249, Generator Loss: 2.1236
D(x): 0.7210, D(G(z)): 0.1800
Epoch: [16/20], Batch Num: [194/600]
Discriminator Loss: 0.6839, Generator Loss: 2.0767
D(x): 0.7740, D(G(z)): 0.2145
Epoch: [16/20], Batch Num: [195/600]
Discriminator Loss: 0.7008, Generator Loss: 1.7763
D(x): 0.7493, D(G(z)): 0.1934
Epoch: [16/20], Batch Num: [196/600]
Discriminator Loss: 0.6444, Generator Loss: 1.6341
D(x): 0.7614, D(G(z)): 0.1907
Epoch: [16/20], Batch Num: [197/600]
Discriminator Loss: 0.6238, Generator Loss: 1.6101
D(x): 0.8050, D(G(z)): 0.2071
Epoch: [16/20], Batch Num: [198/600]
Discriminator Loss: 0.7734, Generator Loss: 1.7347
D(x): 0.8450, D(G(z)): 0.3179
Epoch: [16/20], Batch Num: [199/600]
Discriminator Loss: 0.7372, Generator Loss: 2.2124
D(x): 0.8577, D(G(z)): 0.2910
Epoch: 16, Batch Num: [200/600]

Epoch: [16/20], Batch Num: [200/600]
Discriminator Loss: 0.6935, Generator Loss: 2.4760
D(x): 0.7508, D(G(z)): 0.1911
Epoch: [16/20], Batch Num: [201/600]
Discriminator Loss: 0.9312, Generator Loss: 2.3713
D(x): 0.6415, D(G(z)): 0.1324
Epoch: [16/20], Batch Num: [202/600]
Discriminator Loss: 0.7505, Generator Loss: 2.0513
D(x): 0.7249, D(G(z)): 0.1828
Epoch: [16/20], Batch Num: [203/600]
Discriminator Loss: 0.8335, Generator Loss: 1.6516
D(x): 0.7029, D(G(z)): 0.1859
Epoch: [16/20], Batch Num: [204/600]
Discriminator Loss: 0.7095, Generator Loss: 1.4210
D(x): 0.8311, D(G(z)): 0.2876
Epoch: [16/20], Batch Num: [205/600]
Discriminator Loss: 0.7583, Generator Loss: 1.6694
D(x): 0.8221, D(G(z)): 0.3215
Epoch: [16/20], Batch Num: [206/600]
Discriminator Loss: 0.6922, Generator Loss: 1.8481
D(x): 0.8060, D(G(z)): 0.2854
Epoch: [16/20], Batch Num: [207/600]
Discriminator Loss: 0.7799, Generator Loss: 2.0306
D(x): 0.7729, D(G(z)): 0.2629
Epoch: [16/20], Batch Num: [208/600]
Discriminator Loss: 0.7698, Generator Loss: 2.0875
D(x): 0.7266, D(G(z)): 0.2262
Epoch: [16/20], Batch Num: [209/600]
Discriminator Loss: 0.6707, Generator Loss: 1.9292
D(x): 0.7803, D(G(z)): 0.2289
Epoch: [16/20], Batch Num: [210/600]
Discriminator Loss: 0.8121, Generator Loss: 1.9682
D(x): 0.7110, D(G(z)): 0.2111
Epoch: [16/20], Batch Num: [211/600]
Discriminator Loss: 0.6082, Generator Loss: 1.8508
D(x): 0.8007, D(G(z)): 0.2175
Epoch: [16/20], Batch Num: [212/600]
Discriminator Loss: 0.8339, Generator Loss: 1.7053
D(x): 0.7276, D(G(z)): 0.2440
Epoch: [16/20], Batch Num: [213/600]
Discriminator Loss: 0.6628, Generator Loss: 1.6689
D(x): 0.8010, D(G(z)): 0.2509
Epoch: [16/20], Batch Num: [214/600]
Discriminator Loss: 0.7790, Generator Loss: 1.7117
D(x): 0.7861, D(G(z)): 0.2968
Epoch: [16/20], Batch Num: [215/600]
Discriminator Loss: 0.7352, Generator Loss: 1.7874
D(x): 0.8036, D(G(z)): 0.2797
Epoch: [16/20], Batch Num: [216/600]
Discriminator Loss: 0.8650, Generator Loss: 2.0173
D(x): 0.7482, D(G(z)): 0.2719
Epoch: [16/20], Batch Num: [217/600]
Discriminator Loss: 0.7033, Generator Loss: 2.3145
D(x): 0.7463, D(G(z)): 0.2118
Epoch: [16/20], Batch Num: [218/600]
Discriminator Loss: 0.7248, Generator Loss: 2.2563
D(x): 0.7244, D(G(z)): 0.1895
Epoch: [16/20], Batch Num: [219/600]
Discriminator Loss: 0.6335, Generator Loss: 2.0371
D(x): 0.7808, D(G(z)): 0.2127
Epoch: [16/20], Batch Num: [220/600]
Discriminator Loss: 0.7541, Generator Loss: 1.8287
D(x): 0.7312, D(G(z)): 0.2028
Epoch: [16/20], Batch Num: [221/600]
Discriminator Loss: 0.7218, Generator Loss: 1.7968
D(x): 0.7581, D(G(z)): 0.2292
Epoch: [16/20], Batch Num: [222/600]
Discriminator Loss: 0.7616, Generator Loss: 1.5937
D(x): 0.7487, D(G(z)): 0.2514
Epoch: [16/20], Batch Num: [223/600]
Discriminator Loss: 0.7799, Generator Loss: 1.6231
D(x): 0.8100, D(G(z)): 0.2988
Epoch: [16/20], Batch Num: [224/600]
Discriminator Loss: 0.7751, Generator Loss: 1.8361
D(x): 0.8456, D(G(z)): 0.3196
Epoch: [16/20], Batch Num: [225/600]
Discriminator Loss: 0.7232, Generator Loss: 2.0815
D(x): 0.7468, D(G(z)): 0.2469
Epoch: [16/20], Batch Num: [226/600]
Discriminator Loss: 0.8650, Generator Loss: 2.0971
D(x): 0.7089, D(G(z)): 0.2081
Epoch: [16/20], Batch Num: [227/600]
Discriminator Loss: 0.7968, Generator Loss: 2.1358
D(x): 0.7167, D(G(z)): 0.2032
Epoch: [16/20], Batch Num: [228/600]
Discriminator Loss: 0.7245, Generator Loss: 1.8903
D(x): 0.7375, D(G(z)): 0.1972
Epoch: [16/20], Batch Num: [229/600]
Discriminator Loss: 0.9746, Generator Loss: 1.6743
D(x): 0.7271, D(G(z)): 0.3037
Epoch: [16/20], Batch Num: [230/600]
Discriminator Loss: 0.7951, Generator Loss: 1.7148
D(x): 0.7545, D(G(z)): 0.2679
Epoch: [16/20], Batch Num: [231/600]
Discriminator Loss: 0.8223, Generator Loss: 1.8836
D(x): 0.7721, D(G(z)): 0.2855
Epoch: [16/20], Batch Num: [232/600]
Discriminator Loss: 0.7559, Generator Loss: 1.7860
D(x): 0.7255, D(G(z)): 0.1966
Epoch: [16/20], Batch Num: [233/600]
Discriminator Loss: 0.7190, Generator Loss: 1.6230
D(x): 0.7625, D(G(z)): 0.2394
Epoch: [16/20], Batch Num: [234/600]
Discriminator Loss: 0.7472, Generator Loss: 1.8367
D(x): 0.7888, D(G(z)): 0.2980
Epoch: [16/20], Batch Num: [235/600]
Discriminator Loss: 0.7285, Generator Loss: 1.9744
D(x): 0.7581, D(G(z)): 0.2561
Epoch: [16/20], Batch Num: [236/600]
Discriminator Loss: 0.8269, Generator Loss: 1.7923
D(x): 0.7367, D(G(z)): 0.2810
Epoch: [16/20], Batch Num: [237/600]
Discriminator Loss: 0.7339, Generator Loss: 2.1290
D(x): 0.7796, D(G(z)): 0.2629
Epoch: [16/20], Batch Num: [238/600]
Discriminator Loss: 0.7177, Generator Loss: 2.2599
D(x): 0.7311, D(G(z)): 0.2085
Epoch: [16/20], Batch Num: [239/600]
Discriminator Loss: 0.7484, Generator Loss: 1.9271
D(x): 0.6938, D(G(z)): 0.1762
Epoch: [16/20], Batch Num: [240/600]
Discriminator Loss: 0.7151, Generator Loss: 1.7269
D(x): 0.7771, D(G(z)): 0.2292
Epoch: [16/20], Batch Num: [241/600]
Discriminator Loss: 0.8327, Generator Loss: 1.7147
D(x): 0.8052, D(G(z)): 0.2878
Epoch: [16/20], Batch Num: [242/600]
Discriminator Loss: 0.7648, Generator Loss: 1.7273
D(x): 0.7736, D(G(z)): 0.2701
Epoch: [16/20], Batch Num: [243/600]
Discriminator Loss: 0.7429, Generator Loss: 2.0788
D(x): 0.7868, D(G(z)): 0.2732
Epoch: [16/20], Batch Num: [244/600]
Discriminator Loss: 0.8178, Generator Loss: 2.1222
D(x): 0.7233, D(G(z)): 0.2119
Epoch: [16/20], Batch Num: [245/600]
Discriminator Loss: 0.9663, Generator Loss: 2.1760
D(x): 0.7222, D(G(z)): 0.2438
Epoch: [16/20], Batch Num: [246/600]
Discriminator Loss: 0.8224, Generator Loss: 2.0244
D(x): 0.7846, D(G(z)): 0.2333
Epoch: [16/20], Batch Num: [247/600]
Discriminator Loss: 0.6946, Generator Loss: 2.2181
D(x): 0.7414, D(G(z)): 0.1855
Epoch: [16/20], Batch Num: [248/600]
Discriminator Loss: 0.6415, Generator Loss: 1.6818
D(x): 0.7787, D(G(z)): 0.1973
Epoch: [16/20], Batch Num: [249/600]
Discriminator Loss: 0.7426, Generator Loss: 1.7609
D(x): 0.8305, D(G(z)): 0.2931
Epoch: [16/20], Batch Num: [250/600]
Discriminator Loss: 0.8977, Generator Loss: 1.7836
D(x): 0.7830, D(G(z)): 0.3188
Epoch: [16/20], Batch Num: [251/600]
Discriminator Loss: 0.8025, Generator Loss: 1.9403
D(x): 0.7529, D(G(z)): 0.2666
Epoch: [16/20], Batch Num: [252/600]
Discriminator Loss: 0.7480, Generator Loss: 2.0954
D(x): 0.7373, D(G(z)): 0.2264
Epoch: [16/20], Batch Num: [253/600]
Discriminator Loss: 0.8455, Generator Loss: 1.9021
D(x): 0.7124, D(G(z)): 0.2095
Epoch: [16/20], Batch Num: [254/600]
Discriminator Loss: 0.6943, Generator Loss: 1.9685
D(x): 0.7512, D(G(z)): 0.2039
Epoch: [16/20], Batch Num: [255/600]
Discriminator Loss: 0.6474, Generator Loss: 2.0905
D(x): 0.7837, D(G(z)): 0.2215
Epoch: [16/20], Batch Num: [256/600]
Discriminator Loss: 0.5813, Generator Loss: 1.7454
D(x): 0.8018, D(G(z)): 0.2206
Epoch: [16/20], Batch Num: [257/600]
Discriminator Loss: 0.7988, Generator Loss: 1.8659
D(x): 0.7588, D(G(z)): 0.2465
Epoch: [16/20], Batch Num: [258/600]
Discriminator Loss: 0.8127, Generator Loss: 1.9762
D(x): 0.7502, D(G(z)): 0.2859
Epoch: [16/20], Batch Num: [259/600]
Discriminator Loss: 0.7850, Generator Loss: 2.2208
D(x): 0.7202, D(G(z)): 0.2294
Epoch: [16/20], Batch Num: [260/600]
Discriminator Loss: 0.7954, Generator Loss: 1.8255
D(x): 0.6912, D(G(z)): 0.1811
Epoch: [16/20], Batch Num: [261/600]
Discriminator Loss: 0.7380, Generator Loss: 1.6022
D(x): 0.7993, D(G(z)): 0.2726
Epoch: [16/20], Batch Num: [262/600]
Discriminator Loss: 0.7445, Generator Loss: 1.8510
D(x): 0.7723, D(G(z)): 0.2698
Epoch: [16/20], Batch Num: [263/600]
Discriminator Loss: 0.7003, Generator Loss: 2.0745
D(x): 0.8126, D(G(z)): 0.2597
Epoch: [16/20], Batch Num: [264/600]
Discriminator Loss: 0.6070, Generator Loss: 2.2002
D(x): 0.7797, D(G(z)): 0.1980
Epoch: [16/20], Batch Num: [265/600]
Discriminator Loss: 0.6260, Generator Loss: 2.1607
D(x): 0.7973, D(G(z)): 0.2086
Epoch: [16/20], Batch Num: [266/600]
Discriminator Loss: 0.7097, Generator Loss: 2.0897
D(x): 0.7400, D(G(z)): 0.2055
Epoch: [16/20], Batch Num: [267/600]
Discriminator Loss: 0.7107, Generator Loss: 1.8848
D(x): 0.7638, D(G(z)): 0.2117
Epoch: [16/20], Batch Num: [268/600]
Discriminator Loss: 0.6889, Generator Loss: 1.9833
D(x): 0.7631, D(G(z)): 0.2303
Epoch: [16/20], Batch Num: [269/600]
Discriminator Loss: 0.7167, Generator Loss: 1.9548
D(x): 0.7694, D(G(z)): 0.2332
Epoch: [16/20], Batch Num: [270/600]
Discriminator Loss: 0.8124, Generator Loss: 1.8854
D(x): 0.7468, D(G(z)): 0.2387
Epoch: [16/20], Batch Num: [271/600]
Discriminator Loss: 0.7184, Generator Loss: 1.8672
D(x): 0.8166, D(G(z)): 0.2730
Epoch: [16/20], Batch Num: [272/600]
Discriminator Loss: 0.6674, Generator Loss: 1.7764
D(x): 0.7747, D(G(z)): 0.2144
Epoch: [16/20], Batch Num: [273/600]
Discriminator Loss: 0.8158, Generator Loss: 2.2345
D(x): 0.7817, D(G(z)): 0.2541
Epoch: [16/20], Batch Num: [274/600]
Discriminator Loss: 0.7067, Generator Loss: 2.2102
D(x): 0.6993, D(G(z)): 0.1689
Epoch: [16/20], Batch Num: [275/600]
Discriminator Loss: 0.5951, Generator Loss: 1.8637
D(x): 0.7789, D(G(z)): 0.1776
Epoch: [16/20], Batch Num: [276/600]
Discriminator Loss: 0.6310, Generator Loss: 1.6200
D(x): 0.7798, D(G(z)): 0.2216
Epoch: [16/20], Batch Num: [277/600]
Discriminator Loss: 0.7939, Generator Loss: 1.5472
D(x): 0.7452, D(G(z)): 0.2370
Epoch: [16/20], Batch Num: [278/600]
Discriminator Loss: 0.8400, Generator Loss: 1.5820
D(x): 0.7993, D(G(z)): 0.3268
Epoch: [16/20], Batch Num: [279/600]
Discriminator Loss: 0.7657, Generator Loss: 1.9478
D(x): 0.8047, D(G(z)): 0.2866
Epoch: [16/20], Batch Num: [280/600]
Discriminator Loss: 0.8424, Generator Loss: 2.1582
D(x): 0.7787, D(G(z)): 0.2720
Epoch: [16/20], Batch Num: [281/600]
Discriminator Loss: 0.7398, Generator Loss: 2.2939
D(x): 0.7321, D(G(z)): 0.2215
Epoch: [16/20], Batch Num: [282/600]
Discriminator Loss: 0.9833, Generator Loss: 2.2504
D(x): 0.6515, D(G(z)): 0.2150
Epoch: [16/20], Batch Num: [283/600]
Discriminator Loss: 0.8287, Generator Loss: 1.8510
D(x): 0.6734, D(G(z)): 0.1883
Epoch: [16/20], Batch Num: [284/600]
Discriminator Loss: 0.7648, Generator Loss: 1.6587
D(x): 0.7708, D(G(z)): 0.2385
Epoch: [16/20], Batch Num: [285/600]
Discriminator Loss: 0.7411, Generator Loss: 1.4188
D(x): 0.8305, D(G(z)): 0.3183
Epoch: [16/20], Batch Num: [286/600]
Discriminator Loss: 0.7684, Generator Loss: 1.6768
D(x): 0.8307, D(G(z)): 0.3270
Epoch: [16/20], Batch Num: [287/600]
Discriminator Loss: 0.6304, Generator Loss: 1.9193
D(x): 0.8212, D(G(z)): 0.2531
Epoch: [16/20], Batch Num: [288/600]
Discriminator Loss: 0.6977, Generator Loss: 2.3474
D(x): 0.7306, D(G(z)): 0.1982
Epoch: [16/20], Batch Num: [289/600]
Discriminator Loss: 0.7460, Generator Loss: 2.3241
D(x): 0.7335, D(G(z)): 0.1914
Epoch: [16/20], Batch Num: [290/600]
Discriminator Loss: 0.6583, Generator Loss: 1.9941
D(x): 0.7483, D(G(z)): 0.1525
Epoch: [16/20], Batch Num: [291/600]
Discriminator Loss: 0.6945, Generator Loss: 1.6859
D(x): 0.7419, D(G(z)): 0.1847
Epoch: [16/20], Batch Num: [292/600]
Discriminator Loss: 0.6146, Generator Loss: 1.6545
D(x): 0.8347, D(G(z)): 0.2637
Epoch: [16/20], Batch Num: [293/600]
Discriminator Loss: 0.6962, Generator Loss: 1.6372
D(x): 0.7938, D(G(z)): 0.2561
Epoch: [16/20], Batch Num: [294/600]
Discriminator Loss: 0.7010, Generator Loss: 1.8206
D(x): 0.8291, D(G(z)): 0.3001
Epoch: [16/20], Batch Num: [295/600]
Discriminator Loss: 0.8924, Generator Loss: 2.2165
D(x): 0.7409, D(G(z)): 0.2828
Epoch: [16/20], Batch Num: [296/600]
Discriminator Loss: 0.7356, Generator Loss: 2.1863
D(x): 0.7564, D(G(z)): 0.2142
Epoch: [16/20], Batch Num: [297/600]
Discriminator Loss: 0.7366, Generator Loss: 2.2565
D(x): 0.7226, D(G(z)): 0.1982
Epoch: [16/20], Batch Num: [298/600]
Discriminator Loss: 0.7836, Generator Loss: 1.9031
D(x): 0.7304, D(G(z)): 0.2008
Epoch: [16/20], Batch Num: [299/600]
Discriminator Loss: 0.7480, Generator Loss: 1.6405
D(x): 0.7278, D(G(z)): 0.1957
Epoch: 16, Batch Num: [300/600]

Epoch: [16/20], Batch Num: [300/600]
Discriminator Loss: 0.6529, Generator Loss: 1.4319
D(x): 0.8383, D(G(z)): 0.2908
Epoch: [16/20], Batch Num: [301/600]
Discriminator Loss: 0.6849, Generator Loss: 1.9348
D(x): 0.8432, D(G(z)): 0.3006
Epoch: [16/20], Batch Num: [302/600]
Discriminator Loss: 0.6467, Generator Loss: 2.1183
D(x): 0.8168, D(G(z)): 0.2682
Epoch: [16/20], Batch Num: [303/600]
Discriminator Loss: 0.8414, Generator Loss: 2.4008
D(x): 0.7279, D(G(z)): 0.2270
Epoch: [16/20], Batch Num: [304/600]
Discriminator Loss: 0.8111, Generator Loss: 2.2994
D(x): 0.7114, D(G(z)): 0.1613
Epoch: [16/20], Batch Num: [305/600]
Discriminator Loss: 0.6937, Generator Loss: 2.0457
D(x): 0.7288, D(G(z)): 0.1557
Epoch: [16/20], Batch Num: [306/600]
Discriminator Loss: 0.7161, Generator Loss: 1.6001
D(x): 0.7476, D(G(z)): 0.2119
Epoch: [16/20], Batch Num: [307/600]
Discriminator Loss: 0.8270, Generator Loss: 1.5469
D(x): 0.7931, D(G(z)): 0.3139
Epoch: [16/20], Batch Num: [308/600]
Discriminator Loss: 0.7884, Generator Loss: 1.6085
D(x): 0.7956, D(G(z)): 0.3073
Epoch: [16/20], Batch Num: [309/600]
Discriminator Loss: 0.7248, Generator Loss: 1.7186
D(x): 0.8050, D(G(z)): 0.2744
Epoch: [16/20], Batch Num: [310/600]
Discriminator Loss: 0.6010, Generator Loss: 2.2346
D(x): 0.7974, D(G(z)): 0.2244
Epoch: [16/20], Batch Num: [311/600]
Discriminator Loss: 0.7686, Generator Loss: 2.2676
D(x): 0.7277, D(G(z)): 0.2058
Epoch: [16/20], Batch Num: [312/600]
Discriminator Loss: 0.7985, Generator Loss: 2.1266
D(x): 0.7085, D(G(z)): 0.2185
Epoch: [16/20], Batch Num: [313/600]
Discriminator Loss: 0.8457, Generator Loss: 1.6512
D(x): 0.6566, D(G(z)): 0.1791
Epoch: [16/20], Batch Num: [314/600]
Discriminator Loss: 0.8497, Generator Loss: 1.4133
D(x): 0.7183, D(G(z)): 0.2400
Epoch: [16/20], Batch Num: [315/600]
Discriminator Loss: 0.9409, Generator Loss: 1.4119
D(x): 0.7888, D(G(z)): 0.3509
Epoch: [16/20], Batch Num: [316/600]
Discriminator Loss: 0.8187, Generator Loss: 1.6436
D(x): 0.8378, D(G(z)): 0.3381
Epoch: [16/20], Batch Num: [317/600]
Discriminator Loss: 0.7697, Generator Loss: 2.0395
D(x): 0.8028, D(G(z)): 0.3122
Epoch: [16/20], Batch Num: [318/600]
Discriminator Loss: 0.8855, Generator Loss: 2.3452
D(x): 0.6874, D(G(z)): 0.1966
Epoch: [16/20], Batch Num: [319/600]
Discriminator Loss: 0.8021, Generator Loss: 1.9460
D(x): 0.6850, D(G(z)): 0.1917
Epoch: [16/20], Batch Num: [320/600]
Discriminator Loss: 0.8675, Generator Loss: 1.8661
D(x): 0.6989, D(G(z)): 0.2276
Epoch: [16/20], Batch Num: [321/600]
Discriminator Loss: 0.8482, Generator Loss: 1.4701
D(x): 0.6886, D(G(z)): 0.1997
Epoch: [16/20], Batch Num: [322/600]
Discriminator Loss: 0.8219, Generator Loss: 1.5418
D(x): 0.7965, D(G(z)): 0.3040
Epoch: [16/20], Batch Num: [323/600]
Discriminator Loss: 0.8895, Generator Loss: 1.4763
D(x): 0.7830, D(G(z)): 0.3231
Epoch: [16/20], Batch Num: [324/600]
Discriminator Loss: 0.8106, Generator Loss: 1.5501
D(x): 0.7994, D(G(z)): 0.3182
Epoch: [16/20], Batch Num: [325/600]
Discriminator Loss: 0.7634, Generator Loss: 1.7767
D(x): 0.7971, D(G(z)): 0.2859
Epoch: [16/20], Batch Num: [326/600]
Discriminator Loss: 0.9778, Generator Loss: 1.8194
D(x): 0.6605, D(G(z)): 0.2448
Epoch: [16/20], Batch Num: [327/600]
Discriminator Loss: 0.8993, Generator Loss: 1.7583
D(x): 0.6749, D(G(z)): 0.2213
Epoch: [16/20], Batch Num: [328/600]
Discriminator Loss: 0.9466, Generator Loss: 1.6186
D(x): 0.6323, D(G(z)): 0.2298
Epoch: [16/20], Batch Num: [329/600]
Discriminator Loss: 0.8190, Generator Loss: 1.4094
D(x): 0.7421, D(G(z)): 0.2812
Epoch: [16/20], Batch Num: [330/600]
Discriminator Loss: 0.8134, Generator Loss: 1.3147
D(x): 0.7542, D(G(z)): 0.3077
Epoch: [16/20], Batch Num: [331/600]
Discriminator Loss: 0.7014, Generator Loss: 1.3047
D(x): 0.8052, D(G(z)): 0.2922
Epoch: [16/20], Batch Num: [332/600]
Discriminator Loss: 0.7503, Generator Loss: 1.6596
D(x): 0.7987, D(G(z)): 0.3094
Epoch: [16/20], Batch Num: [333/600]
Discriminator Loss: 0.8476, Generator Loss: 1.8059
D(x): 0.7799, D(G(z)): 0.3344
Epoch: [16/20], Batch Num: [334/600]
Discriminator Loss: 0.7746, Generator Loss: 2.0585
D(x): 0.7191, D(G(z)): 0.2534
Epoch: [16/20], Batch Num: [335/600]
Discriminator Loss: 0.7744, Generator Loss: 2.3137
D(x): 0.6861, D(G(z)): 0.1748
Epoch: [16/20], Batch Num: [336/600]
Discriminator Loss: 0.6452, Generator Loss: 1.9605
D(x): 0.7421, D(G(z)): 0.1716
Epoch: [16/20], Batch Num: [337/600]
Discriminator Loss: 0.9279, Generator Loss: 1.8845
D(x): 0.6653, D(G(z)): 0.2168
Epoch: [16/20], Batch Num: [338/600]
Discriminator Loss: 0.7202, Generator Loss: 1.7101
D(x): 0.7390, D(G(z)): 0.2026
Epoch: [16/20], Batch Num: [339/600]
Discriminator Loss: 0.9366, Generator Loss: 1.5964
D(x): 0.7063, D(G(z)): 0.2764
Epoch: [16/20], Batch Num: [340/600]
Discriminator Loss: 0.7414, Generator Loss: 1.5432
D(x): 0.7883, D(G(z)): 0.2914
Epoch: [16/20], Batch Num: [341/600]
Discriminator Loss: 0.8246, Generator Loss: 1.4663
D(x): 0.7867, D(G(z)): 0.3127
Epoch: [16/20], Batch Num: [342/600]
Discriminator Loss: 0.7053, Generator Loss: 1.7153
D(x): 0.8008, D(G(z)): 0.2571
Epoch: [16/20], Batch Num: [343/600]
Discriminator Loss: 0.7031, Generator Loss: 1.8134
D(x): 0.8243, D(G(z)): 0.2754
Epoch: [16/20], Batch Num: [344/600]
Discriminator Loss: 0.8029, Generator Loss: 1.8259
D(x): 0.6977, D(G(z)): 0.2215
Epoch: [16/20], Batch Num: [345/600]
Discriminator Loss: 0.9154, Generator Loss: 1.7884
D(x): 0.6763, D(G(z)): 0.2276
Epoch: [16/20], Batch Num: [346/600]
Discriminator Loss: 0.6111, Generator Loss: 1.6002
D(x): 0.7987, D(G(z)): 0.2232
Epoch: [16/20], Batch Num: [347/600]
Discriminator Loss: 0.8877, Generator Loss: 1.6564
D(x): 0.6913, D(G(z)): 0.2423
Epoch: [16/20], Batch Num: [348/600]
Discriminator Loss: 0.6714, Generator Loss: 1.5532
D(x): 0.7833, D(G(z)): 0.2509
Epoch: [16/20], Batch Num: [349/600]
Discriminator Loss: 0.7695, Generator Loss: 1.6789
D(x): 0.7587, D(G(z)): 0.2685
Epoch: [16/20], Batch Num: [350/600]
Discriminator Loss: 0.8825, Generator Loss: 1.7365
D(x): 0.7683, D(G(z)): 0.3196
Epoch: [16/20], Batch Num: [351/600]
Discriminator Loss: 0.9025, Generator Loss: 1.8554
D(x): 0.7565, D(G(z)): 0.3287
Epoch: [16/20], Batch Num: [352/600]
Discriminator Loss: 0.8538, Generator Loss: 1.9267
D(x): 0.7071, D(G(z)): 0.2688
Epoch: [16/20], Batch Num: [353/600]
Discriminator Loss: 0.7552, Generator Loss: 1.9469
D(x): 0.7064, D(G(z)): 0.2066
Epoch: [16/20], Batch Num: [354/600]
Discriminator Loss: 0.6791, Generator Loss: 1.5299
D(x): 0.7281, D(G(z)): 0.1878
Epoch: [16/20], Batch Num: [355/600]
Discriminator Loss: 0.7516, Generator Loss: 1.5420
D(x): 0.7483, D(G(z)): 0.2637
Epoch: [16/20], Batch Num: [356/600]
Discriminator Loss: 0.8437, Generator Loss: 1.5187
D(x): 0.7638, D(G(z)): 0.3066
Epoch: [16/20], Batch Num: [357/600]
Discriminator Loss: 0.9677, Generator Loss: 1.7155
D(x): 0.7861, D(G(z)): 0.3720
Epoch: [16/20], Batch Num: [358/600]
Discriminator Loss: 0.9045, Generator Loss: 1.8531
D(x): 0.7275, D(G(z)): 0.3086
Epoch: [16/20], Batch Num: [359/600]
Discriminator Loss: 0.8394, Generator Loss: 2.1773
D(x): 0.7204, D(G(z)): 0.2707
Epoch: [16/20], Batch Num: [360/600]
Discriminator Loss: 0.8127, Generator Loss: 1.9942
D(x): 0.6893, D(G(z)): 0.2038
Epoch: [16/20], Batch Num: [361/600]
Discriminator Loss: 0.9522, Generator Loss: 1.6586
D(x): 0.6331, D(G(z)): 0.2127
Epoch: [16/20], Batch Num: [362/600]
Discriminator Loss: 0.8380, Generator Loss: 1.5099
D(x): 0.7246, D(G(z)): 0.2680
Epoch: [16/20], Batch Num: [363/600]
Discriminator Loss: 0.7138, Generator Loss: 1.4314
D(x): 0.8062, D(G(z)): 0.3065
Epoch: [16/20], Batch Num: [364/600]
Discriminator Loss: 0.9903, Generator Loss: 1.4770
D(x): 0.7631, D(G(z)): 0.3941
Epoch: [16/20], Batch Num: [365/600]
Discriminator Loss: 0.7310, Generator Loss: 1.7386
D(x): 0.7636, D(G(z)): 0.2811
Epoch: [16/20], Batch Num: [366/600]
Discriminator Loss: 0.7614, Generator Loss: 1.8933
D(x): 0.7339, D(G(z)): 0.2581
Epoch: [16/20], Batch Num: [367/600]
Discriminator Loss: 0.7497, Generator Loss: 1.8688
D(x): 0.7210, D(G(z)): 0.2288
Epoch: [16/20], Batch Num: [368/600]
Discriminator Loss: 0.8282, Generator Loss: 1.6467
D(x): 0.6803, D(G(z)): 0.2181
Epoch: [16/20], Batch Num: [369/600]
Discriminator Loss: 0.6688, Generator Loss: 1.5854
D(x): 0.7689, D(G(z)): 0.2230
Epoch: [16/20], Batch Num: [370/600]
Discriminator Loss: 0.8814, Generator Loss: 1.6174
D(x): 0.7468, D(G(z)): 0.3130
Epoch: [16/20], Batch Num: [371/600]
Discriminator Loss: 0.7768, Generator Loss: 1.6058
D(x): 0.7399, D(G(z)): 0.2672
Epoch: [16/20], Batch Num: [372/600]
Discriminator Loss: 0.7640, Generator Loss: 1.7738
D(x): 0.7827, D(G(z)): 0.2798
Epoch: [16/20], Batch Num: [373/600]
Discriminator Loss: 0.7431, Generator Loss: 1.7921
D(x): 0.7387, D(G(z)): 0.2450
Epoch: [16/20], Batch Num: [374/600]
Discriminator Loss: 0.7985, Generator Loss: 1.8413
D(x): 0.7466, D(G(z)): 0.2595
Epoch: [16/20], Batch Num: [375/600]
Discriminator Loss: 0.9780, Generator Loss: 1.8047
D(x): 0.7353, D(G(z)): 0.3247
Epoch: [16/20], Batch Num: [376/600]
Discriminator Loss: 0.6994, Generator Loss: 1.9719
D(x): 0.7657, D(G(z)): 0.2224
Epoch: [16/20], Batch Num: [377/600]
Discriminator Loss: 0.7973, Generator Loss: 1.9823
D(x): 0.7123, D(G(z)): 0.2186
Epoch: [16/20], Batch Num: [378/600]
Discriminator Loss: 0.7856, Generator Loss: 1.7846
D(x): 0.7062, D(G(z)): 0.2231
Epoch: [16/20], Batch Num: [379/600]
Discriminator Loss: 0.7297, Generator Loss: 1.7431
D(x): 0.7426, D(G(z)): 0.2315
Epoch: [16/20], Batch Num: [380/600]
Discriminator Loss: 0.8143, Generator Loss: 1.6750
D(x): 0.7848, D(G(z)): 0.3034
Epoch: [16/20], Batch Num: [381/600]
Discriminator Loss: 0.7525, Generator Loss: 1.6685
D(x): 0.7578, D(G(z)): 0.2438
Epoch: [16/20], Batch Num: [382/600]
Discriminator Loss: 0.7714, Generator Loss: 1.8729
D(x): 0.7414, D(G(z)): 0.2543
Epoch: [16/20], Batch Num: [383/600]
Discriminator Loss: 0.8264, Generator Loss: 1.8100
D(x): 0.7432, D(G(z)): 0.2662
Epoch: [16/20], Batch Num: [384/600]
Discriminator Loss: 0.7549, Generator Loss: 1.9802
D(x): 0.7824, D(G(z)): 0.2629
Epoch: [16/20], Batch Num: [385/600]
Discriminator Loss: 0.8527, Generator Loss: 1.9049
D(x): 0.6994, D(G(z)): 0.2249
Epoch: [16/20], Batch Num: [386/600]
Discriminator Loss: 0.8830, Generator Loss: 1.8617
D(x): 0.6923, D(G(z)): 0.2404
Epoch: [16/20], Batch Num: [387/600]
Discriminator Loss: 0.7903, Generator Loss: 1.6514
D(x): 0.7016, D(G(z)): 0.2138
Epoch: [16/20], Batch Num: [388/600]
Discriminator Loss: 0.7777, Generator Loss: 1.4168
D(x): 0.8180, D(G(z)): 0.3014
Epoch: [16/20], Batch Num: [389/600]
Discriminator Loss: 0.7748, Generator Loss: 1.6411
D(x): 0.7860, D(G(z)): 0.2839
Epoch: [16/20], Batch Num: [390/600]
Discriminator Loss: 0.7687, Generator Loss: 2.0064
D(x): 0.7889, D(G(z)): 0.2769
Epoch: [16/20], Batch Num: [391/600]
Discriminator Loss: 0.6503, Generator Loss: 2.0498
D(x): 0.7784, D(G(z)): 0.2237
Epoch: [16/20], Batch Num: [392/600]
Discriminator Loss: 0.9160, Generator Loss: 2.0580
D(x): 0.7234, D(G(z)): 0.2650
Epoch: [16/20], Batch Num: [393/600]
Discriminator Loss: 0.8182, Generator Loss: 2.0159
D(x): 0.6668, D(G(z)): 0.1907
Epoch: [16/20], Batch Num: [394/600]
Discriminator Loss: 0.8608, Generator Loss: 1.7601
D(x): 0.6982, D(G(z)): 0.2289
Epoch: [16/20], Batch Num: [395/600]
Discriminator Loss: 0.8943, Generator Loss: 1.5872
D(x): 0.7244, D(G(z)): 0.2822
Epoch: [16/20], Batch Num: [396/600]
Discriminator Loss: 0.7299, Generator Loss: 1.4471
D(x): 0.7777, D(G(z)): 0.2844
Epoch: [16/20], Batch Num: [397/600]
Discriminator Loss: 0.8991, Generator Loss: 1.6875
D(x): 0.7810, D(G(z)): 0.3448
Epoch: [16/20], Batch Num: [398/600]
Discriminator Loss: 0.7818, Generator Loss: 1.7811
D(x): 0.7825, D(G(z)): 0.2949
Epoch: [16/20], Batch Num: [399/600]
Discriminator Loss: 0.8066, Generator Loss: 1.9303
D(x): 0.7125, D(G(z)): 0.2463
Epoch: 16, Batch Num: [400/600]

Epoch: [16/20], Batch Num: [400/600]
Discriminator Loss: 0.9169, Generator Loss: 1.9162
D(x): 0.6646, D(G(z)): 0.2331
Epoch: [16/20], Batch Num: [401/600]
Discriminator Loss: 0.6695, Generator Loss: 1.6295
D(x): 0.7469, D(G(z)): 0.2038
Epoch: [16/20], Batch Num: [402/600]
Discriminator Loss: 0.8106, Generator Loss: 1.5015
D(x): 0.7218, D(G(z)): 0.2705
Epoch: [16/20], Batch Num: [403/600]
Discriminator Loss: 0.7243, Generator Loss: 1.6836
D(x): 0.7814, D(G(z)): 0.2666
Epoch: [16/20], Batch Num: [404/600]
Discriminator Loss: 0.8046, Generator Loss: 1.5650
D(x): 0.7357, D(G(z)): 0.2886
Epoch: [16/20], Batch Num: [405/600]
Discriminator Loss: 0.7076, Generator Loss: 1.7168
D(x): 0.8140, D(G(z)): 0.2995
Epoch: [16/20], Batch Num: [406/600]
Discriminator Loss: 0.8274, Generator Loss: 1.9010
D(x): 0.7662, D(G(z)): 0.3141
Epoch: [16/20], Batch Num: [407/600]
Discriminator Loss: 0.8137, Generator Loss: 2.0905
D(x): 0.7132, D(G(z)): 0.2342
Epoch: [16/20], Batch Num: [408/600]
Discriminator Loss: 0.7497, Generator Loss: 1.9514
D(x): 0.7168, D(G(z)): 0.1760
Epoch: [16/20], Batch Num: [409/600]
Discriminator Loss: 0.8025, Generator Loss: 1.8201
D(x): 0.6897, D(G(z)): 0.1912
Epoch: [16/20], Batch Num: [410/600]
Discriminator Loss: 0.7253, Generator Loss: 1.6627
D(x): 0.7781, D(G(z)): 0.2499
Epoch: [16/20], Batch Num: [411/600]
Discriminator Loss: 0.8725, Generator Loss: 1.7120
D(x): 0.7372, D(G(z)): 0.3054
Epoch: [16/20], Batch Num: [412/600]
Discriminator Loss: 0.7890, Generator Loss: 1.6666
D(x): 0.7809, D(G(z)): 0.2818
Epoch: [16/20], Batch Num: [413/600]
Discriminator Loss: 0.7695, Generator Loss: 1.9468
D(x): 0.7943, D(G(z)): 0.3082
Epoch: [16/20], Batch Num: [414/600]
Discriminator Loss: 0.7436, Generator Loss: 2.2846
D(x): 0.7608, D(G(z)): 0.2359
Epoch: [16/20], Batch Num: [415/600]
Discriminator Loss: 0.9228, Generator Loss: 1.9490
D(x): 0.6932, D(G(z)): 0.2491
Epoch: [16/20], Batch Num: [416/600]
Discriminator Loss: 0.8898, Generator Loss: 1.7035
D(x): 0.6465, D(G(z)): 0.2008
Epoch: [16/20], Batch Num: [417/600]
Discriminator Loss: 0.8204, Generator Loss: 1.6510
D(x): 0.7691, D(G(z)): 0.2716
Epoch: [16/20], Batch Num: [418/600]
Discriminator Loss: 0.8405, Generator Loss: 1.7901
D(x): 0.7694, D(G(z)): 0.3182
Epoch: [16/20], Batch Num: [419/600]
Discriminator Loss: 0.8588, Generator Loss: 1.8911
D(x): 0.7801, D(G(z)): 0.3007
Epoch: [16/20], Batch Num: [420/600]
Discriminator Loss: 0.8149, Generator Loss: 2.0399
D(x): 0.7646, D(G(z)): 0.2858
Epoch: [16/20], Batch Num: [421/600]
Discriminator Loss: 0.9668, Generator Loss: 2.0761
D(x): 0.6374, D(G(z)): 0.2393
Epoch: [16/20], Batch Num: [422/600]
Discriminator Loss: 1.0283, Generator Loss: 1.8409
D(x): 0.6386, D(G(z)): 0.2306
Epoch: [16/20], Batch Num: [423/600]
Discriminator Loss: 0.8189, Generator Loss: 1.6755
D(x): 0.6911, D(G(z)): 0.2200
Epoch: [16/20], Batch Num: [424/600]
Discriminator Loss: 0.9217, Generator Loss: 1.4662
D(x): 0.6827, D(G(z)): 0.2635
Epoch: [16/20], Batch Num: [425/600]
Discriminator Loss: 0.8277, Generator Loss: 1.2816
D(x): 0.7772, D(G(z)): 0.3279
Epoch: [16/20], Batch Num: [426/600]
Discriminator Loss: 0.8107, Generator Loss: 1.2907
D(x): 0.8430, D(G(z)): 0.3593
Epoch: [16/20], Batch Num: [427/600]
Discriminator Loss: 0.8425, Generator Loss: 1.4809
D(x): 0.7867, D(G(z)): 0.3492
Epoch: [16/20], Batch Num: [428/600]
Discriminator Loss: 0.9212, Generator Loss: 2.0426
D(x): 0.6971, D(G(z)): 0.2956
Epoch: [16/20], Batch Num: [429/600]
Discriminator Loss: 0.8009, Generator Loss: 1.8070
D(x): 0.6906, D(G(z)): 0.2334
Epoch: [16/20], Batch Num: [430/600]
Discriminator Loss: 0.6544, Generator Loss: 1.9279
D(x): 0.7574, D(G(z)): 0.2199
Epoch: [16/20], Batch Num: [431/600]
Discriminator Loss: 0.8122, Generator Loss: 1.9626
D(x): 0.6873, D(G(z)): 0.2068
Epoch: [16/20], Batch Num: [432/600]
Discriminator Loss: 0.6721, Generator Loss: 1.7190
D(x): 0.7122, D(G(z)): 0.1805
Epoch: [16/20], Batch Num: [433/600]
Discriminator Loss: 0.9202, Generator Loss: 1.5658
D(x): 0.7045, D(G(z)): 0.2674
Epoch: [16/20], Batch Num: [434/600]
Discriminator Loss: 0.7149, Generator Loss: 1.5119
D(x): 0.8316, D(G(z)): 0.3169
Epoch: [16/20], Batch Num: [435/600]
Discriminator Loss: 0.9175, Generator Loss: 1.9042
D(x): 0.7791, D(G(z)): 0.3590
Epoch: [16/20], Batch Num: [436/600]
Discriminator Loss: 0.8276, Generator Loss: 2.1049
D(x): 0.7523, D(G(z)): 0.2597
Epoch: [16/20], Batch Num: [437/600]
Discriminator Loss: 0.7364, Generator Loss: 2.3613
D(x): 0.7045, D(G(z)): 0.1843
Epoch: [16/20], Batch Num: [438/600]
Discriminator Loss: 0.8229, Generator Loss: 1.8877
D(x): 0.6901, D(G(z)): 0.1908
Epoch: [16/20], Batch Num: [439/600]
Discriminator Loss: 0.8045, Generator Loss: 1.7319
D(x): 0.7173, D(G(z)): 0.2300
Epoch: [16/20], Batch Num: [440/600]
Discriminator Loss: 0.7994, Generator Loss: 1.5466
D(x): 0.7636, D(G(z)): 0.2853
Epoch: [16/20], Batch Num: [441/600]
Discriminator Loss: 0.7616, Generator Loss: 1.6195
D(x): 0.7712, D(G(z)): 0.2741
Epoch: [16/20], Batch Num: [442/600]
Discriminator Loss: 0.9203, Generator Loss: 1.6880
D(x): 0.7347, D(G(z)): 0.3180
Epoch: [16/20], Batch Num: [443/600]
Discriminator Loss: 0.8331, Generator Loss: 1.8138
D(x): 0.7725, D(G(z)): 0.3053
Epoch: [16/20], Batch Num: [444/600]
Discriminator Loss: 0.8766, Generator Loss: 1.9147
D(x): 0.6712, D(G(z)): 0.1873
Epoch: [16/20], Batch Num: [445/600]
Discriminator Loss: 0.8325, Generator Loss: 1.5718
D(x): 0.6964, D(G(z)): 0.2309
Epoch: [16/20], Batch Num: [446/600]
Discriminator Loss: 0.9094, Generator Loss: 1.4453
D(x): 0.7135, D(G(z)): 0.2774
Epoch: [16/20], Batch Num: [447/600]
Discriminator Loss: 0.9295, Generator Loss: 1.4259
D(x): 0.6977, D(G(z)): 0.2982
Epoch: [16/20], Batch Num: [448/600]
Discriminator Loss: 0.8322, Generator Loss: 1.2830
D(x): 0.7787, D(G(z)): 0.3446
Epoch: [16/20], Batch Num: [449/600]
Discriminator Loss: 0.7904, Generator Loss: 1.5726
D(x): 0.7371, D(G(z)): 0.2920
Epoch: [16/20], Batch Num: [450/600]
Discriminator Loss: 0.7561, Generator Loss: 1.4849
D(x): 0.7837, D(G(z)): 0.2974
Epoch: [16/20], Batch Num: [451/600]
Discriminator Loss: 0.8143, Generator Loss: 1.8516
D(x): 0.7573, D(G(z)): 0.2957
Epoch: [16/20], Batch Num: [452/600]
Discriminator Loss: 0.6551, Generator Loss: 1.6504
D(x): 0.7521, D(G(z)): 0.2363
Epoch: [16/20], Batch Num: [453/600]
Discriminator Loss: 0.9230, Generator Loss: 1.9296
D(x): 0.6899, D(G(z)): 0.2754
Epoch: [16/20], Batch Num: [454/600]
Discriminator Loss: 0.8472, Generator Loss: 1.8105
D(x): 0.7414, D(G(z)): 0.2993
Epoch: [16/20], Batch Num: [455/600]
Discriminator Loss: 0.9006, Generator Loss: 1.7177
D(x): 0.6589, D(G(z)): 0.2505
Epoch: [16/20], Batch Num: [456/600]
Discriminator Loss: 0.8348, Generator Loss: 1.5472
D(x): 0.7036, D(G(z)): 0.2552
Epoch: [16/20], Batch Num: [457/600]
Discriminator Loss: 0.7243, Generator Loss: 1.6612
D(x): 0.7722, D(G(z)): 0.2714
Epoch: [16/20], Batch Num: [458/600]
Discriminator Loss: 0.7484, Generator Loss: 1.5787
D(x): 0.7716, D(G(z)): 0.2887
Epoch: [16/20], Batch Num: [459/600]
Discriminator Loss: 0.6488, Generator Loss: 1.6075
D(x): 0.7943, D(G(z)): 0.2585
Epoch: [16/20], Batch Num: [460/600]
Discriminator Loss: 0.6946, Generator Loss: 1.6229
D(x): 0.7917, D(G(z)): 0.2829
Epoch: [16/20], Batch Num: [461/600]
Discriminator Loss: 0.7534, Generator Loss: 1.7619
D(x): 0.7972, D(G(z)): 0.2921
Epoch: [16/20], Batch Num: [462/600]
Discriminator Loss: 0.8225, Generator Loss: 1.9425
D(x): 0.7185, D(G(z)): 0.2341
Epoch: [16/20], Batch Num: [463/600]
Discriminator Loss: 0.7532, Generator Loss: 1.8528
D(x): 0.7441, D(G(z)): 0.2335
Epoch: [16/20], Batch Num: [464/600]
Discriminator Loss: 0.7353, Generator Loss: 1.5878
D(x): 0.7386, D(G(z)): 0.2151
Epoch: [16/20], Batch Num: [465/600]
Discriminator Loss: 0.7986, Generator Loss: 1.8659
D(x): 0.7675, D(G(z)): 0.2653
Epoch: [16/20], Batch Num: [466/600]
Discriminator Loss: 0.8323, Generator Loss: 1.7700
D(x): 0.7618, D(G(z)): 0.3000
Epoch: [16/20], Batch Num: [467/600]
Discriminator Loss: 0.6854, Generator Loss: 1.9299
D(x): 0.8040, D(G(z)): 0.2617
Epoch: [16/20], Batch Num: [468/600]
Discriminator Loss: 0.7006, Generator Loss: 2.0209
D(x): 0.7843, D(G(z)): 0.2550
Epoch: [16/20], Batch Num: [469/600]
Discriminator Loss: 0.8190, Generator Loss: 2.4202
D(x): 0.7412, D(G(z)): 0.2296
Epoch: [16/20], Batch Num: [470/600]
Discriminator Loss: 0.6737, Generator Loss: 2.1400
D(x): 0.7372, D(G(z)): 0.1693
Epoch: [16/20], Batch Num: [471/600]
Discriminator Loss: 0.8292, Generator Loss: 1.9974
D(x): 0.6896, D(G(z)): 0.1967
Epoch: [16/20], Batch Num: [472/600]
Discriminator Loss: 0.7951, Generator Loss: 1.8071
D(x): 0.7816, D(G(z)): 0.2784
Epoch: [16/20], Batch Num: [473/600]
Discriminator Loss: 0.7315, Generator Loss: 1.7399
D(x): 0.7829, D(G(z)): 0.2517
Epoch: [16/20], Batch Num: [474/600]
Discriminator Loss: 0.7358, Generator Loss: 1.3539
D(x): 0.7800, D(G(z)): 0.2633
Epoch: [16/20], Batch Num: [475/600]
Discriminator Loss: 0.8775, Generator Loss: 1.7350
D(x): 0.7519, D(G(z)): 0.2655
Epoch: [16/20], Batch Num: [476/600]
Discriminator Loss: 0.9825, Generator Loss: 1.9514
D(x): 0.7987, D(G(z)): 0.3023
Epoch: [16/20], Batch Num: [477/600]
Discriminator Loss: 0.8535, Generator Loss: 2.0305
D(x): 0.7200, D(G(z)): 0.2560
Epoch: [16/20], Batch Num: [478/600]
Discriminator Loss: 0.9144, Generator Loss: 2.0356
D(x): 0.6954, D(G(z)): 0.2406
Epoch: [16/20], Batch Num: [479/600]
Discriminator Loss: 0.8577, Generator Loss: 1.8123
D(x): 0.6832, D(G(z)): 0.1931
Epoch: [16/20], Batch Num: [480/600]
Discriminator Loss: 0.8692, Generator Loss: 1.7655
D(x): 0.7222, D(G(z)): 0.2551
Epoch: [16/20], Batch Num: [481/600]
Discriminator Loss: 0.8907, Generator Loss: 1.7295
D(x): 0.7358, D(G(z)): 0.3025
Epoch: [16/20], Batch Num: [482/600]
Discriminator Loss: 0.8552, Generator Loss: 1.5658
D(x): 0.7237, D(G(z)): 0.2859
Epoch: [16/20], Batch Num: [483/600]
Discriminator Loss: 0.8110, Generator Loss: 1.6282
D(x): 0.7596, D(G(z)): 0.2866
Epoch: [16/20], Batch Num: [484/600]
Discriminator Loss: 0.8855, Generator Loss: 1.6770
D(x): 0.7229, D(G(z)): 0.2938
Epoch: [16/20], Batch Num: [485/600]
Discriminator Loss: 0.7631, Generator Loss: 1.7866
D(x): 0.7573, D(G(z)): 0.2572
Epoch: [16/20], Batch Num: [486/600]
Discriminator Loss: 0.6628, Generator Loss: 1.8932
D(x): 0.7643, D(G(z)): 0.2181
Epoch: [16/20], Batch Num: [487/600]
Discriminator Loss: 0.7373, Generator Loss: 1.9655
D(x): 0.7213, D(G(z)): 0.2092
Epoch: [16/20], Batch Num: [488/600]
Discriminator Loss: 0.9304, Generator Loss: 1.6434
D(x): 0.6847, D(G(z)): 0.2448
Epoch: [16/20], Batch Num: [489/600]
Discriminator Loss: 0.8472, Generator Loss: 1.4235
D(x): 0.7148, D(G(z)): 0.2655
Epoch: [16/20], Batch Num: [490/600]
Discriminator Loss: 0.8126, Generator Loss: 1.3728
D(x): 0.7762, D(G(z)): 0.3199
Epoch: [16/20], Batch Num: [491/600]
Discriminator Loss: 0.7865, Generator Loss: 1.6499
D(x): 0.8207, D(G(z)): 0.3231
Epoch: [16/20], Batch Num: [492/600]
Discriminator Loss: 0.7161, Generator Loss: 1.8130
D(x): 0.8039, D(G(z)): 0.2908
Epoch: [16/20], Batch Num: [493/600]
Discriminator Loss: 0.8518, Generator Loss: 1.9365
D(x): 0.7237, D(G(z)): 0.2418
Epoch: [16/20], Batch Num: [494/600]
Discriminator Loss: 0.8597, Generator Loss: 1.9912
D(x): 0.6458, D(G(z)): 0.1778
Epoch: [16/20], Batch Num: [495/600]
Discriminator Loss: 0.7563, Generator Loss: 1.7788
D(x): 0.7066, D(G(z)): 0.1876
Epoch: [16/20], Batch Num: [496/600]
Discriminator Loss: 0.6967, Generator Loss: 1.7537
D(x): 0.7561, D(G(z)): 0.2454
Epoch: [16/20], Batch Num: [497/600]
Discriminator Loss: 0.7314, Generator Loss: 1.6109
D(x): 0.7675, D(G(z)): 0.2566
Epoch: [16/20], Batch Num: [498/600]
Discriminator Loss: 0.7481, Generator Loss: 1.4148
D(x): 0.7920, D(G(z)): 0.2985
Epoch: [16/20], Batch Num: [499/600]
Discriminator Loss: 0.6470, Generator Loss: 1.6711
D(x): 0.8375, D(G(z)): 0.2705
Epoch: 16, Batch Num: [500/600]

Epoch: [16/20], Batch Num: [500/600]
Discriminator Loss: 0.7398, Generator Loss: 1.6889
D(x): 0.7896, D(G(z)): 0.2717
Epoch: [16/20], Batch Num: [501/600]
Discriminator Loss: 0.7826, Generator Loss: 1.9257
D(x): 0.7419, D(G(z)): 0.2493
Epoch: [16/20], Batch Num: [502/600]
Discriminator Loss: 0.7473, Generator Loss: 2.0345
D(x): 0.7114, D(G(z)): 0.1898
Epoch: [16/20], Batch Num: [503/600]
Discriminator Loss: 0.8524, Generator Loss: 1.7279
D(x): 0.6918, D(G(z)): 0.1979
Epoch: [16/20], Batch Num: [504/600]
Discriminator Loss: 0.7127, Generator Loss: 1.6780
D(x): 0.7504, D(G(z)): 0.2164
Epoch: [16/20], Batch Num: [505/600]
Discriminator Loss: 0.7217, Generator Loss: 1.6003
D(x): 0.7845, D(G(z)): 0.2582
Epoch: [16/20], Batch Num: [506/600]
Discriminator Loss: 0.7138, Generator Loss: 1.6813
D(x): 0.7806, D(G(z)): 0.2530
Epoch: [16/20], Batch Num: [507/600]
Discriminator Loss: 0.6002, Generator Loss: 1.7913
D(x): 0.8089, D(G(z)): 0.2173
Epoch: [16/20], Batch Num: [508/600]
Discriminator Loss: 0.5929, Generator Loss: 1.9835
D(x): 0.8426, D(G(z)): 0.2505
Epoch: [16/20], Batch Num: [509/600]
Discriminator Loss: 0.7279, Generator Loss: 2.2162
D(x): 0.7752, D(G(z)): 0.2344
Epoch: [16/20], Batch Num: [510/600]
Discriminator Loss: 0.6875, Generator Loss: 2.3393
D(x): 0.7393, D(G(z)): 0.1747
Epoch: [16/20], Batch Num: [511/600]
Discriminator Loss: 0.8302, Generator Loss: 2.4044
D(x): 0.6870, D(G(z)): 0.1930
Epoch: [16/20], Batch Num: [512/600]
Discriminator Loss: 0.7893, Generator Loss: 2.0513
D(x): 0.7384, D(G(z)): 0.2027
Epoch: [16/20], Batch Num: [513/600]
Discriminator Loss: 0.8546, Generator Loss: 1.8535
D(x): 0.7598, D(G(z)): 0.2446
Epoch: [16/20], Batch Num: [514/600]
Discriminator Loss: 0.8000, Generator Loss: 1.8798
D(x): 0.7431, D(G(z)): 0.2638
Epoch: [16/20], Batch Num: [515/600]
Discriminator Loss: 0.7562, Generator Loss: 1.9212
D(x): 0.7868, D(G(z)): 0.2592
Epoch: [16/20], Batch Num: [516/600]
Discriminator Loss: 0.7169, Generator Loss: 1.7452
D(x): 0.7742, D(G(z)): 0.2533
Epoch: [16/20], Batch Num: [517/600]
Discriminator Loss: 0.7069, Generator Loss: 2.2568
D(x): 0.7768, D(G(z)): 0.2539
Epoch: [16/20], Batch Num: [518/600]
Discriminator Loss: 0.6657, Generator Loss: 2.0281
D(x): 0.7591, D(G(z)): 0.1883
Epoch: [16/20], Batch Num: [519/600]
Discriminator Loss: 0.6390, Generator Loss: 1.9677
D(x): 0.7749, D(G(z)): 0.1942
Epoch: [16/20], Batch Num: [520/600]
Discriminator Loss: 0.6991, Generator Loss: 1.9627
D(x): 0.7554, D(G(z)): 0.2189
Epoch: [16/20], Batch Num: [521/600]
Discriminator Loss: 0.7202, Generator Loss: 1.9333
D(x): 0.7918, D(G(z)): 0.2674
Epoch: [16/20], Batch Num: [522/600]
Discriminator Loss: 0.7957, Generator Loss: 1.9722
D(x): 0.7512, D(G(z)): 0.2405
Epoch: [16/20], Batch Num: [523/600]
Discriminator Loss: 0.7325, Generator Loss: 2.1210
D(x): 0.7628, D(G(z)): 0.2014
Epoch: [16/20], Batch Num: [524/600]
Discriminator Loss: 0.7786, Generator Loss: 2.0728
D(x): 0.7415, D(G(z)): 0.2282
Epoch: [16/20], Batch Num: [525/600]
Discriminator Loss: 0.8333, Generator Loss: 1.7469
D(x): 0.7142, D(G(z)): 0.1938
Epoch: [16/20], Batch Num: [526/600]
Discriminator Loss: 0.7897, Generator Loss: 1.6643
D(x): 0.7361, D(G(z)): 0.2449
Epoch: [16/20], Batch Num: [527/600]
Discriminator Loss: 0.7206, Generator Loss: 1.4909
D(x): 0.7717, D(G(z)): 0.2421
Epoch: [16/20], Batch Num: [528/600]
Discriminator Loss: 0.8161, Generator Loss: 1.7006
D(x): 0.7788, D(G(z)): 0.3007
Epoch: [16/20], Batch Num: [529/600]
Discriminator Loss: 1.0394, Generator Loss: 1.9643
D(x): 0.7316, D(G(z)): 0.3269
Epoch: [16/20], Batch Num: [530/600]
Discriminator Loss: 1.1061, Generator Loss: 1.9646
D(x): 0.6584, D(G(z)): 0.2670
Epoch: [16/20], Batch Num: [531/600]
Discriminator Loss: 1.0153, Generator Loss: 2.0005
D(x): 0.6372, D(G(z)): 0.2177
Epoch: [16/20], Batch Num: [532/600]
Discriminator Loss: 0.7752, Generator Loss: 1.7164
D(x): 0.6982, D(G(z)): 0.1921
Epoch: [16/20], Batch Num: [533/600]
Discriminator Loss: 0.8828, Generator Loss: 1.3293
D(x): 0.7383, D(G(z)): 0.2922
Epoch: [16/20], Batch Num: [534/600]
Discriminator Loss: 1.0412, Generator Loss: 1.4303
D(x): 0.7671, D(G(z)): 0.3803
Epoch: [16/20], Batch Num: [535/600]
Discriminator Loss: 0.9354, Generator Loss: 1.7517
D(x): 0.7300, D(G(z)): 0.3578
Epoch: [16/20], Batch Num: [536/600]
Discriminator Loss: 0.8010, Generator Loss: 1.8649
D(x): 0.7312, D(G(z)): 0.2758
Epoch: [16/20], Batch Num: [537/600]
Discriminator Loss: 0.8456, Generator Loss: 1.8839
D(x): 0.7007, D(G(z)): 0.2375
Epoch: [16/20], Batch Num: [538/600]
Discriminator Loss: 1.1643, Generator Loss: 1.7352
D(x): 0.6350, D(G(z)): 0.3151
Epoch: [16/20], Batch Num: [539/600]
Discriminator Loss: 0.9121, Generator Loss: 1.4966
D(x): 0.6519, D(G(z)): 0.2381
Epoch: [16/20], Batch Num: [540/600]
Discriminator Loss: 0.8889, Generator Loss: 1.3131
D(x): 0.7046, D(G(z)): 0.2953
Epoch: [16/20], Batch Num: [541/600]
Discriminator Loss: 1.0056, Generator Loss: 1.2098
D(x): 0.7263, D(G(z)): 0.3547
Epoch: [16/20], Batch Num: [542/600]
Discriminator Loss: 0.9671, Generator Loss: 1.2134
D(x): 0.7156, D(G(z)): 0.3538
Epoch: [16/20], Batch Num: [543/600]
Discriminator Loss: 0.9232, Generator Loss: 1.3831
D(x): 0.7695, D(G(z)): 0.3641
Epoch: [16/20], Batch Num: [544/600]
Discriminator Loss: 0.8174, Generator Loss: 1.4359
D(x): 0.7577, D(G(z)): 0.3025
Epoch: [16/20], Batch Num: [545/600]
Discriminator Loss: 0.9774, Generator Loss: 1.6991
D(x): 0.6538, D(G(z)): 0.2738
Epoch: [16/20], Batch Num: [546/600]
Discriminator Loss: 1.0063, Generator Loss: 1.7849
D(x): 0.6251, D(G(z)): 0.2462
Epoch: [16/20], Batch Num: [547/600]
Discriminator Loss: 0.9658, Generator Loss: 1.4481
D(x): 0.6368, D(G(z)): 0.2459
Epoch: [16/20], Batch Num: [548/600]
Discriminator Loss: 0.7884, Generator Loss: 1.3211
D(x): 0.7300, D(G(z)): 0.2699
Epoch: [16/20], Batch Num: [549/600]
Discriminator Loss: 0.8769, Generator Loss: 1.4850
D(x): 0.7369, D(G(z)): 0.3198
Epoch: [16/20], Batch Num: [550/600]
Discriminator Loss: 0.8176, Generator Loss: 1.4221
D(x): 0.7493, D(G(z)): 0.3037
Epoch: [16/20], Batch Num: [551/600]
Discriminator Loss: 0.7927, Generator Loss: 1.4311
D(x): 0.7649, D(G(z)): 0.3083
Epoch: [16/20], Batch Num: [552/600]
Discriminator Loss: 0.8383, Generator Loss: 1.6187
D(x): 0.7532, D(G(z)): 0.3230
Epoch: [16/20], Batch Num: [553/600]
Discriminator Loss: 1.0053, Generator Loss: 1.6847
D(x): 0.6716, D(G(z)): 0.3036
Epoch: [16/20], Batch Num: [554/600]
Discriminator Loss: 0.8250, Generator Loss: 1.6797
D(x): 0.6803, D(G(z)): 0.2331
Epoch: [16/20], Batch Num: [555/600]
Discriminator Loss: 0.7519, Generator Loss: 1.8088
D(x): 0.7364, D(G(z)): 0.2399
Epoch: [16/20], Batch Num: [556/600]
Discriminator Loss: 0.8430, Generator Loss: 1.5575
D(x): 0.6778, D(G(z)): 0.2547
Epoch: [16/20], Batch Num: [557/600]
Discriminator Loss: 0.7941, Generator Loss: 1.4805
D(x): 0.7503, D(G(z)): 0.2819
Epoch: [16/20], Batch Num: [558/600]
Discriminator Loss: 0.8857, Generator Loss: 1.3603
D(x): 0.7292, D(G(z)): 0.3224
Epoch: [16/20], Batch Num: [559/600]
Discriminator Loss: 0.8129, Generator Loss: 1.4796
D(x): 0.7818, D(G(z)): 0.3337
Epoch: [16/20], Batch Num: [560/600]
Discriminator Loss: 0.7506, Generator Loss: 1.8357
D(x): 0.7643, D(G(z)): 0.2707
Epoch: [16/20], Batch Num: [561/600]
Discriminator Loss: 0.8351, Generator Loss: 1.7223
D(x): 0.7079, D(G(z)): 0.2544
Epoch: [16/20], Batch Num: [562/600]
Discriminator Loss: 0.8202, Generator Loss: 1.8787
D(x): 0.7456, D(G(z)): 0.2815
Epoch: [16/20], Batch Num: [563/600]
Discriminator Loss: 0.9247, Generator Loss: 1.7918
D(x): 0.6932, D(G(z)): 0.2693
Epoch: [16/20], Batch Num: [564/600]
Discriminator Loss: 0.7923, Generator Loss: 2.0044
D(x): 0.6877, D(G(z)): 0.2333
Epoch: [16/20], Batch Num: [565/600]
Discriminator Loss: 0.7911, Generator Loss: 1.8952
D(x): 0.7907, D(G(z)): 0.3074
Epoch: [16/20], Batch Num: [566/600]
Discriminator Loss: 0.7391, Generator Loss: 1.9405
D(x): 0.7456, D(G(z)): 0.2266
Epoch: [16/20], Batch Num: [567/600]
Discriminator Loss: 0.7727, Generator Loss: 2.0188
D(x): 0.7369, D(G(z)): 0.2144
Epoch: [16/20], Batch Num: [568/600]
Discriminator Loss: 0.8197, Generator Loss: 1.6699
D(x): 0.7492, D(G(z)): 0.2590
Epoch: [16/20], Batch Num: [569/600]
Discriminator Loss: 0.8598, Generator Loss: 1.8559
D(x): 0.7489, D(G(z)): 0.3030
Epoch: [16/20], Batch Num: [570/600]
Discriminator Loss: 0.7602, Generator Loss: 1.9945
D(x): 0.7763, D(G(z)): 0.2705
Epoch: [16/20], Batch Num: [571/600]
Discriminator Loss: 0.7578, Generator Loss: 2.2328
D(x): 0.7732, D(G(z)): 0.2584
Epoch: [16/20], Batch Num: [572/600]
Discriminator Loss: 0.7568, Generator Loss: 2.0619
D(x): 0.7186, D(G(z)): 0.2032
Epoch: [16/20], Batch Num: [573/600]
Discriminator Loss: 0.8823, Generator Loss: 2.0503
D(x): 0.6671, D(G(z)): 0.2099
Epoch: [16/20], Batch Num: [574/600]
Discriminator Loss: 0.9550, Generator Loss: 1.5811
D(x): 0.6968, D(G(z)): 0.2557
Epoch: [16/20], Batch Num: [575/600]
Discriminator Loss: 0.8781, Generator Loss: 1.5417
D(x): 0.7315, D(G(z)): 0.2405
Epoch: [16/20], Batch Num: [576/600]
Discriminator Loss: 0.8700, Generator Loss: 1.4180
D(x): 0.7772, D(G(z)): 0.3091
Epoch: [16/20], Batch Num: [577/600]
Discriminator Loss: 0.9534, Generator Loss: 1.7128
D(x): 0.7318, D(G(z)): 0.3221
Epoch: [16/20], Batch Num: [578/600]
Discriminator Loss: 1.0935, Generator Loss: 1.6554
D(x): 0.6702, D(G(z)): 0.3365
Epoch: [16/20], Batch Num: [579/600]
Discriminator Loss: 0.9996, Generator Loss: 1.7251
D(x): 0.7307, D(G(z)): 0.3391
Epoch: [16/20], Batch Num: [580/600]
Discriminator Loss: 0.8757, Generator Loss: 1.9911
D(x): 0.6518, D(G(z)): 0.2484
Epoch: [16/20], Batch Num: [581/600]
Discriminator Loss: 0.8879, Generator Loss: 1.8601
D(x): 0.6841, D(G(z)): 0.2661
Epoch: [16/20], Batch Num: [582/600]
Discriminator Loss: 0.9462, Generator Loss: 1.7268
D(x): 0.6586, D(G(z)): 0.2688
Epoch: [16/20], Batch Num: [583/600]
Discriminator Loss: 0.8447, Generator Loss: 1.4377
D(x): 0.7222, D(G(z)): 0.2752
Epoch: [16/20], Batch Num: [584/600]
Discriminator Loss: 0.8544, Generator Loss: 1.3200
D(x): 0.6997, D(G(z)): 0.2670
Epoch: [16/20], Batch Num: [585/600]
Discriminator Loss: 0.8743, Generator Loss: 1.3211
D(x): 0.6762, D(G(z)): 0.2869
Epoch: [16/20], Batch Num: [586/600]
Discriminator Loss: 0.8184, Generator Loss: 1.2323
D(x): 0.7591, D(G(z)): 0.3319
Epoch: [16/20], Batch Num: [587/600]
Discriminator Loss: 1.0154, Generator Loss: 1.3768
D(x): 0.7058, D(G(z)): 0.3327
Epoch: [16/20], Batch Num: [588/600]
Discriminator Loss: 1.0130, Generator Loss: 1.3804
D(x): 0.7181, D(G(z)): 0.3676
Epoch: [16/20], Batch Num: [589/600]
Discriminator Loss: 0.8352, Generator Loss: 1.4786
D(x): 0.7310, D(G(z)): 0.3040
Epoch: [16/20], Batch Num: [590/600]
Discriminator Loss: 0.7605, Generator Loss: 1.9124
D(x): 0.7632, D(G(z)): 0.2931
Epoch: [16/20], Batch Num: [591/600]
Discriminator Loss: 0.8437, Generator Loss: 2.0345
D(x): 0.6714, D(G(z)): 0.2403
Epoch: [16/20], Batch Num: [592/600]
Discriminator Loss: 1.0399, Generator Loss: 1.9684
D(x): 0.6007, D(G(z)): 0.2371
Epoch: [16/20], Batch Num: [593/600]
Discriminator Loss: 0.9212, Generator Loss: 1.5910
D(x): 0.6194, D(G(z)): 0.2045
Epoch: [16/20], Batch Num: [594/600]
Discriminator Loss: 0.8505, Generator Loss: 1.6244
D(x): 0.7810, D(G(z)): 0.3127
Epoch: [16/20], Batch Num: [595/600]
Discriminator Loss: 0.8028, Generator Loss: 1.5084
D(x): 0.7689, D(G(z)): 0.3130
Epoch: [16/20], Batch Num: [596/600]
Discriminator Loss: 0.8571, Generator Loss: 1.6887
D(x): 0.7416, D(G(z)): 0.3017
Epoch: [16/20], Batch Num: [597/600]
Discriminator Loss: 0.8371, Generator Loss: 2.0321
D(x): 0.7517, D(G(z)): 0.2981
Epoch: [16/20], Batch Num: [598/600]
Discriminator Loss: 0.8463, Generator Loss: 2.1507
D(x): 0.7334, D(G(z)): 0.2650
Epoch: [16/20], Batch Num: [599/600]
Discriminator Loss: 0.7831, Generator Loss: 2.0435
D(x): 0.7111, D(G(z)): 0.1874
Epoch: 17, Batch Num: [0/600]

Epoch: [17/20], Batch Num: [0/600]
Discriminator Loss: 0.7036, Generator Loss: 1.8399
D(x): 0.7116, D(G(z)): 0.1926
Epoch: [17/20], Batch Num: [1/600]
Discriminator Loss: 0.8285, Generator Loss: 1.7117
D(x): 0.7191, D(G(z)): 0.2519
Epoch: [17/20], Batch Num: [2/600]
Discriminator Loss: 0.8055, Generator Loss: 1.4464
D(x): 0.7440, D(G(z)): 0.2746
Epoch: [17/20], Batch Num: [3/600]
Discriminator Loss: 0.8730, Generator Loss: 1.7080
D(x): 0.7804, D(G(z)): 0.3421
Epoch: [17/20], Batch Num: [4/600]
Discriminator Loss: 0.8428, Generator Loss: 1.8920
D(x): 0.7708, D(G(z)): 0.3227
Epoch: [17/20], Batch Num: [5/600]
Discriminator Loss: 0.6259, Generator Loss: 2.2427
D(x): 0.7865, D(G(z)): 0.2486
Epoch: [17/20], Batch Num: [6/600]
Discriminator Loss: 0.8360, Generator Loss: 2.2845
D(x): 0.6538, D(G(z)): 0.1941
Epoch: [17/20], Batch Num: [7/600]
Discriminator Loss: 0.7589, Generator Loss: 1.8700
D(x): 0.7093, D(G(z)): 0.1950
Epoch: [17/20], Batch Num: [8/600]
Discriminator Loss: 0.9268, Generator Loss: 1.6717
D(x): 0.7087, D(G(z)): 0.2635
Epoch: [17/20], Batch Num: [9/600]
Discriminator Loss: 0.7734, Generator Loss: 1.2754
D(x): 0.7761, D(G(z)): 0.3032
Epoch: [17/20], Batch Num: [10/600]
Discriminator Loss: 0.8558, Generator Loss: 1.6863
D(x): 0.8173, D(G(z)): 0.3520
Epoch: [17/20], Batch Num: [11/600]
Discriminator Loss: 0.9709, Generator Loss: 1.7781
D(x): 0.6991, D(G(z)): 0.3264
Epoch: [17/20], Batch Num: [12/600]
Discriminator Loss: 0.8150, Generator Loss: 1.8066
D(x): 0.7245, D(G(z)): 0.2586
Epoch: [17/20], Batch Num: [13/600]
Discriminator Loss: 0.8485, Generator Loss: 2.0042
D(x): 0.7149, D(G(z)): 0.2494
Epoch: [17/20], Batch Num: [14/600]
Discriminator Loss: 0.8098, Generator Loss: 1.9741
D(x): 0.7067, D(G(z)): 0.2160
Epoch: [17/20], Batch Num: [15/600]
Discriminator Loss: 0.8365, Generator Loss: 1.6835
D(x): 0.6914, D(G(z)): 0.2450
Epoch: [17/20], Batch Num: [16/600]
Discriminator Loss: 0.9260, Generator Loss: 1.5411
D(x): 0.7100, D(G(z)): 0.3173
Epoch: [17/20], Batch Num: [17/600]
Discriminator Loss: 0.7633, Generator Loss: 1.6997
D(x): 0.8018, D(G(z)): 0.3148
Epoch: [17/20], Batch Num: [18/600]
Discriminator Loss: 0.7853, Generator Loss: 1.6385
D(x): 0.7510, D(G(z)): 0.2684
Epoch: [17/20], Batch Num: [19/600]
Discriminator Loss: 0.6981, Generator Loss: 1.8155
D(x): 0.7656, D(G(z)): 0.2489
Epoch: [17/20], Batch Num: [20/600]
Discriminator Loss: 0.8774, Generator Loss: 1.6257
D(x): 0.6922, D(G(z)): 0.2426
Epoch: [17/20], Batch Num: [21/600]
Discriminator Loss: 0.8385, Generator Loss: 1.4817
D(x): 0.7103, D(G(z)): 0.2749
Epoch: [17/20], Batch Num: [22/600]
Discriminator Loss: 0.7638, Generator Loss: 1.6353
D(x): 0.7769, D(G(z)): 0.2761
Epoch: [17/20], Batch Num: [23/600]
Discriminator Loss: 0.8525, Generator Loss: 1.7556
D(x): 0.6886, D(G(z)): 0.2616
Epoch: [17/20], Batch Num: [24/600]
Discriminator Loss: 0.8473, Generator Loss: 1.7909
D(x): 0.7256, D(G(z)): 0.2594
Epoch: [17/20], Batch Num: [25/600]
Discriminator Loss: 0.7997, Generator Loss: 1.7647
D(x): 0.7978, D(G(z)): 0.3183
Epoch: [17/20], Batch Num: [26/600]
Discriminator Loss: 0.7629, Generator Loss: 1.8793
D(x): 0.7534, D(G(z)): 0.2536
Epoch: [17/20], Batch Num: [27/600]
Discriminator Loss: 0.6525, Generator Loss: 2.0912
D(x): 0.7829, D(G(z)): 0.2120
Epoch: [17/20], Batch Num: [28/600]
Discriminator Loss: 0.7288, Generator Loss: 2.0499
D(x): 0.7541, D(G(z)): 0.2205
Epoch: [17/20], Batch Num: [29/600]
Discriminator Loss: 0.7176, Generator Loss: 1.6340
D(x): 0.7248, D(G(z)): 0.1778
Epoch: [17/20], Batch Num: [30/600]
Discriminator Loss: 0.8260, Generator Loss: 1.7413
D(x): 0.7503, D(G(z)): 0.2747
Epoch: [17/20], Batch Num: [31/600]
Discriminator Loss: 0.7628, Generator Loss: 1.8098
D(x): 0.7893, D(G(z)): 0.3041
Epoch: [17/20], Batch Num: [32/600]
Discriminator Loss: 0.6427, Generator Loss: 1.7393
D(x): 0.7855, D(G(z)): 0.2316
Epoch: [17/20], Batch Num: [33/600]
Discriminator Loss: 0.6906, Generator Loss: 1.7813
D(x): 0.7587, D(G(z)): 0.2322
Epoch: [17/20], Batch Num: [34/600]
Discriminator Loss: 0.7318, Generator Loss: 1.8683
D(x): 0.7645, D(G(z)): 0.2300
Epoch: [17/20], Batch Num: [35/600]
Discriminator Loss: 0.8133, Generator Loss: 1.5867
D(x): 0.7567, D(G(z)): 0.2722
Epoch: [17/20], Batch Num: [36/600]
Discriminator Loss: 0.7036, Generator Loss: 1.9355
D(x): 0.7400, D(G(z)): 0.2096
Epoch: [17/20], Batch Num: [37/600]
Discriminator Loss: 0.7854, Generator Loss: 1.9048
D(x): 0.7576, D(G(z)): 0.2548
Epoch: [17/20], Batch Num: [38/600]
Discriminator Loss: 0.8195, Generator Loss: 1.8871
D(x): 0.7272, D(G(z)): 0.2511
Epoch: [17/20], Batch Num: [39/600]
Discriminator Loss: 0.7102, Generator Loss: 1.6593
D(x): 0.7737, D(G(z)): 0.2578
Epoch: [17/20], Batch Num: [40/600]
Discriminator Loss: 0.7635, Generator Loss: 1.7458
D(x): 0.7929, D(G(z)): 0.2924
Epoch: [17/20], Batch Num: [41/600]
Discriminator Loss: 0.7030, Generator Loss: 2.0850
D(x): 0.7531, D(G(z)): 0.2373
Epoch: [17/20], Batch Num: [42/600]
Discriminator Loss: 0.8080, Generator Loss: 2.0524
D(x): 0.7204, D(G(z)): 0.2484
Epoch: [17/20], Batch Num: [43/600]
Discriminator Loss: 0.9308, Generator Loss: 1.9428
D(x): 0.7525, D(G(z)): 0.2748
Epoch: [17/20], Batch Num: [44/600]
Discriminator Loss: 0.8629, Generator Loss: 2.0705
D(x): 0.7238, D(G(z)): 0.2503
Epoch: [17/20], Batch Num: [45/600]
Discriminator Loss: 0.7701, Generator Loss: 1.9448
D(x): 0.7133, D(G(z)): 0.2105
Epoch: [17/20], Batch Num: [46/600]
Discriminator Loss: 0.6702, Generator Loss: 1.8514
D(x): 0.7980, D(G(z)): 0.2464
Epoch: [17/20], Batch Num: [47/600]
Discriminator Loss: 0.7350, Generator Loss: 1.7987
D(x): 0.7302, D(G(z)): 0.2237
Epoch: [17/20], Batch Num: [48/600]
Discriminator Loss: 0.7668, Generator Loss: 1.4113
D(x): 0.7588, D(G(z)): 0.2645
Epoch: [17/20], Batch Num: [49/600]
Discriminator Loss: 0.8077, Generator Loss: 1.7751
D(x): 0.7655, D(G(z)): 0.2928
Epoch: [17/20], Batch Num: [50/600]
Discriminator Loss: 0.9377, Generator Loss: 1.8527
D(x): 0.7231, D(G(z)): 0.3055
Epoch: [17/20], Batch Num: [51/600]
Discriminator Loss: 0.9007, Generator Loss: 1.7606
D(x): 0.7327, D(G(z)): 0.2823
Epoch: [17/20], Batch Num: [52/600]
Discriminator Loss: 0.8223, Generator Loss: 1.8625
D(x): 0.7109, D(G(z)): 0.2481
Epoch: [17/20], Batch Num: [53/600]
Discriminator Loss: 0.8253, Generator Loss: 1.8159
D(x): 0.7093, D(G(z)): 0.2512
Epoch: [17/20], Batch Num: [54/600]
Discriminator Loss: 0.8116, Generator Loss: 1.5762
D(x): 0.7182, D(G(z)): 0.2695
Epoch: [17/20], Batch Num: [55/600]
Discriminator Loss: 0.9352, Generator Loss: 1.6719
D(x): 0.7143, D(G(z)): 0.3051
Epoch: [17/20], Batch Num: [56/600]
Discriminator Loss: 0.8864, Generator Loss: 1.6170
D(x): 0.7596, D(G(z)): 0.3155
Epoch: [17/20], Batch Num: [57/600]
Discriminator Loss: 0.7539, Generator Loss: 1.6987
D(x): 0.8100, D(G(z)): 0.3351
Epoch: [17/20], Batch Num: [58/600]
Discriminator Loss: 0.7682, Generator Loss: 2.0164
D(x): 0.7654, D(G(z)): 0.2585
Epoch: [17/20], Batch Num: [59/600]
Discriminator Loss: 0.8210, Generator Loss: 2.1431
D(x): 0.6934, D(G(z)): 0.2301
Epoch: [17/20], Batch Num: [60/600]
Discriminator Loss: 0.9186, Generator Loss: 1.8170
D(x): 0.6320, D(G(z)): 0.2008
Epoch: [17/20], Batch Num: [61/600]
Discriminator Loss: 0.7840, Generator Loss: 1.5507
D(x): 0.6801, D(G(z)): 0.2036
Epoch: [17/20], Batch Num: [62/600]
Discriminator Loss: 0.9154, Generator Loss: 1.5655
D(x): 0.7656, D(G(z)): 0.3403
Epoch: [17/20], Batch Num: [63/600]
Discriminator Loss: 1.0510, Generator Loss: 1.7590
D(x): 0.7890, D(G(z)): 0.3975
Epoch: [17/20], Batch Num: [64/600]
Discriminator Loss: 0.7932, Generator Loss: 1.9528
D(x): 0.7856, D(G(z)): 0.3069
Epoch: [17/20], Batch Num: [65/600]
Discriminator Loss: 0.7906, Generator Loss: 2.0420
D(x): 0.7231, D(G(z)): 0.2193
Epoch: [17/20], Batch Num: [66/600]
Discriminator Loss: 0.8331, Generator Loss: 1.9537
D(x): 0.6950, D(G(z)): 0.2340
Epoch: [17/20], Batch Num: [67/600]
Discriminator Loss: 0.8595, Generator Loss: 1.8854
D(x): 0.6637, D(G(z)): 0.2067
Epoch: [17/20], Batch Num: [68/600]
Discriminator Loss: 0.8079, Generator Loss: 1.5178
D(x): 0.7139, D(G(z)): 0.2324
Epoch: [17/20], Batch Num: [69/600]
Discriminator Loss: 0.7914, Generator Loss: 1.4560
D(x): 0.7480, D(G(z)): 0.2729
Epoch: [17/20], Batch Num: [70/600]
Discriminator Loss: 0.7800, Generator Loss: 1.2888
D(x): 0.7835, D(G(z)): 0.2929
Epoch: [17/20], Batch Num: [71/600]
Discriminator Loss: 0.7836, Generator Loss: 1.6206
D(x): 0.8260, D(G(z)): 0.3479
Epoch: [17/20], Batch Num: [72/600]
Discriminator Loss: 0.8259, Generator Loss: 1.6632
D(x): 0.7828, D(G(z)): 0.3005
Epoch: [17/20], Batch Num: [73/600]
Discriminator Loss: 0.9013, Generator Loss: 1.9512
D(x): 0.7263, D(G(z)): 0.2779
Epoch: [17/20], Batch Num: [74/600]
Discriminator Loss: 0.7389, Generator Loss: 2.1884
D(x): 0.7168, D(G(z)): 0.2045
Epoch: [17/20], Batch Num: [75/600]
Discriminator Loss: 0.9312, Generator Loss: 1.9477
D(x): 0.6092, D(G(z)): 0.1835
Epoch: [17/20], Batch Num: [76/600]
Discriminator Loss: 0.8517, Generator Loss: 1.9465
D(x): 0.6953, D(G(z)): 0.2406
Epoch: [17/20], Batch Num: [77/600]
Discriminator Loss: 0.8716, Generator Loss: 1.5611
D(x): 0.6945, D(G(z)): 0.2517
Epoch: [17/20], Batch Num: [78/600]
Discriminator Loss: 0.7942, Generator Loss: 1.5185
D(x): 0.7668, D(G(z)): 0.2891
Epoch: [17/20], Batch Num: [79/600]
Discriminator Loss: 0.8968, Generator Loss: 1.6376
D(x): 0.7781, D(G(z)): 0.3618
Epoch: [17/20], Batch Num: [80/600]
Discriminator Loss: 0.7657, Generator Loss: 1.9299
D(x): 0.7685, D(G(z)): 0.2858
Epoch: [17/20], Batch Num: [81/600]
Discriminator Loss: 0.8400, Generator Loss: 1.8571
D(x): 0.6865, D(G(z)): 0.2231
Epoch: [17/20], Batch Num: [82/600]
Discriminator Loss: 0.7230, Generator Loss: 1.7723
D(x): 0.7413, D(G(z)): 0.2326
Epoch: [17/20], Batch Num: [83/600]
Discriminator Loss: 0.8040, Generator Loss: 1.7280
D(x): 0.7135, D(G(z)): 0.2332
Epoch: [17/20], Batch Num: [84/600]
Discriminator Loss: 0.8935, Generator Loss: 1.6692
D(x): 0.6914, D(G(z)): 0.2563
Epoch: [17/20], Batch Num: [85/600]
Discriminator Loss: 0.9119, Generator Loss: 1.5180
D(x): 0.7560, D(G(z)): 0.3148
Epoch: [17/20], Batch Num: [86/600]
Discriminator Loss: 0.9794, Generator Loss: 1.5890
D(x): 0.7370, D(G(z)): 0.3217
Epoch: [17/20], Batch Num: [87/600]
Discriminator Loss: 0.8458, Generator Loss: 1.7670
D(x): 0.7322, D(G(z)): 0.2861
Epoch: [17/20], Batch Num: [88/600]
Discriminator Loss: 0.9521, Generator Loss: 1.8665
D(x): 0.7207, D(G(z)): 0.3044
Epoch: [17/20], Batch Num: [89/600]
Discriminator Loss: 0.8578, Generator Loss: 1.6685
D(x): 0.7005, D(G(z)): 0.2525
Epoch: [17/20], Batch Num: [90/600]
Discriminator Loss: 0.9016, Generator Loss: 1.7757
D(x): 0.6924, D(G(z)): 0.2482
Epoch: [17/20], Batch Num: [91/600]
Discriminator Loss: 0.9114, Generator Loss: 1.5518
D(x): 0.6440, D(G(z)): 0.2223
Epoch: [17/20], Batch Num: [92/600]
Discriminator Loss: 0.8324, Generator Loss: 1.6047
D(x): 0.7282, D(G(z)): 0.2722
Epoch: [17/20], Batch Num: [93/600]
Discriminator Loss: 0.8738, Generator Loss: 1.2749
D(x): 0.7059, D(G(z)): 0.2855
Epoch: [17/20], Batch Num: [94/600]
Discriminator Loss: 0.7835, Generator Loss: 1.1774
D(x): 0.7966, D(G(z)): 0.3071
Epoch: [17/20], Batch Num: [95/600]
Discriminator Loss: 0.8258, Generator Loss: 1.5752
D(x): 0.8434, D(G(z)): 0.3673
Epoch: [17/20], Batch Num: [96/600]
Discriminator Loss: 0.8353, Generator Loss: 1.7188
D(x): 0.7622, D(G(z)): 0.3081
Epoch: [17/20], Batch Num: [97/600]
Discriminator Loss: 0.9109, Generator Loss: 1.8257
D(x): 0.6633, D(G(z)): 0.2373
Epoch: [17/20], Batch Num: [98/600]
Discriminator Loss: 0.8127, Generator Loss: 2.0379
D(x): 0.6918, D(G(z)): 0.2192
Epoch: [17/20], Batch Num: [99/600]
Discriminator Loss: 0.7684, Generator Loss: 1.8683
D(x): 0.6896, D(G(z)): 0.1786
Epoch: 17, Batch Num: [100/600]

Epoch: [17/20], Batch Num: [100/600]
Discriminator Loss: 0.7837, Generator Loss: 1.5241
D(x): 0.6952, D(G(z)): 0.2026
Epoch: [17/20], Batch Num: [101/600]
Discriminator Loss: 0.7825, Generator Loss: 1.3743
D(x): 0.7622, D(G(z)): 0.2802
Epoch: [17/20], Batch Num: [102/600]
Discriminator Loss: 0.8162, Generator Loss: 1.4109
D(x): 0.8102, D(G(z)): 0.3411
Epoch: [17/20], Batch Num: [103/600]
Discriminator Loss: 0.9178, Generator Loss: 1.6197
D(x): 0.8074, D(G(z)): 0.3460
Epoch: [17/20], Batch Num: [104/600]
Discriminator Loss: 0.7791, Generator Loss: 1.7746
D(x): 0.7758, D(G(z)): 0.3021
Epoch: [17/20], Batch Num: [105/600]
Discriminator Loss: 0.8526, Generator Loss: 1.9023
D(x): 0.6681, D(G(z)): 0.2220
Epoch: [17/20], Batch Num: [106/600]
Discriminator Loss: 0.6609, Generator Loss: 1.8176
D(x): 0.7598, D(G(z)): 0.2085
Epoch: [17/20], Batch Num: [107/600]
Discriminator Loss: 0.6387, Generator Loss: 1.7010
D(x): 0.7358, D(G(z)): 0.1658
Epoch: [17/20], Batch Num: [108/600]
Discriminator Loss: 0.7975, Generator Loss: 1.7768
D(x): 0.7074, D(G(z)): 0.2158
Epoch: [17/20], Batch Num: [109/600]
Discriminator Loss: 0.8461, Generator Loss: 1.6233
D(x): 0.6943, D(G(z)): 0.2539
Epoch: [17/20], Batch Num: [110/600]
Discriminator Loss: 0.7426, Generator Loss: 1.4311
D(x): 0.7821, D(G(z)): 0.2710
Epoch: [17/20], Batch Num: [111/600]
Discriminator Loss: 0.7618, Generator Loss: 1.6056
D(x): 0.8138, D(G(z)): 0.3172
Epoch: [17/20], Batch Num: [112/600]
Discriminator Loss: 0.8568, Generator Loss: 1.6144
D(x): 0.7605, D(G(z)): 0.2822
Epoch: [17/20], Batch Num: [113/600]
Discriminator Loss: 0.8622, Generator Loss: 1.9201
D(x): 0.7083, D(G(z)): 0.2550
Epoch: [17/20], Batch Num: [114/600]
Discriminator Loss: 0.7783, Generator Loss: 1.9051
D(x): 0.6949, D(G(z)): 0.1790
Epoch: [17/20], Batch Num: [115/600]
Discriminator Loss: 0.8829, Generator Loss: 2.0876
D(x): 0.7479, D(G(z)): 0.2700
Epoch: [17/20], Batch Num: [116/600]
Discriminator Loss: 0.7443, Generator Loss: 1.8510
D(x): 0.7854, D(G(z)): 0.2539
Epoch: [17/20], Batch Num: [117/600]
Discriminator Loss: 0.8831, Generator Loss: 1.9495
D(x): 0.6878, D(G(z)): 0.2458
Epoch: [17/20], Batch Num: [118/600]
Discriminator Loss: 0.8372, Generator Loss: 1.6192
D(x): 0.7031, D(G(z)): 0.2254
Epoch: [17/20], Batch Num: [119/600]
Discriminator Loss: 0.8636, Generator Loss: 1.6955
D(x): 0.6944, D(G(z)): 0.2473
Epoch: [17/20], Batch Num: [120/600]
Discriminator Loss: 0.9465, Generator Loss: 1.5857
D(x): 0.7181, D(G(z)): 0.3180
Epoch: [17/20], Batch Num: [121/600]
Discriminator Loss: 0.7384, Generator Loss: 1.6685
D(x): 0.7961, D(G(z)): 0.2997
Epoch: [17/20], Batch Num: [122/600]
Discriminator Loss: 0.8269, Generator Loss: 1.7705
D(x): 0.7795, D(G(z)): 0.3067
Epoch: [17/20], Batch Num: [123/600]
Discriminator Loss: 0.8450, Generator Loss: 1.8659
D(x): 0.7057, D(G(z)): 0.2496
Epoch: [17/20], Batch Num: [124/600]
Discriminator Loss: 0.9337, Generator Loss: 1.8296
D(x): 0.6660, D(G(z)): 0.2449
Epoch: [17/20], Batch Num: [125/600]
Discriminator Loss: 0.8491, Generator Loss: 1.7297
D(x): 0.6782, D(G(z)): 0.2287
Epoch: [17/20], Batch Num: [126/600]
Discriminator Loss: 0.8763, Generator Loss: 1.4196
D(x): 0.7099, D(G(z)): 0.2617
Epoch: [17/20], Batch Num: [127/600]
Discriminator Loss: 1.0360, Generator Loss: 1.3113
D(x): 0.7024, D(G(z)): 0.3238
Epoch: [17/20], Batch Num: [128/600]
Discriminator Loss: 0.9678, Generator Loss: 1.6647
D(x): 0.7753, D(G(z)): 0.3701
Epoch: [17/20], Batch Num: [129/600]
Discriminator Loss: 0.9743, Generator Loss: 1.7413
D(x): 0.7513, D(G(z)): 0.3653
Epoch: [17/20], Batch Num: [130/600]
Discriminator Loss: 0.9567, Generator Loss: 1.9271
D(x): 0.6683, D(G(z)): 0.2823
Epoch: [17/20], Batch Num: [131/600]
Discriminator Loss: 0.9307, Generator Loss: 1.9776
D(x): 0.6238, D(G(z)): 0.2192
Epoch: [17/20], Batch Num: [132/600]
Discriminator Loss: 0.7921, Generator Loss: 1.7238
D(x): 0.6892, D(G(z)): 0.2025
Epoch: [17/20], Batch Num: [133/600]
Discriminator Loss: 0.9309, Generator Loss: 1.3926
D(x): 0.6670, D(G(z)): 0.2686
Epoch: [17/20], Batch Num: [134/600]
Discriminator Loss: 0.8400, Generator Loss: 1.3937
D(x): 0.7165, D(G(z)): 0.3000
Epoch: [17/20], Batch Num: [135/600]
Discriminator Loss: 0.7766, Generator Loss: 1.2627
D(x): 0.7897, D(G(z)): 0.3269
Epoch: [17/20], Batch Num: [136/600]
Discriminator Loss: 0.9641, Generator Loss: 1.3234
D(x): 0.7641, D(G(z)): 0.3855
Epoch: [17/20], Batch Num: [137/600]
Discriminator Loss: 0.8829, Generator Loss: 1.6518
D(x): 0.7416, D(G(z)): 0.3540
Epoch: [17/20], Batch Num: [138/600]
Discriminator Loss: 0.9042, Generator Loss: 1.6264
D(x): 0.7146, D(G(z)): 0.3269
Epoch: [17/20], Batch Num: [139/600]
Discriminator Loss: 0.8430, Generator Loss: 1.6931
D(x): 0.6968, D(G(z)): 0.2554
Epoch: [17/20], Batch Num: [140/600]
Discriminator Loss: 0.9565, Generator Loss: 1.8234
D(x): 0.6201, D(G(z)): 0.2227
Epoch: [17/20], Batch Num: [141/600]
Discriminator Loss: 0.8237, Generator Loss: 1.5876
D(x): 0.7247, D(G(z)): 0.2670
Epoch: [17/20], Batch Num: [142/600]
Discriminator Loss: 0.8512, Generator Loss: 1.5743
D(x): 0.7004, D(G(z)): 0.2584
Epoch: [17/20], Batch Num: [143/600]
Discriminator Loss: 0.8590, Generator Loss: 1.4766
D(x): 0.7153, D(G(z)): 0.2832
Epoch: [17/20], Batch Num: [144/600]
Discriminator Loss: 0.8904, Generator Loss: 1.4436
D(x): 0.7652, D(G(z)): 0.3331
Epoch: [17/20], Batch Num: [145/600]
Discriminator Loss: 0.9930, Generator Loss: 1.4607
D(x): 0.7108, D(G(z)): 0.3390
Epoch: [17/20], Batch Num: [146/600]
Discriminator Loss: 0.7549, Generator Loss: 1.4677
D(x): 0.7825, D(G(z)): 0.2879
Epoch: [17/20], Batch Num: [147/600]
Discriminator Loss: 0.9005, Generator Loss: 1.6230
D(x): 0.6585, D(G(z)): 0.2411
Epoch: [17/20], Batch Num: [148/600]
Discriminator Loss: 0.9011, Generator Loss: 1.7979
D(x): 0.7212, D(G(z)): 0.2899
Epoch: [17/20], Batch Num: [149/600]
Discriminator Loss: 0.8917, Generator Loss: 1.8809
D(x): 0.7382, D(G(z)): 0.2872
Epoch: [17/20], Batch Num: [150/600]
Discriminator Loss: 0.8591, Generator Loss: 1.7359
D(x): 0.7100, D(G(z)): 0.2446
Epoch: [17/20], Batch Num: [151/600]
Discriminator Loss: 0.8344, Generator Loss: 1.6429
D(x): 0.6940, D(G(z)): 0.2395
Epoch: [17/20], Batch Num: [152/600]
Discriminator Loss: 0.6794, Generator Loss: 1.6919
D(x): 0.7571, D(G(z)): 0.2351
Epoch: [17/20], Batch Num: [153/600]
Discriminator Loss: 0.8778, Generator Loss: 1.6088
D(x): 0.7081, D(G(z)): 0.2964
Epoch: [17/20], Batch Num: [154/600]
Discriminator Loss: 0.9008, Generator Loss: 1.4533
D(x): 0.7350, D(G(z)): 0.3258
Epoch: [17/20], Batch Num: [155/600]
Discriminator Loss: 0.7666, Generator Loss: 1.6747
D(x): 0.7544, D(G(z)): 0.2963
Epoch: [17/20], Batch Num: [156/600]
Discriminator Loss: 0.8113, Generator Loss: 2.0275
D(x): 0.7356, D(G(z)): 0.2635
Epoch: [17/20], Batch Num: [157/600]
Discriminator Loss: 0.7920, Generator Loss: 1.7238
D(x): 0.7019, D(G(z)): 0.2452
Epoch: [17/20], Batch Num: [158/600]
Discriminator Loss: 0.7994, Generator Loss: 1.7368
D(x): 0.7129, D(G(z)): 0.2572
Epoch: [17/20], Batch Num: [159/600]
Discriminator Loss: 0.9826, Generator Loss: 1.6518
D(x): 0.6700, D(G(z)): 0.2916
Epoch: [17/20], Batch Num: [160/600]
Discriminator Loss: 0.9891, Generator Loss: 1.5333
D(x): 0.6536, D(G(z)): 0.2815
Epoch: [17/20], Batch Num: [161/600]
Discriminator Loss: 0.6858, Generator Loss: 1.4653
D(x): 0.7695, D(G(z)): 0.2366
Epoch: [17/20], Batch Num: [162/600]
Discriminator Loss: 0.8301, Generator Loss: 1.6125
D(x): 0.7521, D(G(z)): 0.3343
Epoch: [17/20], Batch Num: [163/600]
Discriminator Loss: 0.8905, Generator Loss: 1.7517
D(x): 0.7253, D(G(z)): 0.2945
Epoch: [17/20], Batch Num: [164/600]
Discriminator Loss: 0.7814, Generator Loss: 1.7762
D(x): 0.7146, D(G(z)): 0.2432
Epoch: [17/20], Batch Num: [165/600]
Discriminator Loss: 0.9193, Generator Loss: 1.7976
D(x): 0.6626, D(G(z)): 0.2571
Epoch: [17/20], Batch Num: [166/600]
Discriminator Loss: 0.8598, Generator Loss: 1.7396
D(x): 0.6744, D(G(z)): 0.2359
Epoch: [17/20], Batch Num: [167/600]
Discriminator Loss: 0.7014, Generator Loss: 1.5213
D(x): 0.7511, D(G(z)): 0.2439
Epoch: [17/20], Batch Num: [168/600]
Discriminator Loss: 0.7222, Generator Loss: 1.5454
D(x): 0.7793, D(G(z)): 0.2711
Epoch: [17/20], Batch Num: [169/600]
Discriminator Loss: 0.8681, Generator Loss: 1.6108
D(x): 0.7440, D(G(z)): 0.3106
Epoch: [17/20], Batch Num: [170/600]
Discriminator Loss: 0.7534, Generator Loss: 1.6231
D(x): 0.7443, D(G(z)): 0.2560
Epoch: [17/20], Batch Num: [171/600]
Discriminator Loss: 0.8769, Generator Loss: 1.8396
D(x): 0.7353, D(G(z)): 0.2722
Epoch: [17/20], Batch Num: [172/600]
Discriminator Loss: 0.7874, Generator Loss: 1.7361
D(x): 0.7370, D(G(z)): 0.2356
Epoch: [17/20], Batch Num: [173/600]
Discriminator Loss: 0.6559, Generator Loss: 1.8287
D(x): 0.7965, D(G(z)): 0.2456
Epoch: [17/20], Batch Num: [174/600]
Discriminator Loss: 0.9009, Generator Loss: 1.7889
D(x): 0.6834, D(G(z)): 0.2560
Epoch: [17/20], Batch Num: [175/600]
Discriminator Loss: 0.9090, Generator Loss: 1.4786
D(x): 0.6986, D(G(z)): 0.2587
Epoch: [17/20], Batch Num: [176/600]
Discriminator Loss: 0.6879, Generator Loss: 1.5645
D(x): 0.7916, D(G(z)): 0.2581
Epoch: [17/20], Batch Num: [177/600]
Discriminator Loss: 0.7525, Generator Loss: 1.7387
D(x): 0.7684, D(G(z)): 0.2583
Epoch: [17/20], Batch Num: [178/600]
Discriminator Loss: 0.7004, Generator Loss: 1.5744
D(x): 0.7953, D(G(z)): 0.2649
Epoch: [17/20], Batch Num: [179/600]
Discriminator Loss: 0.7567, Generator Loss: 1.7485
D(x): 0.7556, D(G(z)): 0.2420
Epoch: [17/20], Batch Num: [180/600]
Discriminator Loss: 0.7494, Generator Loss: 1.8924
D(x): 0.7914, D(G(z)): 0.2472
Epoch: [17/20], Batch Num: [181/600]
Discriminator Loss: 0.6329, Generator Loss: 1.8026
D(x): 0.7517, D(G(z)): 0.1850
Epoch: [17/20], Batch Num: [182/600]
Discriminator Loss: 0.7873, Generator Loss: 1.8352
D(x): 0.7540, D(G(z)): 0.2697
Epoch: [17/20], Batch Num: [183/600]
Discriminator Loss: 0.8067, Generator Loss: 1.8736
D(x): 0.7135, D(G(z)): 0.2174
Epoch: [17/20], Batch Num: [184/600]
Discriminator Loss: 0.8094, Generator Loss: 1.6203
D(x): 0.7637, D(G(z)): 0.2588
Epoch: [17/20], Batch Num: [185/600]
Discriminator Loss: 0.7206, Generator Loss: 1.8683
D(x): 0.7946, D(G(z)): 0.2640
Epoch: [17/20], Batch Num: [186/600]
Discriminator Loss: 0.9738, Generator Loss: 1.9369
D(x): 0.7312, D(G(z)): 0.2894
Epoch: [17/20], Batch Num: [187/600]
Discriminator Loss: 0.9658, Generator Loss: 1.8740
D(x): 0.6681, D(G(z)): 0.2420
Epoch: [17/20], Batch Num: [188/600]
Discriminator Loss: 0.7345, Generator Loss: 1.6959
D(x): 0.7632, D(G(z)): 0.2411
Epoch: [17/20], Batch Num: [189/600]
Discriminator Loss: 0.8259, Generator Loss: 1.6547
D(x): 0.7300, D(G(z)): 0.2546
Epoch: [17/20], Batch Num: [190/600]
Discriminator Loss: 0.8143, Generator Loss: 1.5657
D(x): 0.8060, D(G(z)): 0.3309
Epoch: [17/20], Batch Num: [191/600]
Discriminator Loss: 0.8446, Generator Loss: 1.8173
D(x): 0.7716, D(G(z)): 0.2957
Epoch: [17/20], Batch Num: [192/600]
Discriminator Loss: 1.0168, Generator Loss: 1.7351
D(x): 0.6693, D(G(z)): 0.2513
Epoch: [17/20], Batch Num: [193/600]
Discriminator Loss: 0.7749, Generator Loss: 1.7866
D(x): 0.7467, D(G(z)): 0.2438
Epoch: [17/20], Batch Num: [194/600]
Discriminator Loss: 0.9775, Generator Loss: 1.7916
D(x): 0.6836, D(G(z)): 0.2809
Epoch: [17/20], Batch Num: [195/600]
Discriminator Loss: 0.8790, Generator Loss: 1.6357
D(x): 0.7072, D(G(z)): 0.2684
Epoch: [17/20], Batch Num: [196/600]
Discriminator Loss: 1.0427, Generator Loss: 1.6397
D(x): 0.7248, D(G(z)): 0.3280
Epoch: [17/20], Batch Num: [197/600]
Discriminator Loss: 0.8478, Generator Loss: 1.5566
D(x): 0.7095, D(G(z)): 0.2611
Epoch: [17/20], Batch Num: [198/600]
Discriminator Loss: 0.7717, Generator Loss: 1.7016
D(x): 0.8087, D(G(z)): 0.3216
Epoch: [17/20], Batch Num: [199/600]
Discriminator Loss: 0.8168, Generator Loss: 1.6287
D(x): 0.7030, D(G(z)): 0.2466
Epoch: 17, Batch Num: [200/600]

Epoch: [17/20], Batch Num: [200/600]
Discriminator Loss: 0.8582, Generator Loss: 1.6116
D(x): 0.7119, D(G(z)): 0.2634
Epoch: [17/20], Batch Num: [201/600]
Discriminator Loss: 0.7335, Generator Loss: 1.7685
D(x): 0.7613, D(G(z)): 0.2781
Epoch: [17/20], Batch Num: [202/600]
Discriminator Loss: 0.8080, Generator Loss: 1.6245
D(x): 0.7419, D(G(z)): 0.2945
Epoch: [17/20], Batch Num: [203/600]
Discriminator Loss: 0.8040, Generator Loss: 1.5816
D(x): 0.7090, D(G(z)): 0.2651
Epoch: [17/20], Batch Num: [204/600]
Discriminator Loss: 0.9367, Generator Loss: 1.5737
D(x): 0.6656, D(G(z)): 0.2750
Epoch: [17/20], Batch Num: [205/600]
Discriminator Loss: 0.7970, Generator Loss: 1.4187
D(x): 0.7731, D(G(z)): 0.3004
Epoch: [17/20], Batch Num: [206/600]
Discriminator Loss: 0.8752, Generator Loss: 1.8160
D(x): 0.7511, D(G(z)): 0.3018
Epoch: [17/20], Batch Num: [207/600]
Discriminator Loss: 0.8539, Generator Loss: 1.7496
D(x): 0.7341, D(G(z)): 0.3089
Epoch: [17/20], Batch Num: [208/600]
Discriminator Loss: 0.7636, Generator Loss: 1.6870
D(x): 0.7217, D(G(z)): 0.2288
Epoch: [17/20], Batch Num: [209/600]
Discriminator Loss: 0.8086, Generator Loss: 1.8064
D(x): 0.7019, D(G(z)): 0.2421
Epoch: [17/20], Batch Num: [210/600]
Discriminator Loss: 0.9100, Generator Loss: 1.4979
D(x): 0.6703, D(G(z)): 0.2423
Epoch: [17/20], Batch Num: [211/600]
Discriminator Loss: 0.8893, Generator Loss: 1.6578
D(x): 0.7162, D(G(z)): 0.2663
Epoch: [17/20], Batch Num: [212/600]
Discriminator Loss: 0.9424, Generator Loss: 1.4183
D(x): 0.7651, D(G(z)): 0.3438
Epoch: [17/20], Batch Num: [213/600]
Discriminator Loss: 0.7803, Generator Loss: 1.5246
D(x): 0.7991, D(G(z)): 0.3202
Epoch: [17/20], Batch Num: [214/600]
Discriminator Loss: 0.6482, Generator Loss: 1.8921
D(x): 0.7917, D(G(z)): 0.2476
Epoch: [17/20], Batch Num: [215/600]
Discriminator Loss: 0.9656, Generator Loss: 2.0428
D(x): 0.6710, D(G(z)): 0.2584
Epoch: [17/20], Batch Num: [216/600]
Discriminator Loss: 0.7969, Generator Loss: 1.8519
D(x): 0.6995, D(G(z)): 0.2010
Epoch: [17/20], Batch Num: [217/600]
Discriminator Loss: 0.9136, Generator Loss: 1.6542
D(x): 0.6862, D(G(z)): 0.2634
Epoch: [17/20], Batch Num: [218/600]
Discriminator Loss: 0.7940, Generator Loss: 1.6177
D(x): 0.7139, D(G(z)): 0.2401
Epoch: [17/20], Batch Num: [219/600]
Discriminator Loss: 0.8104, Generator Loss: 1.3047
D(x): 0.7577, D(G(z)): 0.3065
Epoch: [17/20], Batch Num: [220/600]
Discriminator Loss: 0.8038, Generator Loss: 1.5453
D(x): 0.7803, D(G(z)): 0.3254
Epoch: [17/20], Batch Num: [221/600]
Discriminator Loss: 0.8471, Generator Loss: 1.5860
D(x): 0.7548, D(G(z)): 0.3345
Epoch: [17/20], Batch Num: [222/600]
Discriminator Loss: 0.6991, Generator Loss: 1.7139
D(x): 0.7623, D(G(z)): 0.2568
Epoch: [17/20], Batch Num: [223/600]
Discriminator Loss: 0.8639, Generator Loss: 1.6923
D(x): 0.7347, D(G(z)): 0.2750
Epoch: [17/20], Batch Num: [224/600]
Discriminator Loss: 0.7216, Generator Loss: 1.8589
D(x): 0.7920, D(G(z)): 0.2743
Epoch: [17/20], Batch Num: [225/600]
Discriminator Loss: 0.8166, Generator Loss: 2.0798
D(x): 0.6920, D(G(z)): 0.2175
Epoch: [17/20], Batch Num: [226/600]
Discriminator Loss: 0.7974, Generator Loss: 1.9426
D(x): 0.6804, D(G(z)): 0.1787
Epoch: [17/20], Batch Num: [227/600]
Discriminator Loss: 0.6793, Generator Loss: 1.5559
D(x): 0.7291, D(G(z)): 0.2124
Epoch: [17/20], Batch Num: [228/600]
Discriminator Loss: 0.8681, Generator Loss: 1.6062
D(x): 0.7498, D(G(z)): 0.3075
Epoch: [17/20], Batch Num: [229/600]
Discriminator Loss: 0.7522, Generator Loss: 1.5853
D(x): 0.8042, D(G(z)): 0.3087
Epoch: [17/20], Batch Num: [230/600]
Discriminator Loss: 0.6856, Generator Loss: 1.5792
D(x): 0.7852, D(G(z)): 0.2549
Epoch: [17/20], Batch Num: [231/600]
Discriminator Loss: 0.8688, Generator Loss: 2.0551
D(x): 0.7553, D(G(z)): 0.3049
Epoch: [17/20], Batch Num: [232/600]
Discriminator Loss: 0.8158, Generator Loss: 2.0914
D(x): 0.7162, D(G(z)): 0.2198
Epoch: [17/20], Batch Num: [233/600]
Discriminator Loss: 0.8717, Generator Loss: 2.0758
D(x): 0.6837, D(G(z)): 0.1896
Epoch: [17/20], Batch Num: [234/600]
Discriminator Loss: 0.9597, Generator Loss: 2.0000
D(x): 0.6534, D(G(z)): 0.2276
Epoch: [17/20], Batch Num: [235/600]
Discriminator Loss: 0.7611, Generator Loss: 1.5686
D(x): 0.7467, D(G(z)): 0.2365
Epoch: [17/20], Batch Num: [236/600]
Discriminator Loss: 0.8116, Generator Loss: 1.4096
D(x): 0.7476, D(G(z)): 0.2910
Epoch: [17/20], Batch Num: [237/600]
Discriminator Loss: 0.7042, Generator Loss: 1.3072
D(x): 0.8150, D(G(z)): 0.3098
Epoch: [17/20], Batch Num: [238/600]
Discriminator Loss: 0.8054, Generator Loss: 1.5853
D(x): 0.7910, D(G(z)): 0.3328
Epoch: [17/20], Batch Num: [239/600]
Discriminator Loss: 0.8374, Generator Loss: 1.7606
D(x): 0.7453, D(G(z)): 0.2996
Epoch: [17/20], Batch Num: [240/600]
Discriminator Loss: 0.7469, Generator Loss: 2.1142
D(x): 0.7990, D(G(z)): 0.2706
Epoch: [17/20], Batch Num: [241/600]
Discriminator Loss: 0.7849, Generator Loss: 2.1382
D(x): 0.7369, D(G(z)): 0.2314
Epoch: [17/20], Batch Num: [242/600]
Discriminator Loss: 0.9434, Generator Loss: 1.9193
D(x): 0.6319, D(G(z)): 0.1834
Epoch: [17/20], Batch Num: [243/600]
Discriminator Loss: 0.8376, Generator Loss: 1.5414
D(x): 0.6827, D(G(z)): 0.2056
Epoch: [17/20], Batch Num: [244/600]
Discriminator Loss: 0.7443, Generator Loss: 1.4265
D(x): 0.7554, D(G(z)): 0.2523
Epoch: [17/20], Batch Num: [245/600]
Discriminator Loss: 0.7143, Generator Loss: 1.4356
D(x): 0.8225, D(G(z)): 0.3185
Epoch: [17/20], Batch Num: [246/600]
Discriminator Loss: 0.8475, Generator Loss: 1.3882
D(x): 0.8177, D(G(z)): 0.3799
Epoch: [17/20], Batch Num: [247/600]
Discriminator Loss: 0.9044, Generator Loss: 1.6667
D(x): 0.8102, D(G(z)): 0.3472
Epoch: [17/20], Batch Num: [248/600]
Discriminator Loss: 0.8198, Generator Loss: 2.0979
D(x): 0.7504, D(G(z)): 0.2745
Epoch: [17/20], Batch Num: [249/600]
Discriminator Loss: 0.8360, Generator Loss: 2.2627
D(x): 0.6734, D(G(z)): 0.1932
Epoch: [17/20], Batch Num: [250/600]
Discriminator Loss: 1.0130, Generator Loss: 2.2220
D(x): 0.6054, D(G(z)): 0.2045
Epoch: [17/20], Batch Num: [251/600]
Discriminator Loss: 0.9059, Generator Loss: 1.9839
D(x): 0.6334, D(G(z)): 0.2034
Epoch: [17/20], Batch Num: [252/600]
Discriminator Loss: 0.8129, Generator Loss: 1.6541
D(x): 0.7310, D(G(z)): 0.2273
Epoch: [17/20], Batch Num: [253/600]
Discriminator Loss: 0.7416, Generator Loss: 1.3234
D(x): 0.7600, D(G(z)): 0.2653
Epoch: [17/20], Batch Num: [254/600]
Discriminator Loss: 0.9417, Generator Loss: 1.3546
D(x): 0.7340, D(G(z)): 0.3431
Epoch: [17/20], Batch Num: [255/600]
Discriminator Loss: 0.7872, Generator Loss: 1.4471
D(x): 0.7703, D(G(z)): 0.3032
Epoch: [17/20], Batch Num: [256/600]
Discriminator Loss: 0.8978, Generator Loss: 1.3932
D(x): 0.7190, D(G(z)): 0.3206
Epoch: [17/20], Batch Num: [257/600]
Discriminator Loss: 0.7776, Generator Loss: 1.6900
D(x): 0.7494, D(G(z)): 0.2693
Epoch: [17/20], Batch Num: [258/600]
Discriminator Loss: 0.8037, Generator Loss: 1.8179
D(x): 0.7475, D(G(z)): 0.2835
Epoch: [17/20], Batch Num: [259/600]
Discriminator Loss: 0.8103, Generator Loss: 1.8599
D(x): 0.7025, D(G(z)): 0.2621
Epoch: [17/20], Batch Num: [260/600]
Discriminator Loss: 0.7989, Generator Loss: 1.7574
D(x): 0.6923, D(G(z)): 0.2243
Epoch: [17/20], Batch Num: [261/600]
Discriminator Loss: 0.7414, Generator Loss: 1.6950
D(x): 0.7310, D(G(z)): 0.2330
Epoch: [17/20], Batch Num: [262/600]
Discriminator Loss: 0.8686, Generator Loss: 1.6980
D(x): 0.7071, D(G(z)): 0.2410
Epoch: [17/20], Batch Num: [263/600]
Discriminator Loss: 0.8175, Generator Loss: 1.6578
D(x): 0.7579, D(G(z)): 0.2852
Epoch: [17/20], Batch Num: [264/600]
Discriminator Loss: 0.8627, Generator Loss: 1.6102
D(x): 0.7570, D(G(z)): 0.3130
Epoch: [17/20], Batch Num: [265/600]
Discriminator Loss: 1.0846, Generator Loss: 1.7814
D(x): 0.7099, D(G(z)): 0.3605
Epoch: [17/20], Batch Num: [266/600]
Discriminator Loss: 0.8588, Generator Loss: 1.5581
D(x): 0.7030, D(G(z)): 0.2753
Epoch: [17/20], Batch Num: [267/600]
Discriminator Loss: 0.7957, Generator Loss: 1.6957
D(x): 0.7538, D(G(z)): 0.2562
Epoch: [17/20], Batch Num: [268/600]
Discriminator Loss: 0.8708, Generator Loss: 1.6049
D(x): 0.7330, D(G(z)): 0.2915
Epoch: [17/20], Batch Num: [269/600]
Discriminator Loss: 0.9259, Generator Loss: 1.7791
D(x): 0.6947, D(G(z)): 0.2771
Epoch: [17/20], Batch Num: [270/600]
Discriminator Loss: 0.8388, Generator Loss: 1.6326
D(x): 0.7201, D(G(z)): 0.2676
Epoch: [17/20], Batch Num: [271/600]
Discriminator Loss: 0.8584, Generator Loss: 1.7234
D(x): 0.7665, D(G(z)): 0.3156
Epoch: [17/20], Batch Num: [272/600]
Discriminator Loss: 0.7003, Generator Loss: 1.8372
D(x): 0.7563, D(G(z)): 0.2455
Epoch: [17/20], Batch Num: [273/600]
Discriminator Loss: 0.7647, Generator Loss: 1.7706
D(x): 0.7423, D(G(z)): 0.2468
Epoch: [17/20], Batch Num: [274/600]
Discriminator Loss: 0.8850, Generator Loss: 1.7821
D(x): 0.7159, D(G(z)): 0.2933
Epoch: [17/20], Batch Num: [275/600]
Discriminator Loss: 0.8300, Generator Loss: 1.7195
D(x): 0.7093, D(G(z)): 0.2613
Epoch: [17/20], Batch Num: [276/600]
Discriminator Loss: 0.7827, Generator Loss: 1.7517
D(x): 0.7250, D(G(z)): 0.2224
Epoch: [17/20], Batch Num: [277/600]
Discriminator Loss: 0.8782, Generator Loss: 1.4011
D(x): 0.6800, D(G(z)): 0.2519
Epoch: [17/20], Batch Num: [278/600]
Discriminator Loss: 0.9116, Generator Loss: 1.5754
D(x): 0.7378, D(G(z)): 0.2960
Epoch: [17/20], Batch Num: [279/600]
Discriminator Loss: 1.0117, Generator Loss: 1.3477
D(x): 0.6786, D(G(z)): 0.3100
Epoch: [17/20], Batch Num: [280/600]
Discriminator Loss: 0.8728, Generator Loss: 1.4925
D(x): 0.7668, D(G(z)): 0.3443
Epoch: [17/20], Batch Num: [281/600]
Discriminator Loss: 0.7880, Generator Loss: 1.5035
D(x): 0.7659, D(G(z)): 0.2989
Epoch: [17/20], Batch Num: [282/600]
Discriminator Loss: 0.9363, Generator Loss: 1.6517
D(x): 0.7258, D(G(z)): 0.3078
Epoch: [17/20], Batch Num: [283/600]
Discriminator Loss: 0.9509, Generator Loss: 1.7880
D(x): 0.6526, D(G(z)): 0.2581
Epoch: [17/20], Batch Num: [284/600]
Discriminator Loss: 0.9324, Generator Loss: 1.5756
D(x): 0.6485, D(G(z)): 0.2563
Epoch: [17/20], Batch Num: [285/600]
Discriminator Loss: 0.8112, Generator Loss: 1.5816
D(x): 0.6931, D(G(z)): 0.2416
Epoch: [17/20], Batch Num: [286/600]
Discriminator Loss: 0.8315, Generator Loss: 1.4024
D(x): 0.7116, D(G(z)): 0.2712
Epoch: [17/20], Batch Num: [287/600]
Discriminator Loss: 0.9571, Generator Loss: 1.3285
D(x): 0.6511, D(G(z)): 0.3051
Epoch: [17/20], Batch Num: [288/600]
Discriminator Loss: 0.7789, Generator Loss: 1.3750
D(x): 0.7669, D(G(z)): 0.3082
Epoch: [17/20], Batch Num: [289/600]
Discriminator Loss: 0.8111, Generator Loss: 1.6276
D(x): 0.7570, D(G(z)): 0.3245
Epoch: [17/20], Batch Num: [290/600]
Discriminator Loss: 0.7782, Generator Loss: 1.5648
D(x): 0.7786, D(G(z)): 0.3249
Epoch: [17/20], Batch Num: [291/600]
Discriminator Loss: 0.7605, Generator Loss: 1.6914
D(x): 0.7284, D(G(z)): 0.2519
Epoch: [17/20], Batch Num: [292/600]
Discriminator Loss: 0.8249, Generator Loss: 1.6242
D(x): 0.6939, D(G(z)): 0.2596
Epoch: [17/20], Batch Num: [293/600]
Discriminator Loss: 0.8325, Generator Loss: 1.5784
D(x): 0.7265, D(G(z)): 0.2813
Epoch: [17/20], Batch Num: [294/600]
Discriminator Loss: 0.7664, Generator Loss: 1.6438
D(x): 0.7420, D(G(z)): 0.2584
Epoch: [17/20], Batch Num: [295/600]
Discriminator Loss: 0.8529, Generator Loss: 1.5910
D(x): 0.6989, D(G(z)): 0.2535
Epoch: [17/20], Batch Num: [296/600]
Discriminator Loss: 0.7631, Generator Loss: 1.6331
D(x): 0.7445, D(G(z)): 0.2622
Epoch: [17/20], Batch Num: [297/600]
Discriminator Loss: 0.8295, Generator Loss: 1.5866
D(x): 0.7583, D(G(z)): 0.2981
Epoch: [17/20], Batch Num: [298/600]
Discriminator Loss: 0.9328, Generator Loss: 1.7011
D(x): 0.7123, D(G(z)): 0.2989
Epoch: [17/20], Batch Num: [299/600]
Discriminator Loss: 0.8819, Generator Loss: 1.7807
D(x): 0.7510, D(G(z)): 0.3026
Epoch: 17, Batch Num: [300/600]

Epoch: [17/20], Batch Num: [300/600]
Discriminator Loss: 0.7801, Generator Loss: 1.8388
D(x): 0.7254, D(G(z)): 0.2419
Epoch: [17/20], Batch Num: [301/600]
Discriminator Loss: 0.9453, Generator Loss: 2.0215
D(x): 0.7054, D(G(z)): 0.2788
Epoch: [17/20], Batch Num: [302/600]
Discriminator Loss: 0.7322, Generator Loss: 1.8254
D(x): 0.7203, D(G(z)): 0.2202
Epoch: [17/20], Batch Num: [303/600]
Discriminator Loss: 0.7095, Generator Loss: 1.8992
D(x): 0.7417, D(G(z)): 0.2119
Epoch: [17/20], Batch Num: [304/600]
Discriminator Loss: 0.8961, Generator Loss: 1.4737
D(x): 0.6826, D(G(z)): 0.2635
Epoch: [17/20], Batch Num: [305/600]
Discriminator Loss: 0.7466, Generator Loss: 1.5658
D(x): 0.7832, D(G(z)): 0.2952
Epoch: [17/20], Batch Num: [306/600]
Discriminator Loss: 1.0062, Generator Loss: 1.6557
D(x): 0.7512, D(G(z)): 0.3198
Epoch: [17/20], Batch Num: [307/600]
Discriminator Loss: 0.7695, Generator Loss: 1.7884
D(x): 0.7800, D(G(z)): 0.2778
Epoch: [17/20], Batch Num: [308/600]
Discriminator Loss: 0.7961, Generator Loss: 1.7895
D(x): 0.7078, D(G(z)): 0.2361
Epoch: [17/20], Batch Num: [309/600]
Discriminator Loss: 0.6707, Generator Loss: 1.8083
D(x): 0.7893, D(G(z)): 0.2327
Epoch: [17/20], Batch Num: [310/600]
Discriminator Loss: 0.7963, Generator Loss: 1.9475
D(x): 0.7568, D(G(z)): 0.2561
Epoch: [17/20], Batch Num: [311/600]
Discriminator Loss: 0.9162, Generator Loss: 1.9896
D(x): 0.6764, D(G(z)): 0.2231
Epoch: [17/20], Batch Num: [312/600]
Discriminator Loss: 0.7815, Generator Loss: 1.8783
D(x): 0.7532, D(G(z)): 0.2545
Epoch: [17/20], Batch Num: [313/600]
Discriminator Loss: 0.9733, Generator Loss: 1.5970
D(x): 0.6712, D(G(z)): 0.2836
Epoch: [17/20], Batch Num: [314/600]
Discriminator Loss: 0.6822, Generator Loss: 1.6539
D(x): 0.7555, D(G(z)): 0.2410
Epoch: [17/20], Batch Num: [315/600]
Discriminator Loss: 0.7835, Generator Loss: 1.7065
D(x): 0.7527, D(G(z)): 0.2670
Epoch: [17/20], Batch Num: [316/600]
Discriminator Loss: 0.8474, Generator Loss: 1.6613
D(x): 0.7022, D(G(z)): 0.2635
Epoch: [17/20], Batch Num: [317/600]
Discriminator Loss: 0.7287, Generator Loss: 1.8434
D(x): 0.8194, D(G(z)): 0.3013
Epoch: [17/20], Batch Num: [318/600]
Discriminator Loss: 0.7984, Generator Loss: 1.7517
D(x): 0.7276, D(G(z)): 0.2201
Epoch: [17/20], Batch Num: [319/600]
Discriminator Loss: 0.9648, Generator Loss: 1.8405
D(x): 0.7178, D(G(z)): 0.3012
Epoch: [17/20], Batch Num: [320/600]
Discriminator Loss: 0.8458, Generator Loss: 1.6497
D(x): 0.7336, D(G(z)): 0.2616
Epoch: [17/20], Batch Num: [321/600]
Discriminator Loss: 0.8433, Generator Loss: 1.7855
D(x): 0.7150, D(G(z)): 0.2407
Epoch: [17/20], Batch Num: [322/600]
Discriminator Loss: 0.8988, Generator Loss: 1.8011
D(x): 0.6911, D(G(z)): 0.2373
Epoch: [17/20], Batch Num: [323/600]
Discriminator Loss: 0.8739, Generator Loss: 1.5500
D(x): 0.7333, D(G(z)): 0.2813
Epoch: [17/20], Batch Num: [324/600]
Discriminator Loss: 0.7270, Generator Loss: 1.6274
D(x): 0.8165, D(G(z)): 0.2985
Epoch: [17/20], Batch Num: [325/600]
Discriminator Loss: 0.8212, Generator Loss: 1.8712
D(x): 0.7768, D(G(z)): 0.2973
Epoch: [17/20], Batch Num: [326/600]
Discriminator Loss: 0.8067, Generator Loss: 1.9552
D(x): 0.7338, D(G(z)): 0.2280
Epoch: [17/20], Batch Num: [327/600]
Discriminator Loss: 0.7743, Generator Loss: 1.9247
D(x): 0.7025, D(G(z)): 0.2081
Epoch: [17/20], Batch Num: [328/600]
Discriminator Loss: 0.7973, Generator Loss: 1.6582
D(x): 0.7002, D(G(z)): 0.2219
Epoch: [17/20], Batch Num: [329/600]
Discriminator Loss: 0.8586, Generator Loss: 1.5860
D(x): 0.7129, D(G(z)): 0.2406
Epoch: [17/20], Batch Num: [330/600]
Discriminator Loss: 0.8139, Generator Loss: 1.4643
D(x): 0.7459, D(G(z)): 0.3010
Epoch: [17/20], Batch Num: [331/600]
Discriminator Loss: 0.9913, Generator Loss: 1.4919
D(x): 0.7869, D(G(z)): 0.3953
Epoch: [17/20], Batch Num: [332/600]
Discriminator Loss: 0.8516, Generator Loss: 1.9771
D(x): 0.8058, D(G(z)): 0.3414
Epoch: [17/20], Batch Num: [333/600]
Discriminator Loss: 0.8864, Generator Loss: 2.1388
D(x): 0.6872, D(G(z)): 0.2338
Epoch: [17/20], Batch Num: [334/600]
Discriminator Loss: 0.8193, Generator Loss: 1.7950
D(x): 0.6936, D(G(z)): 0.2334
Epoch: [17/20], Batch Num: [335/600]
Discriminator Loss: 0.8822, Generator Loss: 1.7127
D(x): 0.6676, D(G(z)): 0.2309
Epoch: [17/20], Batch Num: [336/600]
Discriminator Loss: 0.9255, Generator Loss: 1.5159
D(x): 0.7023, D(G(z)): 0.2886
Epoch: [17/20], Batch Num: [337/600]
Discriminator Loss: 0.7786, Generator Loss: 1.3238
D(x): 0.7462, D(G(z)): 0.2716
Epoch: [17/20], Batch Num: [338/600]
Discriminator Loss: 0.7324, Generator Loss: 1.4254
D(x): 0.7827, D(G(z)): 0.2921
Epoch: [17/20], Batch Num: [339/600]
Discriminator Loss: 0.8174, Generator Loss: 1.4877
D(x): 0.8024, D(G(z)): 0.3335
Epoch: [17/20], Batch Num: [340/600]
Discriminator Loss: 0.8626, Generator Loss: 1.5864
D(x): 0.7549, D(G(z)): 0.3275
Epoch: [17/20], Batch Num: [341/600]
Discriminator Loss: 0.7810, Generator Loss: 1.7835
D(x): 0.7225, D(G(z)): 0.2626
Epoch: [17/20], Batch Num: [342/600]
Discriminator Loss: 0.7315, Generator Loss: 1.8673
D(x): 0.6964, D(G(z)): 0.1970
Epoch: [17/20], Batch Num: [343/600]
Discriminator Loss: 0.9817, Generator Loss: 1.9107
D(x): 0.6502, D(G(z)): 0.2343
Epoch: [17/20], Batch Num: [344/600]
Discriminator Loss: 0.8774, Generator Loss: 1.7954
D(x): 0.6572, D(G(z)): 0.2341
Epoch: [17/20], Batch Num: [345/600]
Discriminator Loss: 0.7429, Generator Loss: 1.7975
D(x): 0.7661, D(G(z)): 0.2927
Epoch: [17/20], Batch Num: [346/600]
Discriminator Loss: 0.8155, Generator Loss: 1.5413
D(x): 0.7365, D(G(z)): 0.3004
Epoch: [17/20], Batch Num: [347/600]
Discriminator Loss: 0.8669, Generator Loss: 1.4474
D(x): 0.6918, D(G(z)): 0.2729
Epoch: [17/20], Batch Num: [348/600]
Discriminator Loss: 0.8411, Generator Loss: 1.5762
D(x): 0.7515, D(G(z)): 0.3169
Epoch: [17/20], Batch Num: [349/600]
Discriminator Loss: 0.8220, Generator Loss: 1.5179
D(x): 0.7518, D(G(z)): 0.2945
Epoch: [17/20], Batch Num: [350/600]
Discriminator Loss: 0.8168, Generator Loss: 1.7165
D(x): 0.7390, D(G(z)): 0.2887
Epoch: [17/20], Batch Num: [351/600]
Discriminator Loss: 0.9365, Generator Loss: 1.7123
D(x): 0.6726, D(G(z)): 0.2692
Epoch: [17/20], Batch Num: [352/600]
Discriminator Loss: 0.9462, Generator Loss: 1.5707
D(x): 0.7017, D(G(z)): 0.2802
Epoch: [17/20], Batch Num: [353/600]
Discriminator Loss: 0.8626, Generator Loss: 1.6367
D(x): 0.7178, D(G(z)): 0.2745
Epoch: [17/20], Batch Num: [354/600]
Discriminator Loss: 0.8545, Generator Loss: 1.7697
D(x): 0.7284, D(G(z)): 0.2863
Epoch: [17/20], Batch Num: [355/600]
Discriminator Loss: 0.7165, Generator Loss: 1.6008
D(x): 0.7858, D(G(z)): 0.2734
Epoch: [17/20], Batch Num: [356/600]
Discriminator Loss: 0.7482, Generator Loss: 1.7488
D(x): 0.7377, D(G(z)): 0.2562
Epoch: [17/20], Batch Num: [357/600]
Discriminator Loss: 0.9038, Generator Loss: 1.6410
D(x): 0.6905, D(G(z)): 0.2660
Epoch: [17/20], Batch Num: [358/600]
Discriminator Loss: 0.8244, Generator Loss: 1.5530
D(x): 0.7394, D(G(z)): 0.2946
Epoch: [17/20], Batch Num: [359/600]
Discriminator Loss: 0.8585, Generator Loss: 1.3566
D(x): 0.7160, D(G(z)): 0.2628
Epoch: [17/20], Batch Num: [360/600]
Discriminator Loss: 0.8793, Generator Loss: 1.4054
D(x): 0.7328, D(G(z)): 0.3126
Epoch: [17/20], Batch Num: [361/600]
Discriminator Loss: 0.9448, Generator Loss: 1.5797
D(x): 0.7613, D(G(z)): 0.3695
Epoch: [17/20], Batch Num: [362/600]
Discriminator Loss: 0.8671, Generator Loss: 1.7102
D(x): 0.7015, D(G(z)): 0.2732
Epoch: [17/20], Batch Num: [363/600]
Discriminator Loss: 0.8101, Generator Loss: 1.8106
D(x): 0.7080, D(G(z)): 0.2502
Epoch: [17/20], Batch Num: [364/600]
Discriminator Loss: 0.8289, Generator Loss: 1.8667
D(x): 0.7071, D(G(z)): 0.2612
Epoch: [17/20], Batch Num: [365/600]
Discriminator Loss: 0.7658, Generator Loss: 1.5738
D(x): 0.7184, D(G(z)): 0.2369
Epoch: [17/20], Batch Num: [366/600]
Discriminator Loss: 0.7785, Generator Loss: 1.5460
D(x): 0.7367, D(G(z)): 0.2714
Epoch: [17/20], Batch Num: [367/600]
Discriminator Loss: 0.8669, Generator Loss: 1.4080
D(x): 0.7130, D(G(z)): 0.2899
Epoch: [17/20], Batch Num: [368/600]
Discriminator Loss: 1.1649, Generator Loss: 1.5462
D(x): 0.7372, D(G(z)): 0.3982
Epoch: [17/20], Batch Num: [369/600]
Discriminator Loss: 0.8151, Generator Loss: 1.7149
D(x): 0.7707, D(G(z)): 0.3019
Epoch: [17/20], Batch Num: [370/600]
Discriminator Loss: 0.6937, Generator Loss: 1.9310
D(x): 0.7539, D(G(z)): 0.2268
Epoch: [17/20], Batch Num: [371/600]
Discriminator Loss: 0.8890, Generator Loss: 2.0887
D(x): 0.6501, D(G(z)): 0.2019
Epoch: [17/20], Batch Num: [372/600]
Discriminator Loss: 0.8116, Generator Loss: 1.7978
D(x): 0.6920, D(G(z)): 0.2085
Epoch: [17/20], Batch Num: [373/600]
Discriminator Loss: 0.7311, Generator Loss: 1.5884
D(x): 0.7399, D(G(z)): 0.2139
Epoch: [17/20], Batch Num: [374/600]
Discriminator Loss: 0.7236, Generator Loss: 1.3629
D(x): 0.7748, D(G(z)): 0.2629
Epoch: [17/20], Batch Num: [375/600]
Discriminator Loss: 0.8939, Generator Loss: 1.3247
D(x): 0.8063, D(G(z)): 0.3350
Epoch: [17/20], Batch Num: [376/600]
Discriminator Loss: 0.8505, Generator Loss: 1.5761
D(x): 0.7962, D(G(z)): 0.3112
Epoch: [17/20], Batch Num: [377/600]
Discriminator Loss: 0.9049, Generator Loss: 1.8336
D(x): 0.7834, D(G(z)): 0.3130
Epoch: [17/20], Batch Num: [378/600]
Discriminator Loss: 0.8084, Generator Loss: 2.0189
D(x): 0.7209, D(G(z)): 0.2119
Epoch: [17/20], Batch Num: [379/600]
Discriminator Loss: 0.6966, Generator Loss: 2.0897
D(x): 0.7200, D(G(z)): 0.1736
Epoch: [17/20], Batch Num: [380/600]
Discriminator Loss: 0.9834, Generator Loss: 1.7624
D(x): 0.6464, D(G(z)): 0.2052
Epoch: [17/20], Batch Num: [381/600]
Discriminator Loss: 0.8671, Generator Loss: 1.4527
D(x): 0.6824, D(G(z)): 0.2281
Epoch: [17/20], Batch Num: [382/600]
Discriminator Loss: 0.7843, Generator Loss: 1.2027
D(x): 0.7340, D(G(z)): 0.2619
Epoch: [17/20], Batch Num: [383/600]
Discriminator Loss: 0.9557, Generator Loss: 1.2727
D(x): 0.7643, D(G(z)): 0.3594
Epoch: [17/20], Batch Num: [384/600]
Discriminator Loss: 0.8387, Generator Loss: 1.3781
D(x): 0.7829, D(G(z)): 0.3429
Epoch: [17/20], Batch Num: [385/600]
Discriminator Loss: 0.8210, Generator Loss: 1.5736
D(x): 0.7874, D(G(z)): 0.3414
Epoch: [17/20], Batch Num: [386/600]
Discriminator Loss: 0.6818, Generator Loss: 1.8619
D(x): 0.8050, D(G(z)): 0.2700
Epoch: [17/20], Batch Num: [387/600]
Discriminator Loss: 0.8013, Generator Loss: 2.0537
D(x): 0.6927, D(G(z)): 0.2168
Epoch: [17/20], Batch Num: [388/600]
Discriminator Loss: 0.9231, Generator Loss: 1.8942
D(x): 0.6199, D(G(z)): 0.2134
Epoch: [17/20], Batch Num: [389/600]
Discriminator Loss: 0.8381, Generator Loss: 1.7051
D(x): 0.6538, D(G(z)): 0.2051
Epoch: [17/20], Batch Num: [390/600]
Discriminator Loss: 0.8374, Generator Loss: 1.5013
D(x): 0.7076, D(G(z)): 0.2760
Epoch: [17/20], Batch Num: [391/600]
Discriminator Loss: 0.7713, Generator Loss: 1.4338
D(x): 0.7898, D(G(z)): 0.3198
Epoch: [17/20], Batch Num: [392/600]
Discriminator Loss: 0.7549, Generator Loss: 1.4249
D(x): 0.7800, D(G(z)): 0.2893
Epoch: [17/20], Batch Num: [393/600]
Discriminator Loss: 0.8394, Generator Loss: 1.6019
D(x): 0.8024, D(G(z)): 0.3387
Epoch: [17/20], Batch Num: [394/600]
Discriminator Loss: 0.7589, Generator Loss: 1.7069
D(x): 0.7251, D(G(z)): 0.2512
Epoch: [17/20], Batch Num: [395/600]
Discriminator Loss: 0.8258, Generator Loss: 1.6981
D(x): 0.7025, D(G(z)): 0.2462
Epoch: [17/20], Batch Num: [396/600]
Discriminator Loss: 0.8030, Generator Loss: 1.7101
D(x): 0.6958, D(G(z)): 0.2249
Epoch: [17/20], Batch Num: [397/600]
Discriminator Loss: 0.8931, Generator Loss: 1.6660
D(x): 0.7314, D(G(z)): 0.2664
Epoch: [17/20], Batch Num: [398/600]
Discriminator Loss: 0.9016, Generator Loss: 1.8078
D(x): 0.6834, D(G(z)): 0.2449
Epoch: [17/20], Batch Num: [399/600]
Discriminator Loss: 0.7802, Generator Loss: 1.5743
D(x): 0.7683, D(G(z)): 0.2755
Epoch: 17, Batch Num: [400/600]

Epoch: [17/20], Batch Num: [400/600]
Discriminator Loss: 0.8209, Generator Loss: 1.7791
D(x): 0.7493, D(G(z)): 0.2655
Epoch: [17/20], Batch Num: [401/600]
Discriminator Loss: 0.9440, Generator Loss: 1.6245
D(x): 0.7262, D(G(z)): 0.2861
Epoch: [17/20], Batch Num: [402/600]
Discriminator Loss: 0.7353, Generator Loss: 1.4755
D(x): 0.7746, D(G(z)): 0.2754
Epoch: [17/20], Batch Num: [403/600]
Discriminator Loss: 0.8091, Generator Loss: 1.6778
D(x): 0.7537, D(G(z)): 0.2682
Epoch: [17/20], Batch Num: [404/600]
Discriminator Loss: 0.7632, Generator Loss: 1.7347
D(x): 0.7356, D(G(z)): 0.2367
Epoch: [17/20], Batch Num: [405/600]
Discriminator Loss: 0.8548, Generator Loss: 1.7987
D(x): 0.7560, D(G(z)): 0.2878
Epoch: [17/20], Batch Num: [406/600]
Discriminator Loss: 0.7459, Generator Loss: 1.8938
D(x): 0.7302, D(G(z)): 0.2312
Epoch: [17/20], Batch Num: [407/600]
Discriminator Loss: 0.7601, Generator Loss: 1.6995
D(x): 0.7219, D(G(z)): 0.2378
Epoch: [17/20], Batch Num: [408/600]
Discriminator Loss: 0.7829, Generator Loss: 1.6512
D(x): 0.7390, D(G(z)): 0.2586
Epoch: [17/20], Batch Num: [409/600]
Discriminator Loss: 0.7295, Generator Loss: 1.6539
D(x): 0.7546, D(G(z)): 0.2349
Epoch: [17/20], Batch Num: [410/600]
Discriminator Loss: 0.7864, Generator Loss: 1.6239
D(x): 0.7572, D(G(z)): 0.2867
Epoch: [17/20], Batch Num: [411/600]
Discriminator Loss: 0.8322, Generator Loss: 1.5655
D(x): 0.7295, D(G(z)): 0.2592
Epoch: [17/20], Batch Num: [412/600]
Discriminator Loss: 0.9402, Generator Loss: 1.6926
D(x): 0.6971, D(G(z)): 0.2937
Epoch: [17/20], Batch Num: [413/600]
Discriminator Loss: 0.9402, Generator Loss: 1.7809
D(x): 0.7388, D(G(z)): 0.3139
Epoch: [17/20], Batch Num: [414/600]
Discriminator Loss: 1.0026, Generator Loss: 1.7316
D(x): 0.6693, D(G(z)): 0.2643
Epoch: [17/20], Batch Num: [415/600]
Discriminator Loss: 0.6897, Generator Loss: 1.6160
D(x): 0.7727, D(G(z)): 0.2417
Epoch: [17/20], Batch Num: [416/600]
Discriminator Loss: 0.9647, Generator Loss: 1.7043
D(x): 0.7365, D(G(z)): 0.2926
Epoch: [17/20], Batch Num: [417/600]
Discriminator Loss: 0.9280, Generator Loss: 1.7381
D(x): 0.7013, D(G(z)): 0.2773
Epoch: [17/20], Batch Num: [418/600]
Discriminator Loss: 0.6793, Generator Loss: 1.6518
D(x): 0.7537, D(G(z)): 0.2100
Epoch: [17/20], Batch Num: [419/600]
Discriminator Loss: 0.7561, Generator Loss: 1.5269
D(x): 0.7496, D(G(z)): 0.2608
Epoch: [17/20], Batch Num: [420/600]
Discriminator Loss: 0.6906, Generator Loss: 1.5140
D(x): 0.7767, D(G(z)): 0.2617
Epoch: [17/20], Batch Num: [421/600]
Discriminator Loss: 0.9245, Generator Loss: 1.6947
D(x): 0.7102, D(G(z)): 0.3103
Epoch: [17/20], Batch Num: [422/600]
Discriminator Loss: 0.8307, Generator Loss: 1.6746
D(x): 0.7045, D(G(z)): 0.2627
Epoch: [17/20], Batch Num: [423/600]
Discriminator Loss: 0.9179, Generator Loss: 1.6557
D(x): 0.6745, D(G(z)): 0.2597
Epoch: [17/20], Batch Num: [424/600]
Discriminator Loss: 0.7746, Generator Loss: 1.5359
D(x): 0.7414, D(G(z)): 0.2797
Epoch: [17/20], Batch Num: [425/600]
Discriminator Loss: 0.7237, Generator Loss: 1.4113
D(x): 0.7792, D(G(z)): 0.2823
Epoch: [17/20], Batch Num: [426/600]
Discriminator Loss: 0.8318, Generator Loss: 1.6922
D(x): 0.7505, D(G(z)): 0.3070
Epoch: [17/20], Batch Num: [427/600]
Discriminator Loss: 0.7700, Generator Loss: 1.6358
D(x): 0.7620, D(G(z)): 0.2784
Epoch: [17/20], Batch Num: [428/600]
Discriminator Loss: 0.7038, Generator Loss: 1.7808
D(x): 0.7745, D(G(z)): 0.2643
Epoch: [17/20], Batch Num: [429/600]
Discriminator Loss: 0.9017, Generator Loss: 1.8821
D(x): 0.6792, D(G(z)): 0.2439
Epoch: [17/20], Batch Num: [430/600]
Discriminator Loss: 0.7672, Generator Loss: 1.6640
D(x): 0.7411, D(G(z)): 0.2493
Epoch: [17/20], Batch Num: [431/600]
Discriminator Loss: 0.6583, Generator Loss: 1.5823
D(x): 0.7615, D(G(z)): 0.2203
Epoch: [17/20], Batch Num: [432/600]
Discriminator Loss: 0.8219, Generator Loss: 1.6643
D(x): 0.7250, D(G(z)): 0.2640
Epoch: [17/20], Batch Num: [433/600]
Discriminator Loss: 0.7706, Generator Loss: 1.5907
D(x): 0.7669, D(G(z)): 0.2850
Epoch: [17/20], Batch Num: [434/600]
Discriminator Loss: 0.8114, Generator Loss: 1.7326
D(x): 0.7489, D(G(z)): 0.2878
Epoch: [17/20], Batch Num: [435/600]
Discriminator Loss: 0.9180, Generator Loss: 1.8891
D(x): 0.7381, D(G(z)): 0.3104
Epoch: [17/20], Batch Num: [436/600]
Discriminator Loss: 0.9532, Generator Loss: 2.0061
D(x): 0.6752, D(G(z)): 0.2514
Epoch: [17/20], Batch Num: [437/600]
Discriminator Loss: 0.8035, Generator Loss: 2.0653
D(x): 0.6842, D(G(z)): 0.1932
Epoch: [17/20], Batch Num: [438/600]
Discriminator Loss: 0.7955, Generator Loss: 1.7731
D(x): 0.6897, D(G(z)): 0.1977
Epoch: [17/20], Batch Num: [439/600]
Discriminator Loss: 0.7357, Generator Loss: 1.6055
D(x): 0.7388, D(G(z)): 0.2462
Epoch: [17/20], Batch Num: [440/600]
Discriminator Loss: 0.7300, Generator Loss: 1.4179
D(x): 0.7659, D(G(z)): 0.2755
Epoch: [17/20], Batch Num: [441/600]
Discriminator Loss: 0.7816, Generator Loss: 1.5217
D(x): 0.7887, D(G(z)): 0.3131
Epoch: [17/20], Batch Num: [442/600]
Discriminator Loss: 0.8960, Generator Loss: 1.4807
D(x): 0.7505, D(G(z)): 0.3274
Epoch: [17/20], Batch Num: [443/600]
Discriminator Loss: 0.7438, Generator Loss: 1.5972
D(x): 0.7510, D(G(z)): 0.2773
Epoch: [17/20], Batch Num: [444/600]
Discriminator Loss: 0.9155, Generator Loss: 1.7127
D(x): 0.7462, D(G(z)): 0.3134
Epoch: [17/20], Batch Num: [445/600]
Discriminator Loss: 0.8645, Generator Loss: 2.2002
D(x): 0.7132, D(G(z)): 0.2701
Epoch: [17/20], Batch Num: [446/600]
Discriminator Loss: 0.8058, Generator Loss: 1.9146
D(x): 0.6877, D(G(z)): 0.2051
Epoch: [17/20], Batch Num: [447/600]
Discriminator Loss: 0.8266, Generator Loss: 1.9535
D(x): 0.6923, D(G(z)): 0.2329
Epoch: [17/20], Batch Num: [448/600]
Discriminator Loss: 0.8876, Generator Loss: 1.7146
D(x): 0.7022, D(G(z)): 0.2503
Epoch: [17/20], Batch Num: [449/600]
Discriminator Loss: 0.7011, Generator Loss: 1.3997
D(x): 0.8043, D(G(z)): 0.2903
Epoch: [17/20], Batch Num: [450/600]
Discriminator Loss: 0.7879, Generator Loss: 1.4546
D(x): 0.7706, D(G(z)): 0.2872
Epoch: [17/20], Batch Num: [451/600]
Discriminator Loss: 0.8307, Generator Loss: 1.7752
D(x): 0.7939, D(G(z)): 0.3111
Epoch: [17/20], Batch Num: [452/600]
Discriminator Loss: 0.7618, Generator Loss: 1.9832
D(x): 0.7307, D(G(z)): 0.2416
Epoch: [17/20], Batch Num: [453/600]
Discriminator Loss: 0.7919, Generator Loss: 1.9392
D(x): 0.7058, D(G(z)): 0.2230
Epoch: [17/20], Batch Num: [454/600]
Discriminator Loss: 1.0612, Generator Loss: 1.5495
D(x): 0.6406, D(G(z)): 0.2432
Epoch: [17/20], Batch Num: [455/600]
Discriminator Loss: 0.7734, Generator Loss: 1.6469
D(x): 0.7679, D(G(z)): 0.2774
Epoch: [17/20], Batch Num: [456/600]
Discriminator Loss: 0.9347, Generator Loss: 1.5780
D(x): 0.7283, D(G(z)): 0.2990
Epoch: [17/20], Batch Num: [457/600]
Discriminator Loss: 0.8750, Generator Loss: 1.5295
D(x): 0.7853, D(G(z)): 0.3405
Epoch: [17/20], Batch Num: [458/600]
Discriminator Loss: 0.9447, Generator Loss: 1.8758
D(x): 0.7525, D(G(z)): 0.3316
Epoch: [17/20], Batch Num: [459/600]
Discriminator Loss: 0.8841, Generator Loss: 1.8801
D(x): 0.6665, D(G(z)): 0.2379
Epoch: [17/20], Batch Num: [460/600]
Discriminator Loss: 0.9679, Generator Loss: 1.7611
D(x): 0.6379, D(G(z)): 0.2362
Epoch: [17/20], Batch Num: [461/600]
Discriminator Loss: 0.9377, Generator Loss: 1.6223
D(x): 0.7055, D(G(z)): 0.2848
Epoch: [17/20], Batch Num: [462/600]
Discriminator Loss: 0.9380, Generator Loss: 1.6041
D(x): 0.7261, D(G(z)): 0.3228
Epoch: [17/20], Batch Num: [463/600]
Discriminator Loss: 0.9848, Generator Loss: 1.6541
D(x): 0.6425, D(G(z)): 0.2702
Epoch: [17/20], Batch Num: [464/600]
Discriminator Loss: 0.6852, Generator Loss: 1.6377
D(x): 0.8172, D(G(z)): 0.2809
Epoch: [17/20], Batch Num: [465/600]
Discriminator Loss: 0.7385, Generator Loss: 1.5559
D(x): 0.7214, D(G(z)): 0.2385
Epoch: [17/20], Batch Num: [466/600]
Discriminator Loss: 0.8259, Generator Loss: 1.5784
D(x): 0.7300, D(G(z)): 0.2925
Epoch: [17/20], Batch Num: [467/600]
Discriminator Loss: 0.8977, Generator Loss: 1.7120
D(x): 0.6857, D(G(z)): 0.2736
Epoch: [17/20], Batch Num: [468/600]
Discriminator Loss: 0.6703, Generator Loss: 1.5715
D(x): 0.7600, D(G(z)): 0.2388
Epoch: [17/20], Batch Num: [469/600]
Discriminator Loss: 0.8737, Generator Loss: 1.7431
D(x): 0.7082, D(G(z)): 0.2950
Epoch: [17/20], Batch Num: [470/600]
Discriminator Loss: 0.8859, Generator Loss: 1.4206
D(x): 0.6712, D(G(z)): 0.2644
Epoch: [17/20], Batch Num: [471/600]
Discriminator Loss: 0.8490, Generator Loss: 1.4233
D(x): 0.7829, D(G(z)): 0.3325
Epoch: [17/20], Batch Num: [472/600]
Discriminator Loss: 0.7452, Generator Loss: 1.4469
D(x): 0.7795, D(G(z)): 0.3015
Epoch: [17/20], Batch Num: [473/600]
Discriminator Loss: 0.7934, Generator Loss: 1.5950
D(x): 0.7055, D(G(z)): 0.2280
Epoch: [17/20], Batch Num: [474/600]
Discriminator Loss: 0.7972, Generator Loss: 1.6721
D(x): 0.7195, D(G(z)): 0.2540
Epoch: [17/20], Batch Num: [475/600]
Discriminator Loss: 0.7782, Generator Loss: 1.6244
D(x): 0.7716, D(G(z)): 0.2895
Epoch: [17/20], Batch Num: [476/600]
Discriminator Loss: 0.8205, Generator Loss: 2.0099
D(x): 0.7443, D(G(z)): 0.2605
Epoch: [17/20], Batch Num: [477/600]
Discriminator Loss: 0.9175, Generator Loss: 1.6014
D(x): 0.6940, D(G(z)): 0.2598
Epoch: [17/20], Batch Num: [478/600]
Discriminator Loss: 0.7655, Generator Loss: 1.8129
D(x): 0.7503, D(G(z)): 0.2631
Epoch: [17/20], Batch Num: [479/600]
Discriminator Loss: 1.0038, Generator Loss: 1.6645
D(x): 0.6871, D(G(z)): 0.2888
Epoch: [17/20], Batch Num: [480/600]
Discriminator Loss: 0.8725, Generator Loss: 1.5844
D(x): 0.7033, D(G(z)): 0.2627
Epoch: [17/20], Batch Num: [481/600]
Discriminator Loss: 0.7350, Generator Loss: 1.3005
D(x): 0.7628, D(G(z)): 0.2718
Epoch: [17/20], Batch Num: [482/600]
Discriminator Loss: 0.7509, Generator Loss: 1.4179
D(x): 0.7982, D(G(z)): 0.2797
Epoch: [17/20], Batch Num: [483/600]
Discriminator Loss: 0.9581, Generator Loss: 1.8672
D(x): 0.7634, D(G(z)): 0.3422
Epoch: [17/20], Batch Num: [484/600]
Discriminator Loss: 0.7018, Generator Loss: 2.0453
D(x): 0.7570, D(G(z)): 0.2484
Epoch: [17/20], Batch Num: [485/600]
Discriminator Loss: 0.9077, Generator Loss: 1.8056
D(x): 0.6881, D(G(z)): 0.2330
Epoch: [17/20], Batch Num: [486/600]
Discriminator Loss: 0.7310, Generator Loss: 1.9358
D(x): 0.7337, D(G(z)): 0.2176
Epoch: [17/20], Batch Num: [487/600]
Discriminator Loss: 0.9345, Generator Loss: 1.7197
D(x): 0.6782, D(G(z)): 0.2418
Epoch: [17/20], Batch Num: [488/600]
Discriminator Loss: 0.8396, Generator Loss: 1.5020
D(x): 0.7730, D(G(z)): 0.2898
Epoch: [17/20], Batch Num: [489/600]
Discriminator Loss: 0.8159, Generator Loss: 1.7479
D(x): 0.7601, D(G(z)): 0.2846
Epoch: [17/20], Batch Num: [490/600]
Discriminator Loss: 0.7574, Generator Loss: 1.5665
D(x): 0.7837, D(G(z)): 0.2928
Epoch: [17/20], Batch Num: [491/600]
Discriminator Loss: 0.9078, Generator Loss: 1.7154
D(x): 0.6934, D(G(z)): 0.2690
Epoch: [17/20], Batch Num: [492/600]
Discriminator Loss: 0.9111, Generator Loss: 1.7461
D(x): 0.7134, D(G(z)): 0.2719
Epoch: [17/20], Batch Num: [493/600]
Discriminator Loss: 0.7254, Generator Loss: 1.9650
D(x): 0.7414, D(G(z)): 0.2361
Epoch: [17/20], Batch Num: [494/600]
Discriminator Loss: 0.8677, Generator Loss: 1.6188
D(x): 0.7144, D(G(z)): 0.2606
Epoch: [17/20], Batch Num: [495/600]
Discriminator Loss: 0.8581, Generator Loss: 1.6831
D(x): 0.7117, D(G(z)): 0.2556
Epoch: [17/20], Batch Num: [496/600]
Discriminator Loss: 0.8484, Generator Loss: 1.4983
D(x): 0.7030, D(G(z)): 0.2470
Epoch: [17/20], Batch Num: [497/600]
Discriminator Loss: 0.8149, Generator Loss: 1.4866
D(x): 0.7811, D(G(z)): 0.3097
Epoch: [17/20], Batch Num: [498/600]
Discriminator Loss: 0.8212, Generator Loss: 1.6121
D(x): 0.7432, D(G(z)): 0.2757
Epoch: [17/20], Batch Num: [499/600]
Discriminator Loss: 0.9647, Generator Loss: 1.6708
D(x): 0.7415, D(G(z)): 0.3189
Epoch: 17, Batch Num: [500/600]

Epoch: [17/20], Batch Num: [500/600]
Discriminator Loss: 0.8231, Generator Loss: 1.7366
D(x): 0.7043, D(G(z)): 0.2646
Epoch: [17/20], Batch Num: [501/600]
Discriminator Loss: 0.8061, Generator Loss: 1.7525
D(x): 0.7453, D(G(z)): 0.2798
Epoch: [17/20], Batch Num: [502/600]
Discriminator Loss: 1.1181, Generator Loss: 1.6979
D(x): 0.6273, D(G(z)): 0.3034
Epoch: [17/20], Batch Num: [503/600]
Discriminator Loss: 0.8220, Generator Loss: 1.6859
D(x): 0.7393, D(G(z)): 0.2966
Epoch: [17/20], Batch Num: [504/600]
Discriminator Loss: 0.8523, Generator Loss: 1.6124
D(x): 0.6666, D(G(z)): 0.2355
Epoch: [17/20], Batch Num: [505/600]
Discriminator Loss: 0.8565, Generator Loss: 1.6064
D(x): 0.7460, D(G(z)): 0.3064
Epoch: [17/20], Batch Num: [506/600]
Discriminator Loss: 0.6959, Generator Loss: 1.6429
D(x): 0.7427, D(G(z)): 0.2414
Epoch: [17/20], Batch Num: [507/600]
Discriminator Loss: 0.8637, Generator Loss: 1.5436
D(x): 0.7185, D(G(z)): 0.3106
Epoch: [17/20], Batch Num: [508/600]
Discriminator Loss: 0.7796, Generator Loss: 1.6057
D(x): 0.7409, D(G(z)): 0.2588
Epoch: [17/20], Batch Num: [509/600]
Discriminator Loss: 0.8459, Generator Loss: 1.5677
D(x): 0.7444, D(G(z)): 0.2978
Epoch: [17/20], Batch Num: [510/600]
Discriminator Loss: 0.8340, Generator Loss: 1.8729
D(x): 0.7473, D(G(z)): 0.2670
Epoch: [17/20], Batch Num: [511/600]
Discriminator Loss: 0.8191, Generator Loss: 1.9031
D(x): 0.7343, D(G(z)): 0.2551
Epoch: [17/20], Batch Num: [512/600]
Discriminator Loss: 0.9114, Generator Loss: 1.8319
D(x): 0.6825, D(G(z)): 0.2480
Epoch: [17/20], Batch Num: [513/600]
Discriminator Loss: 0.7566, Generator Loss: 1.6650
D(x): 0.7750, D(G(z)): 0.2627
Epoch: [17/20], Batch Num: [514/600]
Discriminator Loss: 0.9229, Generator Loss: 1.8073
D(x): 0.6889, D(G(z)): 0.2696
Epoch: [17/20], Batch Num: [515/600]
Discriminator Loss: 0.7514, Generator Loss: 1.8527
D(x): 0.7552, D(G(z)): 0.2512
Epoch: [17/20], Batch Num: [516/600]
Discriminator Loss: 0.7891, Generator Loss: 1.6326
D(x): 0.7245, D(G(z)): 0.2458
Epoch: [17/20], Batch Num: [517/600]
Discriminator Loss: 0.8243, Generator Loss: 1.5094
D(x): 0.7342, D(G(z)): 0.2751
Epoch: [17/20], Batch Num: [518/600]
Discriminator Loss: 0.8617, Generator Loss: 1.4587
D(x): 0.7516, D(G(z)): 0.3144
Epoch: [17/20], Batch Num: [519/600]
Discriminator Loss: 0.7218, Generator Loss: 1.7722
D(x): 0.8382, D(G(z)): 0.3138
Epoch: [17/20], Batch Num: [520/600]
Discriminator Loss: 0.7456, Generator Loss: 1.7908
D(x): 0.7351, D(G(z)): 0.2390
Epoch: [17/20], Batch Num: [521/600]
Discriminator Loss: 0.7315, Generator Loss: 1.7690
D(x): 0.7116, D(G(z)): 0.2048
Epoch: [17/20], Batch Num: [522/600]
Discriminator Loss: 0.9058, Generator Loss: 1.7313
D(x): 0.6632, D(G(z)): 0.2313
Epoch: [17/20], Batch Num: [523/600]
Discriminator Loss: 0.7922, Generator Loss: 1.4850
D(x): 0.7258, D(G(z)): 0.2726
Epoch: [17/20], Batch Num: [524/600]
Discriminator Loss: 0.8085, Generator Loss: 1.5707
D(x): 0.7650, D(G(z)): 0.2999
Epoch: [17/20], Batch Num: [525/600]
Discriminator Loss: 0.8843, Generator Loss: 1.5403
D(x): 0.7639, D(G(z)): 0.3167
Epoch: [17/20], Batch Num: [526/600]
Discriminator Loss: 0.8563, Generator Loss: 1.7386
D(x): 0.7505, D(G(z)): 0.3006
Epoch: [17/20], Batch Num: [527/600]
Discriminator Loss: 0.7456, Generator Loss: 1.6964
D(x): 0.7389, D(G(z)): 0.2299
Epoch: [17/20], Batch Num: [528/600]
Discriminator Loss: 0.7657, Generator Loss: 1.9610
D(x): 0.7259, D(G(z)): 0.2379
Epoch: [17/20], Batch Num: [529/600]
Discriminator Loss: 0.8494, Generator Loss: 1.9767
D(x): 0.6999, D(G(z)): 0.2326
Epoch: [17/20], Batch Num: [530/600]
Discriminator Loss: 0.8392, Generator Loss: 1.7688
D(x): 0.7142, D(G(z)): 0.2293
Epoch: [17/20], Batch Num: [531/600]
Discriminator Loss: 0.8784, Generator Loss: 1.5255
D(x): 0.7144, D(G(z)): 0.2575
Epoch: [17/20], Batch Num: [532/600]
Discriminator Loss: 0.9987, Generator Loss: 1.4911
D(x): 0.7380, D(G(z)): 0.3370
Epoch: [17/20], Batch Num: [533/600]
Discriminator Loss: 0.8746, Generator Loss: 1.4516
D(x): 0.7572, D(G(z)): 0.3122
Epoch: [17/20], Batch Num: [534/600]
Discriminator Loss: 0.6677, Generator Loss: 1.6710
D(x): 0.8250, D(G(z)): 0.2835
Epoch: [17/20], Batch Num: [535/600]
Discriminator Loss: 0.7461, Generator Loss: 1.6068
D(x): 0.7632, D(G(z)): 0.2539
Epoch: [17/20], Batch Num: [536/600]
Discriminator Loss: 0.7326, Generator Loss: 1.9834
D(x): 0.7489, D(G(z)): 0.2466
Epoch: [17/20], Batch Num: [537/600]
Discriminator Loss: 0.9097, Generator Loss: 1.8057
D(x): 0.6507, D(G(z)): 0.2121
Epoch: [17/20], Batch Num: [538/600]
Discriminator Loss: 0.8645, Generator Loss: 1.7665
D(x): 0.6904, D(G(z)): 0.2302
Epoch: [17/20], Batch Num: [539/600]
Discriminator Loss: 0.9024, Generator Loss: 1.4053
D(x): 0.6631, D(G(z)): 0.2434
Epoch: [17/20], Batch Num: [540/600]
Discriminator Loss: 0.9185, Generator Loss: 1.3735
D(x): 0.7319, D(G(z)): 0.3239
Epoch: [17/20], Batch Num: [541/600]
Discriminator Loss: 1.0176, Generator Loss: 1.3065
D(x): 0.7446, D(G(z)): 0.3636
Epoch: [17/20], Batch Num: [542/600]
Discriminator Loss: 0.9938, Generator Loss: 1.5226
D(x): 0.7233, D(G(z)): 0.3460
Epoch: [17/20], Batch Num: [543/600]
Discriminator Loss: 0.8973, Generator Loss: 1.6967
D(x): 0.7323, D(G(z)): 0.3138
Epoch: [17/20], Batch Num: [544/600]
Discriminator Loss: 0.8277, Generator Loss: 1.6397
D(x): 0.6922, D(G(z)): 0.2393
Epoch: [17/20], Batch Num: [545/600]
Discriminator Loss: 0.9036, Generator Loss: 1.7648
D(x): 0.6922, D(G(z)): 0.2877
Epoch: [17/20], Batch Num: [546/600]
Discriminator Loss: 0.9192, Generator Loss: 1.7603
D(x): 0.6924, D(G(z)): 0.2839
Epoch: [17/20], Batch Num: [547/600]
Discriminator Loss: 0.8836, Generator Loss: 1.6472
D(x): 0.6869, D(G(z)): 0.2409
Epoch: [17/20], Batch Num: [548/600]
Discriminator Loss: 0.8412, Generator Loss: 1.5287
D(x): 0.7394, D(G(z)): 0.2846
Epoch: [17/20], Batch Num: [549/600]
Discriminator Loss: 0.9266, Generator Loss: 1.3856
D(x): 0.7223, D(G(z)): 0.3318
Epoch: [17/20], Batch Num: [550/600]
Discriminator Loss: 1.0173, Generator Loss: 1.5090
D(x): 0.7521, D(G(z)): 0.3704
Epoch: [17/20], Batch Num: [551/600]
Discriminator Loss: 0.8751, Generator Loss: 1.6319
D(x): 0.7642, D(G(z)): 0.3181
Epoch: [17/20], Batch Num: [552/600]
Discriminator Loss: 0.8312, Generator Loss: 1.6482
D(x): 0.7176, D(G(z)): 0.2496
Epoch: [17/20], Batch Num: [553/600]
Discriminator Loss: 0.7409, Generator Loss: 1.7878
D(x): 0.7276, D(G(z)): 0.2299
Epoch: [17/20], Batch Num: [554/600]
Discriminator Loss: 0.9494, Generator Loss: 1.7348
D(x): 0.6642, D(G(z)): 0.2561
Epoch: [17/20], Batch Num: [555/600]
Discriminator Loss: 0.9751, Generator Loss: 1.5444
D(x): 0.6481, D(G(z)): 0.2738
Epoch: [17/20], Batch Num: [556/600]
Discriminator Loss: 0.8683, Generator Loss: 1.5183
D(x): 0.6952, D(G(z)): 0.2580
Epoch: [17/20], Batch Num: [557/600]
Discriminator Loss: 0.9020, Generator Loss: 1.5066
D(x): 0.7275, D(G(z)): 0.3156
Epoch: [17/20], Batch Num: [558/600]
Discriminator Loss: 0.7799, Generator Loss: 1.4130
D(x): 0.7676, D(G(z)): 0.3009
Epoch: [17/20], Batch Num: [559/600]
Discriminator Loss: 0.9983, Generator Loss: 1.4714
D(x): 0.7151, D(G(z)): 0.3642
Epoch: [17/20], Batch Num: [560/600]
Discriminator Loss: 0.8172, Generator Loss: 1.4579
D(x): 0.7712, D(G(z)): 0.3161
Epoch: [17/20], Batch Num: [561/600]
Discriminator Loss: 0.9298, Generator Loss: 1.7589
D(x): 0.7050, D(G(z)): 0.3122
Epoch: [17/20], Batch Num: [562/600]
Discriminator Loss: 0.8363, Generator Loss: 1.7849
D(x): 0.6858, D(G(z)): 0.2609
Epoch: [17/20], Batch Num: [563/600]
Discriminator Loss: 0.8126, Generator Loss: 1.7397
D(x): 0.7164, D(G(z)): 0.2429
Epoch: [17/20], Batch Num: [564/600]
Discriminator Loss: 0.8185, Generator Loss: 1.6043
D(x): 0.7139, D(G(z)): 0.2670
Epoch: [17/20], Batch Num: [565/600]
Discriminator Loss: 0.9049, Generator Loss: 1.5134
D(x): 0.6797, D(G(z)): 0.2706
Epoch: [17/20], Batch Num: [566/600]
Discriminator Loss: 0.8488, Generator Loss: 1.5736
D(x): 0.7291, D(G(z)): 0.2999
Epoch: [17/20], Batch Num: [567/600]
Discriminator Loss: 0.8682, Generator Loss: 1.4995
D(x): 0.7240, D(G(z)): 0.2930
Epoch: [17/20], Batch Num: [568/600]
Discriminator Loss: 0.7926, Generator Loss: 1.5619
D(x): 0.7581, D(G(z)): 0.2960
Epoch: [17/20], Batch Num: [569/600]
Discriminator Loss: 0.9039, Generator Loss: 1.5336
D(x): 0.6634, D(G(z)): 0.2531
Epoch: [17/20], Batch Num: [570/600]
Discriminator Loss: 0.8685, Generator Loss: 1.4818
D(x): 0.7211, D(G(z)): 0.2883
Epoch: [17/20], Batch Num: [571/600]
Discriminator Loss: 0.8314, Generator Loss: 1.3915
D(x): 0.7131, D(G(z)): 0.2629
Epoch: [17/20], Batch Num: [572/600]
Discriminator Loss: 0.8245, Generator Loss: 1.3660
D(x): 0.7436, D(G(z)): 0.3086
Epoch: [17/20], Batch Num: [573/600]
Discriminator Loss: 0.8713, Generator Loss: 1.6359
D(x): 0.7533, D(G(z)): 0.3319
Epoch: [17/20], Batch Num: [574/600]
Discriminator Loss: 0.8073, Generator Loss: 1.7567
D(x): 0.7538, D(G(z)): 0.2893
Epoch: [17/20], Batch Num: [575/600]
Discriminator Loss: 0.7651, Generator Loss: 2.0545
D(x): 0.7569, D(G(z)): 0.2584
Epoch: [17/20], Batch Num: [576/600]
Discriminator Loss: 0.9455, Generator Loss: 1.8774
D(x): 0.6658, D(G(z)): 0.2411
Epoch: [17/20], Batch Num: [577/600]
Discriminator Loss: 0.8169, Generator Loss: 2.0093
D(x): 0.7221, D(G(z)): 0.2150
Epoch: [17/20], Batch Num: [578/600]
Discriminator Loss: 1.0466, Generator Loss: 1.7492
D(x): 0.6324, D(G(z)): 0.2427
Epoch: [17/20], Batch Num: [579/600]
Discriminator Loss: 0.8612, Generator Loss: 1.4530
D(x): 0.7188, D(G(z)): 0.2682
Epoch: [17/20], Batch Num: [580/600]
Discriminator Loss: 0.8109, Generator Loss: 1.4097
D(x): 0.7945, D(G(z)): 0.3259
Epoch: [17/20], Batch Num: [581/600]
Discriminator Loss: 0.7496, Generator Loss: 1.5501
D(x): 0.8236, D(G(z)): 0.3117
Epoch: [17/20], Batch Num: [582/600]
Discriminator Loss: 0.7593, Generator Loss: 1.9174
D(x): 0.8091, D(G(z)): 0.2884
Epoch: [17/20], Batch Num: [583/600]
Discriminator Loss: 0.8085, Generator Loss: 2.0986
D(x): 0.7329, D(G(z)): 0.2499
Epoch: [17/20], Batch Num: [584/600]
Discriminator Loss: 0.9975, Generator Loss: 2.1227
D(x): 0.6401, D(G(z)): 0.2040
Epoch: [17/20], Batch Num: [585/600]
Discriminator Loss: 0.8614, Generator Loss: 1.8356
D(x): 0.6627, D(G(z)): 0.1808
Epoch: [17/20], Batch Num: [586/600]
Discriminator Loss: 0.9178, Generator Loss: 1.8350
D(x): 0.6865, D(G(z)): 0.2327
Epoch: [17/20], Batch Num: [587/600]
Discriminator Loss: 0.8080, Generator Loss: 1.4450
D(x): 0.7380, D(G(z)): 0.2362
Epoch: [17/20], Batch Num: [588/600]
Discriminator Loss: 0.8978, Generator Loss: 1.2137
D(x): 0.7790, D(G(z)): 0.3127
Epoch: [17/20], Batch Num: [589/600]
Discriminator Loss: 0.8594, Generator Loss: 1.2794
D(x): 0.7833, D(G(z)): 0.3281
Epoch: [17/20], Batch Num: [590/600]
Discriminator Loss: 0.7160, Generator Loss: 1.6481
D(x): 0.8353, D(G(z)): 0.3394
Epoch: [17/20], Batch Num: [591/600]
Discriminator Loss: 0.7523, Generator Loss: 1.8433
D(x): 0.7381, D(G(z)): 0.2349
Epoch: [17/20], Batch Num: [592/600]
Discriminator Loss: 0.8232, Generator Loss: 2.0202
D(x): 0.6948, D(G(z)): 0.2257
Epoch: [17/20], Batch Num: [593/600]
Discriminator Loss: 0.9682, Generator Loss: 1.9377
D(x): 0.6385, D(G(z)): 0.2067
Epoch: [17/20], Batch Num: [594/600]
Discriminator Loss: 0.9307, Generator Loss: 1.5607
D(x): 0.6848, D(G(z)): 0.2586
Epoch: [17/20], Batch Num: [595/600]
Discriminator Loss: 0.8333, Generator Loss: 1.4002
D(x): 0.7268, D(G(z)): 0.2618
Epoch: [17/20], Batch Num: [596/600]
Discriminator Loss: 0.9129, Generator Loss: 1.4478
D(x): 0.7352, D(G(z)): 0.2854
Epoch: [17/20], Batch Num: [597/600]
Discriminator Loss: 1.0828, Generator Loss: 1.4426
D(x): 0.7426, D(G(z)): 0.4007
Epoch: [17/20], Batch Num: [598/600]
Discriminator Loss: 0.8513, Generator Loss: 1.4694
D(x): 0.7427, D(G(z)): 0.3056
Epoch: [17/20], Batch Num: [599/600]
Discriminator Loss: 0.8493, Generator Loss: 1.6522
D(x): 0.7592, D(G(z)): 0.3235
Epoch: 18, Batch Num: [0/600]

Epoch: [18/20], Batch Num: [0/600]
Discriminator Loss: 0.8393, Generator Loss: 1.7921
D(x): 0.6920, D(G(z)): 0.2399
Epoch: [18/20], Batch Num: [1/600]
Discriminator Loss: 0.7689, Generator Loss: 1.7211
D(x): 0.7053, D(G(z)): 0.2160
Epoch: [18/20], Batch Num: [2/600]
Discriminator Loss: 0.8712, Generator Loss: 1.3693
D(x): 0.6765, D(G(z)): 0.2455
Epoch: [18/20], Batch Num: [3/600]
Discriminator Loss: 0.9612, Generator Loss: 1.4841
D(x): 0.7074, D(G(z)): 0.3213
Epoch: [18/20], Batch Num: [4/600]
Discriminator Loss: 0.9947, Generator Loss: 1.3596
D(x): 0.7309, D(G(z)): 0.3286
Epoch: [18/20], Batch Num: [5/600]
Discriminator Loss: 0.8441, Generator Loss: 1.4950
D(x): 0.7546, D(G(z)): 0.3128
Epoch: [18/20], Batch Num: [6/600]
Discriminator Loss: 1.0423, Generator Loss: 1.5350
D(x): 0.7344, D(G(z)): 0.3736
Epoch: [18/20], Batch Num: [7/600]
Discriminator Loss: 0.8513, Generator Loss: 1.6337
D(x): 0.7219, D(G(z)): 0.2733
Epoch: [18/20], Batch Num: [8/600]
Discriminator Loss: 0.8600, Generator Loss: 1.4400
D(x): 0.6784, D(G(z)): 0.2542
Epoch: [18/20], Batch Num: [9/600]
Discriminator Loss: 0.8545, Generator Loss: 1.5423
D(x): 0.7059, D(G(z)): 0.2861
Epoch: [18/20], Batch Num: [10/600]
Discriminator Loss: 0.9213, Generator Loss: 1.5072
D(x): 0.6953, D(G(z)): 0.2832
Epoch: [18/20], Batch Num: [11/600]
Discriminator Loss: 1.0424, Generator Loss: 1.6907
D(x): 0.6726, D(G(z)): 0.3284
Epoch: [18/20], Batch Num: [12/600]
Discriminator Loss: 1.0191, Generator Loss: 1.7631
D(x): 0.6811, D(G(z)): 0.3020
Epoch: [18/20], Batch Num: [13/600]
Discriminator Loss: 0.8985, Generator Loss: 1.6968
D(x): 0.7022, D(G(z)): 0.2754
Epoch: [18/20], Batch Num: [14/600]
Discriminator Loss: 0.9733, Generator Loss: 1.5779
D(x): 0.7128, D(G(z)): 0.2917
Epoch: [18/20], Batch Num: [15/600]
Discriminator Loss: 0.8668, Generator Loss: 1.7339
D(x): 0.7374, D(G(z)): 0.3026
Epoch: [18/20], Batch Num: [16/600]
Discriminator Loss: 0.8963, Generator Loss: 1.6523
D(x): 0.6806, D(G(z)): 0.2528
Epoch: [18/20], Batch Num: [17/600]
Discriminator Loss: 0.8294, Generator Loss: 1.4836
D(x): 0.7126, D(G(z)): 0.2711
Epoch: [18/20], Batch Num: [18/600]
Discriminator Loss: 0.8074, Generator Loss: 1.4968
D(x): 0.7123, D(G(z)): 0.2602
Epoch: [18/20], Batch Num: [19/600]
Discriminator Loss: 0.8325, Generator Loss: 1.5728
D(x): 0.7373, D(G(z)): 0.2876
Epoch: [18/20], Batch Num: [20/600]
Discriminator Loss: 0.8710, Generator Loss: 1.4200
D(x): 0.7087, D(G(z)): 0.2929
Epoch: [18/20], Batch Num: [21/600]
Discriminator Loss: 0.8075, Generator Loss: 1.4816
D(x): 0.7878, D(G(z)): 0.3274
Epoch: [18/20], Batch Num: [22/600]
Discriminator Loss: 0.8528, Generator Loss: 1.8289
D(x): 0.7284, D(G(z)): 0.3018
Epoch: [18/20], Batch Num: [23/600]
Discriminator Loss: 0.7376, Generator Loss: 2.1161
D(x): 0.7476, D(G(z)): 0.2255
Epoch: [18/20], Batch Num: [24/600]
Discriminator Loss: 0.9556, Generator Loss: 1.7886
D(x): 0.6362, D(G(z)): 0.2403
Epoch: [18/20], Batch Num: [25/600]
Discriminator Loss: 0.8410, Generator Loss: 1.8723
D(x): 0.6934, D(G(z)): 0.2173
Epoch: [18/20], Batch Num: [26/600]
Discriminator Loss: 0.9195, Generator Loss: 1.7040
D(x): 0.7393, D(G(z)): 0.3350
Epoch: [18/20], Batch Num: [27/600]
Discriminator Loss: 0.8758, Generator Loss: 1.5935
D(x): 0.6960, D(G(z)): 0.2628
Epoch: [18/20], Batch Num: [28/600]
Discriminator Loss: 0.9062, Generator Loss: 1.6257
D(x): 0.7212, D(G(z)): 0.2844
Epoch: [18/20], Batch Num: [29/600]
Discriminator Loss: 0.8290, Generator Loss: 1.6385
D(x): 0.7187, D(G(z)): 0.2573
Epoch: [18/20], Batch Num: [30/600]
Discriminator Loss: 0.8790, Generator Loss: 1.5340
D(x): 0.7422, D(G(z)): 0.3090
Epoch: [18/20], Batch Num: [31/600]
Discriminator Loss: 0.8375, Generator Loss: 1.6300
D(x): 0.7855, D(G(z)): 0.3333
Epoch: [18/20], Batch Num: [32/600]
Discriminator Loss: 0.6912, Generator Loss: 1.9204
D(x): 0.7452, D(G(z)): 0.2160
Epoch: [18/20], Batch Num: [33/600]
Discriminator Loss: 0.9007, Generator Loss: 1.9858
D(x): 0.6990, D(G(z)): 0.2641
Epoch: [18/20], Batch Num: [34/600]
Discriminator Loss: 1.0481, Generator Loss: 1.6589
D(x): 0.6829, D(G(z)): 0.3138
Epoch: [18/20], Batch Num: [35/600]
Discriminator Loss: 0.9106, Generator Loss: 1.5789
D(x): 0.6515, D(G(z)): 0.2318
Epoch: [18/20], Batch Num: [36/600]
Discriminator Loss: 1.0037, Generator Loss: 1.4478
D(x): 0.7314, D(G(z)): 0.3539
Epoch: [18/20], Batch Num: [37/600]
Discriminator Loss: 0.9796, Generator Loss: 1.5418
D(x): 0.7306, D(G(z)): 0.3802
Epoch: [18/20], Batch Num: [38/600]
Discriminator Loss: 0.8254, Generator Loss: 1.7617
D(x): 0.7253, D(G(z)): 0.2737
Epoch: [18/20], Batch Num: [39/600]
Discriminator Loss: 0.8638, Generator Loss: 1.5495
D(x): 0.7268, D(G(z)): 0.2950
Epoch: [18/20], Batch Num: [40/600]
Discriminator Loss: 0.8871, Generator Loss: 1.6710
D(x): 0.6754, D(G(z)): 0.2809
Epoch: [18/20], Batch Num: [41/600]
Discriminator Loss: 0.8618, Generator Loss: 1.5064
D(x): 0.6548, D(G(z)): 0.2417
Epoch: [18/20], Batch Num: [42/600]
Discriminator Loss: 0.7694, Generator Loss: 1.3710
D(x): 0.7304, D(G(z)): 0.2719
Epoch: [18/20], Batch Num: [43/600]
Discriminator Loss: 0.8627, Generator Loss: 1.3924
D(x): 0.7208, D(G(z)): 0.3172
Epoch: [18/20], Batch Num: [44/600]
Discriminator Loss: 1.0045, Generator Loss: 1.3125
D(x): 0.6730, D(G(z)): 0.3221
Epoch: [18/20], Batch Num: [45/600]
Discriminator Loss: 0.8889, Generator Loss: 1.3836
D(x): 0.7605, D(G(z)): 0.3523
Epoch: [18/20], Batch Num: [46/600]
Discriminator Loss: 0.8740, Generator Loss: 1.4998
D(x): 0.7238, D(G(z)): 0.3130
Epoch: [18/20], Batch Num: [47/600]
Discriminator Loss: 0.8697, Generator Loss: 1.5620
D(x): 0.7127, D(G(z)): 0.2823
Epoch: [18/20], Batch Num: [48/600]
Discriminator Loss: 0.8802, Generator Loss: 1.7457
D(x): 0.7196, D(G(z)): 0.3042
Epoch: [18/20], Batch Num: [49/600]
Discriminator Loss: 0.9474, Generator Loss: 1.7949
D(x): 0.6461, D(G(z)): 0.2478
Epoch: [18/20], Batch Num: [50/600]
Discriminator Loss: 0.8830, Generator Loss: 1.6214
D(x): 0.6751, D(G(z)): 0.2504
Epoch: [18/20], Batch Num: [51/600]
Discriminator Loss: 0.9520, Generator Loss: 1.7320
D(x): 0.7143, D(G(z)): 0.3081
Epoch: [18/20], Batch Num: [52/600]
Discriminator Loss: 1.0617, Generator Loss: 1.5289
D(x): 0.6373, D(G(z)): 0.2773
Epoch: [18/20], Batch Num: [53/600]
Discriminator Loss: 0.7811, Generator Loss: 1.4650
D(x): 0.7502, D(G(z)): 0.2895
Epoch: [18/20], Batch Num: [54/600]
Discriminator Loss: 0.9691, Generator Loss: 1.4338
D(x): 0.7352, D(G(z)): 0.3554
Epoch: [18/20], Batch Num: [55/600]
Discriminator Loss: 0.8788, Generator Loss: 1.6672
D(x): 0.7278, D(G(z)): 0.2858
Epoch: [18/20], Batch Num: [56/600]
Discriminator Loss: 0.9166, Generator Loss: 1.7685
D(x): 0.6786, D(G(z)): 0.2677
Epoch: [18/20], Batch Num: [57/600]
Discriminator Loss: 0.9112, Generator Loss: 1.5606
D(x): 0.7372, D(G(z)): 0.3133
Epoch: [18/20], Batch Num: [58/600]
Discriminator Loss: 0.7424, Generator Loss: 1.6561
D(x): 0.7353, D(G(z)): 0.2567
Epoch: [18/20], Batch Num: [59/600]
Discriminator Loss: 0.9287, Generator Loss: 1.7264
D(x): 0.6953, D(G(z)): 0.3057
Epoch: [18/20], Batch Num: [60/600]
Discriminator Loss: 0.8845, Generator Loss: 1.5479
D(x): 0.6787, D(G(z)): 0.2686
Epoch: [18/20], Batch Num: [61/600]
Discriminator Loss: 1.0065, Generator Loss: 1.5842
D(x): 0.7025, D(G(z)): 0.3394
Epoch: [18/20], Batch Num: [62/600]
Discriminator Loss: 0.8885, Generator Loss: 1.4656
D(x): 0.7364, D(G(z)): 0.3370
Epoch: [18/20], Batch Num: [63/600]
Discriminator Loss: 0.9367, Generator Loss: 1.5268
D(x): 0.6787, D(G(z)): 0.2751
Epoch: [18/20], Batch Num: [64/600]
Discriminator Loss: 0.9552, Generator Loss: 1.4195
D(x): 0.7263, D(G(z)): 0.3214
Epoch: [18/20], Batch Num: [65/600]
Discriminator Loss: 0.8203, Generator Loss: 1.4455
D(x): 0.7642, D(G(z)): 0.2995
Epoch: [18/20], Batch Num: [66/600]
Discriminator Loss: 0.8916, Generator Loss: 1.4002
D(x): 0.7060, D(G(z)): 0.2912
Epoch: [18/20], Batch Num: [67/600]
Discriminator Loss: 0.9205, Generator Loss: 1.5267
D(x): 0.6806, D(G(z)): 0.2960
Epoch: [18/20], Batch Num: [68/600]
Discriminator Loss: 1.0722, Generator Loss: 1.5155
D(x): 0.6511, D(G(z)): 0.3170
Epoch: [18/20], Batch Num: [69/600]
Discriminator Loss: 1.0367, Generator Loss: 1.4149
D(x): 0.6813, D(G(z)): 0.3348
Epoch: [18/20], Batch Num: [70/600]
Discriminator Loss: 0.8498, Generator Loss: 1.5144
D(x): 0.7227, D(G(z)): 0.2820
Epoch: [18/20], Batch Num: [71/600]
Discriminator Loss: 1.0246, Generator Loss: 1.6343
D(x): 0.6697, D(G(z)): 0.3283
Epoch: [18/20], Batch Num: [72/600]
Discriminator Loss: 0.9698, Generator Loss: 1.5604
D(x): 0.6742, D(G(z)): 0.3050
Epoch: [18/20], Batch Num: [73/600]
Discriminator Loss: 0.9492, Generator Loss: 1.4618
D(x): 0.6909, D(G(z)): 0.2985
Epoch: [18/20], Batch Num: [74/600]
Discriminator Loss: 1.0485, Generator Loss: 1.6091
D(x): 0.6760, D(G(z)): 0.3482
Epoch: [18/20], Batch Num: [75/600]
Discriminator Loss: 0.9456, Generator Loss: 1.8174
D(x): 0.7053, D(G(z)): 0.3145
Epoch: [18/20], Batch Num: [76/600]
Discriminator Loss: 1.0072, Generator Loss: 1.7408
D(x): 0.6834, D(G(z)): 0.3223
Epoch: [18/20], Batch Num: [77/600]
Discriminator Loss: 1.0284, Generator Loss: 1.9279
D(x): 0.6591, D(G(z)): 0.3088
Epoch: [18/20], Batch Num: [78/600]
Discriminator Loss: 1.0295, Generator Loss: 1.6195
D(x): 0.5927, D(G(z)): 0.2534
Epoch: [18/20], Batch Num: [79/600]
Discriminator Loss: 0.9436, Generator Loss: 1.4383
D(x): 0.6430, D(G(z)): 0.2717
Epoch: [18/20], Batch Num: [80/600]
Discriminator Loss: 0.9630, Generator Loss: 1.2197
D(x): 0.6587, D(G(z)): 0.2826
Epoch: [18/20], Batch Num: [81/600]
Discriminator Loss: 1.0778, Generator Loss: 1.2376
D(x): 0.7540, D(G(z)): 0.4279
Epoch: [18/20], Batch Num: [82/600]
Discriminator Loss: 1.0273, Generator Loss: 1.3632
D(x): 0.7462, D(G(z)): 0.3917
Epoch: [18/20], Batch Num: [83/600]
Discriminator Loss: 0.9334, Generator Loss: 1.5386
D(x): 0.6778, D(G(z)): 0.3077
Epoch: [18/20], Batch Num: [84/600]
Discriminator Loss: 1.1013, Generator Loss: 1.6114
D(x): 0.6200, D(G(z)): 0.3201
Epoch: [18/20], Batch Num: [85/600]
Discriminator Loss: 0.8543, Generator Loss: 1.6682
D(x): 0.6697, D(G(z)): 0.2595
Epoch: [18/20], Batch Num: [86/600]
Discriminator Loss: 0.9562, Generator Loss: 1.4997
D(x): 0.6109, D(G(z)): 0.2434
Epoch: [18/20], Batch Num: [87/600]
Discriminator Loss: 1.0193, Generator Loss: 1.4915
D(x): 0.6133, D(G(z)): 0.2945
Epoch: [18/20], Batch Num: [88/600]
Discriminator Loss: 0.9432, Generator Loss: 1.0926
D(x): 0.6668, D(G(z)): 0.3084
Epoch: [18/20], Batch Num: [89/600]
Discriminator Loss: 0.8531, Generator Loss: 1.1594
D(x): 0.7675, D(G(z)): 0.3699
Epoch: [18/20], Batch Num: [90/600]
Discriminator Loss: 0.9302, Generator Loss: 1.3035
D(x): 0.7842, D(G(z)): 0.4033
Epoch: [18/20], Batch Num: [91/600]
Discriminator Loss: 0.9644, Generator Loss: 1.7770
D(x): 0.7032, D(G(z)): 0.3566
Epoch: [18/20], Batch Num: [92/600]
Discriminator Loss: 0.8476, Generator Loss: 1.9128
D(x): 0.6738, D(G(z)): 0.2569
Epoch: [18/20], Batch Num: [93/600]
Discriminator Loss: 0.9841, Generator Loss: 1.9498
D(x): 0.6139, D(G(z)): 0.2502
Epoch: [18/20], Batch Num: [94/600]
Discriminator Loss: 0.8427, Generator Loss: 1.8732
D(x): 0.6379, D(G(z)): 0.1969
Epoch: [18/20], Batch Num: [95/600]
Discriminator Loss: 0.8799, Generator Loss: 1.6827
D(x): 0.6727, D(G(z)): 0.2533
Epoch: [18/20], Batch Num: [96/600]
Discriminator Loss: 0.9315, Generator Loss: 1.5080
D(x): 0.6807, D(G(z)): 0.3041
Epoch: [18/20], Batch Num: [97/600]
Discriminator Loss: 0.7966, Generator Loss: 1.4381
D(x): 0.7565, D(G(z)): 0.3107
Epoch: [18/20], Batch Num: [98/600]
Discriminator Loss: 0.8163, Generator Loss: 1.6101
D(x): 0.7558, D(G(z)): 0.3104
Epoch: [18/20], Batch Num: [99/600]
Discriminator Loss: 0.8445, Generator Loss: 1.6274
D(x): 0.7587, D(G(z)): 0.3191
Epoch: 18, Batch Num: [100/600]

Epoch: [18/20], Batch Num: [100/600]
Discriminator Loss: 0.8567, Generator Loss: 2.0586
D(x): 0.6848, D(G(z)): 0.2550
Epoch: [18/20], Batch Num: [101/600]
Discriminator Loss: 0.9331, Generator Loss: 2.0193
D(x): 0.6558, D(G(z)): 0.2818
Epoch: [18/20], Batch Num: [102/600]
Discriminator Loss: 1.1064, Generator Loss: 1.9576
D(x): 0.6043, D(G(z)): 0.2644
Epoch: [18/20], Batch Num: [103/600]
Discriminator Loss: 0.9017, Generator Loss: 1.7441
D(x): 0.6494, D(G(z)): 0.2604
Epoch: [18/20], Batch Num: [104/600]
Discriminator Loss: 0.9104, Generator Loss: 1.5347
D(x): 0.7269, D(G(z)): 0.3143
Epoch: [18/20], Batch Num: [105/600]
Discriminator Loss: 0.9696, Generator Loss: 1.3943
D(x): 0.7291, D(G(z)): 0.3656
Epoch: [18/20], Batch Num: [106/600]
Discriminator Loss: 0.8162, Generator Loss: 1.3182
D(x): 0.7675, D(G(z)): 0.3240
Epoch: [18/20], Batch Num: [107/600]
Discriminator Loss: 0.9759, Generator Loss: 1.5554
D(x): 0.6579, D(G(z)): 0.3126
Epoch: [18/20], Batch Num: [108/600]
Discriminator Loss: 0.8771, Generator Loss: 1.7667
D(x): 0.7307, D(G(z)): 0.3103
Epoch: [18/20], Batch Num: [109/600]
Discriminator Loss: 0.9691, Generator Loss: 1.6141
D(x): 0.6724, D(G(z)): 0.3093
Epoch: [18/20], Batch Num: [110/600]
Discriminator Loss: 0.9347, Generator Loss: 1.7905
D(x): 0.6459, D(G(z)): 0.2503
Epoch: [18/20], Batch Num: [111/600]
Discriminator Loss: 0.9394, Generator Loss: 1.4400
D(x): 0.6321, D(G(z)): 0.2432
Epoch: [18/20], Batch Num: [112/600]
Discriminator Loss: 0.9402, Generator Loss: 1.3225
D(x): 0.7053, D(G(z)): 0.3247
Epoch: [18/20], Batch Num: [113/600]
Discriminator Loss: 1.0892, Generator Loss: 1.3355
D(x): 0.7035, D(G(z)): 0.3730
Epoch: [18/20], Batch Num: [114/600]
Discriminator Loss: 0.9489, Generator Loss: 1.3859
D(x): 0.7667, D(G(z)): 0.3770
Epoch: [18/20], Batch Num: [115/600]
Discriminator Loss: 0.8231, Generator Loss: 1.5554
D(x): 0.7294, D(G(z)): 0.2981
Epoch: [18/20], Batch Num: [116/600]
Discriminator Loss: 0.8164, Generator Loss: 1.6078
D(x): 0.7329, D(G(z)): 0.2771
Epoch: [18/20], Batch Num: [117/600]
Discriminator Loss: 0.8789, Generator Loss: 1.7361
D(x): 0.6566, D(G(z)): 0.2355
Epoch: [18/20], Batch Num: [118/600]
Discriminator Loss: 0.9890, Generator Loss: 1.5927
D(x): 0.6381, D(G(z)): 0.2480
Epoch: [18/20], Batch Num: [119/600]
Discriminator Loss: 0.8424, Generator Loss: 1.3639
D(x): 0.7112, D(G(z)): 0.2775
Epoch: [18/20], Batch Num: [120/600]
Discriminator Loss: 1.0301, Generator Loss: 1.1748
D(x): 0.6783, D(G(z)): 0.3234
Epoch: [18/20], Batch Num: [121/600]
Discriminator Loss: 1.0240, Generator Loss: 1.3622
D(x): 0.7109, D(G(z)): 0.3514
Epoch: [18/20], Batch Num: [122/600]
Discriminator Loss: 0.8624, Generator Loss: 1.5736
D(x): 0.8066, D(G(z)): 0.3725
Epoch: [18/20], Batch Num: [123/600]
Discriminator Loss: 0.9621, Generator Loss: 1.5919
D(x): 0.6673, D(G(z)): 0.2804
Epoch: [18/20], Batch Num: [124/600]
Discriminator Loss: 0.9027, Generator Loss: 1.5790
D(x): 0.6812, D(G(z)): 0.2919
Epoch: [18/20], Batch Num: [125/600]
Discriminator Loss: 0.8985, Generator Loss: 1.5024
D(x): 0.7018, D(G(z)): 0.2883
Epoch: [18/20], Batch Num: [126/600]
Discriminator Loss: 0.8836, Generator Loss: 1.4681
D(x): 0.6669, D(G(z)): 0.2427
Epoch: [18/20], Batch Num: [127/600]
Discriminator Loss: 0.9969, Generator Loss: 1.3965
D(x): 0.6538, D(G(z)): 0.2807
Epoch: [18/20], Batch Num: [128/600]
Discriminator Loss: 0.8579, Generator Loss: 1.3360
D(x): 0.7512, D(G(z)): 0.3178
Epoch: [18/20], Batch Num: [129/600]
Discriminator Loss: 0.9117, Generator Loss: 1.4160
D(x): 0.7432, D(G(z)): 0.3464
Epoch: [18/20], Batch Num: [130/600]
Discriminator Loss: 0.7966, Generator Loss: 1.5642
D(x): 0.7819, D(G(z)): 0.3271
Epoch: [18/20], Batch Num: [131/600]
Discriminator Loss: 0.7657, Generator Loss: 1.8628
D(x): 0.7315, D(G(z)): 0.2598
Epoch: [18/20], Batch Num: [132/600]
Discriminator Loss: 0.9694, Generator Loss: 1.8697
D(x): 0.6244, D(G(z)): 0.2383
Epoch: [18/20], Batch Num: [133/600]
Discriminator Loss: 0.8226, Generator Loss: 1.6852
D(x): 0.7023, D(G(z)): 0.2393
Epoch: [18/20], Batch Num: [134/600]
Discriminator Loss: 0.7421, Generator Loss: 1.5318
D(x): 0.7281, D(G(z)): 0.2325
Epoch: [18/20], Batch Num: [135/600]
Discriminator Loss: 0.8269, Generator Loss: 1.6713
D(x): 0.7449, D(G(z)): 0.3020
Epoch: [18/20], Batch Num: [136/600]
Discriminator Loss: 0.7991, Generator Loss: 1.6398
D(x): 0.7349, D(G(z)): 0.2834
Epoch: [18/20], Batch Num: [137/600]
Discriminator Loss: 0.7985, Generator Loss: 1.5590
D(x): 0.7634, D(G(z)): 0.3014
Epoch: [18/20], Batch Num: [138/600]
Discriminator Loss: 0.8347, Generator Loss: 1.9358
D(x): 0.7370, D(G(z)): 0.2963
Epoch: [18/20], Batch Num: [139/600]
Discriminator Loss: 0.7489, Generator Loss: 1.9560
D(x): 0.7450, D(G(z)): 0.2434
Epoch: [18/20], Batch Num: [140/600]
Discriminator Loss: 0.8548, Generator Loss: 1.9837
D(x): 0.7018, D(G(z)): 0.2524
Epoch: [18/20], Batch Num: [141/600]
Discriminator Loss: 0.8098, Generator Loss: 1.9065
D(x): 0.6996, D(G(z)): 0.2140
Epoch: [18/20], Batch Num: [142/600]
Discriminator Loss: 0.8511, Generator Loss: 1.7595
D(x): 0.7441, D(G(z)): 0.2682
Epoch: [18/20], Batch Num: [143/600]
Discriminator Loss: 0.8650, Generator Loss: 1.6752
D(x): 0.7126, D(G(z)): 0.2522
Epoch: [18/20], Batch Num: [144/600]
Discriminator Loss: 0.8942, Generator Loss: 1.3169
D(x): 0.6847, D(G(z)): 0.2680
Epoch: [18/20], Batch Num: [145/600]
Discriminator Loss: 0.8271, Generator Loss: 1.5772
D(x): 0.7892, D(G(z)): 0.3287
Epoch: [18/20], Batch Num: [146/600]
Discriminator Loss: 0.8344, Generator Loss: 1.4674
D(x): 0.7706, D(G(z)): 0.3273
Epoch: [18/20], Batch Num: [147/600]
Discriminator Loss: 0.9728, Generator Loss: 1.7543
D(x): 0.7440, D(G(z)): 0.3475
Epoch: [18/20], Batch Num: [148/600]
Discriminator Loss: 0.8977, Generator Loss: 1.9815
D(x): 0.7317, D(G(z)): 0.3079
Epoch: [18/20], Batch Num: [149/600]
Discriminator Loss: 0.9294, Generator Loss: 1.7196
D(x): 0.6395, D(G(z)): 0.2166
Epoch: [18/20], Batch Num: [150/600]
Discriminator Loss: 0.9952, Generator Loss: 1.7116
D(x): 0.6556, D(G(z)): 0.2857
Epoch: [18/20], Batch Num: [151/600]
Discriminator Loss: 0.8636, Generator Loss: 1.5932
D(x): 0.7065, D(G(z)): 0.2702
Epoch: [18/20], Batch Num: [152/600]
Discriminator Loss: 0.9495, Generator Loss: 1.4911
D(x): 0.7222, D(G(z)): 0.3536
Epoch: [18/20], Batch Num: [153/600]
Discriminator Loss: 0.9101, Generator Loss: 1.5221
D(x): 0.7661, D(G(z)): 0.3506
Epoch: [18/20], Batch Num: [154/600]
Discriminator Loss: 0.9383, Generator Loss: 1.6494
D(x): 0.7544, D(G(z)): 0.3656
Epoch: [18/20], Batch Num: [155/600]
Discriminator Loss: 0.9996, Generator Loss: 1.6197
D(x): 0.6824, D(G(z)): 0.3397
Epoch: [18/20], Batch Num: [156/600]
Discriminator Loss: 0.8850, Generator Loss: 1.5446
D(x): 0.6882, D(G(z)): 0.2725
Epoch: [18/20], Batch Num: [157/600]
Discriminator Loss: 1.0121, Generator Loss: 1.5090
D(x): 0.6331, D(G(z)): 0.2683
Epoch: [18/20], Batch Num: [158/600]
Discriminator Loss: 1.0268, Generator Loss: 1.6138
D(x): 0.6733, D(G(z)): 0.3360
Epoch: [18/20], Batch Num: [159/600]
Discriminator Loss: 1.0630, Generator Loss: 1.3501
D(x): 0.6226, D(G(z)): 0.3150
Epoch: [18/20], Batch Num: [160/600]
Discriminator Loss: 0.8323, Generator Loss: 1.3750
D(x): 0.7433, D(G(z)): 0.3189
Epoch: [18/20], Batch Num: [161/600]
Discriminator Loss: 0.8595, Generator Loss: 1.3859
D(x): 0.7161, D(G(z)): 0.3191
Epoch: [18/20], Batch Num: [162/600]
Discriminator Loss: 0.8719, Generator Loss: 1.3544
D(x): 0.7167, D(G(z)): 0.3215
Epoch: [18/20], Batch Num: [163/600]
Discriminator Loss: 1.0740, Generator Loss: 1.2705
D(x): 0.6873, D(G(z)): 0.3668
Epoch: [18/20], Batch Num: [164/600]
Discriminator Loss: 0.9934, Generator Loss: 1.4524
D(x): 0.6781, D(G(z)): 0.3328
Epoch: [18/20], Batch Num: [165/600]
Discriminator Loss: 0.9770, Generator Loss: 1.3972
D(x): 0.6766, D(G(z)): 0.3395
Epoch: [18/20], Batch Num: [166/600]
Discriminator Loss: 0.8374, Generator Loss: 1.5844
D(x): 0.6935, D(G(z)): 0.2744
Epoch: [18/20], Batch Num: [167/600]
Discriminator Loss: 0.9024, Generator Loss: 1.5833
D(x): 0.6590, D(G(z)): 0.2643
Epoch: [18/20], Batch Num: [168/600]
Discriminator Loss: 0.9721, Generator Loss: 1.3958
D(x): 0.6667, D(G(z)): 0.2961
Epoch: [18/20], Batch Num: [169/600]
Discriminator Loss: 0.9360, Generator Loss: 1.4322
D(x): 0.6652, D(G(z)): 0.2962
Epoch: [18/20], Batch Num: [170/600]
Discriminator Loss: 0.9685, Generator Loss: 1.2364
D(x): 0.6666, D(G(z)): 0.3094
Epoch: [18/20], Batch Num: [171/600]
Discriminator Loss: 0.9037, Generator Loss: 1.2835
D(x): 0.7269, D(G(z)): 0.3413
Epoch: [18/20], Batch Num: [172/600]
Discriminator Loss: 0.8976, Generator Loss: 1.4148
D(x): 0.7166, D(G(z)): 0.3467
Epoch: [18/20], Batch Num: [173/600]
Discriminator Loss: 0.8178, Generator Loss: 1.4530
D(x): 0.7580, D(G(z)): 0.3245
Epoch: [18/20], Batch Num: [174/600]
Discriminator Loss: 0.8860, Generator Loss: 1.5590
D(x): 0.7148, D(G(z)): 0.3070
Epoch: [18/20], Batch Num: [175/600]
Discriminator Loss: 0.8828, Generator Loss: 1.5544
D(x): 0.7160, D(G(z)): 0.2938
Epoch: [18/20], Batch Num: [176/600]
Discriminator Loss: 0.9677, Generator Loss: 1.7011
D(x): 0.6575, D(G(z)): 0.2800
Epoch: [18/20], Batch Num: [177/600]
Discriminator Loss: 1.0681, Generator Loss: 1.5363
D(x): 0.6454, D(G(z)): 0.2977
Epoch: [18/20], Batch Num: [178/600]
Discriminator Loss: 0.9075, Generator Loss: 1.4461
D(x): 0.6938, D(G(z)): 0.2775
Epoch: [18/20], Batch Num: [179/600]
Discriminator Loss: 0.8709, Generator Loss: 1.5373
D(x): 0.6792, D(G(z)): 0.2587
Epoch: [18/20], Batch Num: [180/600]
Discriminator Loss: 0.9688, Generator Loss: 1.5045
D(x): 0.7386, D(G(z)): 0.3486
Epoch: [18/20], Batch Num: [181/600]
Discriminator Loss: 0.9190, Generator Loss: 1.4892
D(x): 0.6754, D(G(z)): 0.2983
Epoch: [18/20], Batch Num: [182/600]
Discriminator Loss: 1.0948, Generator Loss: 1.4606
D(x): 0.6637, D(G(z)): 0.3412
Epoch: [18/20], Batch Num: [183/600]
Discriminator Loss: 0.8432, Generator Loss: 1.6090
D(x): 0.7043, D(G(z)): 0.2914
Epoch: [18/20], Batch Num: [184/600]
Discriminator Loss: 0.9312, Generator Loss: 1.6042
D(x): 0.6585, D(G(z)): 0.2632
Epoch: [18/20], Batch Num: [185/600]
Discriminator Loss: 0.7906, Generator Loss: 1.4932
D(x): 0.7108, D(G(z)): 0.2770
Epoch: [18/20], Batch Num: [186/600]
Discriminator Loss: 0.8412, Generator Loss: 1.4938
D(x): 0.7128, D(G(z)): 0.2831
Epoch: [18/20], Batch Num: [187/600]
Discriminator Loss: 0.8980, Generator Loss: 1.5703
D(x): 0.6953, D(G(z)): 0.2911
Epoch: [18/20], Batch Num: [188/600]
Discriminator Loss: 0.7472, Generator Loss: 1.5562
D(x): 0.7928, D(G(z)): 0.3043
Epoch: [18/20], Batch Num: [189/600]
Discriminator Loss: 0.8156, Generator Loss: 1.6362
D(x): 0.7328, D(G(z)): 0.2978
Epoch: [18/20], Batch Num: [190/600]
Discriminator Loss: 0.8153, Generator Loss: 1.5272
D(x): 0.7237, D(G(z)): 0.2677
Epoch: [18/20], Batch Num: [191/600]
Discriminator Loss: 0.7481, Generator Loss: 1.5271
D(x): 0.7214, D(G(z)): 0.2444
Epoch: [18/20], Batch Num: [192/600]
Discriminator Loss: 0.9339, Generator Loss: 1.4448
D(x): 0.6873, D(G(z)): 0.3046
Epoch: [18/20], Batch Num: [193/600]
Discriminator Loss: 0.8998, Generator Loss: 1.5358
D(x): 0.6914, D(G(z)): 0.2819
Epoch: [18/20], Batch Num: [194/600]
Discriminator Loss: 0.8455, Generator Loss: 1.6430
D(x): 0.7191, D(G(z)): 0.2865
Epoch: [18/20], Batch Num: [195/600]
Discriminator Loss: 0.9185, Generator Loss: 1.5123
D(x): 0.7078, D(G(z)): 0.3086
Epoch: [18/20], Batch Num: [196/600]
Discriminator Loss: 1.0174, Generator Loss: 1.6766
D(x): 0.6909, D(G(z)): 0.3418
Epoch: [18/20], Batch Num: [197/600]
Discriminator Loss: 1.0110, Generator Loss: 1.7470
D(x): 0.7091, D(G(z)): 0.3066
Epoch: [18/20], Batch Num: [198/600]
Discriminator Loss: 0.9527, Generator Loss: 1.5457
D(x): 0.6760, D(G(z)): 0.2925
Epoch: [18/20], Batch Num: [199/600]
Discriminator Loss: 0.8670, Generator Loss: 1.5110
D(x): 0.7152, D(G(z)): 0.2801
Epoch: 18, Batch Num: [200/600]

Epoch: [18/20], Batch Num: [200/600]
Discriminator Loss: 0.8281, Generator Loss: 1.6863
D(x): 0.7451, D(G(z)): 0.3089
Epoch: [18/20], Batch Num: [201/600]
Discriminator Loss: 0.9003, Generator Loss: 1.6920
D(x): 0.6982, D(G(z)): 0.2810
Epoch: [18/20], Batch Num: [202/600]
Discriminator Loss: 0.8863, Generator Loss: 1.7686
D(x): 0.6984, D(G(z)): 0.2807
Epoch: [18/20], Batch Num: [203/600]
Discriminator Loss: 1.0084, Generator Loss: 1.5805
D(x): 0.6550, D(G(z)): 0.2974
Epoch: [18/20], Batch Num: [204/600]
Discriminator Loss: 0.9374, Generator Loss: 1.4976
D(x): 0.6536, D(G(z)): 0.2803
Epoch: [18/20], Batch Num: [205/600]
Discriminator Loss: 1.0025, Generator Loss: 1.3092
D(x): 0.6967, D(G(z)): 0.3324
Epoch: [18/20], Batch Num: [206/600]
Discriminator Loss: 1.0025, Generator Loss: 1.3333
D(x): 0.7296, D(G(z)): 0.3771
Epoch: [18/20], Batch Num: [207/600]
Discriminator Loss: 1.0839, Generator Loss: 1.4497
D(x): 0.7397, D(G(z)): 0.4251
Epoch: [18/20], Batch Num: [208/600]
Discriminator Loss: 0.9089, Generator Loss: 1.5636
D(x): 0.7287, D(G(z)): 0.3321
Epoch: [18/20], Batch Num: [209/600]
Discriminator Loss: 0.9603, Generator Loss: 1.6704
D(x): 0.6643, D(G(z)): 0.2819
Epoch: [18/20], Batch Num: [210/600]
Discriminator Loss: 0.8854, Generator Loss: 1.6422
D(x): 0.6735, D(G(z)): 0.2438
Epoch: [18/20], Batch Num: [211/600]
Discriminator Loss: 0.9901, Generator Loss: 1.4268
D(x): 0.6382, D(G(z)): 0.2896
Epoch: [18/20], Batch Num: [212/600]
Discriminator Loss: 0.9537, Generator Loss: 1.3221
D(x): 0.7347, D(G(z)): 0.3401
Epoch: [18/20], Batch Num: [213/600]
Discriminator Loss: 0.9399, Generator Loss: 1.3974
D(x): 0.6856, D(G(z)): 0.3067
Epoch: [18/20], Batch Num: [214/600]
Discriminator Loss: 0.8399, Generator Loss: 1.3260
D(x): 0.7306, D(G(z)): 0.3308
Epoch: [18/20], Batch Num: [215/600]
Discriminator Loss: 1.0541, Generator Loss: 1.4761
D(x): 0.7113, D(G(z)): 0.3772
Epoch: [18/20], Batch Num: [216/600]
Discriminator Loss: 0.9395, Generator Loss: 1.5552
D(x): 0.7092, D(G(z)): 0.3162
Epoch: [18/20], Batch Num: [217/600]
Discriminator Loss: 0.9515, Generator Loss: 1.5578
D(x): 0.6380, D(G(z)): 0.2497
Epoch: [18/20], Batch Num: [218/600]
Discriminator Loss: 1.0346, Generator Loss: 1.5535
D(x): 0.6234, D(G(z)): 0.2715
Epoch: [18/20], Batch Num: [219/600]
Discriminator Loss: 0.9309, Generator Loss: 1.3767
D(x): 0.6843, D(G(z)): 0.3078
Epoch: [18/20], Batch Num: [220/600]
Discriminator Loss: 0.8857, Generator Loss: 1.4054
D(x): 0.6574, D(G(z)): 0.2641
Epoch: [18/20], Batch Num: [221/600]
Discriminator Loss: 0.9911, Generator Loss: 1.3629
D(x): 0.6703, D(G(z)): 0.3125
Epoch: [18/20], Batch Num: [222/600]
Discriminator Loss: 1.1146, Generator Loss: 1.3190
D(x): 0.6946, D(G(z)): 0.3676
Epoch: [18/20], Batch Num: [223/600]
Discriminator Loss: 1.0562, Generator Loss: 1.5289
D(x): 0.7228, D(G(z)): 0.3924
Epoch: [18/20], Batch Num: [224/600]
Discriminator Loss: 0.8714, Generator Loss: 1.6554
D(x): 0.7257, D(G(z)): 0.3177
Epoch: [18/20], Batch Num: [225/600]
Discriminator Loss: 0.8565, Generator Loss: 1.7970
D(x): 0.6899, D(G(z)): 0.2858
Epoch: [18/20], Batch Num: [226/600]
Discriminator Loss: 0.8378, Generator Loss: 1.5698
D(x): 0.6348, D(G(z)): 0.2207
Epoch: [18/20], Batch Num: [227/600]
Discriminator Loss: 0.9332, Generator Loss: 1.5552
D(x): 0.6563, D(G(z)): 0.2690
Epoch: [18/20], Batch Num: [228/600]
Discriminator Loss: 0.9134, Generator Loss: 1.4751
D(x): 0.6542, D(G(z)): 0.2825
Epoch: [18/20], Batch Num: [229/600]
Discriminator Loss: 0.8911, Generator Loss: 1.2060
D(x): 0.7104, D(G(z)): 0.3285
Epoch: [18/20], Batch Num: [230/600]
Discriminator Loss: 0.9453, Generator Loss: 1.2169
D(x): 0.7197, D(G(z)): 0.3414
Epoch: [18/20], Batch Num: [231/600]
Discriminator Loss: 0.7762, Generator Loss: 1.2634
D(x): 0.7454, D(G(z)): 0.3125
Epoch: [18/20], Batch Num: [232/600]
Discriminator Loss: 0.9041, Generator Loss: 1.4834
D(x): 0.7609, D(G(z)): 0.3741
Epoch: [18/20], Batch Num: [233/600]
Discriminator Loss: 0.7824, Generator Loss: 1.6503
D(x): 0.7445, D(G(z)): 0.2609
Epoch: [18/20], Batch Num: [234/600]
Discriminator Loss: 0.8557, Generator Loss: 1.7556
D(x): 0.6732, D(G(z)): 0.2441
Epoch: [18/20], Batch Num: [235/600]
Discriminator Loss: 0.8814, Generator Loss: 1.8752
D(x): 0.6896, D(G(z)): 0.2640
Epoch: [18/20], Batch Num: [236/600]
Discriminator Loss: 0.9750, Generator Loss: 1.6966
D(x): 0.6613, D(G(z)): 0.2681
Epoch: [18/20], Batch Num: [237/600]
Discriminator Loss: 0.8508, Generator Loss: 1.4685
D(x): 0.6913, D(G(z)): 0.2647
Epoch: [18/20], Batch Num: [238/600]
Discriminator Loss: 0.7454, Generator Loss: 1.4301
D(x): 0.7404, D(G(z)): 0.2561
Epoch: [18/20], Batch Num: [239/600]
Discriminator Loss: 0.8058, Generator Loss: 1.4191
D(x): 0.7696, D(G(z)): 0.3259
Epoch: [18/20], Batch Num: [240/600]
Discriminator Loss: 0.9523, Generator Loss: 1.4848
D(x): 0.7523, D(G(z)): 0.3581
Epoch: [18/20], Batch Num: [241/600]
Discriminator Loss: 0.9290, Generator Loss: 1.7490
D(x): 0.7095, D(G(z)): 0.2965
Epoch: [18/20], Batch Num: [242/600]
Discriminator Loss: 0.7600, Generator Loss: 1.8265
D(x): 0.7229, D(G(z)): 0.2458
Epoch: [18/20], Batch Num: [243/600]
Discriminator Loss: 0.7069, Generator Loss: 1.8233
D(x): 0.7352, D(G(z)): 0.2176
Epoch: [18/20], Batch Num: [244/600]
Discriminator Loss: 0.8921, Generator Loss: 1.8961
D(x): 0.6904, D(G(z)): 0.2602
Epoch: [18/20], Batch Num: [245/600]
Discriminator Loss: 0.9718, Generator Loss: 1.8697
D(x): 0.6978, D(G(z)): 0.2957
Epoch: [18/20], Batch Num: [246/600]
Discriminator Loss: 0.7447, Generator Loss: 1.6833
D(x): 0.7404, D(G(z)): 0.2510
Epoch: [18/20], Batch Num: [247/600]
Discriminator Loss: 0.7893, Generator Loss: 1.6301
D(x): 0.7505, D(G(z)): 0.2730
Epoch: [18/20], Batch Num: [248/600]
Discriminator Loss: 0.8429, Generator Loss: 1.7217
D(x): 0.7852, D(G(z)): 0.3417
Epoch: [18/20], Batch Num: [249/600]
Discriminator Loss: 0.8350, Generator Loss: 1.7453
D(x): 0.6791, D(G(z)): 0.2339
Epoch: [18/20], Batch Num: [250/600]
Discriminator Loss: 0.8680, Generator Loss: 1.5925
D(x): 0.6994, D(G(z)): 0.2508
Epoch: [18/20], Batch Num: [251/600]
Discriminator Loss: 0.9710, Generator Loss: 1.4002
D(x): 0.6448, D(G(z)): 0.2582
Epoch: [18/20], Batch Num: [252/600]
Discriminator Loss: 1.0084, Generator Loss: 1.5348
D(x): 0.7249, D(G(z)): 0.3520
Epoch: [18/20], Batch Num: [253/600]
Discriminator Loss: 0.8658, Generator Loss: 1.4241
D(x): 0.7537, D(G(z)): 0.3120
Epoch: [18/20], Batch Num: [254/600]
Discriminator Loss: 0.8333, Generator Loss: 1.6813
D(x): 0.7274, D(G(z)): 0.3007
Epoch: [18/20], Batch Num: [255/600]
Discriminator Loss: 0.7223, Generator Loss: 1.7354
D(x): 0.7651, D(G(z)): 0.2559
Epoch: [18/20], Batch Num: [256/600]
Discriminator Loss: 0.8480, Generator Loss: 1.6400
D(x): 0.6544, D(G(z)): 0.2332
Epoch: [18/20], Batch Num: [257/600]
Discriminator Loss: 0.8233, Generator Loss: 1.5153
D(x): 0.6801, D(G(z)): 0.2444
Epoch: [18/20], Batch Num: [258/600]
Discriminator Loss: 0.8154, Generator Loss: 1.5580
D(x): 0.7631, D(G(z)): 0.3000
Epoch: [18/20], Batch Num: [259/600]
Discriminator Loss: 0.8515, Generator Loss: 1.5785
D(x): 0.7558, D(G(z)): 0.3107
Epoch: [18/20], Batch Num: [260/600]
Discriminator Loss: 0.8304, Generator Loss: 1.5465
D(x): 0.7254, D(G(z)): 0.2893
Epoch: [18/20], Batch Num: [261/600]
Discriminator Loss: 0.9256, Generator Loss: 1.6152
D(x): 0.7319, D(G(z)): 0.3243
Epoch: [18/20], Batch Num: [262/600]
Discriminator Loss: 0.8262, Generator Loss: 1.7329
D(x): 0.6983, D(G(z)): 0.2596
Epoch: [18/20], Batch Num: [263/600]
Discriminator Loss: 0.8630, Generator Loss: 1.7446
D(x): 0.7035, D(G(z)): 0.2797
Epoch: [18/20], Batch Num: [264/600]
Discriminator Loss: 0.8654, Generator Loss: 1.5641
D(x): 0.7102, D(G(z)): 0.2694
Epoch: [18/20], Batch Num: [265/600]
Discriminator Loss: 0.9205, Generator Loss: 1.6882
D(x): 0.7084, D(G(z)): 0.2822
Epoch: [18/20], Batch Num: [266/600]
Discriminator Loss: 0.9118, Generator Loss: 1.7554
D(x): 0.6925, D(G(z)): 0.2767
Epoch: [18/20], Batch Num: [267/600]
Discriminator Loss: 0.8757, Generator Loss: 1.7644
D(x): 0.7187, D(G(z)): 0.2669
Epoch: [18/20], Batch Num: [268/600]
Discriminator Loss: 0.8542, Generator Loss: 1.6790
D(x): 0.6993, D(G(z)): 0.2662
Epoch: [18/20], Batch Num: [269/600]
Discriminator Loss: 0.9868, Generator Loss: 1.5669
D(x): 0.6736, D(G(z)): 0.2851
Epoch: [18/20], Batch Num: [270/600]
Discriminator Loss: 0.8410, Generator Loss: 1.4747
D(x): 0.7417, D(G(z)): 0.3017
Epoch: [18/20], Batch Num: [271/600]
Discriminator Loss: 0.9015, Generator Loss: 1.7170
D(x): 0.7230, D(G(z)): 0.3122
Epoch: [18/20], Batch Num: [272/600]
Discriminator Loss: 1.0202, Generator Loss: 1.4892
D(x): 0.6568, D(G(z)): 0.3027
Epoch: [18/20], Batch Num: [273/600]
Discriminator Loss: 0.9392, Generator Loss: 1.4860
D(x): 0.6902, D(G(z)): 0.2814
Epoch: [18/20], Batch Num: [274/600]
Discriminator Loss: 0.9664, Generator Loss: 1.4634
D(x): 0.6864, D(G(z)): 0.2990
Epoch: [18/20], Batch Num: [275/600]
Discriminator Loss: 0.7625, Generator Loss: 1.5422
D(x): 0.7772, D(G(z)): 0.2930
Epoch: [18/20], Batch Num: [276/600]
Discriminator Loss: 1.1020, Generator Loss: 1.7498
D(x): 0.6571, D(G(z)): 0.3148
Epoch: [18/20], Batch Num: [277/600]
Discriminator Loss: 0.9645, Generator Loss: 1.7892
D(x): 0.6928, D(G(z)): 0.3226
Epoch: [18/20], Batch Num: [278/600]
Discriminator Loss: 0.9992, Generator Loss: 1.4790
D(x): 0.6469, D(G(z)): 0.2749
Epoch: [18/20], Batch Num: [279/600]
Discriminator Loss: 0.9895, Generator Loss: 1.7092
D(x): 0.7003, D(G(z)): 0.3249
Epoch: [18/20], Batch Num: [280/600]
Discriminator Loss: 0.9212, Generator Loss: 1.6036
D(x): 0.7153, D(G(z)): 0.3046
Epoch: [18/20], Batch Num: [281/600]
Discriminator Loss: 0.7508, Generator Loss: 1.6161
D(x): 0.7393, D(G(z)): 0.2695
Epoch: [18/20], Batch Num: [282/600]
Discriminator Loss: 0.9169, Generator Loss: 1.6330
D(x): 0.7183, D(G(z)): 0.2981
Epoch: [18/20], Batch Num: [283/600]
Discriminator Loss: 0.9055, Generator Loss: 1.5632
D(x): 0.6877, D(G(z)): 0.2897
Epoch: [18/20], Batch Num: [284/600]
Discriminator Loss: 0.9753, Generator Loss: 1.6214
D(x): 0.6731, D(G(z)): 0.3042
Epoch: [18/20], Batch Num: [285/600]
Discriminator Loss: 0.8417, Generator Loss: 1.5399
D(x): 0.7253, D(G(z)): 0.2831
Epoch: [18/20], Batch Num: [286/600]
Discriminator Loss: 0.7993, Generator Loss: 1.6208
D(x): 0.7244, D(G(z)): 0.2753
Epoch: [18/20], Batch Num: [287/600]
Discriminator Loss: 0.8900, Generator Loss: 1.8499
D(x): 0.6999, D(G(z)): 0.2781
Epoch: [18/20], Batch Num: [288/600]
Discriminator Loss: 0.7910, Generator Loss: 1.6867
D(x): 0.7552, D(G(z)): 0.2982
Epoch: [18/20], Batch Num: [289/600]
Discriminator Loss: 0.8597, Generator Loss: 1.6357
D(x): 0.6819, D(G(z)): 0.2494
Epoch: [18/20], Batch Num: [290/600]
Discriminator Loss: 0.9903, Generator Loss: 1.6274
D(x): 0.6732, D(G(z)): 0.2889
Epoch: [18/20], Batch Num: [291/600]
Discriminator Loss: 1.0612, Generator Loss: 1.4027
D(x): 0.6803, D(G(z)): 0.3217
Epoch: [18/20], Batch Num: [292/600]
Discriminator Loss: 0.9412, Generator Loss: 1.4250
D(x): 0.7471, D(G(z)): 0.3206
Epoch: [18/20], Batch Num: [293/600]
Discriminator Loss: 1.0161, Generator Loss: 1.6656
D(x): 0.6978, D(G(z)): 0.3441
Epoch: [18/20], Batch Num: [294/600]
Discriminator Loss: 0.8179, Generator Loss: 1.5463
D(x): 0.7364, D(G(z)): 0.2843
Epoch: [18/20], Batch Num: [295/600]
Discriminator Loss: 0.7947, Generator Loss: 1.6944
D(x): 0.7244, D(G(z)): 0.2733
Epoch: [18/20], Batch Num: [296/600]
Discriminator Loss: 0.8828, Generator Loss: 1.7011
D(x): 0.6931, D(G(z)): 0.2552
Epoch: [18/20], Batch Num: [297/600]
Discriminator Loss: 0.8384, Generator Loss: 1.4097
D(x): 0.6809, D(G(z)): 0.2374
Epoch: [18/20], Batch Num: [298/600]
Discriminator Loss: 1.0086, Generator Loss: 1.4217
D(x): 0.6775, D(G(z)): 0.3221
Epoch: [18/20], Batch Num: [299/600]
Discriminator Loss: 0.9955, Generator Loss: 1.4940
D(x): 0.7011, D(G(z)): 0.3346
Epoch: 18, Batch Num: [300/600]

Epoch: [18/20], Batch Num: [300/600]
Discriminator Loss: 0.9643, Generator Loss: 1.3914
D(x): 0.7102, D(G(z)): 0.3508
Epoch: [18/20], Batch Num: [301/600]
Discriminator Loss: 1.0123, Generator Loss: 1.4129
D(x): 0.7268, D(G(z)): 0.3594
Epoch: [18/20], Batch Num: [302/600]
Discriminator Loss: 0.9633, Generator Loss: 1.5067
D(x): 0.6555, D(G(z)): 0.2886
Epoch: [18/20], Batch Num: [303/600]
Discriminator Loss: 1.0647, Generator Loss: 1.5148
D(x): 0.6671, D(G(z)): 0.3409
Epoch: [18/20], Batch Num: [304/600]
Discriminator Loss: 0.9845, Generator Loss: 1.4344
D(x): 0.6397, D(G(z)): 0.2767
Epoch: [18/20], Batch Num: [305/600]
Discriminator Loss: 0.9921, Generator Loss: 1.4807
D(x): 0.7226, D(G(z)): 0.3488
Epoch: [18/20], Batch Num: [306/600]
Discriminator Loss: 0.9969, Generator Loss: 1.3175
D(x): 0.6694, D(G(z)): 0.3286
Epoch: [18/20], Batch Num: [307/600]
Discriminator Loss: 1.0173, Generator Loss: 1.3486
D(x): 0.6868, D(G(z)): 0.3499
Epoch: [18/20], Batch Num: [308/600]
Discriminator Loss: 1.0543, Generator Loss: 1.4592
D(x): 0.6484, D(G(z)): 0.3368
Epoch: [18/20], Batch Num: [309/600]
Discriminator Loss: 1.1216, Generator Loss: 1.4559
D(x): 0.6918, D(G(z)): 0.3748
Epoch: [18/20], Batch Num: [310/600]
Discriminator Loss: 0.9323, Generator Loss: 1.4216
D(x): 0.7001, D(G(z)): 0.3260
Epoch: [18/20], Batch Num: [311/600]
Discriminator Loss: 0.9463, Generator Loss: 1.5292
D(x): 0.6899, D(G(z)): 0.3093
Epoch: [18/20], Batch Num: [312/600]
Discriminator Loss: 0.9559, Generator Loss: 1.6077
D(x): 0.6400, D(G(z)): 0.2667
Epoch: [18/20], Batch Num: [313/600]
Discriminator Loss: 1.0050, Generator Loss: 1.4401
D(x): 0.6456, D(G(z)): 0.3126
Epoch: [18/20], Batch Num: [314/600]
Discriminator Loss: 1.0820, Generator Loss: 1.5165
D(x): 0.6641, D(G(z)): 0.3494
Epoch: [18/20], Batch Num: [315/600]
Discriminator Loss: 0.9611, Generator Loss: 1.2524
D(x): 0.6722, D(G(z)): 0.3098
Epoch: [18/20], Batch Num: [316/600]
Discriminator Loss: 0.8409, Generator Loss: 1.3411
D(x): 0.7212, D(G(z)): 0.3259
Epoch: [18/20], Batch Num: [317/600]
Discriminator Loss: 1.0310, Generator Loss: 1.4343
D(x): 0.6882, D(G(z)): 0.3517
Epoch: [18/20], Batch Num: [318/600]
Discriminator Loss: 0.9081, Generator Loss: 1.2159
D(x): 0.6851, D(G(z)): 0.3130
Epoch: [18/20], Batch Num: [319/600]
Discriminator Loss: 0.9304, Generator Loss: 1.3777
D(x): 0.7256, D(G(z)): 0.3667
Epoch: [18/20], Batch Num: [320/600]
Discriminator Loss: 0.9664, Generator Loss: 1.5270
D(x): 0.6952, D(G(z)): 0.3390
Epoch: [18/20], Batch Num: [321/600]
Discriminator Loss: 0.9161, Generator Loss: 1.2725
D(x): 0.6660, D(G(z)): 0.2928
Epoch: [18/20], Batch Num: [322/600]
Discriminator Loss: 0.8816, Generator Loss: 1.3533
D(x): 0.6613, D(G(z)): 0.2950
Epoch: [18/20], Batch Num: [323/600]
Discriminator Loss: 0.9572, Generator Loss: 1.3100
D(x): 0.6841, D(G(z)): 0.3103
Epoch: [18/20], Batch Num: [324/600]
Discriminator Loss: 0.9865, Generator Loss: 1.2588
D(x): 0.6888, D(G(z)): 0.3552
Epoch: [18/20], Batch Num: [325/600]
Discriminator Loss: 0.9294, Generator Loss: 1.2284
D(x): 0.7117, D(G(z)): 0.3455
Epoch: [18/20], Batch Num: [326/600]
Discriminator Loss: 1.0343, Generator Loss: 1.3615
D(x): 0.7057, D(G(z)): 0.3793
Epoch: [18/20], Batch Num: [327/600]
Discriminator Loss: 0.9488, Generator Loss: 1.4640
D(x): 0.7222, D(G(z)): 0.3511
Epoch: [18/20], Batch Num: [328/600]
Discriminator Loss: 1.0165, Generator Loss: 1.5148
D(x): 0.6199, D(G(z)): 0.2871
Epoch: [18/20], Batch Num: [329/600]
Discriminator Loss: 0.8235, Generator Loss: 1.5247
D(x): 0.6985, D(G(z)): 0.2722
Epoch: [18/20], Batch Num: [330/600]
Discriminator Loss: 0.9599, Generator Loss: 1.5919
D(x): 0.6442, D(G(z)): 0.2723
Epoch: [18/20], Batch Num: [331/600]
Discriminator Loss: 0.8572, Generator Loss: 1.5268
D(x): 0.6812, D(G(z)): 0.2666
Epoch: [18/20], Batch Num: [332/600]
Discriminator Loss: 0.9532, Generator Loss: 1.2549
D(x): 0.6563, D(G(z)): 0.3043
Epoch: [18/20], Batch Num: [333/600]
Discriminator Loss: 1.0097, Generator Loss: 1.2614
D(x): 0.7193, D(G(z)): 0.3574
Epoch: [18/20], Batch Num: [334/600]
Discriminator Loss: 0.9877, Generator Loss: 1.3463
D(x): 0.7116, D(G(z)): 0.3446
Epoch: [18/20], Batch Num: [335/600]
Discriminator Loss: 0.9054, Generator Loss: 1.3162
D(x): 0.7295, D(G(z)): 0.3418
Epoch: [18/20], Batch Num: [336/600]
Discriminator Loss: 0.9215, Generator Loss: 1.4750
D(x): 0.7125, D(G(z)): 0.3402
Epoch: [18/20], Batch Num: [337/600]
Discriminator Loss: 1.0578, Generator Loss: 1.5856
D(x): 0.6225, D(G(z)): 0.2861
Epoch: [18/20], Batch Num: [338/600]
Discriminator Loss: 0.9547, Generator Loss: 1.5454
D(x): 0.6154, D(G(z)): 0.2433
Epoch: [18/20], Batch Num: [339/600]
Discriminator Loss: 0.9408, Generator Loss: 1.5396
D(x): 0.6535, D(G(z)): 0.2731
Epoch: [18/20], Batch Num: [340/600]
Discriminator Loss: 0.8219, Generator Loss: 1.2387
D(x): 0.7329, D(G(z)): 0.3233
Epoch: [18/20], Batch Num: [341/600]
Discriminator Loss: 0.8183, Generator Loss: 1.1485
D(x): 0.7422, D(G(z)): 0.3266
Epoch: [18/20], Batch Num: [342/600]
Discriminator Loss: 0.8163, Generator Loss: 1.2365
D(x): 0.7576, D(G(z)): 0.3476
Epoch: [18/20], Batch Num: [343/600]
Discriminator Loss: 0.8801, Generator Loss: 1.3746
D(x): 0.7129, D(G(z)): 0.3042
Epoch: [18/20], Batch Num: [344/600]
Discriminator Loss: 0.9097, Generator Loss: 1.5454
D(x): 0.7213, D(G(z)): 0.3430
Epoch: [18/20], Batch Num: [345/600]
Discriminator Loss: 0.8172, Generator Loss: 1.6591
D(x): 0.7424, D(G(z)): 0.3145
Epoch: [18/20], Batch Num: [346/600]
Discriminator Loss: 0.9830, Generator Loss: 1.7305
D(x): 0.6374, D(G(z)): 0.2821
Epoch: [18/20], Batch Num: [347/600]
Discriminator Loss: 0.9015, Generator Loss: 1.5179
D(x): 0.6422, D(G(z)): 0.2540
Epoch: [18/20], Batch Num: [348/600]
Discriminator Loss: 0.8757, Generator Loss: 1.5753
D(x): 0.6690, D(G(z)): 0.2468
Epoch: [18/20], Batch Num: [349/600]
Discriminator Loss: 0.9612, Generator Loss: 1.3292
D(x): 0.7080, D(G(z)): 0.3390
Epoch: [18/20], Batch Num: [350/600]
Discriminator Loss: 0.9239, Generator Loss: 1.2216
D(x): 0.7584, D(G(z)): 0.3708
Epoch: [18/20], Batch Num: [351/600]
Discriminator Loss: 0.9939, Generator Loss: 1.3663
D(x): 0.7183, D(G(z)): 0.3550
Epoch: [18/20], Batch Num: [352/600]
Discriminator Loss: 0.9660, Generator Loss: 1.5145
D(x): 0.6968, D(G(z)): 0.3152
Epoch: [18/20], Batch Num: [353/600]
Discriminator Loss: 0.8770, Generator Loss: 1.6623
D(x): 0.7078, D(G(z)): 0.2882
Epoch: [18/20], Batch Num: [354/600]
Discriminator Loss: 0.9675, Generator Loss: 1.7604
D(x): 0.6511, D(G(z)): 0.2826
Epoch: [18/20], Batch Num: [355/600]
Discriminator Loss: 0.8954, Generator Loss: 1.7140
D(x): 0.6803, D(G(z)): 0.2531
Epoch: [18/20], Batch Num: [356/600]
Discriminator Loss: 0.9147, Generator Loss: 1.6821
D(x): 0.6843, D(G(z)): 0.2725
Epoch: [18/20], Batch Num: [357/600]
Discriminator Loss: 0.9216, Generator Loss: 1.4779
D(x): 0.6890, D(G(z)): 0.2823
Epoch: [18/20], Batch Num: [358/600]
Discriminator Loss: 0.8323, Generator Loss: 1.5099
D(x): 0.7329, D(G(z)): 0.2851
Epoch: [18/20], Batch Num: [359/600]
Discriminator Loss: 0.8612, Generator Loss: 1.4551
D(x): 0.7135, D(G(z)): 0.2977
Epoch: [18/20], Batch Num: [360/600]
Discriminator Loss: 0.9209, Generator Loss: 1.4163
D(x): 0.7230, D(G(z)): 0.3295
Epoch: [18/20], Batch Num: [361/600]
Discriminator Loss: 0.8810, Generator Loss: 1.5074
D(x): 0.7320, D(G(z)): 0.3068
Epoch: [18/20], Batch Num: [362/600]
Discriminator Loss: 0.9591, Generator Loss: 1.5565
D(x): 0.6514, D(G(z)): 0.2884
Epoch: [18/20], Batch Num: [363/600]
Discriminator Loss: 1.0133, Generator Loss: 1.6088
D(x): 0.6610, D(G(z)): 0.2994
Epoch: [18/20], Batch Num: [364/600]
Discriminator Loss: 0.9865, Generator Loss: 1.5236
D(x): 0.6682, D(G(z)): 0.3077
Epoch: [18/20], Batch Num: [365/600]
Discriminator Loss: 0.9182, Generator Loss: 1.4142
D(x): 0.7030, D(G(z)): 0.3167
Epoch: [18/20], Batch Num: [366/600]
Discriminator Loss: 0.8282, Generator Loss: 1.3671
D(x): 0.7742, D(G(z)): 0.3563
Epoch: [18/20], Batch Num: [367/600]
Discriminator Loss: 0.7513, Generator Loss: 1.4552
D(x): 0.7483, D(G(z)): 0.2899
Epoch: [18/20], Batch Num: [368/600]
Discriminator Loss: 0.9418, Generator Loss: 1.6395
D(x): 0.7042, D(G(z)): 0.3062
Epoch: [18/20], Batch Num: [369/600]
Discriminator Loss: 0.8711, Generator Loss: 1.7885
D(x): 0.7241, D(G(z)): 0.3128
Epoch: [18/20], Batch Num: [370/600]
Discriminator Loss: 0.8959, Generator Loss: 1.6750
D(x): 0.6525, D(G(z)): 0.2761
Epoch: [18/20], Batch Num: [371/600]
Discriminator Loss: 0.9363, Generator Loss: 1.6103
D(x): 0.6787, D(G(z)): 0.2707
Epoch: [18/20], Batch Num: [372/600]
Discriminator Loss: 1.0097, Generator Loss: 1.3675
D(x): 0.6850, D(G(z)): 0.3331
Epoch: [18/20], Batch Num: [373/600]
Discriminator Loss: 1.0742, Generator Loss: 1.4444
D(x): 0.7482, D(G(z)): 0.4258
Epoch: [18/20], Batch Num: [374/600]
Discriminator Loss: 0.9367, Generator Loss: 1.6647
D(x): 0.7240, D(G(z)): 0.3215
Epoch: [18/20], Batch Num: [375/600]
Discriminator Loss: 0.9349, Generator Loss: 1.9212
D(x): 0.6919, D(G(z)): 0.2885
Epoch: [18/20], Batch Num: [376/600]
Discriminator Loss: 0.8923, Generator Loss: 1.7315
D(x): 0.6194, D(G(z)): 0.1995
Epoch: [18/20], Batch Num: [377/600]
Discriminator Loss: 0.9119, Generator Loss: 1.7134
D(x): 0.6725, D(G(z)): 0.2785
Epoch: [18/20], Batch Num: [378/600]
Discriminator Loss: 0.9795, Generator Loss: 1.4794
D(x): 0.6938, D(G(z)): 0.3174
Epoch: [18/20], Batch Num: [379/600]
Discriminator Loss: 0.8145, Generator Loss: 1.3541
D(x): 0.7646, D(G(z)): 0.3091
Epoch: [18/20], Batch Num: [380/600]
Discriminator Loss: 0.9625, Generator Loss: 1.5161
D(x): 0.7180, D(G(z)): 0.3363
Epoch: [18/20], Batch Num: [381/600]
Discriminator Loss: 0.8791, Generator Loss: 1.6513
D(x): 0.7451, D(G(z)): 0.3249
Epoch: [18/20], Batch Num: [382/600]
Discriminator Loss: 0.8615, Generator Loss: 1.7048
D(x): 0.7232, D(G(z)): 0.2965
Epoch: [18/20], Batch Num: [383/600]
Discriminator Loss: 1.1339, Generator Loss: 1.6324
D(x): 0.6038, D(G(z)): 0.2839
Epoch: [18/20], Batch Num: [384/600]
Discriminator Loss: 1.1035, Generator Loss: 1.4571
D(x): 0.6771, D(G(z)): 0.3342
Epoch: [18/20], Batch Num: [385/600]
Discriminator Loss: 0.8481, Generator Loss: 1.3029
D(x): 0.7058, D(G(z)): 0.2752
Epoch: [18/20], Batch Num: [386/600]
Discriminator Loss: 0.8340, Generator Loss: 1.3758
D(x): 0.7263, D(G(z)): 0.3039
Epoch: [18/20], Batch Num: [387/600]
Discriminator Loss: 0.9573, Generator Loss: 1.3488
D(x): 0.7053, D(G(z)): 0.3211
Epoch: [18/20], Batch Num: [388/600]
Discriminator Loss: 1.0044, Generator Loss: 1.6161
D(x): 0.7039, D(G(z)): 0.3539
Epoch: [18/20], Batch Num: [389/600]
Discriminator Loss: 0.9668, Generator Loss: 1.4673
D(x): 0.6928, D(G(z)): 0.3216
Epoch: [18/20], Batch Num: [390/600]
Discriminator Loss: 0.9109, Generator Loss: 1.4603
D(x): 0.6813, D(G(z)): 0.2882
Epoch: [18/20], Batch Num: [391/600]
Discriminator Loss: 0.8635, Generator Loss: 1.6045
D(x): 0.6910, D(G(z)): 0.2772
Epoch: [18/20], Batch Num: [392/600]
Discriminator Loss: 0.8990, Generator Loss: 1.4379
D(x): 0.6655, D(G(z)): 0.2681
Epoch: [18/20], Batch Num: [393/600]
Discriminator Loss: 1.1446, Generator Loss: 1.4573
D(x): 0.6266, D(G(z)): 0.3470
Epoch: [18/20], Batch Num: [394/600]
Discriminator Loss: 0.8910, Generator Loss: 1.3257
D(x): 0.6986, D(G(z)): 0.2994
Epoch: [18/20], Batch Num: [395/600]
Discriminator Loss: 0.8879, Generator Loss: 1.4065
D(x): 0.7383, D(G(z)): 0.3239
Epoch: [18/20], Batch Num: [396/600]
Discriminator Loss: 1.0482, Generator Loss: 1.3502
D(x): 0.7166, D(G(z)): 0.3637
Epoch: [18/20], Batch Num: [397/600]
Discriminator Loss: 1.0185, Generator Loss: 1.6320
D(x): 0.7308, D(G(z)): 0.3416
Epoch: [18/20], Batch Num: [398/600]
Discriminator Loss: 0.9580, Generator Loss: 1.5706
D(x): 0.6624, D(G(z)): 0.2814
Epoch: [18/20], Batch Num: [399/600]
Discriminator Loss: 0.9001, Generator Loss: 1.5625
D(x): 0.6618, D(G(z)): 0.2396
Epoch: 18, Batch Num: [400/600]

Epoch: [18/20], Batch Num: [400/600]
Discriminator Loss: 1.0089, Generator Loss: 1.6094
D(x): 0.6367, D(G(z)): 0.2685
Epoch: [18/20], Batch Num: [401/600]
Discriminator Loss: 1.0399, Generator Loss: 1.3522
D(x): 0.6197, D(G(z)): 0.2716
Epoch: [18/20], Batch Num: [402/600]
Discriminator Loss: 1.0044, Generator Loss: 1.2227
D(x): 0.6800, D(G(z)): 0.3311
Epoch: [18/20], Batch Num: [403/600]
Discriminator Loss: 0.9252, Generator Loss: 1.1864
D(x): 0.7477, D(G(z)): 0.3600
Epoch: [18/20], Batch Num: [404/600]
Discriminator Loss: 0.8946, Generator Loss: 1.2055
D(x): 0.7476, D(G(z)): 0.3289
Epoch: [18/20], Batch Num: [405/600]
Discriminator Loss: 0.9772, Generator Loss: 1.2351
D(x): 0.7303, D(G(z)): 0.3518
Epoch: [18/20], Batch Num: [406/600]
Discriminator Loss: 0.8106, Generator Loss: 1.3557
D(x): 0.7251, D(G(z)): 0.2947
Epoch: [18/20], Batch Num: [407/600]
Discriminator Loss: 0.8235, Generator Loss: 1.4035
D(x): 0.7250, D(G(z)): 0.2886
Epoch: [18/20], Batch Num: [408/600]
Discriminator Loss: 1.1173, Generator Loss: 1.5181
D(x): 0.6161, D(G(z)): 0.3146
Epoch: [18/20], Batch Num: [409/600]
Discriminator Loss: 0.9746, Generator Loss: 1.4691
D(x): 0.6431, D(G(z)): 0.2765
Epoch: [18/20], Batch Num: [410/600]
Discriminator Loss: 0.9514, Generator Loss: 1.2999
D(x): 0.6252, D(G(z)): 0.2568
Epoch: [18/20], Batch Num: [411/600]
Discriminator Loss: 0.9522, Generator Loss: 1.3775
D(x): 0.7160, D(G(z)): 0.3373
Epoch: [18/20], Batch Num: [412/600]
Discriminator Loss: 0.9399, Generator Loss: 1.2573
D(x): 0.7195, D(G(z)): 0.3637
Epoch: [18/20], Batch Num: [413/600]
Discriminator Loss: 1.0259, Generator Loss: 1.2869
D(x): 0.6754, D(G(z)): 0.3644
Epoch: [18/20], Batch Num: [414/600]
Discriminator Loss: 0.8308, Generator Loss: 1.3264
D(x): 0.7412, D(G(z)): 0.3052
Epoch: [18/20], Batch Num: [415/600]
Discriminator Loss: 0.9568, Generator Loss: 1.3644
D(x): 0.6896, D(G(z)): 0.3333
Epoch: [18/20], Batch Num: [416/600]
Discriminator Loss: 0.8997, Generator Loss: 1.4915
D(x): 0.6965, D(G(z)): 0.3068
Epoch: [18/20], Batch Num: [417/600]
Discriminator Loss: 1.0453, Generator Loss: 1.6126
D(x): 0.6622, D(G(z)): 0.3046
Epoch: [18/20], Batch Num: [418/600]
Discriminator Loss: 1.0520, Generator Loss: 1.3938
D(x): 0.5876, D(G(z)): 0.2613
Epoch: [18/20], Batch Num: [419/600]
Discriminator Loss: 1.0301, Generator Loss: 1.3785
D(x): 0.5949, D(G(z)): 0.2832
Epoch: [18/20], Batch Num: [420/600]
Discriminator Loss: 1.0891, Generator Loss: 1.2558
D(x): 0.6927, D(G(z)): 0.3799
Epoch: [18/20], Batch Num: [421/600]
Discriminator Loss: 0.9127, Generator Loss: 1.2459
D(x): 0.7590, D(G(z)): 0.3592
Epoch: [18/20], Batch Num: [422/600]
Discriminator Loss: 1.1020, Generator Loss: 1.3232
D(x): 0.6827, D(G(z)): 0.3861
Epoch: [18/20], Batch Num: [423/600]
Discriminator Loss: 0.8798, Generator Loss: 1.6349
D(x): 0.6999, D(G(z)): 0.3093
Epoch: [18/20], Batch Num: [424/600]
Discriminator Loss: 1.0585, Generator Loss: 1.6272
D(x): 0.6155, D(G(z)): 0.3158
Epoch: [18/20], Batch Num: [425/600]
Discriminator Loss: 1.0018, Generator Loss: 1.5444
D(x): 0.6223, D(G(z)): 0.2913
Epoch: [18/20], Batch Num: [426/600]
Discriminator Loss: 1.0282, Generator Loss: 1.4219
D(x): 0.6275, D(G(z)): 0.2964
Epoch: [18/20], Batch Num: [427/600]
Discriminator Loss: 0.9771, Generator Loss: 1.2902
D(x): 0.6348, D(G(z)): 0.2834
Epoch: [18/20], Batch Num: [428/600]
Discriminator Loss: 0.9044, Generator Loss: 1.2228
D(x): 0.7033, D(G(z)): 0.3367
Epoch: [18/20], Batch Num: [429/600]
Discriminator Loss: 1.0119, Generator Loss: 1.1533
D(x): 0.7115, D(G(z)): 0.4066
Epoch: [18/20], Batch Num: [430/600]
Discriminator Loss: 0.8982, Generator Loss: 1.2887
D(x): 0.7414, D(G(z)): 0.3712
Epoch: [18/20], Batch Num: [431/600]
Discriminator Loss: 0.9418, Generator Loss: 1.2493
D(x): 0.7423, D(G(z)): 0.3753
Epoch: [18/20], Batch Num: [432/600]
Discriminator Loss: 0.8616, Generator Loss: 1.3688
D(x): 0.7188, D(G(z)): 0.3269
Epoch: [18/20], Batch Num: [433/600]
Discriminator Loss: 0.8723, Generator Loss: 1.4765
D(x): 0.6758, D(G(z)): 0.2782
Epoch: [18/20], Batch Num: [434/600]
Discriminator Loss: 1.0653, Generator Loss: 1.5315
D(x): 0.6033, D(G(z)): 0.3060
Epoch: [18/20], Batch Num: [435/600]
Discriminator Loss: 0.9509, Generator Loss: 1.3442
D(x): 0.6465, D(G(z)): 0.3024
Epoch: [18/20], Batch Num: [436/600]
Discriminator Loss: 1.0565, Generator Loss: 1.4596
D(x): 0.6156, D(G(z)): 0.3121
Epoch: [18/20], Batch Num: [437/600]
Discriminator Loss: 1.0048, Generator Loss: 1.2809
D(x): 0.6759, D(G(z)): 0.3440
Epoch: [18/20], Batch Num: [438/600]
Discriminator Loss: 0.9644, Generator Loss: 1.1840
D(x): 0.6823, D(G(z)): 0.3334
Epoch: [18/20], Batch Num: [439/600]
Discriminator Loss: 0.9974, Generator Loss: 1.2913
D(x): 0.6737, D(G(z)): 0.3207
Epoch: [18/20], Batch Num: [440/600]
Discriminator Loss: 0.9438, Generator Loss: 1.2693
D(x): 0.7167, D(G(z)): 0.3475
Epoch: [18/20], Batch Num: [441/600]
Discriminator Loss: 0.9205, Generator Loss: 1.3266
D(x): 0.6929, D(G(z)): 0.3219
Epoch: [18/20], Batch Num: [442/600]
Discriminator Loss: 0.8460, Generator Loss: 1.3733
D(x): 0.7098, D(G(z)): 0.3132
Epoch: [18/20], Batch Num: [443/600]
Discriminator Loss: 0.9669, Generator Loss: 1.4404
D(x): 0.6651, D(G(z)): 0.3108
Epoch: [18/20], Batch Num: [444/600]
Discriminator Loss: 0.8857, Generator Loss: 1.3728
D(x): 0.7161, D(G(z)): 0.3006
Epoch: [18/20], Batch Num: [445/600]
Discriminator Loss: 1.0213, Generator Loss: 1.3331
D(x): 0.6735, D(G(z)): 0.3439
Epoch: [18/20], Batch Num: [446/600]
Discriminator Loss: 1.0939, Generator Loss: 1.3919
D(x): 0.6511, D(G(z)): 0.3458
Epoch: [18/20], Batch Num: [447/600]
Discriminator Loss: 1.0417, Generator Loss: 1.4833
D(x): 0.6434, D(G(z)): 0.3356
Epoch: [18/20], Batch Num: [448/600]
Discriminator Loss: 0.9506, Generator Loss: 1.2442
D(x): 0.6320, D(G(z)): 0.2807
Epoch: [18/20], Batch Num: [449/600]
Discriminator Loss: 0.8843, Generator Loss: 1.2591
D(x): 0.7080, D(G(z)): 0.3084
Epoch: [18/20], Batch Num: [450/600]
Discriminator Loss: 0.9437, Generator Loss: 1.2184
D(x): 0.6756, D(G(z)): 0.3142
Epoch: [18/20], Batch Num: [451/600]
Discriminator Loss: 1.0146, Generator Loss: 1.1522
D(x): 0.6970, D(G(z)): 0.3709
Epoch: [18/20], Batch Num: [452/600]
Discriminator Loss: 0.9790, Generator Loss: 1.2362
D(x): 0.7335, D(G(z)): 0.3794
Epoch: [18/20], Batch Num: [453/600]
Discriminator Loss: 1.0466, Generator Loss: 1.3973
D(x): 0.6908, D(G(z)): 0.3839
Epoch: [18/20], Batch Num: [454/600]
Discriminator Loss: 0.9587, Generator Loss: 1.5179
D(x): 0.6939, D(G(z)): 0.3487
Epoch: [18/20], Batch Num: [455/600]
Discriminator Loss: 0.9568, Generator Loss: 1.5647
D(x): 0.6640, D(G(z)): 0.2957
Epoch: [18/20], Batch Num: [456/600]
Discriminator Loss: 0.9711, Generator Loss: 1.5729
D(x): 0.6190, D(G(z)): 0.2635
Epoch: [18/20], Batch Num: [457/600]
Discriminator Loss: 0.9485, Generator Loss: 1.4504
D(x): 0.6737, D(G(z)): 0.3108
Epoch: [18/20], Batch Num: [458/600]
Discriminator Loss: 1.0153, Generator Loss: 1.5177
D(x): 0.6598, D(G(z)): 0.3343
Epoch: [18/20], Batch Num: [459/600]
Discriminator Loss: 0.9536, Generator Loss: 1.3071
D(x): 0.6938, D(G(z)): 0.3278
Epoch: [18/20], Batch Num: [460/600]
Discriminator Loss: 0.8869, Generator Loss: 1.4594
D(x): 0.7073, D(G(z)): 0.3086
Epoch: [18/20], Batch Num: [461/600]
Discriminator Loss: 0.8148, Generator Loss: 1.3311
D(x): 0.7383, D(G(z)): 0.3103
Epoch: [18/20], Batch Num: [462/600]
Discriminator Loss: 0.9166, Generator Loss: 1.4959
D(x): 0.7466, D(G(z)): 0.3446
Epoch: [18/20], Batch Num: [463/600]
Discriminator Loss: 0.8829, Generator Loss: 1.7589
D(x): 0.7261, D(G(z)): 0.3250
Epoch: [18/20], Batch Num: [464/600]
Discriminator Loss: 0.9486, Generator Loss: 1.8279
D(x): 0.6601, D(G(z)): 0.2681
Epoch: [18/20], Batch Num: [465/600]
Discriminator Loss: 0.9352, Generator Loss: 1.7896
D(x): 0.6337, D(G(z)): 0.2654
Epoch: [18/20], Batch Num: [466/600]
Discriminator Loss: 0.9497, Generator Loss: 1.5348
D(x): 0.6381, D(G(z)): 0.2611
Epoch: [18/20], Batch Num: [467/600]
Discriminator Loss: 0.9249, Generator Loss: 1.3624
D(x): 0.6841, D(G(z)): 0.2912
Epoch: [18/20], Batch Num: [468/600]
Discriminator Loss: 0.9882, Generator Loss: 1.3280
D(x): 0.7020, D(G(z)): 0.3511
Epoch: [18/20], Batch Num: [469/600]
Discriminator Loss: 0.9604, Generator Loss: 1.4563
D(x): 0.7414, D(G(z)): 0.3566
Epoch: [18/20], Batch Num: [470/600]
Discriminator Loss: 0.8795, Generator Loss: 1.5913
D(x): 0.7530, D(G(z)): 0.3224
Epoch: [18/20], Batch Num: [471/600]
Discriminator Loss: 0.8189, Generator Loss: 1.6250
D(x): 0.7474, D(G(z)): 0.2995
Epoch: [18/20], Batch Num: [472/600]
Discriminator Loss: 0.9260, Generator Loss: 1.8236
D(x): 0.6643, D(G(z)): 0.2891
Epoch: [18/20], Batch Num: [473/600]
Discriminator Loss: 0.9219, Generator Loss: 1.5567
D(x): 0.6303, D(G(z)): 0.2247
Epoch: [18/20], Batch Num: [474/600]
Discriminator Loss: 0.9353, Generator Loss: 1.5128
D(x): 0.6902, D(G(z)): 0.2935
Epoch: [18/20], Batch Num: [475/600]
Discriminator Loss: 0.9554, Generator Loss: 1.4031
D(x): 0.7042, D(G(z)): 0.3024
Epoch: [18/20], Batch Num: [476/600]
Discriminator Loss: 0.7772, Generator Loss: 1.3871
D(x): 0.7480, D(G(z)): 0.2972
Epoch: [18/20], Batch Num: [477/600]
Discriminator Loss: 0.8862, Generator Loss: 1.4360
D(x): 0.7205, D(G(z)): 0.3435
Epoch: [18/20], Batch Num: [478/600]
Discriminator Loss: 0.8938, Generator Loss: 1.6044
D(x): 0.7161, D(G(z)): 0.3231
Epoch: [18/20], Batch Num: [479/600]
Discriminator Loss: 0.9392, Generator Loss: 1.6353
D(x): 0.6499, D(G(z)): 0.2938
Epoch: [18/20], Batch Num: [480/600]
Discriminator Loss: 0.9075, Generator Loss: 1.4882
D(x): 0.6749, D(G(z)): 0.2882
Epoch: [18/20], Batch Num: [481/600]
Discriminator Loss: 0.8143, Generator Loss: 1.4488
D(x): 0.7221, D(G(z)): 0.2669
Epoch: [18/20], Batch Num: [482/600]
Discriminator Loss: 0.8549, Generator Loss: 1.3547
D(x): 0.7502, D(G(z)): 0.3008
Epoch: [18/20], Batch Num: [483/600]
Discriminator Loss: 0.7628, Generator Loss: 1.6383
D(x): 0.7503, D(G(z)): 0.2775
Epoch: [18/20], Batch Num: [484/600]
Discriminator Loss: 0.8195, Generator Loss: 1.8324
D(x): 0.7296, D(G(z)): 0.2798
Epoch: [18/20], Batch Num: [485/600]
Discriminator Loss: 0.7275, Generator Loss: 1.9275
D(x): 0.7325, D(G(z)): 0.2319
Epoch: [18/20], Batch Num: [486/600]
Discriminator Loss: 0.9070, Generator Loss: 1.5910
D(x): 0.7339, D(G(z)): 0.2844
Epoch: [18/20], Batch Num: [487/600]
Discriminator Loss: 0.8640, Generator Loss: 1.8525
D(x): 0.7280, D(G(z)): 0.2753
Epoch: [18/20], Batch Num: [488/600]
Discriminator Loss: 0.7802, Generator Loss: 1.6542
D(x): 0.7520, D(G(z)): 0.2560
Epoch: [18/20], Batch Num: [489/600]
Discriminator Loss: 0.7845, Generator Loss: 1.7037
D(x): 0.7440, D(G(z)): 0.2452
Epoch: [18/20], Batch Num: [490/600]
Discriminator Loss: 0.8618, Generator Loss: 1.6605
D(x): 0.7307, D(G(z)): 0.2799
Epoch: [18/20], Batch Num: [491/600]
Discriminator Loss: 0.8953, Generator Loss: 1.5295
D(x): 0.7332, D(G(z)): 0.2984
Epoch: [18/20], Batch Num: [492/600]
Discriminator Loss: 0.8666, Generator Loss: 1.6333
D(x): 0.7425, D(G(z)): 0.2950
Epoch: [18/20], Batch Num: [493/600]
Discriminator Loss: 0.9226, Generator Loss: 1.7833
D(x): 0.7193, D(G(z)): 0.2699
Epoch: [18/20], Batch Num: [494/600]
Discriminator Loss: 0.7661, Generator Loss: 1.8883
D(x): 0.7545, D(G(z)): 0.2565
Epoch: [18/20], Batch Num: [495/600]
Discriminator Loss: 0.9028, Generator Loss: 2.0401
D(x): 0.7229, D(G(z)): 0.2540
Epoch: [18/20], Batch Num: [496/600]
Discriminator Loss: 0.8579, Generator Loss: 1.7933
D(x): 0.6879, D(G(z)): 0.2270
Epoch: [18/20], Batch Num: [497/600]
Discriminator Loss: 1.0341, Generator Loss: 1.5102
D(x): 0.6453, D(G(z)): 0.2774
Epoch: [18/20], Batch Num: [498/600]
Discriminator Loss: 1.0005, Generator Loss: 1.3525
D(x): 0.7399, D(G(z)): 0.3392
Epoch: [18/20], Batch Num: [499/600]
Discriminator Loss: 1.1712, Generator Loss: 1.5782
D(x): 0.7491, D(G(z)): 0.4317
Epoch: 18, Batch Num: [500/600]

Epoch: [18/20], Batch Num: [500/600]
Discriminator Loss: 0.8109, Generator Loss: 2.0280
D(x): 0.7643, D(G(z)): 0.2965
Epoch: [18/20], Batch Num: [501/600]
Discriminator Loss: 0.8353, Generator Loss: 1.9085
D(x): 0.6909, D(G(z)): 0.2411
Epoch: [18/20], Batch Num: [502/600]
Discriminator Loss: 0.9029, Generator Loss: 1.9607
D(x): 0.6765, D(G(z)): 0.2379
Epoch: [18/20], Batch Num: [503/600]
Discriminator Loss: 0.8398, Generator Loss: 1.8110
D(x): 0.7291, D(G(z)): 0.2626
Epoch: [18/20], Batch Num: [504/600]
Discriminator Loss: 0.9049, Generator Loss: 1.7112
D(x): 0.7026, D(G(z)): 0.2503
Epoch: [18/20], Batch Num: [505/600]
Discriminator Loss: 0.9285, Generator Loss: 1.6919
D(x): 0.6744, D(G(z)): 0.2285
Epoch: [18/20], Batch Num: [506/600]
Discriminator Loss: 0.9837, Generator Loss: 1.5293
D(x): 0.6880, D(G(z)): 0.3033
Epoch: [18/20], Batch Num: [507/600]
Discriminator Loss: 0.8717, Generator Loss: 1.3878
D(x): 0.7110, D(G(z)): 0.2808
Epoch: [18/20], Batch Num: [508/600]
Discriminator Loss: 0.9316, Generator Loss: 1.1320
D(x): 0.7106, D(G(z)): 0.3382
Epoch: [18/20], Batch Num: [509/600]
Discriminator Loss: 0.9923, Generator Loss: 1.5135
D(x): 0.7205, D(G(z)): 0.3583
Epoch: [18/20], Batch Num: [510/600]
Discriminator Loss: 1.0715, Generator Loss: 1.3912
D(x): 0.7217, D(G(z)): 0.4010
Epoch: [18/20], Batch Num: [511/600]
Discriminator Loss: 0.9289, Generator Loss: 1.7195
D(x): 0.7608, D(G(z)): 0.3722
Epoch: [18/20], Batch Num: [512/600]
Discriminator Loss: 0.8871, Generator Loss: 1.7145
D(x): 0.6655, D(G(z)): 0.2634
Epoch: [18/20], Batch Num: [513/600]
Discriminator Loss: 0.8964, Generator Loss: 1.7146
D(x): 0.6689, D(G(z)): 0.2443
Epoch: [18/20], Batch Num: [514/600]
Discriminator Loss: 0.8845, Generator Loss: 1.4825
D(x): 0.6664, D(G(z)): 0.2773
Epoch: [18/20], Batch Num: [515/600]
Discriminator Loss: 0.9512, Generator Loss: 1.2618
D(x): 0.6553, D(G(z)): 0.3001
Epoch: [18/20], Batch Num: [516/600]
Discriminator Loss: 0.8618, Generator Loss: 1.0892
D(x): 0.7027, D(G(z)): 0.3120
Epoch: [18/20], Batch Num: [517/600]
Discriminator Loss: 0.8696, Generator Loss: 1.1754
D(x): 0.7553, D(G(z)): 0.3444
Epoch: [18/20], Batch Num: [518/600]
Discriminator Loss: 0.8944, Generator Loss: 1.2386
D(x): 0.7306, D(G(z)): 0.3459
Epoch: [18/20], Batch Num: [519/600]
Discriminator Loss: 1.0260, Generator Loss: 1.3337
D(x): 0.6842, D(G(z)): 0.3506
Epoch: [18/20], Batch Num: [520/600]
Discriminator Loss: 1.0902, Generator Loss: 1.4480
D(x): 0.6873, D(G(z)): 0.3806
Epoch: [18/20], Batch Num: [521/600]
Discriminator Loss: 1.0860, Generator Loss: 1.5740
D(x): 0.6031, D(G(z)): 0.2795
Epoch: [18/20], Batch Num: [522/600]
Discriminator Loss: 0.9813, Generator Loss: 1.5786
D(x): 0.6484, D(G(z)): 0.2938
Epoch: [18/20], Batch Num: [523/600]
Discriminator Loss: 0.8789, Generator Loss: 1.4376
D(x): 0.6879, D(G(z)): 0.2875
Epoch: [18/20], Batch Num: [524/600]
Discriminator Loss: 1.0347, Generator Loss: 1.4438
D(x): 0.6654, D(G(z)): 0.3360
Epoch: [18/20], Batch Num: [525/600]
Discriminator Loss: 0.9727, Generator Loss: 1.3446
D(x): 0.6846, D(G(z)): 0.3333
Epoch: [18/20], Batch Num: [526/600]
Discriminator Loss: 0.9636, Generator Loss: 1.4220
D(x): 0.7450, D(G(z)): 0.3828
Epoch: [18/20], Batch Num: [527/600]
Discriminator Loss: 0.8541, Generator Loss: 1.5775
D(x): 0.6810, D(G(z)): 0.2836
Epoch: [18/20], Batch Num: [528/600]
Discriminator Loss: 0.9304, Generator Loss: 1.5912
D(x): 0.6668, D(G(z)): 0.3093
Epoch: [18/20], Batch Num: [529/600]
Discriminator Loss: 0.9830, Generator Loss: 1.5661
D(x): 0.6075, D(G(z)): 0.2594
Epoch: [18/20], Batch Num: [530/600]
Discriminator Loss: 0.8653, Generator Loss: 1.7557
D(x): 0.6873, D(G(z)): 0.2940
Epoch: [18/20], Batch Num: [531/600]
Discriminator Loss: 0.8998, Generator Loss: 1.2994
D(x): 0.6960, D(G(z)): 0.3152
Epoch: [18/20], Batch Num: [532/600]
Discriminator Loss: 0.8476, Generator Loss: 1.3687
D(x): 0.7702, D(G(z)): 0.3324
Epoch: [18/20], Batch Num: [533/600]
Discriminator Loss: 0.8100, Generator Loss: 1.3389
D(x): 0.7462, D(G(z)): 0.3002
Epoch: [18/20], Batch Num: [534/600]
Discriminator Loss: 0.9810, Generator Loss: 1.6272
D(x): 0.7099, D(G(z)): 0.3575
Epoch: [18/20], Batch Num: [535/600]
Discriminator Loss: 0.8001, Generator Loss: 1.6534
D(x): 0.7389, D(G(z)): 0.2755
Epoch: [18/20], Batch Num: [536/600]
Discriminator Loss: 0.7387, Generator Loss: 1.7901
D(x): 0.7047, D(G(z)): 0.2289
Epoch: [18/20], Batch Num: [537/600]
Discriminator Loss: 0.7604, Generator Loss: 1.6440
D(x): 0.7073, D(G(z)): 0.2268
Epoch: [18/20], Batch Num: [538/600]
Discriminator Loss: 0.8168, Generator Loss: 1.6035
D(x): 0.7177, D(G(z)): 0.2772
Epoch: [18/20], Batch Num: [539/600]
Discriminator Loss: 0.9122, Generator Loss: 1.5835
D(x): 0.7013, D(G(z)): 0.2913
Epoch: [18/20], Batch Num: [540/600]
Discriminator Loss: 0.8451, Generator Loss: 1.6275
D(x): 0.7369, D(G(z)): 0.2815
Epoch: [18/20], Batch Num: [541/600]
Discriminator Loss: 0.8545, Generator Loss: 1.7690
D(x): 0.7559, D(G(z)): 0.2929
Epoch: [18/20], Batch Num: [542/600]
Discriminator Loss: 0.7453, Generator Loss: 1.6251
D(x): 0.7664, D(G(z)): 0.2841
Epoch: [18/20], Batch Num: [543/600]
Discriminator Loss: 0.9237, Generator Loss: 1.7580
D(x): 0.7035, D(G(z)): 0.2686
Epoch: [18/20], Batch Num: [544/600]
Discriminator Loss: 0.8619, Generator Loss: 1.7687
D(x): 0.6961, D(G(z)): 0.2699
Epoch: [18/20], Batch Num: [545/600]
Discriminator Loss: 0.8681, Generator Loss: 1.4908
D(x): 0.7068, D(G(z)): 0.2644
Epoch: [18/20], Batch Num: [546/600]
Discriminator Loss: 0.8715, Generator Loss: 1.8134
D(x): 0.7485, D(G(z)): 0.3121
Epoch: [18/20], Batch Num: [547/600]
Discriminator Loss: 0.7934, Generator Loss: 1.8749
D(x): 0.7599, D(G(z)): 0.2724
Epoch: [18/20], Batch Num: [548/600]
Discriminator Loss: 0.8799, Generator Loss: 1.6535
D(x): 0.7004, D(G(z)): 0.2423
Epoch: [18/20], Batch Num: [549/600]
Discriminator Loss: 0.8819, Generator Loss: 1.6160
D(x): 0.6788, D(G(z)): 0.2452
Epoch: [18/20], Batch Num: [550/600]
Discriminator Loss: 0.8114, Generator Loss: 1.4959
D(x): 0.7146, D(G(z)): 0.2596
Epoch: [18/20], Batch Num: [551/600]
Discriminator Loss: 0.8940, Generator Loss: 1.5553
D(x): 0.7498, D(G(z)): 0.3087
Epoch: [18/20], Batch Num: [552/600]
Discriminator Loss: 0.8062, Generator Loss: 1.6763
D(x): 0.7916, D(G(z)): 0.3183
Epoch: [18/20], Batch Num: [553/600]
Discriminator Loss: 0.8662, Generator Loss: 1.8020
D(x): 0.7135, D(G(z)): 0.2999
Epoch: [18/20], Batch Num: [554/600]
Discriminator Loss: 1.0501, Generator Loss: 1.8002
D(x): 0.6235, D(G(z)): 0.2869
Epoch: [18/20], Batch Num: [555/600]
Discriminator Loss: 0.8173, Generator Loss: 1.8268
D(x): 0.6899, D(G(z)): 0.2170
Epoch: [18/20], Batch Num: [556/600]
Discriminator Loss: 1.0068, Generator Loss: 1.7087
D(x): 0.6673, D(G(z)): 0.3012
Epoch: [18/20], Batch Num: [557/600]
Discriminator Loss: 0.9033, Generator Loss: 1.6284
D(x): 0.6874, D(G(z)): 0.2750
Epoch: [18/20], Batch Num: [558/600]
Discriminator Loss: 0.9592, Generator Loss: 1.5100
D(x): 0.7147, D(G(z)): 0.3136
Epoch: [18/20], Batch Num: [559/600]
Discriminator Loss: 0.8460, Generator Loss: 1.6123
D(x): 0.7025, D(G(z)): 0.2639
Epoch: [18/20], Batch Num: [560/600]
Discriminator Loss: 0.9513, Generator Loss: 1.6735
D(x): 0.6944, D(G(z)): 0.3124
Epoch: [18/20], Batch Num: [561/600]
Discriminator Loss: 0.9296, Generator Loss: 1.6335
D(x): 0.6906, D(G(z)): 0.2963
Epoch: [18/20], Batch Num: [562/600]
Discriminator Loss: 0.7978, Generator Loss: 1.5017
D(x): 0.7204, D(G(z)): 0.2884
Epoch: [18/20], Batch Num: [563/600]
Discriminator Loss: 1.0183, Generator Loss: 1.2339
D(x): 0.6569, D(G(z)): 0.2909
Epoch: [18/20], Batch Num: [564/600]
Discriminator Loss: 1.0052, Generator Loss: 1.1952
D(x): 0.7064, D(G(z)): 0.3721
Epoch: [18/20], Batch Num: [565/600]
Discriminator Loss: 0.9727, Generator Loss: 1.3269
D(x): 0.7102, D(G(z)): 0.3670
Epoch: [18/20], Batch Num: [566/600]
Discriminator Loss: 1.0569, Generator Loss: 1.2967
D(x): 0.6574, D(G(z)): 0.3580
Epoch: [18/20], Batch Num: [567/600]
Discriminator Loss: 0.8930, Generator Loss: 1.7088
D(x): 0.7216, D(G(z)): 0.3443
Epoch: [18/20], Batch Num: [568/600]
Discriminator Loss: 0.9266, Generator Loss: 1.5533
D(x): 0.6526, D(G(z)): 0.2797
Epoch: [18/20], Batch Num: [569/600]
Discriminator Loss: 0.8699, Generator Loss: 1.5443
D(x): 0.6631, D(G(z)): 0.2473
Epoch: [18/20], Batch Num: [570/600]
Discriminator Loss: 0.8520, Generator Loss: 1.3018
D(x): 0.7033, D(G(z)): 0.2712
Epoch: [18/20], Batch Num: [571/600]
Discriminator Loss: 0.8565, Generator Loss: 1.3089
D(x): 0.7133, D(G(z)): 0.3075
Epoch: [18/20], Batch Num: [572/600]
Discriminator Loss: 0.9635, Generator Loss: 1.4648
D(x): 0.7463, D(G(z)): 0.3426
Epoch: [18/20], Batch Num: [573/600]
Discriminator Loss: 0.8345, Generator Loss: 1.5302
D(x): 0.7266, D(G(z)): 0.3034
Epoch: [18/20], Batch Num: [574/600]
Discriminator Loss: 0.8280, Generator Loss: 1.5987
D(x): 0.7100, D(G(z)): 0.2803
Epoch: [18/20], Batch Num: [575/600]
Discriminator Loss: 0.9435, Generator Loss: 1.5420
D(x): 0.6735, D(G(z)): 0.2900
Epoch: [18/20], Batch Num: [576/600]
Discriminator Loss: 0.8955, Generator Loss: 1.5412
D(x): 0.7221, D(G(z)): 0.2930
Epoch: [18/20], Batch Num: [577/600]
Discriminator Loss: 0.8481, Generator Loss: 1.6462
D(x): 0.6764, D(G(z)): 0.2287
Epoch: [18/20], Batch Num: [578/600]
Discriminator Loss: 0.7580, Generator Loss: 1.5386
D(x): 0.7362, D(G(z)): 0.2637
Epoch: [18/20], Batch Num: [579/600]
Discriminator Loss: 0.8573, Generator Loss: 1.5159
D(x): 0.7334, D(G(z)): 0.2974
Epoch: [18/20], Batch Num: [580/600]
Discriminator Loss: 0.8116, Generator Loss: 1.7516
D(x): 0.7797, D(G(z)): 0.3169
Epoch: [18/20], Batch Num: [581/600]
Discriminator Loss: 0.9127, Generator Loss: 1.7763
D(x): 0.7200, D(G(z)): 0.2725
Epoch: [18/20], Batch Num: [582/600]
Discriminator Loss: 0.9867, Generator Loss: 1.6591
D(x): 0.7092, D(G(z)): 0.3032
Epoch: [18/20], Batch Num: [583/600]
Discriminator Loss: 0.8219, Generator Loss: 1.8605
D(x): 0.7097, D(G(z)): 0.2661
Epoch: [18/20], Batch Num: [584/600]
Discriminator Loss: 0.9250, Generator Loss: 1.7837
D(x): 0.6913, D(G(z)): 0.2682
Epoch: [18/20], Batch Num: [585/600]
Discriminator Loss: 0.9310, Generator Loss: 1.6461
D(x): 0.6752, D(G(z)): 0.2715
Epoch: [18/20], Batch Num: [586/600]
Discriminator Loss: 0.8548, Generator Loss: 1.4815
D(x): 0.7302, D(G(z)): 0.2956
Epoch: [18/20], Batch Num: [587/600]
Discriminator Loss: 0.9182, Generator Loss: 1.5171
D(x): 0.7095, D(G(z)): 0.3134
Epoch: [18/20], Batch Num: [588/600]
Discriminator Loss: 0.8247, Generator Loss: 1.5083
D(x): 0.7719, D(G(z)): 0.3202
Epoch: [18/20], Batch Num: [589/600]
Discriminator Loss: 0.9041, Generator Loss: 1.6967
D(x): 0.7692, D(G(z)): 0.3348
Epoch: [18/20], Batch Num: [590/600]
Discriminator Loss: 1.0198, Generator Loss: 2.1648
D(x): 0.6187, D(G(z)): 0.2353
Epoch: [18/20], Batch Num: [591/600]
Discriminator Loss: 0.9324, Generator Loss: 1.7613
D(x): 0.6165, D(G(z)): 0.2201
Epoch: [18/20], Batch Num: [592/600]
Discriminator Loss: 0.7837, Generator Loss: 1.6957
D(x): 0.7354, D(G(z)): 0.2842
Epoch: [18/20], Batch Num: [593/600]
Discriminator Loss: 0.8243, Generator Loss: 1.3757
D(x): 0.7063, D(G(z)): 0.2594
Epoch: [18/20], Batch Num: [594/600]
Discriminator Loss: 0.8401, Generator Loss: 1.4106
D(x): 0.7340, D(G(z)): 0.3132
Epoch: [18/20], Batch Num: [595/600]
Discriminator Loss: 0.7786, Generator Loss: 1.4500
D(x): 0.8049, D(G(z)): 0.3501
Epoch: [18/20], Batch Num: [596/600]
Discriminator Loss: 1.0172, Generator Loss: 1.6212
D(x): 0.7187, D(G(z)): 0.3644
Epoch: [18/20], Batch Num: [597/600]
Discriminator Loss: 0.9712, Generator Loss: 1.8407
D(x): 0.6493, D(G(z)): 0.2696
Epoch: [18/20], Batch Num: [598/600]
Discriminator Loss: 0.8587, Generator Loss: 1.7014
D(x): 0.6578, D(G(z)): 0.2172
Epoch: [18/20], Batch Num: [599/600]
Discriminator Loss: 0.9792, Generator Loss: 1.3030
D(x): 0.6294, D(G(z)): 0.2349
Epoch: 19, Batch Num: [0/600]

Epoch: [19/20], Batch Num: [0/600]
Discriminator Loss: 0.9647, Generator Loss: 1.1736
D(x): 0.7117, D(G(z)): 0.3385
Epoch: [19/20], Batch Num: [1/600]
Discriminator Loss: 1.1578, Generator Loss: 1.2891
D(x): 0.7645, D(G(z)): 0.4390
Epoch: [19/20], Batch Num: [2/600]
Discriminator Loss: 1.0803, Generator Loss: 1.5122
D(x): 0.7727, D(G(z)): 0.4304
Epoch: [19/20], Batch Num: [3/600]
Discriminator Loss: 1.0946, Generator Loss: 1.9243
D(x): 0.6210, D(G(z)): 0.3105
Epoch: [19/20], Batch Num: [4/600]
Discriminator Loss: 1.0329, Generator Loss: 1.9470
D(x): 0.5850, D(G(z)): 0.2219
Epoch: [19/20], Batch Num: [5/600]
Discriminator Loss: 0.9081, Generator Loss: 1.5375
D(x): 0.6138, D(G(z)): 0.2264
Epoch: [19/20], Batch Num: [6/600]
Discriminator Loss: 0.9388, Generator Loss: 1.1529
D(x): 0.6489, D(G(z)): 0.2549
Epoch: [19/20], Batch Num: [7/600]
Discriminator Loss: 1.0073, Generator Loss: 1.2467
D(x): 0.6936, D(G(z)): 0.3560
Epoch: [19/20], Batch Num: [8/600]
Discriminator Loss: 1.0109, Generator Loss: 1.2319
D(x): 0.7449, D(G(z)): 0.3857
Epoch: [19/20], Batch Num: [9/600]
Discriminator Loss: 0.8614, Generator Loss: 1.3304
D(x): 0.7535, D(G(z)): 0.3369
Epoch: [19/20], Batch Num: [10/600]
Discriminator Loss: 0.8457, Generator Loss: 1.4378
D(x): 0.7605, D(G(z)): 0.3577
Epoch: [19/20], Batch Num: [11/600]
Discriminator Loss: 0.9142, Generator Loss: 1.4370
D(x): 0.7243, D(G(z)): 0.3340
Epoch: [19/20], Batch Num: [12/600]
Discriminator Loss: 1.0500, Generator Loss: 1.5234
D(x): 0.5975, D(G(z)): 0.2801
Epoch: [19/20], Batch Num: [13/600]
Discriminator Loss: 0.9379, Generator Loss: 1.5199
D(x): 0.6466, D(G(z)): 0.2798
Epoch: [19/20], Batch Num: [14/600]
Discriminator Loss: 1.0046, Generator Loss: 1.4268
D(x): 0.6248, D(G(z)): 0.2752
Epoch: [19/20], Batch Num: [15/600]
Discriminator Loss: 0.8570, Generator Loss: 1.2704
D(x): 0.6935, D(G(z)): 0.2864
Epoch: [19/20], Batch Num: [16/600]
Discriminator Loss: 0.9549, Generator Loss: 1.1791
D(x): 0.7176, D(G(z)): 0.3694
Epoch: [19/20], Batch Num: [17/600]
Discriminator Loss: 0.9595, Generator Loss: 1.2964
D(x): 0.7050, D(G(z)): 0.3446
Epoch: [19/20], Batch Num: [18/600]
Discriminator Loss: 0.8268, Generator Loss: 1.4241
D(x): 0.7744, D(G(z)): 0.3536
Epoch: [19/20], Batch Num: [19/600]
Discriminator Loss: 1.0291, Generator Loss: 1.3946
D(x): 0.6761, D(G(z)): 0.3373
Epoch: [19/20], Batch Num: [20/600]
Discriminator Loss: 1.0983, Generator Loss: 1.5894
D(x): 0.6007, D(G(z)): 0.2942
Epoch: [19/20], Batch Num: [21/600]
Discriminator Loss: 1.0430, Generator Loss: 1.6502
D(x): 0.6166, D(G(z)): 0.2765
Epoch: [19/20], Batch Num: [22/600]
Discriminator Loss: 0.8860, Generator Loss: 1.5970
D(x): 0.6993, D(G(z)): 0.2994
Epoch: [19/20], Batch Num: [23/600]
Discriminator Loss: 0.8677, Generator Loss: 1.4150
D(x): 0.6902, D(G(z)): 0.2814
Epoch: [19/20], Batch Num: [24/600]
Discriminator Loss: 0.8595, Generator Loss: 1.4075
D(x): 0.6848, D(G(z)): 0.2621
Epoch: [19/20], Batch Num: [25/600]
Discriminator Loss: 0.9334, Generator Loss: 1.2311
D(x): 0.7248, D(G(z)): 0.3262
Epoch: [19/20], Batch Num: [26/600]
Discriminator Loss: 0.9454, Generator Loss: 1.2939
D(x): 0.6945, D(G(z)): 0.3309
Epoch: [19/20], Batch Num: [27/600]
Discriminator Loss: 1.0236, Generator Loss: 1.3906
D(x): 0.7110, D(G(z)): 0.3788
Epoch: [19/20], Batch Num: [28/600]
Discriminator Loss: 1.0131, Generator Loss: 1.3700
D(x): 0.6736, D(G(z)): 0.3450
Epoch: [19/20], Batch Num: [29/600]
Discriminator Loss: 1.0863, Generator Loss: 1.5921
D(x): 0.6574, D(G(z)): 0.3602
Epoch: [19/20], Batch Num: [30/600]
Discriminator Loss: 0.9684, Generator Loss: 1.4803
D(x): 0.6710, D(G(z)): 0.3070
Epoch: [19/20], Batch Num: [31/600]
Discriminator Loss: 0.9487, Generator Loss: 1.5085
D(x): 0.6690, D(G(z)): 0.2956
Epoch: [19/20], Batch Num: [32/600]
Discriminator Loss: 0.9754, Generator Loss: 1.7323
D(x): 0.6502, D(G(z)): 0.2718
Epoch: [19/20], Batch Num: [33/600]
Discriminator Loss: 0.9331, Generator Loss: 1.5220
D(x): 0.6307, D(G(z)): 0.2316
Epoch: [19/20], Batch Num: [34/600]
Discriminator Loss: 0.9309, Generator Loss: 1.3063
D(x): 0.6836, D(G(z)): 0.2920
Epoch: [19/20], Batch Num: [35/600]
Discriminator Loss: 0.7962, Generator Loss: 1.2501
D(x): 0.7082, D(G(z)): 0.2703
Epoch: [19/20], Batch Num: [36/600]
Discriminator Loss: 0.9103, Generator Loss: 1.1850
D(x): 0.7327, D(G(z)): 0.3431
Epoch: [19/20], Batch Num: [37/600]
Discriminator Loss: 0.8156, Generator Loss: 1.2957
D(x): 0.7411, D(G(z)): 0.3179
Epoch: [19/20], Batch Num: [38/600]
Discriminator Loss: 0.9158, Generator Loss: 1.3065
D(x): 0.7545, D(G(z)): 0.3759
Epoch: [19/20], Batch Num: [39/600]
Discriminator Loss: 0.8233, Generator Loss: 1.5455
D(x): 0.7411, D(G(z)): 0.3120
Epoch: [19/20], Batch Num: [40/600]
Discriminator Loss: 0.9274, Generator Loss: 1.5831
D(x): 0.6991, D(G(z)): 0.3306
Epoch: [19/20], Batch Num: [41/600]
Discriminator Loss: 0.8312, Generator Loss: 1.8387
D(x): 0.6752, D(G(z)): 0.2500
Epoch: [19/20], Batch Num: [42/600]
Discriminator Loss: 0.9789, Generator Loss: 1.6394
D(x): 0.6137, D(G(z)): 0.2530
Epoch: [19/20], Batch Num: [43/600]
Discriminator Loss: 0.8984, Generator Loss: 1.6469
D(x): 0.6549, D(G(z)): 0.2519
Epoch: [19/20], Batch Num: [44/600]
Discriminator Loss: 0.8743, Generator Loss: 1.5287
D(x): 0.6775, D(G(z)): 0.2758
Epoch: [19/20], Batch Num: [45/600]
Discriminator Loss: 0.8480, Generator Loss: 1.4629
D(x): 0.7491, D(G(z)): 0.3245
Epoch: [19/20], Batch Num: [46/600]
Discriminator Loss: 0.9130, Generator Loss: 1.4376
D(x): 0.7115, D(G(z)): 0.3410
Epoch: [19/20], Batch Num: [47/600]
Discriminator Loss: 0.8443, Generator Loss: 1.5106
D(x): 0.7561, D(G(z)): 0.3282
Epoch: [19/20], Batch Num: [48/600]
Discriminator Loss: 0.9474, Generator Loss: 1.6743
D(x): 0.6817, D(G(z)): 0.3237
Epoch: [19/20], Batch Num: [49/600]
Discriminator Loss: 0.8658, Generator Loss: 1.8076
D(x): 0.7281, D(G(z)): 0.3047
Epoch: [19/20], Batch Num: [50/600]
Discriminator Loss: 0.8271, Generator Loss: 1.8707
D(x): 0.6952, D(G(z)): 0.2528
Epoch: [19/20], Batch Num: [51/600]
Discriminator Loss: 0.8958, Generator Loss: 1.7946
D(x): 0.6779, D(G(z)): 0.2566
Epoch: [19/20], Batch Num: [52/600]
Discriminator Loss: 0.9583, Generator Loss: 1.4081
D(x): 0.6548, D(G(z)): 0.2488
Epoch: [19/20], Batch Num: [53/600]
Discriminator Loss: 0.9951, Generator Loss: 1.3988
D(x): 0.7123, D(G(z)): 0.3330
Epoch: [19/20], Batch Num: [54/600]
Discriminator Loss: 0.9536, Generator Loss: 1.5259
D(x): 0.7545, D(G(z)): 0.3790
Epoch: [19/20], Batch Num: [55/600]
Discriminator Loss: 1.0453, Generator Loss: 1.5621
D(x): 0.6956, D(G(z)): 0.3293
Epoch: [19/20], Batch Num: [56/600]
Discriminator Loss: 0.9746, Generator Loss: 1.7097
D(x): 0.6921, D(G(z)): 0.3002
Epoch: [19/20], Batch Num: [57/600]
Discriminator Loss: 0.8260, Generator Loss: 1.5692
D(x): 0.7219, D(G(z)): 0.2756
Epoch: [19/20], Batch Num: [58/600]
Discriminator Loss: 0.8940, Generator Loss: 1.6412
D(x): 0.6809, D(G(z)): 0.2750
Epoch: [19/20], Batch Num: [59/600]
Discriminator Loss: 0.8773, Generator Loss: 1.4802
D(x): 0.6865, D(G(z)): 0.2481
Epoch: [19/20], Batch Num: [60/600]
Discriminator Loss: 1.0127, Generator Loss: 1.5354
D(x): 0.7480, D(G(z)): 0.3828
Epoch: [19/20], Batch Num: [61/600]
Discriminator Loss: 1.0100, Generator Loss: 1.5285
D(x): 0.6866, D(G(z)): 0.3000
Epoch: [19/20], Batch Num: [62/600]
Discriminator Loss: 1.1514, Generator Loss: 1.6729
D(x): 0.6615, D(G(z)): 0.3259
Epoch: [19/20], Batch Num: [63/600]
Discriminator Loss: 0.8959, Generator Loss: 1.4455
D(x): 0.6775, D(G(z)): 0.2794
Epoch: [19/20], Batch Num: [64/600]
Discriminator Loss: 0.8488, Generator Loss: 1.5421
D(x): 0.6745, D(G(z)): 0.2488
Epoch: [19/20], Batch Num: [65/600]
Discriminator Loss: 0.8361, Generator Loss: 1.5212
D(x): 0.7335, D(G(z)): 0.3148
Epoch: [19/20], Batch Num: [66/600]
Discriminator Loss: 0.9673, Generator Loss: 1.4918
D(x): 0.6886, D(G(z)): 0.3345
Epoch: [19/20], Batch Num: [67/600]
Discriminator Loss: 1.0413, Generator Loss: 1.4306
D(x): 0.6491, D(G(z)): 0.3002
Epoch: [19/20], Batch Num: [68/600]
Discriminator Loss: 0.8379, Generator Loss: 1.4717
D(x): 0.7331, D(G(z)): 0.2981
Epoch: [19/20], Batch Num: [69/600]
Discriminator Loss: 0.8971, Generator Loss: 1.2905
D(x): 0.7104, D(G(z)): 0.3184
Epoch: [19/20], Batch Num: [70/600]
Discriminator Loss: 1.0195, Generator Loss: 1.5412
D(x): 0.6882, D(G(z)): 0.3430
Epoch: [19/20], Batch Num: [71/600]
Discriminator Loss: 0.9211, Generator Loss: 1.5807
D(x): 0.6974, D(G(z)): 0.3114
Epoch: [19/20], Batch Num: [72/600]
Discriminator Loss: 0.9680, Generator Loss: 1.5067
D(x): 0.6574, D(G(z)): 0.2924
Epoch: [19/20], Batch Num: [73/600]
Discriminator Loss: 0.9767, Generator Loss: 1.4234
D(x): 0.6715, D(G(z)): 0.3039
Epoch: [19/20], Batch Num: [74/600]
Discriminator Loss: 1.0057, Generator Loss: 1.4292
D(x): 0.6532, D(G(z)): 0.3122
Epoch: [19/20], Batch Num: [75/600]
Discriminator Loss: 0.9790, Generator Loss: 1.2783
D(x): 0.6867, D(G(z)): 0.3240
Epoch: [19/20], Batch Num: [76/600]
Discriminator Loss: 0.8416, Generator Loss: 1.1867
D(x): 0.6983, D(G(z)): 0.2719
Epoch: [19/20], Batch Num: [77/600]
Discriminator Loss: 0.9602, Generator Loss: 1.2999
D(x): 0.7464, D(G(z)): 0.3865
Epoch: [19/20], Batch Num: [78/600]
Discriminator Loss: 0.9818, Generator Loss: 1.3129
D(x): 0.7161, D(G(z)): 0.3769
Epoch: [19/20], Batch Num: [79/600]
Discriminator Loss: 0.9019, Generator Loss: 1.6120
D(x): 0.7011, D(G(z)): 0.3135
Epoch: [19/20], Batch Num: [80/600]
Discriminator Loss: 0.8647, Generator Loss: 1.5825
D(x): 0.6940, D(G(z)): 0.2577
Epoch: [19/20], Batch Num: [81/600]
Discriminator Loss: 1.0243, Generator Loss: 1.7100
D(x): 0.6199, D(G(z)): 0.2614
Epoch: [19/20], Batch Num: [82/600]
Discriminator Loss: 0.8078, Generator Loss: 1.6879
D(x): 0.6945, D(G(z)): 0.2685
Epoch: [19/20], Batch Num: [83/600]
Discriminator Loss: 0.8748, Generator Loss: 1.3910
D(x): 0.7087, D(G(z)): 0.2882
Epoch: [19/20], Batch Num: [84/600]
Discriminator Loss: 0.7890, Generator Loss: 1.3537
D(x): 0.7150, D(G(z)): 0.2517
Epoch: [19/20], Batch Num: [85/600]
Discriminator Loss: 0.8501, Generator Loss: 1.3735
D(x): 0.7344, D(G(z)): 0.3022
Epoch: [19/20], Batch Num: [86/600]
Discriminator Loss: 1.0233, Generator Loss: 1.3154
D(x): 0.7628, D(G(z)): 0.4079
Epoch: [19/20], Batch Num: [87/600]
Discriminator Loss: 0.8532, Generator Loss: 1.5894
D(x): 0.6952, D(G(z)): 0.2771
Epoch: [19/20], Batch Num: [88/600]
Discriminator Loss: 0.8664, Generator Loss: 1.7698
D(x): 0.7254, D(G(z)): 0.2962
Epoch: [19/20], Batch Num: [89/600]
Discriminator Loss: 0.7291, Generator Loss: 1.7858
D(x): 0.7414, D(G(z)): 0.2362
Epoch: [19/20], Batch Num: [90/600]
Discriminator Loss: 0.9207, Generator Loss: 1.7898
D(x): 0.7003, D(G(z)): 0.2804
Epoch: [19/20], Batch Num: [91/600]
Discriminator Loss: 0.8781, Generator Loss: 1.9221
D(x): 0.6583, D(G(z)): 0.2193
Epoch: [19/20], Batch Num: [92/600]
Discriminator Loss: 0.6982, Generator Loss: 1.6918
D(x): 0.7342, D(G(z)): 0.2162
Epoch: [19/20], Batch Num: [93/600]
Discriminator Loss: 0.9473, Generator Loss: 1.5118
D(x): 0.7167, D(G(z)): 0.2857
Epoch: [19/20], Batch Num: [94/600]
Discriminator Loss: 0.9198, Generator Loss: 1.4684
D(x): 0.7393, D(G(z)): 0.3111
Epoch: [19/20], Batch Num: [95/600]
Discriminator Loss: 0.8709, Generator Loss: 1.4724
D(x): 0.7200, D(G(z)): 0.2700
Epoch: [19/20], Batch Num: [96/600]
Discriminator Loss: 0.8763, Generator Loss: 1.4846
D(x): 0.7501, D(G(z)): 0.3107
Epoch: [19/20], Batch Num: [97/600]
Discriminator Loss: 0.8609, Generator Loss: 1.5561
D(x): 0.7326, D(G(z)): 0.2783
Epoch: [19/20], Batch Num: [98/600]
Discriminator Loss: 0.9739, Generator Loss: 1.4598
D(x): 0.6514, D(G(z)): 0.2545
Epoch: [19/20], Batch Num: [99/600]
Discriminator Loss: 0.9460, Generator Loss: 1.5308
D(x): 0.6792, D(G(z)): 0.2704
Epoch: 19, Batch Num: [100/600]

Epoch: [19/20], Batch Num: [100/600]
Discriminator Loss: 0.9800, Generator Loss: 1.3924
D(x): 0.6869, D(G(z)): 0.2948
Epoch: [19/20], Batch Num: [101/600]
Discriminator Loss: 0.8135, Generator Loss: 1.2850
D(x): 0.7564, D(G(z)): 0.3058
Epoch: [19/20], Batch Num: [102/600]
Discriminator Loss: 0.8296, Generator Loss: 1.4918
D(x): 0.7818, D(G(z)): 0.3595
Epoch: [19/20], Batch Num: [103/600]
Discriminator Loss: 0.8700, Generator Loss: 1.3564
D(x): 0.6999, D(G(z)): 0.2968
Epoch: [19/20], Batch Num: [104/600]
Discriminator Loss: 0.8178, Generator Loss: 1.5716
D(x): 0.7494, D(G(z)): 0.3037
Epoch: [19/20], Batch Num: [105/600]
Discriminator Loss: 0.8099, Generator Loss: 1.5713
D(x): 0.7063, D(G(z)): 0.2897
Epoch: [19/20], Batch Num: [106/600]
Discriminator Loss: 1.0170, Generator Loss: 1.5526
D(x): 0.6623, D(G(z)): 0.2899
Epoch: [19/20], Batch Num: [107/600]
Discriminator Loss: 0.9456, Generator Loss: 1.5736
D(x): 0.6943, D(G(z)): 0.2962
Epoch: [19/20], Batch Num: [108/600]
Discriminator Loss: 0.9406, Generator Loss: 1.6081
D(x): 0.7247, D(G(z)): 0.3155
Epoch: [19/20], Batch Num: [109/600]
Discriminator Loss: 0.9007, Generator Loss: 1.5260
D(x): 0.6815, D(G(z)): 0.2760
Epoch: [19/20], Batch Num: [110/600]
Discriminator Loss: 0.9308, Generator Loss: 1.4743
D(x): 0.7073, D(G(z)): 0.3111
Epoch: [19/20], Batch Num: [111/600]
Discriminator Loss: 1.0010, Generator Loss: 1.5058
D(x): 0.6619, D(G(z)): 0.2886
Epoch: [19/20], Batch Num: [112/600]
Discriminator Loss: 0.9788, Generator Loss: 1.4783
D(x): 0.6776, D(G(z)): 0.2890
Epoch: [19/20], Batch Num: [113/600]
Discriminator Loss: 1.0441, Generator Loss: 1.4208
D(x): 0.7285, D(G(z)): 0.3569
Epoch: [19/20], Batch Num: [114/600]
Discriminator Loss: 0.8379, Generator Loss: 1.5831
D(x): 0.7441, D(G(z)): 0.3079
Epoch: [19/20], Batch Num: [115/600]
Discriminator Loss: 0.8778, Generator Loss: 1.5301
D(x): 0.6657, D(G(z)): 0.2507
Epoch: [19/20], Batch Num: [116/600]
Discriminator Loss: 0.8892, Generator Loss: 1.5607
D(x): 0.7041, D(G(z)): 0.2787
Epoch: [19/20], Batch Num: [117/600]
Discriminator Loss: 0.8412, Generator Loss: 1.4837
D(x): 0.7012, D(G(z)): 0.2683
Epoch: [19/20], Batch Num: [118/600]
Discriminator Loss: 0.8598, Generator Loss: 1.4087
D(x): 0.7051, D(G(z)): 0.2832
Epoch: [19/20], Batch Num: [119/600]
Discriminator Loss: 0.9085, Generator Loss: 1.4486
D(x): 0.6818, D(G(z)): 0.2875
Epoch: [19/20], Batch Num: [120/600]
Discriminator Loss: 0.9461, Generator Loss: 1.3536
D(x): 0.7053, D(G(z)): 0.3492
Epoch: [19/20], Batch Num: [121/600]
Discriminator Loss: 0.8200, Generator Loss: 1.5265
D(x): 0.7347, D(G(z)): 0.3024
Epoch: [19/20], Batch Num: [122/600]
Discriminator Loss: 0.7937, Generator Loss: 1.5479
D(x): 0.7307, D(G(z)): 0.2996
Epoch: [19/20], Batch Num: [123/600]
Discriminator Loss: 0.9045, Generator Loss: 1.5819
D(x): 0.7378, D(G(z)): 0.3312
Epoch: [19/20], Batch Num: [124/600]
Discriminator Loss: 0.9521, Generator Loss: 1.5686
D(x): 0.6907, D(G(z)): 0.3265
Epoch: [19/20], Batch Num: [125/600]
Discriminator Loss: 0.8000, Generator Loss: 1.6239
D(x): 0.7371, D(G(z)): 0.2761
Epoch: [19/20], Batch Num: [126/600]
Discriminator Loss: 0.9228, Generator Loss: 1.6442
D(x): 0.6678, D(G(z)): 0.2742
Epoch: [19/20], Batch Num: [127/600]
Discriminator Loss: 0.8724, Generator Loss: 1.5165
D(x): 0.6961, D(G(z)): 0.2869
Epoch: [19/20], Batch Num: [128/600]
Discriminator Loss: 0.9007, Generator Loss: 1.5654
D(x): 0.6762, D(G(z)): 0.2699
Epoch: [19/20], Batch Num: [129/600]
Discriminator Loss: 1.0092, Generator Loss: 1.4416
D(x): 0.6554, D(G(z)): 0.2860
Epoch: [19/20], Batch Num: [130/600]
Discriminator Loss: 1.0256, Generator Loss: 1.3341
D(x): 0.7085, D(G(z)): 0.3417
Epoch: [19/20], Batch Num: [131/600]
Discriminator Loss: 0.8608, Generator Loss: 1.4272
D(x): 0.7241, D(G(z)): 0.2864
Epoch: [19/20], Batch Num: [132/600]
Discriminator Loss: 0.7901, Generator Loss: 1.4543
D(x): 0.7287, D(G(z)): 0.2689
Epoch: [19/20], Batch Num: [133/600]
Discriminator Loss: 0.9212, Generator Loss: 1.5508
D(x): 0.7324, D(G(z)): 0.3104
Epoch: [19/20], Batch Num: [134/600]
Discriminator Loss: 0.8998, Generator Loss: 1.6315
D(x): 0.7028, D(G(z)): 0.2988
Epoch: [19/20], Batch Num: [135/600]
Discriminator Loss: 0.8127, Generator Loss: 1.9348
D(x): 0.7610, D(G(z)): 0.3059
Epoch: [19/20], Batch Num: [136/600]
Discriminator Loss: 0.8542, Generator Loss: 1.9761
D(x): 0.6989, D(G(z)): 0.2461
Epoch: [19/20], Batch Num: [137/600]
Discriminator Loss: 0.7740, Generator Loss: 1.8050
D(x): 0.7057, D(G(z)): 0.2106
Epoch: [19/20], Batch Num: [138/600]
Discriminator Loss: 0.8065, Generator Loss: 1.6738
D(x): 0.7254, D(G(z)): 0.2567
Epoch: [19/20], Batch Num: [139/600]
Discriminator Loss: 0.8939, Generator Loss: 1.5044
D(x): 0.6848, D(G(z)): 0.2469
Epoch: [19/20], Batch Num: [140/600]
Discriminator Loss: 0.8964, Generator Loss: 1.3931
D(x): 0.7050, D(G(z)): 0.2866
Epoch: [19/20], Batch Num: [141/600]
Discriminator Loss: 0.8691, Generator Loss: 1.2930
D(x): 0.7493, D(G(z)): 0.3105
Epoch: [19/20], Batch Num: [142/600]
Discriminator Loss: 0.9627, Generator Loss: 1.5249
D(x): 0.7611, D(G(z)): 0.3678
Epoch: [19/20], Batch Num: [143/600]
Discriminator Loss: 0.7602, Generator Loss: 1.7292
D(x): 0.7749, D(G(z)): 0.2884
Epoch: [19/20], Batch Num: [144/600]
Discriminator Loss: 0.8676, Generator Loss: 1.7369
D(x): 0.6659, D(G(z)): 0.2386
Epoch: [19/20], Batch Num: [145/600]
Discriminator Loss: 0.8393, Generator Loss: 1.8670
D(x): 0.6838, D(G(z)): 0.2192
Epoch: [19/20], Batch Num: [146/600]
Discriminator Loss: 0.9527, Generator Loss: 1.3126
D(x): 0.6541, D(G(z)): 0.2702
Epoch: [19/20], Batch Num: [147/600]
Discriminator Loss: 0.9518, Generator Loss: 1.4770
D(x): 0.7122, D(G(z)): 0.3026
Epoch: [19/20], Batch Num: [148/600]
Discriminator Loss: 0.9233, Generator Loss: 1.4536
D(x): 0.7100, D(G(z)): 0.3075
Epoch: [19/20], Batch Num: [149/600]
Discriminator Loss: 0.9344, Generator Loss: 1.2996
D(x): 0.7233, D(G(z)): 0.3110
Epoch: [19/20], Batch Num: [150/600]
Discriminator Loss: 0.7477, Generator Loss: 1.4318
D(x): 0.7878, D(G(z)): 0.2961
Epoch: [19/20], Batch Num: [151/600]
Discriminator Loss: 0.8983, Generator Loss: 1.8421
D(x): 0.7073, D(G(z)): 0.3144
Epoch: [19/20], Batch Num: [152/600]
Discriminator Loss: 0.8219, Generator Loss: 1.7723
D(x): 0.7116, D(G(z)): 0.2391
Epoch: [19/20], Batch Num: [153/600]
Discriminator Loss: 0.6683, Generator Loss: 1.7845
D(x): 0.7564, D(G(z)): 0.2471
Epoch: [19/20], Batch Num: [154/600]
Discriminator Loss: 0.8786, Generator Loss: 1.6971
D(x): 0.6819, D(G(z)): 0.2504
Epoch: [19/20], Batch Num: [155/600]
Discriminator Loss: 0.8119, Generator Loss: 1.5742
D(x): 0.7118, D(G(z)): 0.2573
Epoch: [19/20], Batch Num: [156/600]
Discriminator Loss: 0.8526, Generator Loss: 1.3661
D(x): 0.7344, D(G(z)): 0.3150
Epoch: [19/20], Batch Num: [157/600]
Discriminator Loss: 0.8321, Generator Loss: 1.4271
D(x): 0.7558, D(G(z)): 0.3220
Epoch: [19/20], Batch Num: [158/600]
Discriminator Loss: 0.7834, Generator Loss: 1.6900
D(x): 0.7912, D(G(z)): 0.3346
Epoch: [19/20], Batch Num: [159/600]
Discriminator Loss: 0.9098, Generator Loss: 1.8792
D(x): 0.7224, D(G(z)): 0.3142
Epoch: [19/20], Batch Num: [160/600]
Discriminator Loss: 0.9203, Generator Loss: 1.9701
D(x): 0.6830, D(G(z)): 0.2685
Epoch: [19/20], Batch Num: [161/600]
Discriminator Loss: 0.8184, Generator Loss: 1.9762
D(x): 0.6987, D(G(z)): 0.2195
Epoch: [19/20], Batch Num: [162/600]
Discriminator Loss: 0.9863, Generator Loss: 1.8183
D(x): 0.6417, D(G(z)): 0.2521
Epoch: [19/20], Batch Num: [163/600]
Discriminator Loss: 0.8724, Generator Loss: 1.5967
D(x): 0.7115, D(G(z)): 0.2741
Epoch: [19/20], Batch Num: [164/600]
Discriminator Loss: 1.0237, Generator Loss: 1.4460
D(x): 0.7130, D(G(z)): 0.3154
Epoch: [19/20], Batch Num: [165/600]
Discriminator Loss: 1.0421, Generator Loss: 1.4746
D(x): 0.7278, D(G(z)): 0.3549
Epoch: [19/20], Batch Num: [166/600]
Discriminator Loss: 0.8458, Generator Loss: 1.4966
D(x): 0.7705, D(G(z)): 0.3283
Epoch: [19/20], Batch Num: [167/600]
Discriminator Loss: 0.9731, Generator Loss: 1.5806
D(x): 0.7267, D(G(z)): 0.3427
Epoch: [19/20], Batch Num: [168/600]
Discriminator Loss: 0.8817, Generator Loss: 1.7561
D(x): 0.7096, D(G(z)): 0.2791
Epoch: [19/20], Batch Num: [169/600]
Discriminator Loss: 1.2152, Generator Loss: 1.6482
D(x): 0.5561, D(G(z)): 0.2691
Epoch: [19/20], Batch Num: [170/600]
Discriminator Loss: 0.9262, Generator Loss: 1.6385
D(x): 0.7099, D(G(z)): 0.2751
Epoch: [19/20], Batch Num: [171/600]
Discriminator Loss: 0.8967, Generator Loss: 1.2528
D(x): 0.6513, D(G(z)): 0.2515
Epoch: [19/20], Batch Num: [172/600]
Discriminator Loss: 0.9345, Generator Loss: 1.3599
D(x): 0.7576, D(G(z)): 0.3602
Epoch: [19/20], Batch Num: [173/600]
Discriminator Loss: 1.0534, Generator Loss: 1.2649
D(x): 0.6876, D(G(z)): 0.3743
Epoch: [19/20], Batch Num: [174/600]
Discriminator Loss: 0.9250, Generator Loss: 1.3847
D(x): 0.7568, D(G(z)): 0.3757
Epoch: [19/20], Batch Num: [175/600]
Discriminator Loss: 0.9750, Generator Loss: 1.2864
D(x): 0.6667, D(G(z)): 0.3105
Epoch: [19/20], Batch Num: [176/600]
Discriminator Loss: 0.9333, Generator Loss: 1.4051
D(x): 0.7172, D(G(z)): 0.3172
Epoch: [19/20], Batch Num: [177/600]
Discriminator Loss: 0.9964, Generator Loss: 1.6387
D(x): 0.6453, D(G(z)): 0.3027
Epoch: [19/20], Batch Num: [178/600]
Discriminator Loss: 0.9414, Generator Loss: 1.4945
D(x): 0.6843, D(G(z)): 0.3140
Epoch: [19/20], Batch Num: [179/600]
Discriminator Loss: 0.9167, Generator Loss: 1.4165
D(x): 0.6484, D(G(z)): 0.2632
Epoch: [19/20], Batch Num: [180/600]
Discriminator Loss: 0.8993, Generator Loss: 1.2965
D(x): 0.6906, D(G(z)): 0.2864
Epoch: [19/20], Batch Num: [181/600]
Discriminator Loss: 0.9566, Generator Loss: 1.3028
D(x): 0.6895, D(G(z)): 0.3205
Epoch: [19/20], Batch Num: [182/600]
Discriminator Loss: 0.9623, Generator Loss: 1.3521
D(x): 0.6810, D(G(z)): 0.3203
Epoch: [19/20], Batch Num: [183/600]
Discriminator Loss: 0.8813, Generator Loss: 1.3869
D(x): 0.7080, D(G(z)): 0.3097
Epoch: [19/20], Batch Num: [184/600]
Discriminator Loss: 1.0015, Generator Loss: 1.2512
D(x): 0.7092, D(G(z)): 0.3484
Epoch: [19/20], Batch Num: [185/600]
Discriminator Loss: 0.8670, Generator Loss: 1.5007
D(x): 0.7661, D(G(z)): 0.3540
Epoch: [19/20], Batch Num: [186/600]
Discriminator Loss: 0.8825, Generator Loss: 1.4775
D(x): 0.7181, D(G(z)): 0.3062
Epoch: [19/20], Batch Num: [187/600]
Discriminator Loss: 0.9726, Generator Loss: 1.6888
D(x): 0.6367, D(G(z)): 0.2807
Epoch: [19/20], Batch Num: [188/600]
Discriminator Loss: 0.9012, Generator Loss: 1.7244
D(x): 0.6740, D(G(z)): 0.2868
Epoch: [19/20], Batch Num: [189/600]
Discriminator Loss: 0.8395, Generator Loss: 1.6500
D(x): 0.7127, D(G(z)): 0.2586
Epoch: [19/20], Batch Num: [190/600]
Discriminator Loss: 0.8845, Generator Loss: 1.3507
D(x): 0.6930, D(G(z)): 0.2697
Epoch: [19/20], Batch Num: [191/600]
Discriminator Loss: 0.9552, Generator Loss: 1.3555
D(x): 0.6835, D(G(z)): 0.3126
Epoch: [19/20], Batch Num: [192/600]
Discriminator Loss: 0.8283, Generator Loss: 1.1808
D(x): 0.7288, D(G(z)): 0.3028
Epoch: [19/20], Batch Num: [193/600]
Discriminator Loss: 0.9157, Generator Loss: 1.3389
D(x): 0.7483, D(G(z)): 0.3542
Epoch: [19/20], Batch Num: [194/600]
Discriminator Loss: 0.8190, Generator Loss: 1.4812
D(x): 0.7520, D(G(z)): 0.2923
Epoch: [19/20], Batch Num: [195/600]
Discriminator Loss: 0.8694, Generator Loss: 1.6857
D(x): 0.7239, D(G(z)): 0.3000
Epoch: [19/20], Batch Num: [196/600]
Discriminator Loss: 0.8965, Generator Loss: 1.6278
D(x): 0.7305, D(G(z)): 0.2997
Epoch: [19/20], Batch Num: [197/600]
Discriminator Loss: 0.8137, Generator Loss: 1.7152
D(x): 0.7069, D(G(z)): 0.2257
Epoch: [19/20], Batch Num: [198/600]
Discriminator Loss: 0.9427, Generator Loss: 1.6854
D(x): 0.6642, D(G(z)): 0.2489
Epoch: [19/20], Batch Num: [199/600]
Discriminator Loss: 1.0480, Generator Loss: 1.6172
D(x): 0.6671, D(G(z)): 0.3000
Epoch: 19, Batch Num: [200/600]

Epoch: [19/20], Batch Num: [200/600]
Discriminator Loss: 0.9058, Generator Loss: 1.4391
D(x): 0.6909, D(G(z)): 0.2592
Epoch: [19/20], Batch Num: [201/600]
Discriminator Loss: 0.7562, Generator Loss: 1.3552
D(x): 0.7720, D(G(z)): 0.2836
Epoch: [19/20], Batch Num: [202/600]
Discriminator Loss: 0.8986, Generator Loss: 1.4110
D(x): 0.7315, D(G(z)): 0.3231
Epoch: [19/20], Batch Num: [203/600]
Discriminator Loss: 0.9569, Generator Loss: 1.5063
D(x): 0.7213, D(G(z)): 0.3262
Epoch: [19/20], Batch Num: [204/600]
Discriminator Loss: 0.8611, Generator Loss: 1.6077
D(x): 0.7576, D(G(z)): 0.3254
Epoch: [19/20], Batch Num: [205/600]
Discriminator Loss: 1.0166, Generator Loss: 1.7772
D(x): 0.6527, D(G(z)): 0.3008
Epoch: [19/20], Batch Num: [206/600]
Discriminator Loss: 1.0794, Generator Loss: 1.5981
D(x): 0.5907, D(G(z)): 0.2518
Epoch: [19/20], Batch Num: [207/600]
Discriminator Loss: 0.8582, Generator Loss: 1.4476
D(x): 0.7119, D(G(z)): 0.2854
Epoch: [19/20], Batch Num: [208/600]
Discriminator Loss: 0.8872, Generator Loss: 1.4285
D(x): 0.6960, D(G(z)): 0.2891
Epoch: [19/20], Batch Num: [209/600]
Discriminator Loss: 0.9472, Generator Loss: 1.3840
D(x): 0.7130, D(G(z)): 0.3301
Epoch: [19/20], Batch Num: [210/600]
Discriminator Loss: 0.9180, Generator Loss: 1.3428
D(x): 0.7399, D(G(z)): 0.3398
Epoch: [19/20], Batch Num: [211/600]
Discriminator Loss: 0.8215, Generator Loss: 1.5588
D(x): 0.7351, D(G(z)): 0.2951
Epoch: [19/20], Batch Num: [212/600]
Discriminator Loss: 0.9381, Generator Loss: 1.6439
D(x): 0.6763, D(G(z)): 0.2989
Epoch: [19/20], Batch Num: [213/600]
Discriminator Loss: 0.8798, Generator Loss: 1.6256
D(x): 0.6655, D(G(z)): 0.2651
Epoch: [19/20], Batch Num: [214/600]
Discriminator Loss: 0.9909, Generator Loss: 1.5401
D(x): 0.6050, D(G(z)): 0.2616
Epoch: [19/20], Batch Num: [215/600]
Discriminator Loss: 0.9312, Generator Loss: 1.5625
D(x): 0.6470, D(G(z)): 0.2704
Epoch: [19/20], Batch Num: [216/600]
Discriminator Loss: 0.8880, Generator Loss: 1.3741
D(x): 0.6750, D(G(z)): 0.2972
Epoch: [19/20], Batch Num: [217/600]
Discriminator Loss: 0.8646, Generator Loss: 1.3208
D(x): 0.7388, D(G(z)): 0.3189
Epoch: [19/20], Batch Num: [218/600]
Discriminator Loss: 0.9361, Generator Loss: 1.4339
D(x): 0.7710, D(G(z)): 0.3727
Epoch: [19/20], Batch Num: [219/600]
Discriminator Loss: 0.8735, Generator Loss: 1.5858
D(x): 0.7602, D(G(z)): 0.3411
Epoch: [19/20], Batch Num: [220/600]
Discriminator Loss: 0.8452, Generator Loss: 1.9485
D(x): 0.6914, D(G(z)): 0.2653
Epoch: [19/20], Batch Num: [221/600]
Discriminator Loss: 0.9088, Generator Loss: 1.7353
D(x): 0.6417, D(G(z)): 0.2423
Epoch: [19/20], Batch Num: [222/600]
Discriminator Loss: 0.9094, Generator Loss: 1.7105
D(x): 0.6455, D(G(z)): 0.2529
Epoch: [19/20], Batch Num: [223/600]
Discriminator Loss: 0.8996, Generator Loss: 1.5444
D(x): 0.6727, D(G(z)): 0.2603
Epoch: [19/20], Batch Num: [224/600]
Discriminator Loss: 0.9510, Generator Loss: 1.3343
D(x): 0.6815, D(G(z)): 0.2859
Epoch: [19/20], Batch Num: [225/600]
Discriminator Loss: 0.7854, Generator Loss: 1.3128
D(x): 0.7446, D(G(z)): 0.2913
Epoch: [19/20], Batch Num: [226/600]
Discriminator Loss: 0.8203, Generator Loss: 1.3611
D(x): 0.7931, D(G(z)): 0.3459
Epoch: [19/20], Batch Num: [227/600]
Discriminator Loss: 1.0959, Generator Loss: 1.6287
D(x): 0.7597, D(G(z)): 0.4262
Epoch: [19/20], Batch Num: [228/600]
Discriminator Loss: 0.8661, Generator Loss: 2.0260
D(x): 0.6965, D(G(z)): 0.2641
Epoch: [19/20], Batch Num: [229/600]
Discriminator Loss: 0.9421, Generator Loss: 1.9051
D(x): 0.6235, D(G(z)): 0.2018
Epoch: [19/20], Batch Num: [230/600]
Discriminator Loss: 1.0308, Generator Loss: 1.8152
D(x): 0.6135, D(G(z)): 0.2340
Epoch: [19/20], Batch Num: [231/600]
Discriminator Loss: 0.8568, Generator Loss: 1.5261
D(x): 0.7224, D(G(z)): 0.2715
Epoch: [19/20], Batch Num: [232/600]
Discriminator Loss: 0.9281, Generator Loss: 1.3603
D(x): 0.7248, D(G(z)): 0.2964
Epoch: [19/20], Batch Num: [233/600]
Discriminator Loss: 1.0042, Generator Loss: 1.3400
D(x): 0.7332, D(G(z)): 0.3546
Epoch: [19/20], Batch Num: [234/600]
Discriminator Loss: 0.9803, Generator Loss: 1.2618
D(x): 0.7222, D(G(z)): 0.3489
Epoch: [19/20], Batch Num: [235/600]
Discriminator Loss: 0.7940, Generator Loss: 1.4796
D(x): 0.7533, D(G(z)): 0.2999
Epoch: [19/20], Batch Num: [236/600]
Discriminator Loss: 0.9067, Generator Loss: 1.4509
D(x): 0.6962, D(G(z)): 0.3186
Epoch: [19/20], Batch Num: [237/600]
Discriminator Loss: 0.8537, Generator Loss: 1.5112
D(x): 0.7433, D(G(z)): 0.3334
Epoch: [19/20], Batch Num: [238/600]
Discriminator Loss: 0.9766, Generator Loss: 1.7196
D(x): 0.6579, D(G(z)): 0.2918
Epoch: [19/20], Batch Num: [239/600]
Discriminator Loss: 1.0358, Generator Loss: 1.6338
D(x): 0.6664, D(G(z)): 0.3106
Epoch: [19/20], Batch Num: [240/600]
Discriminator Loss: 0.9550, Generator Loss: 1.4426
D(x): 0.6357, D(G(z)): 0.2629
Epoch: [19/20], Batch Num: [241/600]
Discriminator Loss: 0.8864, Generator Loss: 1.5020
D(x): 0.6933, D(G(z)): 0.3038
Epoch: [19/20], Batch Num: [242/600]
Discriminator Loss: 0.8218, Generator Loss: 1.2980
D(x): 0.7036, D(G(z)): 0.2767
Epoch: [19/20], Batch Num: [243/600]
Discriminator Loss: 0.9916, Generator Loss: 1.2596
D(x): 0.7089, D(G(z)): 0.3340
Epoch: [19/20], Batch Num: [244/600]
Discriminator Loss: 0.7627, Generator Loss: 1.3511
D(x): 0.7688, D(G(z)): 0.3187
Epoch: [19/20], Batch Num: [245/600]
Discriminator Loss: 1.0480, Generator Loss: 1.2694
D(x): 0.6930, D(G(z)): 0.3657
Epoch: [19/20], Batch Num: [246/600]
Discriminator Loss: 0.8873, Generator Loss: 1.4302
D(x): 0.7264, D(G(z)): 0.3156
Epoch: [19/20], Batch Num: [247/600]
Discriminator Loss: 1.0604, Generator Loss: 1.5110
D(x): 0.6775, D(G(z)): 0.3252
Epoch: [19/20], Batch Num: [248/600]
Discriminator Loss: 0.8886, Generator Loss: 1.4386
D(x): 0.6909, D(G(z)): 0.2896
Epoch: [19/20], Batch Num: [249/600]
Discriminator Loss: 0.9621, Generator Loss: 1.4295
D(x): 0.6510, D(G(z)): 0.2802
Epoch: [19/20], Batch Num: [250/600]
Discriminator Loss: 0.8720, Generator Loss: 1.3530
D(x): 0.6866, D(G(z)): 0.2887
Epoch: [19/20], Batch Num: [251/600]
Discriminator Loss: 0.8824, Generator Loss: 1.4072
D(x): 0.7104, D(G(z)): 0.3133
Epoch: [19/20], Batch Num: [252/600]
Discriminator Loss: 0.9498, Generator Loss: 1.4265
D(x): 0.7167, D(G(z)): 0.3574
Epoch: [19/20], Batch Num: [253/600]
Discriminator Loss: 0.9872, Generator Loss: 1.5225
D(x): 0.6752, D(G(z)): 0.3240
Epoch: [19/20], Batch Num: [254/600]
Discriminator Loss: 0.9332, Generator Loss: 1.3343
D(x): 0.7019, D(G(z)): 0.3137
Epoch: [19/20], Batch Num: [255/600]
Discriminator Loss: 0.8151, Generator Loss: 1.4275
D(x): 0.7557, D(G(z)): 0.3153
Epoch: [19/20], Batch Num: [256/600]
Discriminator Loss: 0.8675, Generator Loss: 1.6311
D(x): 0.6921, D(G(z)): 0.2700
Epoch: [19/20], Batch Num: [257/600]
Discriminator Loss: 0.8683, Generator Loss: 1.7418
D(x): 0.6974, D(G(z)): 0.2912
Epoch: [19/20], Batch Num: [258/600]
Discriminator Loss: 0.9808, Generator Loss: 1.6744
D(x): 0.6455, D(G(z)): 0.2695
Epoch: [19/20], Batch Num: [259/600]
Discriminator Loss: 0.8421, Generator Loss: 1.5100
D(x): 0.7202, D(G(z)): 0.2823
Epoch: [19/20], Batch Num: [260/600]
Discriminator Loss: 0.9586, Generator Loss: 1.4635
D(x): 0.6645, D(G(z)): 0.2742
Epoch: [19/20], Batch Num: [261/600]
Discriminator Loss: 0.9628, Generator Loss: 1.3879
D(x): 0.7126, D(G(z)): 0.3143
Epoch: [19/20], Batch Num: [262/600]
Discriminator Loss: 0.7958, Generator Loss: 1.4765
D(x): 0.7684, D(G(z)): 0.3219
Epoch: [19/20], Batch Num: [263/600]
Discriminator Loss: 0.9403, Generator Loss: 1.5386
D(x): 0.7027, D(G(z)): 0.3112
Epoch: [19/20], Batch Num: [264/600]
Discriminator Loss: 0.8561, Generator Loss: 1.7755
D(x): 0.7026, D(G(z)): 0.2785
Epoch: [19/20], Batch Num: [265/600]
Discriminator Loss: 0.9069, Generator Loss: 1.7213
D(x): 0.6922, D(G(z)): 0.2773
Epoch: [19/20], Batch Num: [266/600]
Discriminator Loss: 0.8027, Generator Loss: 1.7318
D(x): 0.7016, D(G(z)): 0.2311
Epoch: [19/20], Batch Num: [267/600]
Discriminator Loss: 0.9825, Generator Loss: 1.6228
D(x): 0.6809, D(G(z)): 0.2927
Epoch: [19/20], Batch Num: [268/600]
Discriminator Loss: 0.9209, Generator Loss: 1.3923
D(x): 0.7214, D(G(z)): 0.3212
Epoch: [19/20], Batch Num: [269/600]
Discriminator Loss: 1.0645, Generator Loss: 1.6311
D(x): 0.7684, D(G(z)): 0.4129
Epoch: [19/20], Batch Num: [270/600]
Discriminator Loss: 1.1350, Generator Loss: 1.6772
D(x): 0.6992, D(G(z)): 0.3533
Epoch: [19/20], Batch Num: [271/600]
Discriminator Loss: 0.9493, Generator Loss: 1.7333
D(x): 0.6627, D(G(z)): 0.2618
Epoch: [19/20], Batch Num: [272/600]
Discriminator Loss: 0.9468, Generator Loss: 1.8948
D(x): 0.7014, D(G(z)): 0.2779
Epoch: [19/20], Batch Num: [273/600]
Discriminator Loss: 0.9255, Generator Loss: 1.5818
D(x): 0.6495, D(G(z)): 0.2813
Epoch: [19/20], Batch Num: [274/600]
Discriminator Loss: 0.8728, Generator Loss: 1.1281
D(x): 0.6575, D(G(z)): 0.2430
Epoch: [19/20], Batch Num: [275/600]
Discriminator Loss: 0.9399, Generator Loss: 1.0661
D(x): 0.7129, D(G(z)): 0.3395
Epoch: [19/20], Batch Num: [276/600]
Discriminator Loss: 0.9653, Generator Loss: 1.1899
D(x): 0.7675, D(G(z)): 0.4014
Epoch: [19/20], Batch Num: [277/600]
Discriminator Loss: 0.9454, Generator Loss: 1.2360
D(x): 0.7879, D(G(z)): 0.4045
Epoch: [19/20], Batch Num: [278/600]
Discriminator Loss: 0.8497, Generator Loss: 1.5130
D(x): 0.7711, D(G(z)): 0.3460
Epoch: [19/20], Batch Num: [279/600]
Discriminator Loss: 0.8865, Generator Loss: 1.8037
D(x): 0.6555, D(G(z)): 0.2462
Epoch: [19/20], Batch Num: [280/600]
Discriminator Loss: 1.1027, Generator Loss: 1.6474
D(x): 0.5572, D(G(z)): 0.2296
Epoch: [19/20], Batch Num: [281/600]
Discriminator Loss: 0.9462, Generator Loss: 1.6611
D(x): 0.6685, D(G(z)): 0.2900
Epoch: [19/20], Batch Num: [282/600]
Discriminator Loss: 0.8333, Generator Loss: 1.2848
D(x): 0.6899, D(G(z)): 0.2801
Epoch: [19/20], Batch Num: [283/600]
Discriminator Loss: 0.8337, Generator Loss: 1.2431
D(x): 0.7398, D(G(z)): 0.2943
Epoch: [19/20], Batch Num: [284/600]
Discriminator Loss: 0.9691, Generator Loss: 1.2425
D(x): 0.7526, D(G(z)): 0.3740
Epoch: [19/20], Batch Num: [285/600]
Discriminator Loss: 0.9326, Generator Loss: 1.5399
D(x): 0.7831, D(G(z)): 0.3477
Epoch: [19/20], Batch Num: [286/600]
Discriminator Loss: 0.8852, Generator Loss: 1.6006
D(x): 0.7189, D(G(z)): 0.3000
Epoch: [19/20], Batch Num: [287/600]
Discriminator Loss: 0.8811, Generator Loss: 1.5916
D(x): 0.6674, D(G(z)): 0.2397
Epoch: [19/20], Batch Num: [288/600]
Discriminator Loss: 0.7760, Generator Loss: 1.7070
D(x): 0.6987, D(G(z)): 0.2357
Epoch: [19/20], Batch Num: [289/600]
Discriminator Loss: 0.8923, Generator Loss: 1.4348
D(x): 0.6813, D(G(z)): 0.2710
Epoch: [19/20], Batch Num: [290/600]
Discriminator Loss: 1.1127, Generator Loss: 1.3814
D(x): 0.6306, D(G(z)): 0.3411
Epoch: [19/20], Batch Num: [291/600]
Discriminator Loss: 0.9143, Generator Loss: 1.3523
D(x): 0.7003, D(G(z)): 0.3248
Epoch: [19/20], Batch Num: [292/600]
Discriminator Loss: 0.8379, Generator Loss: 1.3045
D(x): 0.7471, D(G(z)): 0.3210
Epoch: [19/20], Batch Num: [293/600]
Discriminator Loss: 0.9777, Generator Loss: 1.4087
D(x): 0.7360, D(G(z)): 0.3657
Epoch: [19/20], Batch Num: [294/600]
Discriminator Loss: 0.9211, Generator Loss: 1.5203
D(x): 0.6766, D(G(z)): 0.2881
Epoch: [19/20], Batch Num: [295/600]
Discriminator Loss: 0.9349, Generator Loss: 1.4592
D(x): 0.6523, D(G(z)): 0.2612
Epoch: [19/20], Batch Num: [296/600]
Discriminator Loss: 0.9068, Generator Loss: 1.4206
D(x): 0.6848, D(G(z)): 0.2916
Epoch: [19/20], Batch Num: [297/600]
Discriminator Loss: 0.8525, Generator Loss: 1.1751
D(x): 0.7147, D(G(z)): 0.2922
Epoch: [19/20], Batch Num: [298/600]
Discriminator Loss: 0.9600, Generator Loss: 1.2369
D(x): 0.6949, D(G(z)): 0.3280
Epoch: [19/20], Batch Num: [299/600]
Discriminator Loss: 1.0510, Generator Loss: 1.2876
D(x): 0.6995, D(G(z)): 0.3845
Epoch: 19, Batch Num: [300/600]

Epoch: [19/20], Batch Num: [300/600]
Discriminator Loss: 0.8833, Generator Loss: 1.2749
D(x): 0.7254, D(G(z)): 0.3354
Epoch: [19/20], Batch Num: [301/600]
Discriminator Loss: 1.0194, Generator Loss: 1.4937
D(x): 0.6987, D(G(z)): 0.3665
Epoch: [19/20], Batch Num: [302/600]
Discriminator Loss: 0.9390, Generator Loss: 1.5337
D(x): 0.6434, D(G(z)): 0.2609
Epoch: [19/20], Batch Num: [303/600]
Discriminator Loss: 0.8608, Generator Loss: 1.5724
D(x): 0.6772, D(G(z)): 0.2524
Epoch: [19/20], Batch Num: [304/600]
Discriminator Loss: 0.8539, Generator Loss: 1.4819
D(x): 0.6990, D(G(z)): 0.2613
Epoch: [19/20], Batch Num: [305/600]
Discriminator Loss: 0.9004, Generator Loss: 1.4921
D(x): 0.6947, D(G(z)): 0.2939
Epoch: [19/20], Batch Num: [306/600]
Discriminator Loss: 0.8893, Generator Loss: 1.4048
D(x): 0.6917, D(G(z)): 0.3054
Epoch: [19/20], Batch Num: [307/600]
Discriminator Loss: 0.9978, Generator Loss: 1.2864
D(x): 0.7124, D(G(z)): 0.3285
Epoch: [19/20], Batch Num: [308/600]
Discriminator Loss: 0.9456, Generator Loss: 1.3427
D(x): 0.7393, D(G(z)): 0.3607
Epoch: [19/20], Batch Num: [309/600]
Discriminator Loss: 0.8684, Generator Loss: 1.4248
D(x): 0.7224, D(G(z)): 0.3278
Epoch: [19/20], Batch Num: [310/600]
Discriminator Loss: 0.9908, Generator Loss: 1.6642
D(x): 0.6719, D(G(z)): 0.3017
Epoch: [19/20], Batch Num: [311/600]
Discriminator Loss: 1.0134, Generator Loss: 1.6247
D(x): 0.6440, D(G(z)): 0.2773
Epoch: [19/20], Batch Num: [312/600]
Discriminator Loss: 1.0139, Generator Loss: 1.6435
D(x): 0.6221, D(G(z)): 0.2773
Epoch: [19/20], Batch Num: [313/600]
Discriminator Loss: 0.9194, Generator Loss: 1.4973
D(x): 0.6740, D(G(z)): 0.2852
Epoch: [19/20], Batch Num: [314/600]
Discriminator Loss: 0.9892, Generator Loss: 1.3741
D(x): 0.6793, D(G(z)): 0.3165
Epoch: [19/20], Batch Num: [315/600]
Discriminator Loss: 1.1073, Generator Loss: 1.4017
D(x): 0.6024, D(G(z)): 0.3114
Epoch: [19/20], Batch Num: [316/600]
Discriminator Loss: 0.8855, Generator Loss: 1.3107
D(x): 0.7091, D(G(z)): 0.2953
Epoch: [19/20], Batch Num: [317/600]
Discriminator Loss: 0.8900, Generator Loss: 1.1912
D(x): 0.7228, D(G(z)): 0.3262
Epoch: [19/20], Batch Num: [318/600]
Discriminator Loss: 0.8631, Generator Loss: 1.3377
D(x): 0.7205, D(G(z)): 0.3390
Epoch: [19/20], Batch Num: [319/600]
Discriminator Loss: 0.9616, Generator Loss: 1.2934
D(x): 0.7086, D(G(z)): 0.3238
Epoch: [19/20], Batch Num: [320/600]
Discriminator Loss: 0.9466, Generator Loss: 1.3359
D(x): 0.6801, D(G(z)): 0.3272
Epoch: [19/20], Batch Num: [321/600]
Discriminator Loss: 0.8255, Generator Loss: 1.3108
D(x): 0.7117, D(G(z)): 0.2898
Epoch: [19/20], Batch Num: [322/600]
Discriminator Loss: 0.9162, Generator Loss: 1.4812
D(x): 0.6623, D(G(z)): 0.2893
Epoch: [19/20], Batch Num: [323/600]
Discriminator Loss: 0.9008, Generator Loss: 1.4525
D(x): 0.7280, D(G(z)): 0.3166
Epoch: [19/20], Batch Num: [324/600]
Discriminator Loss: 0.9023, Generator Loss: 1.4071
D(x): 0.7244, D(G(z)): 0.3519
Epoch: [19/20], Batch Num: [325/600]
Discriminator Loss: 0.9275, Generator Loss: 1.5161
D(x): 0.6400, D(G(z)): 0.2805
Epoch: [19/20], Batch Num: [326/600]
Discriminator Loss: 0.8361, Generator Loss: 1.2989
D(x): 0.7224, D(G(z)): 0.2901
Epoch: [19/20], Batch Num: [327/600]
Discriminator Loss: 0.9740, Generator Loss: 1.3328
D(x): 0.6863, D(G(z)): 0.3203
Epoch: [19/20], Batch Num: [328/600]
Discriminator Loss: 0.9079, Generator Loss: 1.3956
D(x): 0.6971, D(G(z)): 0.3071
Epoch: [19/20], Batch Num: [329/600]
Discriminator Loss: 0.9038, Generator Loss: 1.4425
D(x): 0.7112, D(G(z)): 0.3438
Epoch: [19/20], Batch Num: [330/600]
Discriminator Loss: 0.9828, Generator Loss: 1.4709
D(x): 0.6536, D(G(z)): 0.2988
Epoch: [19/20], Batch Num: [331/600]
Discriminator Loss: 0.7834, Generator Loss: 1.3883
D(x): 0.7290, D(G(z)): 0.2711
Epoch: [19/20], Batch Num: [332/600]
Discriminator Loss: 0.7475, Generator Loss: 1.5433
D(x): 0.7572, D(G(z)): 0.2869
Epoch: [19/20], Batch Num: [333/600]
Discriminator Loss: 0.8541, Generator Loss: 1.5727
D(x): 0.6800, D(G(z)): 0.2696
Epoch: [19/20], Batch Num: [334/600]
Discriminator Loss: 0.8476, Generator Loss: 1.2439
D(x): 0.6877, D(G(z)): 0.2705
Epoch: [19/20], Batch Num: [335/600]
Discriminator Loss: 0.9286, Generator Loss: 1.3491
D(x): 0.7111, D(G(z)): 0.3271
Epoch: [19/20], Batch Num: [336/600]
Discriminator Loss: 0.8494, Generator Loss: 1.4931
D(x): 0.7443, D(G(z)): 0.3352
Epoch: [19/20], Batch Num: [337/600]
Discriminator Loss: 0.9225, Generator Loss: 1.5341
D(x): 0.7653, D(G(z)): 0.3620
Epoch: [19/20], Batch Num: [338/600]
Discriminator Loss: 0.8997, Generator Loss: 1.6939
D(x): 0.7028, D(G(z)): 0.2910
Epoch: [19/20], Batch Num: [339/600]
Discriminator Loss: 0.9460, Generator Loss: 1.5425
D(x): 0.6606, D(G(z)): 0.2551
Epoch: [19/20], Batch Num: [340/600]
Discriminator Loss: 0.8150, Generator Loss: 1.8371
D(x): 0.7138, D(G(z)): 0.2469
Epoch: [19/20], Batch Num: [341/600]
Discriminator Loss: 0.9357, Generator Loss: 1.5773
D(x): 0.6963, D(G(z)): 0.2719
Epoch: [19/20], Batch Num: [342/600]
Discriminator Loss: 0.9483, Generator Loss: 1.4162
D(x): 0.6939, D(G(z)): 0.2674
Epoch: [19/20], Batch Num: [343/600]
Discriminator Loss: 0.8242, Generator Loss: 1.6183
D(x): 0.7717, D(G(z)): 0.3228
Epoch: [19/20], Batch Num: [344/600]
Discriminator Loss: 1.0265, Generator Loss: 1.5420
D(x): 0.6985, D(G(z)): 0.3246
Epoch: [19/20], Batch Num: [345/600]
Discriminator Loss: 0.8013, Generator Loss: 1.5772
D(x): 0.7238, D(G(z)): 0.2255
Epoch: [19/20], Batch Num: [346/600]
Discriminator Loss: 0.8406, Generator Loss: 1.5416
D(x): 0.7272, D(G(z)): 0.2775
Epoch: [19/20], Batch Num: [347/600]
Discriminator Loss: 0.8985, Generator Loss: 1.6316
D(x): 0.7308, D(G(z)): 0.2873
Epoch: [19/20], Batch Num: [348/600]
Discriminator Loss: 1.0337, Generator Loss: 1.6273
D(x): 0.6846, D(G(z)): 0.3336
Epoch: [19/20], Batch Num: [349/600]
Discriminator Loss: 1.0206, Generator Loss: 1.5397
D(x): 0.6390, D(G(z)): 0.2883
Epoch: [19/20], Batch Num: [350/600]
Discriminator Loss: 0.8431, Generator Loss: 1.5367
D(x): 0.7123, D(G(z)): 0.2850
Epoch: [19/20], Batch Num: [351/600]
Discriminator Loss: 0.9545, Generator Loss: 1.4802
D(x): 0.6892, D(G(z)): 0.3117
Epoch: [19/20], Batch Num: [352/600]
Discriminator Loss: 0.8243, Generator Loss: 1.4572
D(x): 0.7511, D(G(z)): 0.3112
Epoch: [19/20], Batch Num: [353/600]
Discriminator Loss: 0.9521, Generator Loss: 1.5797
D(x): 0.6936, D(G(z)): 0.2980
Epoch: [19/20], Batch Num: [354/600]
Discriminator Loss: 1.1070, Generator Loss: 1.4859
D(x): 0.6553, D(G(z)): 0.3484
Epoch: [19/20], Batch Num: [355/600]
Discriminator Loss: 1.0311, Generator Loss: 1.4480
D(x): 0.6526, D(G(z)): 0.3010
Epoch: [19/20], Batch Num: [356/600]
Discriminator Loss: 0.8835, Generator Loss: 1.4551
D(x): 0.7141, D(G(z)): 0.3137
Epoch: [19/20], Batch Num: [357/600]
Discriminator Loss: 1.0116, Generator Loss: 1.4314
D(x): 0.7002, D(G(z)): 0.3361
Epoch: [19/20], Batch Num: [358/600]
Discriminator Loss: 0.9048, Generator Loss: 1.5120
D(x): 0.6999, D(G(z)): 0.2903
Epoch: [19/20], Batch Num: [359/600]
Discriminator Loss: 1.0061, Generator Loss: 1.5113
D(x): 0.6487, D(G(z)): 0.2987
Epoch: [19/20], Batch Num: [360/600]
Discriminator Loss: 0.8866, Generator Loss: 1.5852
D(x): 0.6901, D(G(z)): 0.2806
Epoch: [19/20], Batch Num: [361/600]
Discriminator Loss: 0.9714, Generator Loss: 1.3541
D(x): 0.6905, D(G(z)): 0.3124
Epoch: [19/20], Batch Num: [362/600]
Discriminator Loss: 1.0146, Generator Loss: 1.4068
D(x): 0.6748, D(G(z)): 0.3097
Epoch: [19/20], Batch Num: [363/600]
Discriminator Loss: 0.9817, Generator Loss: 1.4195
D(x): 0.7031, D(G(z)): 0.3542
Epoch: [19/20], Batch Num: [364/600]
Discriminator Loss: 0.9122, Generator Loss: 1.3868
D(x): 0.6654, D(G(z)): 0.2832
Epoch: [19/20], Batch Num: [365/600]
Discriminator Loss: 0.8474, Generator Loss: 1.3846
D(x): 0.7175, D(G(z)): 0.3086
Epoch: [19/20], Batch Num: [366/600]
Discriminator Loss: 0.7968, Generator Loss: 1.3878
D(x): 0.7220, D(G(z)): 0.2718
Epoch: [19/20], Batch Num: [367/600]
Discriminator Loss: 0.9146, Generator Loss: 1.3708
D(x): 0.6946, D(G(z)): 0.3100
Epoch: [19/20], Batch Num: [368/600]
Discriminator Loss: 1.0151, Generator Loss: 1.4364
D(x): 0.6875, D(G(z)): 0.3323
Epoch: [19/20], Batch Num: [369/600]
Discriminator Loss: 0.8794, Generator Loss: 1.3058
D(x): 0.7451, D(G(z)): 0.3113
Epoch: [19/20], Batch Num: [370/600]
Discriminator Loss: 0.9148, Generator Loss: 1.4881
D(x): 0.6901, D(G(z)): 0.3015
Epoch: [19/20], Batch Num: [371/600]
Discriminator Loss: 0.8250, Generator Loss: 1.4066
D(x): 0.7319, D(G(z)): 0.2938
Epoch: [19/20], Batch Num: [372/600]
Discriminator Loss: 0.9939, Generator Loss: 1.2309
D(x): 0.6338, D(G(z)): 0.2703
Epoch: [19/20], Batch Num: [373/600]
Discriminator Loss: 1.0114, Generator Loss: 1.2351
D(x): 0.7398, D(G(z)): 0.3925
Epoch: [19/20], Batch Num: [374/600]
Discriminator Loss: 1.1513, Generator Loss: 1.5246
D(x): 0.7117, D(G(z)): 0.3967
Epoch: [19/20], Batch Num: [375/600]
Discriminator Loss: 0.9238, Generator Loss: 1.4688
D(x): 0.6889, D(G(z)): 0.3031
Epoch: [19/20], Batch Num: [376/600]
Discriminator Loss: 0.9939, Generator Loss: 1.6345
D(x): 0.6323, D(G(z)): 0.2577
Epoch: [19/20], Batch Num: [377/600]
Discriminator Loss: 1.0764, Generator Loss: 1.3668
D(x): 0.6182, D(G(z)): 0.2934
Epoch: [19/20], Batch Num: [378/600]
Discriminator Loss: 1.0092, Generator Loss: 1.4371
D(x): 0.6628, D(G(z)): 0.3202
Epoch: [19/20], Batch Num: [379/600]
Discriminator Loss: 0.9794, Generator Loss: 1.3384
D(x): 0.7092, D(G(z)): 0.3384
Epoch: [19/20], Batch Num: [380/600]
Discriminator Loss: 1.0451, Generator Loss: 1.3399
D(x): 0.6743, D(G(z)): 0.3503
Epoch: [19/20], Batch Num: [381/600]
Discriminator Loss: 0.9681, Generator Loss: 1.4320
D(x): 0.7149, D(G(z)): 0.3561
Epoch: [19/20], Batch Num: [382/600]
Discriminator Loss: 0.8363, Generator Loss: 1.3730
D(x): 0.7275, D(G(z)): 0.3037
Epoch: [19/20], Batch Num: [383/600]
Discriminator Loss: 0.8880, Generator Loss: 1.5108
D(x): 0.6902, D(G(z)): 0.2941
Epoch: [19/20], Batch Num: [384/600]
Discriminator Loss: 0.9824, Generator Loss: 1.6140
D(x): 0.6445, D(G(z)): 0.2817
Epoch: [19/20], Batch Num: [385/600]
Discriminator Loss: 0.9437, Generator Loss: 1.5307
D(x): 0.6963, D(G(z)): 0.3132
Epoch: [19/20], Batch Num: [386/600]
Discriminator Loss: 0.9256, Generator Loss: 1.5000
D(x): 0.6367, D(G(z)): 0.2642
Epoch: [19/20], Batch Num: [387/600]
Discriminator Loss: 0.9838, Generator Loss: 1.3930
D(x): 0.6693, D(G(z)): 0.2978
Epoch: [19/20], Batch Num: [388/600]
Discriminator Loss: 1.0418, Generator Loss: 1.4735
D(x): 0.6857, D(G(z)): 0.3410
Epoch: [19/20], Batch Num: [389/600]
Discriminator Loss: 0.9370, Generator Loss: 1.4860
D(x): 0.7071, D(G(z)): 0.3306
Epoch: [19/20], Batch Num: [390/600]
Discriminator Loss: 0.9957, Generator Loss: 1.5358
D(x): 0.7044, D(G(z)): 0.3512
Epoch: [19/20], Batch Num: [391/600]
Discriminator Loss: 0.9920, Generator Loss: 1.5996
D(x): 0.6595, D(G(z)): 0.2749
Epoch: [19/20], Batch Num: [392/600]
Discriminator Loss: 0.8537, Generator Loss: 1.5082
D(x): 0.6850, D(G(z)): 0.2751
Epoch: [19/20], Batch Num: [393/600]
Discriminator Loss: 0.8794, Generator Loss: 1.4833
D(x): 0.7044, D(G(z)): 0.2941
Epoch: [19/20], Batch Num: [394/600]
Discriminator Loss: 0.8343, Generator Loss: 1.4576
D(x): 0.6936, D(G(z)): 0.2658
Epoch: [19/20], Batch Num: [395/600]
Discriminator Loss: 1.0266, Generator Loss: 1.4131
D(x): 0.6513, D(G(z)): 0.3154
Epoch: [19/20], Batch Num: [396/600]
Discriminator Loss: 0.9034, Generator Loss: 1.4118
D(x): 0.7007, D(G(z)): 0.3112
Epoch: [19/20], Batch Num: [397/600]
Discriminator Loss: 0.9648, Generator Loss: 1.4194
D(x): 0.7059, D(G(z)): 0.3431
Epoch: [19/20], Batch Num: [398/600]
Discriminator Loss: 0.9458, Generator Loss: 1.3774
D(x): 0.6974, D(G(z)): 0.3306
Epoch: [19/20], Batch Num: [399/600]
Discriminator Loss: 0.9680, Generator Loss: 1.4491
D(x): 0.6811, D(G(z)): 0.3273
Epoch: 19, Batch Num: [400/600]

Epoch: [19/20], Batch Num: [400/600]
Discriminator Loss: 0.9445, Generator Loss: 1.4475
D(x): 0.6982, D(G(z)): 0.3188
Epoch: [19/20], Batch Num: [401/600]
Discriminator Loss: 0.9236, Generator Loss: 1.5144
D(x): 0.6871, D(G(z)): 0.2946
Epoch: [19/20], Batch Num: [402/600]
Discriminator Loss: 1.0021, Generator Loss: 1.6166
D(x): 0.6736, D(G(z)): 0.3140
Epoch: [19/20], Batch Num: [403/600]
Discriminator Loss: 0.8174, Generator Loss: 1.5969
D(x): 0.6949, D(G(z)): 0.2440
Epoch: [19/20], Batch Num: [404/600]
Discriminator Loss: 0.9388, Generator Loss: 1.6817
D(x): 0.6594, D(G(z)): 0.2692
Epoch: [19/20], Batch Num: [405/600]
Discriminator Loss: 0.8976, Generator Loss: 1.5372
D(x): 0.6853, D(G(z)): 0.2875
Epoch: [19/20], Batch Num: [406/600]
Discriminator Loss: 0.9453, Generator Loss: 1.5401
D(x): 0.7359, D(G(z)): 0.3454
Epoch: [19/20], Batch Num: [407/600]
Discriminator Loss: 0.9886, Generator Loss: 1.6095
D(x): 0.6902, D(G(z)): 0.3296
Epoch: [19/20], Batch Num: [408/600]
Discriminator Loss: 0.9097, Generator Loss: 1.9551
D(x): 0.7224, D(G(z)): 0.3031
Epoch: [19/20], Batch Num: [409/600]
Discriminator Loss: 0.9731, Generator Loss: 1.7063
D(x): 0.6431, D(G(z)): 0.2563
Epoch: [19/20], Batch Num: [410/600]
Discriminator Loss: 0.7903, Generator Loss: 1.6554
D(x): 0.6794, D(G(z)): 0.2104
Epoch: [19/20], Batch Num: [411/600]
Discriminator Loss: 0.9132, Generator Loss: 1.4775
D(x): 0.6562, D(G(z)): 0.2657
Epoch: [19/20], Batch Num: [412/600]
Discriminator Loss: 0.8057, Generator Loss: 1.4907
D(x): 0.7602, D(G(z)): 0.3167
Epoch: [19/20], Batch Num: [413/600]
Discriminator Loss: 0.8974, Generator Loss: 1.3366
D(x): 0.7472, D(G(z)): 0.3324
Epoch: [19/20], Batch Num: [414/600]
Discriminator Loss: 0.8147, Generator Loss: 1.5528
D(x): 0.7758, D(G(z)): 0.3262
Epoch: [19/20], Batch Num: [415/600]
Discriminator Loss: 0.8375, Generator Loss: 1.7157
D(x): 0.7260, D(G(z)): 0.2951
Epoch: [19/20], Batch Num: [416/600]
Discriminator Loss: 0.8292, Generator Loss: 1.6264
D(x): 0.7230, D(G(z)): 0.2880
Epoch: [19/20], Batch Num: [417/600]
Discriminator Loss: 1.0730, Generator Loss: 1.5214
D(x): 0.5954, D(G(z)): 0.2465
Epoch: [19/20], Batch Num: [418/600]
Discriminator Loss: 0.9441, Generator Loss: 1.5870
D(x): 0.6658, D(G(z)): 0.2832
Epoch: [19/20], Batch Num: [419/600]
Discriminator Loss: 0.8721, Generator Loss: 1.4586
D(x): 0.6964, D(G(z)): 0.2810
Epoch: [19/20], Batch Num: [420/600]
Discriminator Loss: 0.9022, Generator Loss: 1.4221
D(x): 0.7062, D(G(z)): 0.3209
Epoch: [19/20], Batch Num: [421/600]
Discriminator Loss: 0.8749, Generator Loss: 1.5262
D(x): 0.7644, D(G(z)): 0.3454
Epoch: [19/20], Batch Num: [422/600]
Discriminator Loss: 0.8638, Generator Loss: 1.5599
D(x): 0.7633, D(G(z)): 0.3563
Epoch: [19/20], Batch Num: [423/600]
Discriminator Loss: 0.8486, Generator Loss: 1.6739
D(x): 0.7025, D(G(z)): 0.2897
Epoch: [19/20], Batch Num: [424/600]
Discriminator Loss: 0.8924, Generator Loss: 1.6268
D(x): 0.6706, D(G(z)): 0.2721
Epoch: [19/20], Batch Num: [425/600]
Discriminator Loss: 0.8383, Generator Loss: 1.5241
D(x): 0.6954, D(G(z)): 0.2632
Epoch: [19/20], Batch Num: [426/600]
Discriminator Loss: 0.8212, Generator Loss: 1.4800
D(x): 0.6912, D(G(z)): 0.2657
Epoch: [19/20], Batch Num: [427/600]
Discriminator Loss: 0.8193, Generator Loss: 1.4413
D(x): 0.7471, D(G(z)): 0.3255
Epoch: [19/20], Batch Num: [428/600]
Discriminator Loss: 0.9020, Generator Loss: 1.3751
D(x): 0.7331, D(G(z)): 0.3311
Epoch: [19/20], Batch Num: [429/600]
Discriminator Loss: 0.8914, Generator Loss: 1.4327
D(x): 0.7087, D(G(z)): 0.2908
Epoch: [19/20], Batch Num: [430/600]
Discriminator Loss: 1.1458, Generator Loss: 1.5701
D(x): 0.6606, D(G(z)): 0.3401
Epoch: [19/20], Batch Num: [431/600]
Discriminator Loss: 0.7721, Generator Loss: 1.4885
D(x): 0.7288, D(G(z)): 0.2548
Epoch: [19/20], Batch Num: [432/600]
Discriminator Loss: 0.9487, Generator Loss: 1.4926
D(x): 0.6950, D(G(z)): 0.3178
Epoch: [19/20], Batch Num: [433/600]
Discriminator Loss: 0.9165, Generator Loss: 1.4947
D(x): 0.6593, D(G(z)): 0.2864
Epoch: [19/20], Batch Num: [434/600]
Discriminator Loss: 0.8149, Generator Loss: 1.3498
D(x): 0.7611, D(G(z)): 0.3131
Epoch: [19/20], Batch Num: [435/600]
Discriminator Loss: 1.0545, Generator Loss: 1.4175
D(x): 0.6929, D(G(z)): 0.3323
Epoch: [19/20], Batch Num: [436/600]
Discriminator Loss: 0.8732, Generator Loss: 1.4974
D(x): 0.7345, D(G(z)): 0.3146
Epoch: [19/20], Batch Num: [437/600]
Discriminator Loss: 0.7867, Generator Loss: 1.4971
D(x): 0.7294, D(G(z)): 0.2774
Epoch: [19/20], Batch Num: [438/600]
Discriminator Loss: 1.0285, Generator Loss: 1.5239
D(x): 0.6455, D(G(z)): 0.2789
Epoch: [19/20], Batch Num: [439/600]
Discriminator Loss: 0.9346, Generator Loss: 1.5103
D(x): 0.7311, D(G(z)): 0.3134
Epoch: [19/20], Batch Num: [440/600]
Discriminator Loss: 0.9390, Generator Loss: 1.5444
D(x): 0.6491, D(G(z)): 0.2585
Epoch: [19/20], Batch Num: [441/600]
Discriminator Loss: 0.9199, Generator Loss: 1.3715
D(x): 0.6879, D(G(z)): 0.2770
Epoch: [19/20], Batch Num: [442/600]
Discriminator Loss: 0.8946, Generator Loss: 1.4381
D(x): 0.7517, D(G(z)): 0.3285
Epoch: [19/20], Batch Num: [443/600]
Discriminator Loss: 0.9740, Generator Loss: 1.6566
D(x): 0.6823, D(G(z)): 0.3142
Epoch: [19/20], Batch Num: [444/600]
Discriminator Loss: 0.7992, Generator Loss: 1.4792
D(x): 0.7167, D(G(z)): 0.2829
Epoch: [19/20], Batch Num: [445/600]
Discriminator Loss: 0.7832, Generator Loss: 1.5715
D(x): 0.7501, D(G(z)): 0.2913
Epoch: [19/20], Batch Num: [446/600]
Discriminator Loss: 0.9311, Generator Loss: 1.4590
D(x): 0.7113, D(G(z)): 0.3328
Epoch: [19/20], Batch Num: [447/600]
Discriminator Loss: 0.8662, Generator Loss: 1.5933
D(x): 0.7035, D(G(z)): 0.2941
Epoch: [19/20], Batch Num: [448/600]
Discriminator Loss: 0.7909, Generator Loss: 1.7367
D(x): 0.7196, D(G(z)): 0.2487
Epoch: [19/20], Batch Num: [449/600]
Discriminator Loss: 0.8353, Generator Loss: 1.5221
D(x): 0.7124, D(G(z)): 0.2595
Epoch: [19/20], Batch Num: [450/600]
Discriminator Loss: 0.8661, Generator Loss: 1.6672
D(x): 0.6939, D(G(z)): 0.2581
Epoch: [19/20], Batch Num: [451/600]
Discriminator Loss: 0.9027, Generator Loss: 1.5752
D(x): 0.7194, D(G(z)): 0.3078
Epoch: [19/20], Batch Num: [452/600]
Discriminator Loss: 0.8191, Generator Loss: 1.6614
D(x): 0.7689, D(G(z)): 0.2958
Epoch: [19/20], Batch Num: [453/600]
Discriminator Loss: 0.8321, Generator Loss: 1.6689
D(x): 0.7285, D(G(z)): 0.2879
Epoch: [19/20], Batch Num: [454/600]
Discriminator Loss: 0.9558, Generator Loss: 1.7131
D(x): 0.6876, D(G(z)): 0.2875
Epoch: [19/20], Batch Num: [455/600]
Discriminator Loss: 1.2208, Generator Loss: 1.7662
D(x): 0.6265, D(G(z)): 0.3076
Epoch: [19/20], Batch Num: [456/600]
Discriminator Loss: 0.8809, Generator Loss: 1.6456
D(x): 0.7195, D(G(z)): 0.2848
Epoch: [19/20], Batch Num: [457/600]
Discriminator Loss: 0.8215, Generator Loss: 1.5484
D(x): 0.6876, D(G(z)): 0.2423
Epoch: [19/20], Batch Num: [458/600]
Discriminator Loss: 0.8750, Generator Loss: 1.4695
D(x): 0.7002, D(G(z)): 0.2899
Epoch: [19/20], Batch Num: [459/600]
Discriminator Loss: 0.9447, Generator Loss: 1.3995
D(x): 0.7384, D(G(z)): 0.3468
Epoch: [19/20], Batch Num: [460/600]
Discriminator Loss: 0.9696, Generator Loss: 1.4326
D(x): 0.6650, D(G(z)): 0.2863
Epoch: [19/20], Batch Num: [461/600]
Discriminator Loss: 0.9950, Generator Loss: 1.4966
D(x): 0.6788, D(G(z)): 0.3164
Epoch: [19/20], Batch Num: [462/600]
Discriminator Loss: 1.0770, Generator Loss: 1.4837
D(x): 0.6942, D(G(z)): 0.3563
Epoch: [19/20], Batch Num: [463/600]
Discriminator Loss: 0.9212, Generator Loss: 1.4422
D(x): 0.6919, D(G(z)): 0.3068
Epoch: [19/20], Batch Num: [464/600]
Discriminator Loss: 1.0003, Generator Loss: 1.3862
D(x): 0.6581, D(G(z)): 0.2906
Epoch: [19/20], Batch Num: [465/600]
Discriminator Loss: 0.9384, Generator Loss: 1.3826
D(x): 0.7071, D(G(z)): 0.3103
Epoch: [19/20], Batch Num: [466/600]
Discriminator Loss: 1.0085, Generator Loss: 1.2515
D(x): 0.6638, D(G(z)): 0.3199
Epoch: [19/20], Batch Num: [467/600]
Discriminator Loss: 0.9745, Generator Loss: 1.2703
D(x): 0.6620, D(G(z)): 0.3263
Epoch: [19/20], Batch Num: [468/600]
Discriminator Loss: 1.0436, Generator Loss: 1.3097
D(x): 0.6772, D(G(z)): 0.3611
Epoch: [19/20], Batch Num: [469/600]
Discriminator Loss: 1.0158, Generator Loss: 1.2378
D(x): 0.6515, D(G(z)): 0.3340
Epoch: [19/20], Batch Num: [470/600]
Discriminator Loss: 0.8403, Generator Loss: 1.3967
D(x): 0.7535, D(G(z)): 0.3458
Epoch: [19/20], Batch Num: [471/600]
Discriminator Loss: 0.8689, Generator Loss: 1.4054
D(x): 0.7385, D(G(z)): 0.3235
Epoch: [19/20], Batch Num: [472/600]
Discriminator Loss: 1.0265, Generator Loss: 1.3897
D(x): 0.6118, D(G(z)): 0.2802
Epoch: [19/20], Batch Num: [473/600]
Discriminator Loss: 0.9127, Generator Loss: 1.5483
D(x): 0.6884, D(G(z)): 0.3071
Epoch: [19/20], Batch Num: [474/600]
Discriminator Loss: 0.9611, Generator Loss: 1.4552
D(x): 0.6470, D(G(z)): 0.2950
Epoch: [19/20], Batch Num: [475/600]
Discriminator Loss: 0.9891, Generator Loss: 1.3450
D(x): 0.6986, D(G(z)): 0.3386
Epoch: [19/20], Batch Num: [476/600]
Discriminator Loss: 1.0920, Generator Loss: 1.4305
D(x): 0.6611, D(G(z)): 0.3692
Epoch: [19/20], Batch Num: [477/600]
Discriminator Loss: 1.0474, Generator Loss: 1.4586
D(x): 0.6875, D(G(z)): 0.3502
Epoch: [19/20], Batch Num: [478/600]
Discriminator Loss: 0.9716, Generator Loss: 1.3450
D(x): 0.6750, D(G(z)): 0.3080
Epoch: [19/20], Batch Num: [479/600]
Discriminator Loss: 0.9635, Generator Loss: 1.4373
D(x): 0.6474, D(G(z)): 0.2970
Epoch: [19/20], Batch Num: [480/600]
Discriminator Loss: 0.9610, Generator Loss: 1.5230
D(x): 0.7093, D(G(z)): 0.3485
Epoch: [19/20], Batch Num: [481/600]
Discriminator Loss: 0.9359, Generator Loss: 1.5456
D(x): 0.7175, D(G(z)): 0.3295
Epoch: [19/20], Batch Num: [482/600]
Discriminator Loss: 0.9921, Generator Loss: 1.6597
D(x): 0.6793, D(G(z)): 0.3286
Epoch: [19/20], Batch Num: [483/600]
Discriminator Loss: 1.0011, Generator Loss: 1.4284
D(x): 0.6654, D(G(z)): 0.2926
Epoch: [19/20], Batch Num: [484/600]
Discriminator Loss: 0.8915, Generator Loss: 1.3961
D(x): 0.7038, D(G(z)): 0.3018
Epoch: [19/20], Batch Num: [485/600]
Discriminator Loss: 0.9530, Generator Loss: 1.5229
D(x): 0.6781, D(G(z)): 0.2953
Epoch: [19/20], Batch Num: [486/600]
Discriminator Loss: 0.8909, Generator Loss: 1.4403
D(x): 0.6950, D(G(z)): 0.2985
Epoch: [19/20], Batch Num: [487/600]
Discriminator Loss: 0.9282, Generator Loss: 1.1718
D(x): 0.6807, D(G(z)): 0.2885
Epoch: [19/20], Batch Num: [488/600]
Discriminator Loss: 0.8702, Generator Loss: 1.3827
D(x): 0.7213, D(G(z)): 0.3087
Epoch: [19/20], Batch Num: [489/600]
Discriminator Loss: 0.9179, Generator Loss: 1.3139
D(x): 0.7044, D(G(z)): 0.3467
Epoch: [19/20], Batch Num: [490/600]
Discriminator Loss: 0.9102, Generator Loss: 1.4655
D(x): 0.7499, D(G(z)): 0.3482
Epoch: [19/20], Batch Num: [491/600]
Discriminator Loss: 1.0410, Generator Loss: 1.5969
D(x): 0.6228, D(G(z)): 0.2930
Epoch: [19/20], Batch Num: [492/600]
Discriminator Loss: 0.9534, Generator Loss: 1.7191
D(x): 0.6868, D(G(z)): 0.3210
Epoch: [19/20], Batch Num: [493/600]
Discriminator Loss: 0.9653, Generator Loss: 1.6214
D(x): 0.6731, D(G(z)): 0.2919
Epoch: [19/20], Batch Num: [494/600]
Discriminator Loss: 0.9662, Generator Loss: 1.6669
D(x): 0.6355, D(G(z)): 0.2580
Epoch: [19/20], Batch Num: [495/600]
Discriminator Loss: 0.8889, Generator Loss: 1.3986
D(x): 0.6781, D(G(z)): 0.2768
Epoch: [19/20], Batch Num: [496/600]
Discriminator Loss: 1.0252, Generator Loss: 1.2843
D(x): 0.6959, D(G(z)): 0.3366
Epoch: [19/20], Batch Num: [497/600]
Discriminator Loss: 0.9360, Generator Loss: 1.3549
D(x): 0.7359, D(G(z)): 0.3601
Epoch: [19/20], Batch Num: [498/600]
Discriminator Loss: 0.9228, Generator Loss: 1.2662
D(x): 0.7063, D(G(z)): 0.3364
Epoch: [19/20], Batch Num: [499/600]
Discriminator Loss: 1.0092, Generator Loss: 1.5632
D(x): 0.7098, D(G(z)): 0.3614
Epoch: 19, Batch Num: [500/600]

Epoch: [19/20], Batch Num: [500/600]
Discriminator Loss: 0.9347, Generator Loss: 1.5522
D(x): 0.6698, D(G(z)): 0.2857
Epoch: [19/20], Batch Num: [501/600]
Discriminator Loss: 0.9140, Generator Loss: 1.3782
D(x): 0.6783, D(G(z)): 0.2860
Epoch: [19/20], Batch Num: [502/600]
Discriminator Loss: 0.9800, Generator Loss: 1.3941
D(x): 0.6688, D(G(z)): 0.3045
Epoch: [19/20], Batch Num: [503/600]
Discriminator Loss: 1.1533, Generator Loss: 1.2678
D(x): 0.6366, D(G(z)): 0.3525
Epoch: [19/20], Batch Num: [504/600]
Discriminator Loss: 0.8968, Generator Loss: 1.3221
D(x): 0.7052, D(G(z)): 0.3221
Epoch: [19/20], Batch Num: [505/600]
Discriminator Loss: 0.9858, Generator Loss: 1.2397
D(x): 0.7289, D(G(z)): 0.3627
Epoch: [19/20], Batch Num: [506/600]
Discriminator Loss: 0.9394, Generator Loss: 1.4261
D(x): 0.7477, D(G(z)): 0.3578
Epoch: [19/20], Batch Num: [507/600]
Discriminator Loss: 0.9437, Generator Loss: 1.4752
D(x): 0.6628, D(G(z)): 0.2915
Epoch: [19/20], Batch Num: [508/600]
Discriminator Loss: 0.9428, Generator Loss: 1.3687
D(x): 0.6906, D(G(z)): 0.3179
Epoch: [19/20], Batch Num: [509/600]
Discriminator Loss: 0.9870, Generator Loss: 1.3017
D(x): 0.6456, D(G(z)): 0.2825
Epoch: [19/20], Batch Num: [510/600]
Discriminator Loss: 0.9454, Generator Loss: 1.4479
D(x): 0.6890, D(G(z)): 0.3262
Epoch: [19/20], Batch Num: [511/600]
Discriminator Loss: 0.9357, Generator Loss: 1.2655
D(x): 0.7093, D(G(z)): 0.3425
Epoch: [19/20], Batch Num: [512/600]
Discriminator Loss: 1.0222, Generator Loss: 1.3623
D(x): 0.6802, D(G(z)): 0.3399
Epoch: [19/20], Batch Num: [513/600]
Discriminator Loss: 1.0532, Generator Loss: 1.3549
D(x): 0.7037, D(G(z)): 0.3723
Epoch: [19/20], Batch Num: [514/600]
Discriminator Loss: 0.9095, Generator Loss: 1.3985
D(x): 0.6892, D(G(z)): 0.3047
Epoch: [19/20], Batch Num: [515/600]
Discriminator Loss: 0.8705, Generator Loss: 1.5239
D(x): 0.7085, D(G(z)): 0.2981
Epoch: [19/20], Batch Num: [516/600]
Discriminator Loss: 0.8772, Generator Loss: 1.4575
D(x): 0.6887, D(G(z)): 0.2910
Epoch: [19/20], Batch Num: [517/600]
Discriminator Loss: 1.0083, Generator Loss: 1.5113
D(x): 0.6620, D(G(z)): 0.3049
Epoch: [19/20], Batch Num: [518/600]
Discriminator Loss: 0.8605, Generator Loss: 1.3335
D(x): 0.7111, D(G(z)): 0.3119
Epoch: [19/20], Batch Num: [519/600]
Discriminator Loss: 1.0151, Generator Loss: 1.4636
D(x): 0.6515, D(G(z)): 0.3082
Epoch: [19/20], Batch Num: [520/600]
Discriminator Loss: 0.9379, Generator Loss: 1.4544
D(x): 0.7033, D(G(z)): 0.3113
Epoch: [19/20], Batch Num: [521/600]
Discriminator Loss: 0.9727, Generator Loss: 1.3760
D(x): 0.6801, D(G(z)): 0.3157
Epoch: [19/20], Batch Num: [522/600]
Discriminator Loss: 1.0550, Generator Loss: 1.6233
D(x): 0.6675, D(G(z)): 0.3506
Epoch: [19/20], Batch Num: [523/600]
Discriminator Loss: 0.8579, Generator Loss: 1.3389
D(x): 0.6711, D(G(z)): 0.2527
Epoch: [19/20], Batch Num: [524/600]
Discriminator Loss: 1.0397, Generator Loss: 1.3060
D(x): 0.6105, D(G(z)): 0.3115
Epoch: [19/20], Batch Num: [525/600]
Discriminator Loss: 1.0479, Generator Loss: 1.2726
D(x): 0.6515, D(G(z)): 0.3289
Epoch: [19/20], Batch Num: [526/600]
Discriminator Loss: 0.8815, Generator Loss: 1.1930
D(x): 0.7174, D(G(z)): 0.3288
Epoch: [19/20], Batch Num: [527/600]
Discriminator Loss: 0.9806, Generator Loss: 1.2494
D(x): 0.6839, D(G(z)): 0.3452
Epoch: [19/20], Batch Num: [528/600]
Discriminator Loss: 0.9456, Generator Loss: 1.4499
D(x): 0.7168, D(G(z)): 0.3646
Epoch: [19/20], Batch Num: [529/600]
Discriminator Loss: 0.9738, Generator Loss: 1.4408
D(x): 0.6693, D(G(z)): 0.3220
Epoch: [19/20], Batch Num: [530/600]
Discriminator Loss: 0.9051, Generator Loss: 1.5337
D(x): 0.6695, D(G(z)): 0.2955
Epoch: [19/20], Batch Num: [531/600]
Discriminator Loss: 0.9490, Generator Loss: 1.4677
D(x): 0.6392, D(G(z)): 0.2706
Epoch: [19/20], Batch Num: [532/600]
Discriminator Loss: 0.8770, Generator Loss: 1.4454
D(x): 0.6654, D(G(z)): 0.2628
Epoch: [19/20], Batch Num: [533/600]
Discriminator Loss: 0.8768, Generator Loss: 1.3899
D(x): 0.6885, D(G(z)): 0.2971
Epoch: [19/20], Batch Num: [534/600]
Discriminator Loss: 0.8875, Generator Loss: 1.2206
D(x): 0.7011, D(G(z)): 0.2977
Epoch: [19/20], Batch Num: [535/600]
Discriminator Loss: 0.9320, Generator Loss: 1.2490
D(x): 0.7792, D(G(z)): 0.3800
Epoch: [19/20], Batch Num: [536/600]
Discriminator Loss: 1.0315, Generator Loss: 1.5724
D(x): 0.7181, D(G(z)): 0.3940
Epoch: [19/20], Batch Num: [537/600]
Discriminator Loss: 0.8446, Generator Loss: 1.5662
D(x): 0.7084, D(G(z)): 0.2717
Epoch: [19/20], Batch Num: [538/600]
Discriminator Loss: 0.9172, Generator Loss: 1.7035
D(x): 0.6379, D(G(z)): 0.2342
Epoch: [19/20], Batch Num: [539/600]
Discriminator Loss: 0.8595, Generator Loss: 1.5574
D(x): 0.6684, D(G(z)): 0.2301
Epoch: [19/20], Batch Num: [540/600]
Discriminator Loss: 0.9794, Generator Loss: 1.5750
D(x): 0.6505, D(G(z)): 0.2802
Epoch: [19/20], Batch Num: [541/600]
Discriminator Loss: 0.8423, Generator Loss: 1.4047
D(x): 0.6944, D(G(z)): 0.2721
Epoch: [19/20], Batch Num: [542/600]
Discriminator Loss: 0.8565, Generator Loss: 1.2502
D(x): 0.7367, D(G(z)): 0.3188
Epoch: [19/20], Batch Num: [543/600]
Discriminator Loss: 0.9156, Generator Loss: 1.2518
D(x): 0.7384, D(G(z)): 0.3232
Epoch: [19/20], Batch Num: [544/600]
Discriminator Loss: 0.8805, Generator Loss: 1.4900
D(x): 0.7436, D(G(z)): 0.3296
Epoch: [19/20], Batch Num: [545/600]
Discriminator Loss: 0.7759, Generator Loss: 1.7566
D(x): 0.7508, D(G(z)): 0.2919
Epoch: [19/20], Batch Num: [546/600]
Discriminator Loss: 0.8627, Generator Loss: 1.6455
D(x): 0.7191, D(G(z)): 0.3046
Epoch: [19/20], Batch Num: [547/600]
Discriminator Loss: 0.8053, Generator Loss: 1.7827
D(x): 0.6843, D(G(z)): 0.2436
Epoch: [19/20], Batch Num: [548/600]
Discriminator Loss: 0.8807, Generator Loss: 1.7628
D(x): 0.6638, D(G(z)): 0.2347
Epoch: [19/20], Batch Num: [549/600]
Discriminator Loss: 0.8122, Generator Loss: 1.5125
D(x): 0.7006, D(G(z)): 0.2625
Epoch: [19/20], Batch Num: [550/600]
Discriminator Loss: 0.9888, Generator Loss: 1.5194
D(x): 0.6723, D(G(z)): 0.2951
Epoch: [19/20], Batch Num: [551/600]
Discriminator Loss: 0.8347, Generator Loss: 1.4668
D(x): 0.7626, D(G(z)): 0.3041
Epoch: [19/20], Batch Num: [552/600]
Discriminator Loss: 1.0164, Generator Loss: 1.3445
D(x): 0.7251, D(G(z)): 0.3588
Epoch: [19/20], Batch Num: [553/600]
Discriminator Loss: 0.8249, Generator Loss: 1.5224
D(x): 0.7526, D(G(z)): 0.2990
Epoch: [19/20], Batch Num: [554/600]
Discriminator Loss: 1.1084, Generator Loss: 1.6662
D(x): 0.6833, D(G(z)): 0.3432
Epoch: [19/20], Batch Num: [555/600]
Discriminator Loss: 0.9509, Generator Loss: 1.6981
D(x): 0.7064, D(G(z)): 0.3012
Epoch: [19/20], Batch Num: [556/600]
Discriminator Loss: 0.8632, Generator Loss: 1.9439
D(x): 0.7149, D(G(z)): 0.2560
Epoch: [19/20], Batch Num: [557/600]
Discriminator Loss: 0.9870, Generator Loss: 1.6163
D(x): 0.6205, D(G(z)): 0.2165
Epoch: [19/20], Batch Num: [558/600]
Discriminator Loss: 0.9499, Generator Loss: 1.4881
D(x): 0.6653, D(G(z)): 0.2876
Epoch: [19/20], Batch Num: [559/600]
Discriminator Loss: 0.9328, Generator Loss: 1.3306
D(x): 0.7236, D(G(z)): 0.3277
Epoch: [19/20], Batch Num: [560/600]
Discriminator Loss: 1.1425, Generator Loss: 1.4574
D(x): 0.7599, D(G(z)): 0.4256
Epoch: [19/20], Batch Num: [561/600]
Discriminator Loss: 0.9190, Generator Loss: 1.7029
D(x): 0.7282, D(G(z)): 0.3421
Epoch: [19/20], Batch Num: [562/600]
Discriminator Loss: 0.8444, Generator Loss: 1.8544
D(x): 0.6951, D(G(z)): 0.2689
Epoch: [19/20], Batch Num: [563/600]
Discriminator Loss: 0.9701, Generator Loss: 1.4462
D(x): 0.5882, D(G(z)): 0.2115
Epoch: [19/20], Batch Num: [564/600]
Discriminator Loss: 0.9251, Generator Loss: 1.3214
D(x): 0.6325, D(G(z)): 0.2523
Epoch: [19/20], Batch Num: [565/600]
Discriminator Loss: 1.0439, Generator Loss: 1.1818
D(x): 0.7110, D(G(z)): 0.3637
Epoch: [19/20], Batch Num: [566/600]
Discriminator Loss: 0.9524, Generator Loss: 1.1332
D(x): 0.7151, D(G(z)): 0.3519
Epoch: [19/20], Batch Num: [567/600]
Discriminator Loss: 0.9407, Generator Loss: 1.3459
D(x): 0.7469, D(G(z)): 0.3758
Epoch: [19/20], Batch Num: [568/600]
Discriminator Loss: 0.9846, Generator Loss: 1.2985
D(x): 0.6831, D(G(z)): 0.3332
Epoch: [19/20], Batch Num: [569/600]
Discriminator Loss: 0.9753, Generator Loss: 1.5268
D(x): 0.7115, D(G(z)): 0.3563
Epoch: [19/20], Batch Num: [570/600]
Discriminator Loss: 0.9035, Generator Loss: 1.6883
D(x): 0.6729, D(G(z)): 0.3033
Epoch: [19/20], Batch Num: [571/600]
Discriminator Loss: 0.9108, Generator Loss: 1.7449
D(x): 0.6514, D(G(z)): 0.2521
Epoch: [19/20], Batch Num: [572/600]
Discriminator Loss: 1.0743, Generator Loss: 1.7815
D(x): 0.5876, D(G(z)): 0.2788
Epoch: [19/20], Batch Num: [573/600]
Discriminator Loss: 0.9010, Generator Loss: 1.4728
D(x): 0.6733, D(G(z)): 0.2756
Epoch: [19/20], Batch Num: [574/600]
Discriminator Loss: 0.9593, Generator Loss: 1.3822
D(x): 0.6981, D(G(z)): 0.3282
Epoch: [19/20], Batch Num: [575/600]
Discriminator Loss: 0.9676, Generator Loss: 1.3502
D(x): 0.6750, D(G(z)): 0.3321
Epoch: [19/20], Batch Num: [576/600]
Discriminator Loss: 0.8961, Generator Loss: 1.3806
D(x): 0.7207, D(G(z)): 0.3254
Epoch: [19/20], Batch Num: [577/600]
Discriminator Loss: 0.9443, Generator Loss: 1.5184
D(x): 0.7638, D(G(z)): 0.3844
Epoch: [19/20], Batch Num: [578/600]
Discriminator Loss: 0.8601, Generator Loss: 1.6301
D(x): 0.6972, D(G(z)): 0.2998
Epoch: [19/20], Batch Num: [579/600]
Discriminator Loss: 0.9200, Generator Loss: 1.7937
D(x): 0.6662, D(G(z)): 0.2711
Epoch: [19/20], Batch Num: [580/600]
Discriminator Loss: 0.8753, Generator Loss: 1.7153
D(x): 0.6358, D(G(z)): 0.2417
Epoch: [19/20], Batch Num: [581/600]
Discriminator Loss: 0.9943, Generator Loss: 1.3385
D(x): 0.6376, D(G(z)): 0.2702
Epoch: [19/20], Batch Num: [582/600]
Discriminator Loss: 1.0073, Generator Loss: 1.3969
D(x): 0.6579, D(G(z)): 0.3058
Epoch: [19/20], Batch Num: [583/600]
Discriminator Loss: 1.0473, Generator Loss: 1.1281
D(x): 0.7292, D(G(z)): 0.3840
Epoch: [19/20], Batch Num: [584/600]
Discriminator Loss: 0.9112, Generator Loss: 1.2916
D(x): 0.7443, D(G(z)): 0.3760
Epoch: [19/20], Batch Num: [585/600]
Discriminator Loss: 0.9126, Generator Loss: 1.5122
D(x): 0.7471, D(G(z)): 0.3721
Epoch: [19/20], Batch Num: [586/600]
Discriminator Loss: 0.9752, Generator Loss: 1.6812
D(x): 0.6870, D(G(z)): 0.3177
Epoch: [19/20], Batch Num: [587/600]
Discriminator Loss: 0.8806, Generator Loss: 1.5999
D(x): 0.6845, D(G(z)): 0.2880
Epoch: [19/20], Batch Num: [588/600]
Discriminator Loss: 0.9719, Generator Loss: 1.5398
D(x): 0.6117, D(G(z)): 0.2629
Epoch: [19/20], Batch Num: [589/600]
Discriminator Loss: 0.8858, Generator Loss: 1.2548
D(x): 0.6553, D(G(z)): 0.2700
Epoch: [19/20], Batch Num: [590/600]
Discriminator Loss: 0.9199, Generator Loss: 1.1884
D(x): 0.6768, D(G(z)): 0.3173
Epoch: [19/20], Batch Num: [591/600]
Discriminator Loss: 1.1235, Generator Loss: 1.1826
D(x): 0.6902, D(G(z)): 0.4004
Epoch: [19/20], Batch Num: [592/600]
Discriminator Loss: 0.8713, Generator Loss: 1.2220
D(x): 0.7789, D(G(z)): 0.3824
Epoch: [19/20], Batch Num: [593/600]
Discriminator Loss: 0.8021, Generator Loss: 1.4873
D(x): 0.7725, D(G(z)): 0.3357
Epoch: [19/20], Batch Num: [594/600]
Discriminator Loss: 0.9842, Generator Loss: 1.6934
D(x): 0.6687, D(G(z)): 0.3114
Epoch: [19/20], Batch Num: [595/600]
Discriminator Loss: 1.0628, Generator Loss: 1.6284
D(x): 0.5692, D(G(z)): 0.2551
Epoch: [19/20], Batch Num: [596/600]
Discriminator Loss: 0.9686, Generator Loss: 1.6454
D(x): 0.6382, D(G(z)): 0.2790
Epoch: [19/20], Batch Num: [597/600]
Discriminator Loss: 0.8623, Generator Loss: 1.4669
D(x): 0.6872, D(G(z)): 0.2639
Epoch: [19/20], Batch Num: [598/600]
Discriminator Loss: 0.9441, Generator Loss: 1.4213
D(x): 0.7073, D(G(z)): 0.3250
Epoch: [19/20], Batch Num: [599/600]
Discriminator Loss: 1.0135, Generator Loss: 1.3210
D(x): 0.7029, D(G(z)): 0.3679

L'entraînement des modèles GAN demande beaucoup de patience en raison de sa profondeur. Nous avons décidé de resumer ce que l'on a appris à travers la modification des paramètres sous forme de conseils:

• Problème : Les images générées ressemblent à du bruit.
• Solution: Utiliser le dropout sur le discriminateur et le générateur. De faibles valeurs de dropout (0,3 à 0,6) génèrent des images plus réalistes.

• Problème : La perte du Discriminator converge rapidement vers zéro, empêchant ainsi le Generator d'apprendre.
• Solution : Ne pas pré-entraîner le discriminateur. Au lieu de cela, rendre son taux d'apprentissage plus grand que le taux d'apprentissage du modèle Adversarial. Utiliser un échantillon de bruit d'entraînement différent pour le générateur.

• Problème : Les images du générateur ressemblent toujours à du bruit.
• Solution: Vérifier si l'activation, la normalisation du lot et le dropout sont appliqués dans la bonne séquence.

• Problème : Déterminer les paramètres d'entraînement/modèle corrects.
• Solution: Commencer avec des valeurs de travail connues provenant d'articles de recherche et de codes publiés et ajustez un paramètre à la fois. Avant de s'entraîner sur 200 epoch ou plus, observez l'effet de l'ajustement des valeurs des paramètres à environ 50 ou 100 époch.